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+
+from speakers.common.registry import registry
+import os
+
+root_dir = os.path.dirname(os.path.abspath(__file__))
+registry.register_path("bark_library_root", root_dir)
+
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diff --git a/bark/assets/prompts/readme.md b/bark/assets/prompts/readme.md
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+# Example Prompts Data
+
+## Version Two
+The `v2` prompts are better engineered to follow text with a consistent voice.
+To use them, simply include `v2` in the prompt. For example
+```python
+from bark import generate_audio
+text_prompt = "madam I'm adam"
+audio_array = generate_audio(text_prompt, history_prompt="v2/en_speaker_1")
+```
+
+## Prompt Format
+The provided data is in the .npz format, which is a file format used in Python for storing arrays and data. The data contains three arrays: semantic_prompt, coarse_prompt, and fine_prompt.
+
+```semantic_prompt```
+
+The semantic_prompt array contains a sequence of token IDs generated by the BERT tokenizer from Hugging Face. These tokens encode the text input and are used as an input to generate the audio output. The shape of this array is (n,), where n is the number of tokens in the input text.
+
+```coarse_prompt```
+
+The coarse_prompt array is an intermediate output of the text-to-speech pipeline, and contains token IDs generated by the first two codebooks of the EnCodec Codec from Facebook. This step converts the semantic tokens into a different representation that is better suited for the subsequent step. The shape of this array is (2, m), where m is the number of tokens after conversion by the EnCodec Codec.
+
+```fine_prompt```
+
+The fine_prompt array is a further processed output of the pipeline, and contains 8 codebooks from the EnCodec Codec. These codebooks represent the final stage of tokenization, and the resulting tokens are used to generate the audio output. The shape of this array is (8, p), where p is the number of tokens after further processing by the EnCodec Codec.
+
+Overall, these arrays represent different stages of a text-to-speech pipeline that converts text input into synthesized audio output. The semantic_prompt array represents the input text, while coarse_prompt and fine_prompt represent intermediate and final stages of tokenization, respectively.
+
+
+
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diff --git a/bark/mode_load.py b/bark/mode_load.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4bd8bf8950b40febc68084c24daaaa97f52e822
--- /dev/null
+++ b/bark/mode_load.py
@@ -0,0 +1,710 @@
+from bark.model_fine import FineGPT, FineGPTConfig
+from bark.model import GPT, GPTConfig
+from huggingface_hub import hf_hub_download
+from typing import Type, TypeVar
+from transformers import BertTokenizer
+from scipy.special import softmax
+from encodec import EncodecModel
+import logging
+import torch
+import os
+import torch.nn as nn
+import torch.nn.functional as F
+import re
+import numpy as np
+import contextlib
+import funcy
+import tqdm
+import numpy as np
+logger = logging.getLogger('bark_model_load')
+
+
+def set_bark_model_load_logger(l):
+    global logger
+    logger = l
+
+
+
+if (
+        torch.cuda.is_available() and
+        hasattr(torch.cuda, "amp") and
+        hasattr(torch.cuda.amp, "autocast") and
+        hasattr(torch.cuda, "is_bf16_supported") and
+        torch.cuda.is_bf16_supported()
+):
+    autocast = funcy.partial(torch.cuda.amp.autocast, dtype=torch.bfloat16)
+else:
+    @contextlib.contextmanager
+    def autocast():
+        yield
+
+
+class InferenceContext:
+    def __init__(self, benchmark=False):
+        # we can't expect inputs to be the same length, so disable benchmarking by default
+        self._chosen_cudnn_benchmark = benchmark
+        self._cudnn_benchmark = None
+
+    def __enter__(self):
+        self._cudnn_benchmark = torch.backends.cudnn.benchmark
+        torch.backends.cudnn.benchmark = self._chosen_cudnn_benchmark
+
+    def __exit__(self, exc_type, exc_value, exc_traceback):
+        torch.backends.cudnn.benchmark = self._cudnn_benchmark
+
+
+@contextlib.contextmanager
+def _inference_mode():
+    with InferenceContext(), torch.inference_mode(), torch.no_grad(), autocast():
+        yield
+
+
+# 定义一个泛型类型变量
+G = TypeVar('G', bound='GPT')
+C = TypeVar('C', bound='GPTConfig')
+
+
+class ModelType:
+    def __init__(self, model_type: str, model_class: Type[G], model_config: Type[C]):
+        """
+        模型类型适配
+        :param model_type:
+        :param model_class:
+        :param model_config:
+        """
+        self.model_type = model_type
+        self.model_class = model_class
+        self.model_config = model_config
+
+
+CONTEXT_WINDOW_SIZE = 1024
+
+SEMANTIC_RATE_HZ = 49.9
+SEMANTIC_VOCAB_SIZE = 10_000
+
+CODEBOOK_SIZE = 1024
+N_COARSE_CODEBOOKS = 2
+N_FINE_CODEBOOKS = 8
+COARSE_RATE_HZ = 75
+
+SAMPLE_RATE = 24_000
+
+REMOTE_MODEL_PATHS = {
+    "text": {
+        "repo_id": "suno/bark",
+        "file_name": "text_2.pt",
+    },
+    "coarse": {
+        "repo_id": "suno/bark",
+        "file_name": "coarse_2.pt",
+    },
+    "fine": {
+        "repo_id": "suno/bark",
+        "file_name": "fine_2.pt",
+    },
+}
+SUPPORTED_LANGS = [
+    ("English", "en"),
+    ("German", "de"),
+    ("Spanish", "es"),
+    ("French", "fr"),
+    ("Hindi", "hi"),
+    ("Italian", "it"),
+    ("Japanese", "ja"),
+    ("Korean", "ko"),
+    ("Polish", "pl"),
+    ("Portuguese", "pt"),
+    ("Russian", "ru"),
+    ("Turkish", "tr"),
+    ("Chinese", "zh"),
+]
+# init prompt_speaker_np
+ALLOWED_PROMPTS = {"announcer"}
+for _, lang in SUPPORTED_LANGS:
+    for prefix in ("", f"v2{os.path.sep}"):
+        for n in range(10):
+            ALLOWED_PROMPTS.add(f"{prefix}{lang}_speaker_{n}")
+
+TEXT_ENCODING_OFFSET = 10_048
+SEMANTIC_PAD_TOKEN = 10_000
+TEXT_PAD_TOKEN = 129_595
+SEMANTIC_INFER_TOKEN = 129_599
+
+COARSE_SEMANTIC_PAD_TOKEN = 12_048
+COARSE_INFER_TOKEN = 12_050
+
+
+def _download(self, from_hf_path, file_name, local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+    hf_hub_download(repo_id=from_hf_path, filename=file_name, local_dir=local_dir)
+
+
+def _load_codec_model(device):
+    model = EncodecModel.encodec_model_24khz()
+    model.set_target_bandwidth(6.0)
+    model.eval()
+    model.to(device)
+    return model
+
+
+def _normalize_whitespace(text):
+    return re.sub(r"\s+", " ", text).strip()
+
+
+def _load_history_prompt(history_prompt_dir: str, history_prompt_input: str):
+    # make sure this works on non-ubuntu
+
+    history_prompt_input = os.path.join(*history_prompt_input.split("/"))
+    if history_prompt_input not in ALLOWED_PROMPTS:
+        raise ValueError("history prompt not found")
+    history_prompt = np.load(
+        os.path.join(history_prompt_dir, "assets", "prompts", f"{history_prompt_input}.npz")
+    )
+    return history_prompt
+
+
+def _flatten_codebooks(arr, offset_size=CODEBOOK_SIZE):
+    assert len(arr.shape) == 2
+    arr = arr.copy()
+    if offset_size is not None:
+        for n in range(1, arr.shape[0]):
+            arr[n, :] += offset_size * n
+    flat_arr = arr.ravel("F")
+    return flat_arr
+
+
+class ModelCheckPointInfo:
+    _model_type: ModelType
+    _model: nn.Module
+    _model_path: str
+
+    def __init__(self, model_type: ModelType):
+        self._model_type = model_type
+
+    @property
+    def model_type(self) -> ModelType:
+        return self._model_type
+
+    @property
+    def model(self) -> nn.Module:
+        return self._model
+
+    @property
+    def model_path(self) -> str:
+        return self._model_path
+
+    @model.setter
+    def model(self, value):
+        self._model = value
+
+    @model_path.setter
+    def model_path(self, value):
+        self._model_path = value
+
+
+class BarkModelLoader:
+    _text_model: ModelCheckPointInfo = ModelCheckPointInfo(
+        model_type=ModelType(model_type="text_model", model_class=GPT, model_config=GPTConfig))
+    _coarse_model: ModelCheckPointInfo = ModelCheckPointInfo(
+        model_type=ModelType(model_type="coarse_model", model_class=GPT, model_config=GPTConfig))
+    _fine_model: ModelCheckPointInfo = ModelCheckPointInfo(
+        model_type=ModelType(model_type="fine_model", model_class=FineGPT, model_config=FineGPTConfig))
+
+    _tokenizer: BertTokenizer
+    _tokenizer_path: str = "bert-base-multilingual-cased"
+    _encodec: EncodecModel
+
+    def __init__(self, tokenizer_path: str, text_path: str, coarse_path: str, fine_path: str, device: str):
+
+        if tokenizer_path:
+            self._tokenizer_path = tokenizer_path
+        logger.info(f"BertTokenizer load.")
+        self._tokenizer = BertTokenizer.from_pretrained(self._tokenizer_path)
+        logger.info(f"BertTokenizer loaded")
+
+        self._text_model.model_path = text_path
+        self._coarse_model.model_path = coarse_path
+        self._fine_model.model_path = fine_path
+
+        # if not os.path.exists(self._text_model.model_path):
+        #     model_info = REMOTE_MODEL_PATHS['text']
+        #     logger.info(f"text model not found, downloading into `{self._text_model.model_path}`.")
+        #
+        #     _download(model_info["repo_id"], model_info["file_name"], self._text_model.model_path)
+        # if not os.path.exists(self._coarse_model.model_path):
+        #     model_info = REMOTE_MODEL_PATHS['coarse']
+        #     logger.info(f"coarse model not found, downloading into `{self._coarse_model.model_path}`.")
+        #     _download(model_info["repo_id"], model_info["file_name"], self._coarse_model.model_path)
+        # if not os.path.exists(self._fine_model.model_path):
+        #     model_info = REMOTE_MODEL_PATHS['fine']
+        #     logger.info(f"fine model not found, downloading into `{self._fine_model.model_path}`.")
+        #     _download(model_info["repo_id"], model_info["file_name"], self._fine_model.model_path)
+
+        self._load_moad(model_type=self._text_model.model_type, ckpt_path=self._text_model.model_path, device=device)
+        self._load_moad(model_type=self._coarse_model.model_type, ckpt_path=self._coarse_model.model_path, device=device)
+        self._load_moad(model_type=self._fine_model.model_type, ckpt_path=self._fine_model.model_path, device=device)
+
+    def _load_moad(self, model_type: ModelType, ckpt_path: str, device: str):
+        if not os.path.exists(self._fine_model.model_path):
+            raise RuntimeError("loader model path is not exists")
+
+        device = torch.device(device)
+        checkpoint = torch.load(ckpt_path, map_location=device)
+        # this is a hack
+        model_args = checkpoint["model_args"]
+        if "input_vocab_size" not in model_args:
+            model_args["input_vocab_size"] = model_args["vocab_size"]
+            model_args["output_vocab_size"] = model_args["vocab_size"]
+            del model_args["vocab_size"]
+        gptconf = model_type.model_config(**checkpoint["model_args"])
+        model = model_type.model_class(gptconf)
+        state_dict = checkpoint["model"]
+        # fixup checkpoint
+        unwanted_prefix = "_orig_mod."
+        for k, v in list(state_dict.items()):
+            if k.startswith(unwanted_prefix):
+                state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+        extra_keys = set(state_dict.keys()) - set(model.state_dict().keys())
+        extra_keys = set([k for k in extra_keys if not k.endswith(".attn.bias")])
+        missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
+        missing_keys = set([k for k in missing_keys if not k.endswith(".attn.bias")])
+        if len(extra_keys) != 0:
+            raise ValueError(f"extra keys found: {extra_keys}")
+        if len(missing_keys) != 0:
+            raise ValueError(f"missing keys: {missing_keys}")
+        model.load_state_dict(state_dict, strict=False)
+        n_params = model.get_num_params()
+        val_loss = checkpoint["best_val_loss"].item()
+        logger.info(
+            f"model {model_type.model_type} loaded: {round(n_params / 1e6, 1)}M params, {round(val_loss, 3)} loss")
+        model.eval()
+        model.to(device)
+        del checkpoint, state_dict
+        if model_type.model_type == "text_model":
+            self._text_model.model = model
+        elif model_type.model_type == "coarse_model":
+            self._coarse_model.model = model
+        elif model_type.model_type == "fine_model":
+            self._fine_model.model = model
+        self._encodec = _load_codec_model(device)
+
+    def generate_text_semantic(
+            self,
+            text,
+            history_prompt=None,
+            history_prompt_dir=None,
+            temp=0.7,
+            top_k=None,
+            top_p=None,
+            silent=False,
+            min_eos_p=0.2,
+            max_gen_duration_s=None,
+            allow_early_stop=True,
+            use_kv_caching=False,
+    ):
+        """Generate semantic tokens from text."""
+        assert isinstance(text, str)
+        text = _normalize_whitespace(text)
+        assert len(text.strip()) > 0
+        if history_prompt is not None:
+            history_prompt = _load_history_prompt(history_prompt_dir=history_prompt_dir, history_prompt_input=history_prompt)
+            semantic_history = history_prompt["semantic_prompt"]
+            assert (
+                    isinstance(semantic_history, np.ndarray)
+                    and len(semantic_history.shape) == 1
+                    and len(semantic_history) > 0
+                    and semantic_history.min() >= 0
+                    and semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1
+            )
+        else:
+            semantic_history = None
+        model = self._text_model.model
+        tokenizer = self._tokenizer
+        encoded_text = np.array(tokenizer.encode(text, add_special_tokens=False)) + TEXT_ENCODING_OFFSET
+        # if OFFLOAD_CPU:
+        #     model.to(models_devices["text"])
+        device = next(model.parameters()).device
+        if len(encoded_text) > 256:
+            p = round((len(encoded_text) - 256) / len(encoded_text) * 100, 1)
+            logger.warning(f"warning, text too long, lopping of last {p}%")
+            encoded_text = encoded_text[:256]
+        encoded_text = np.pad(
+            encoded_text,
+            (0, 256 - len(encoded_text)),
+            constant_values=TEXT_PAD_TOKEN,
+            mode="constant",
+        )
+        if semantic_history is not None:
+            semantic_history = semantic_history.astype(np.int64)
+            # lop off if history is too long, pad if needed
+            semantic_history = semantic_history[-256:]
+            semantic_history = np.pad(
+                semantic_history,
+                (0, 256 - len(semantic_history)),
+                constant_values=SEMANTIC_PAD_TOKEN,
+                mode="constant",
+            )
+        else:
+            semantic_history = np.array([SEMANTIC_PAD_TOKEN] * 256)
+        x = torch.from_numpy(
+            np.hstack([
+                encoded_text, semantic_history, np.array([SEMANTIC_INFER_TOKEN])
+            ]).astype(np.int64)
+        )[None]
+        assert x.shape[1] == 256 + 256 + 1
+        with _inference_mode():
+            x = x.to(device)
+            n_tot_steps = 768
+            # custom tqdm updates since we don't know when eos will occur
+            pbar = tqdm.tqdm(disable=silent, total=n_tot_steps)
+            pbar_state = 0
+            tot_generated_duration_s = 0
+            kv_cache = None
+            for n in range(n_tot_steps):
+                if use_kv_caching and kv_cache is not None:
+                    x_input = x[:, [-1]]
+                else:
+                    x_input = x
+                logits, kv_cache = model(
+                    x_input, merge_context=True, use_cache=use_kv_caching, past_kv=kv_cache
+                )
+                relevant_logits = logits[0, 0, :SEMANTIC_VOCAB_SIZE]
+                if allow_early_stop:
+                    relevant_logits = torch.hstack(
+                        (relevant_logits, logits[0, 0, [SEMANTIC_PAD_TOKEN]])  # eos
+                    )
+                if top_p is not None:
+                    # faster to convert to numpy
+                    original_device = relevant_logits.device
+                    relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
+                    sorted_indices = np.argsort(relevant_logits)[::-1]
+                    sorted_logits = relevant_logits[sorted_indices]
+                    cumulative_probs = np.cumsum(softmax(sorted_logits))
+                    sorted_indices_to_remove = cumulative_probs > top_p
+                    sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
+                    sorted_indices_to_remove[0] = False
+                    relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
+                    relevant_logits = torch.from_numpy(relevant_logits)
+                    relevant_logits = relevant_logits.to(original_device)
+                if top_k is not None:
+                    v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
+                    relevant_logits[relevant_logits < v[-1]] = -float("Inf")
+                probs = F.softmax(relevant_logits / temp, dim=-1)
+                item_next = torch.multinomial(probs, num_samples=1).to(torch.int32)
+                if allow_early_stop and (
+                        item_next == SEMANTIC_VOCAB_SIZE
+                        or (min_eos_p is not None and probs[-1] >= min_eos_p)
+                ):
+                    # eos found, so break
+                    pbar.update(n - pbar_state)
+                    break
+                x = torch.cat((x, item_next[None]), dim=1)
+                tot_generated_duration_s += 1 / SEMANTIC_RATE_HZ
+                if max_gen_duration_s is not None and tot_generated_duration_s > max_gen_duration_s:
+                    pbar.update(n - pbar_state)
+                    break
+                if n == n_tot_steps - 1:
+                    pbar.update(n - pbar_state)
+                    break
+                del logits, relevant_logits, probs, item_next
+
+                if n > pbar_state:
+                    if n > pbar.total:
+                        pbar.total = n
+                    pbar.update(n - pbar_state)
+                pbar_state = n
+            pbar.total = n
+            pbar.refresh()
+            pbar.close()
+            out = x.detach().cpu().numpy().squeeze()[256 + 256 + 1:]
+
+        assert all(0 <= out) and all(out < SEMANTIC_VOCAB_SIZE)
+
+        return out
+
+    def generate_coarse(
+            self,
+            x_semantic,
+            history_prompt=None,
+            history_prompt_dir=None,
+            temp=0.7,
+            top_k=None,
+            top_p=None,
+            silent=False,
+            max_coarse_history=630,  # min 60 (faster), max 630 (more context)
+            sliding_window_len=60,
+            use_kv_caching=False,
+    ):
+        """Generate coarse audio codes from semantic tokens."""
+        assert (
+                isinstance(x_semantic, np.ndarray)
+                and len(x_semantic.shape) == 1
+                and len(x_semantic) > 0
+                and x_semantic.min() >= 0
+                and x_semantic.max() <= SEMANTIC_VOCAB_SIZE - 1
+        )
+        assert 60 <= max_coarse_history <= 630
+        assert max_coarse_history + sliding_window_len <= 1024 - 256
+        semantic_to_coarse_ratio = COARSE_RATE_HZ / SEMANTIC_RATE_HZ * N_COARSE_CODEBOOKS
+        max_semantic_history = int(np.floor(max_coarse_history / semantic_to_coarse_ratio))
+        if history_prompt is not None:
+            history_prompt = _load_history_prompt(history_prompt_dir=history_prompt_dir,history_prompt_input=history_prompt)
+            x_semantic_history = history_prompt["semantic_prompt"]
+            x_coarse_history = history_prompt["coarse_prompt"]
+            assert (
+                    isinstance(x_semantic_history, np.ndarray)
+                    and len(x_semantic_history.shape) == 1
+                    and len(x_semantic_history) > 0
+                    and x_semantic_history.min() >= 0
+                    and x_semantic_history.max() <= SEMANTIC_VOCAB_SIZE - 1
+                    and isinstance(x_coarse_history, np.ndarray)
+                    and len(x_coarse_history.shape) == 2
+                    and x_coarse_history.shape[0] == N_COARSE_CODEBOOKS
+                    and x_coarse_history.shape[-1] >= 0
+                    and x_coarse_history.min() >= 0
+                    and x_coarse_history.max() <= CODEBOOK_SIZE - 1
+                    and (
+                            round(x_coarse_history.shape[-1] / len(x_semantic_history), 1)
+                            == round(semantic_to_coarse_ratio / N_COARSE_CODEBOOKS, 1)
+                    )
+            )
+            x_coarse_history = _flatten_codebooks(x_coarse_history) + SEMANTIC_VOCAB_SIZE
+            # trim histories correctly
+            n_semantic_hist_provided = np.min(
+                [
+                    max_semantic_history,
+                    len(x_semantic_history) - len(x_semantic_history) % 2,
+                    int(np.floor(len(x_coarse_history) / semantic_to_coarse_ratio)),
+                ]
+            )
+            n_coarse_hist_provided = int(round(n_semantic_hist_provided * semantic_to_coarse_ratio))
+            x_semantic_history = x_semantic_history[-n_semantic_hist_provided:].astype(np.int32)
+            x_coarse_history = x_coarse_history[-n_coarse_hist_provided:].astype(np.int32)
+            # TODO: bit of a hack for time alignment (sounds better)
+            x_coarse_history = x_coarse_history[:-2]
+        else:
+            x_semantic_history = np.array([], dtype=np.int32)
+            x_coarse_history = np.array([], dtype=np.int32)
+
+        model = self._coarse_model.model
+        # if OFFLOAD_CPU:
+        #     model.to(models_devices["coarse"])
+        device = next(model.parameters()).device
+        # start loop
+        n_steps = int(
+            round(
+                np.floor(len(x_semantic) * semantic_to_coarse_ratio / N_COARSE_CODEBOOKS)
+                * N_COARSE_CODEBOOKS
+            )
+        )
+        assert n_steps > 0 and n_steps % N_COARSE_CODEBOOKS == 0
+        x_semantic = np.hstack([x_semantic_history, x_semantic]).astype(np.int32)
+        x_coarse = x_coarse_history.astype(np.int32)
+        base_semantic_idx = len(x_semantic_history)
+        with _inference_mode():
+            x_semantic_in = torch.from_numpy(x_semantic)[None].to(device)
+            x_coarse_in = torch.from_numpy(x_coarse)[None].to(device)
+            n_window_steps = int(np.ceil(n_steps / sliding_window_len))
+            n_step = 0
+            for _ in tqdm.tqdm(range(n_window_steps), total=n_window_steps, disable=silent):
+                semantic_idx = base_semantic_idx + int(round(n_step / semantic_to_coarse_ratio))
+                # pad from right side
+                x_in = x_semantic_in[:, np.max([0, semantic_idx - max_semantic_history]):]
+                x_in = x_in[:, :256]
+                x_in = F.pad(
+                    x_in,
+                    (0, 256 - x_in.shape[-1]),
+                    "constant",
+                    COARSE_SEMANTIC_PAD_TOKEN,
+                )
+                x_in = torch.hstack(
+                    [
+                        x_in,
+                        torch.tensor([COARSE_INFER_TOKEN])[None].to(device),
+                        x_coarse_in[:, -max_coarse_history:],
+                    ]
+                )
+                kv_cache = None
+                for _ in range(sliding_window_len):
+                    if n_step >= n_steps:
+                        continue
+                    is_major_step = n_step % N_COARSE_CODEBOOKS == 0
+
+                    if use_kv_caching and kv_cache is not None:
+                        x_input = x_in[:, [-1]]
+                    else:
+                        x_input = x_in
+
+                    logits, kv_cache = model(x_input, use_cache=use_kv_caching, past_kv=kv_cache)
+                    logit_start_idx = (
+                            SEMANTIC_VOCAB_SIZE + (1 - int(is_major_step)) * CODEBOOK_SIZE
+                    )
+                    logit_end_idx = (
+                            SEMANTIC_VOCAB_SIZE + (2 - int(is_major_step)) * CODEBOOK_SIZE
+                    )
+                    relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx]
+                    if top_p is not None:
+                        # faster to convert to numpy
+                        original_device = relevant_logits.device
+                        relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
+                        sorted_indices = np.argsort(relevant_logits)[::-1]
+                        sorted_logits = relevant_logits[sorted_indices]
+                        cumulative_probs = np.cumsum(softmax(sorted_logits))
+                        sorted_indices_to_remove = cumulative_probs > top_p
+                        sorted_indices_to_remove[1:] = sorted_indices_to_remove[:-1].copy()
+                        sorted_indices_to_remove[0] = False
+                        relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
+                        relevant_logits = torch.from_numpy(relevant_logits)
+                        relevant_logits = relevant_logits.to(original_device)
+                    if top_k is not None:
+                        v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
+                        relevant_logits[relevant_logits < v[-1]] = -float("Inf")
+                    probs = F.softmax(relevant_logits / temp, dim=-1)
+                    item_next = torch.multinomial(probs, num_samples=1).to(torch.int32)
+                    item_next += logit_start_idx
+                    x_coarse_in = torch.cat((x_coarse_in, item_next[None]), dim=1)
+                    x_in = torch.cat((x_in, item_next[None]), dim=1)
+                    del logits, relevant_logits, probs, item_next
+                    n_step += 1
+                del x_in
+            del x_semantic_in
+        # if OFFLOAD_CPU:
+        #     model.to("cpu")
+        gen_coarse_arr = x_coarse_in.detach().cpu().numpy().squeeze()[len(x_coarse_history):]
+        del x_coarse_in
+        assert len(gen_coarse_arr) == n_steps
+        gen_coarse_audio_arr = gen_coarse_arr.reshape(-1, N_COARSE_CODEBOOKS).T - SEMANTIC_VOCAB_SIZE
+        for n in range(1, N_COARSE_CODEBOOKS):
+            gen_coarse_audio_arr[n, :] -= n * CODEBOOK_SIZE
+        # _clear_cuda_cache()
+        return gen_coarse_audio_arr
+
+    def generate_fine(
+            self,
+            x_coarse_gen,
+            history_prompt=None,
+            history_prompt_dir=None,
+            temp=0.5,
+            silent=True,
+    ):
+        """Generate full audio codes from coarse audio codes."""
+        assert (
+                isinstance(x_coarse_gen, np.ndarray)
+                and len(x_coarse_gen.shape) == 2
+                and 1 <= x_coarse_gen.shape[0] <= N_FINE_CODEBOOKS - 1
+                and x_coarse_gen.shape[1] > 0
+                and x_coarse_gen.min() >= 0
+                and x_coarse_gen.max() <= CODEBOOK_SIZE - 1
+        )
+        if history_prompt is not None:
+            history_prompt = _load_history_prompt(history_prompt_dir=history_prompt_dir,history_prompt_input=history_prompt)
+            x_fine_history = history_prompt["fine_prompt"]
+            assert (
+                    isinstance(x_fine_history, np.ndarray)
+                    and len(x_fine_history.shape) == 2
+                    and x_fine_history.shape[0] == N_FINE_CODEBOOKS
+                    and x_fine_history.shape[1] >= 0
+                    and x_fine_history.min() >= 0
+                    and x_fine_history.max() <= CODEBOOK_SIZE - 1
+            )
+        else:
+            x_fine_history = None
+        n_coarse = x_coarse_gen.shape[0]
+
+        model = self._fine_model.model
+        # if OFFLOAD_CPU:
+        #     model.to(models_devices["fine"])
+        device = next(model.parameters()).device
+        # make input arr
+        in_arr = np.vstack(
+            [
+                x_coarse_gen,
+                np.zeros((N_FINE_CODEBOOKS - n_coarse, x_coarse_gen.shape[1]))
+                + CODEBOOK_SIZE,  # padding
+            ]
+        ).astype(np.int32)
+        # prepend history if available (max 512)
+        if x_fine_history is not None:
+            x_fine_history = x_fine_history.astype(np.int32)
+            in_arr = np.hstack(
+                [
+                    x_fine_history[:, -512:].astype(np.int32),
+                    in_arr,
+                ]
+            )
+            n_history = x_fine_history[:, -512:].shape[1]
+        else:
+            n_history = 0
+        n_remove_from_end = 0
+        # need to pad if too short (since non-causal model)
+        if in_arr.shape[1] < 1024:
+            n_remove_from_end = 1024 - in_arr.shape[1]
+            in_arr = np.hstack(
+                [
+                    in_arr,
+                    np.zeros((N_FINE_CODEBOOKS, n_remove_from_end), dtype=np.int32) + CODEBOOK_SIZE,
+                ]
+            )
+        # we can be lazy about fractional loop and just keep overwriting codebooks
+        n_loops = np.max([0, int(np.ceil((x_coarse_gen.shape[1] - (1024 - n_history)) / 512))]) + 1
+        with _inference_mode():
+            in_arr = torch.tensor(in_arr.T).to(device)
+            for n in tqdm.tqdm(range(n_loops), disable=silent):
+                start_idx = np.min([n * 512, in_arr.shape[0] - 1024])
+                start_fill_idx = np.min([n_history + n * 512, in_arr.shape[0] - 512])
+                rel_start_fill_idx = start_fill_idx - start_idx
+                in_buffer = in_arr[start_idx: start_idx + 1024, :][None]
+                for nn in range(n_coarse, N_FINE_CODEBOOKS):
+                    logits = model(nn, in_buffer)
+                    if temp is None:
+                        relevant_logits = logits[0, rel_start_fill_idx:, :CODEBOOK_SIZE]
+                        codebook_preds = torch.argmax(relevant_logits, -1)
+                    else:
+                        relevant_logits = logits[0, :, :CODEBOOK_SIZE] / temp
+                        probs = F.softmax(relevant_logits, dim=-1)
+                        codebook_preds = torch.multinomial(
+                            probs[rel_start_fill_idx:1024], num_samples=1
+                        ).reshape(-1)
+                    codebook_preds = codebook_preds.to(torch.int32)
+                    in_buffer[0, rel_start_fill_idx:, nn] = codebook_preds
+                    del logits, codebook_preds
+                # transfer over info into model_in and convert to numpy
+                for nn in range(n_coarse, N_FINE_CODEBOOKS):
+                    in_arr[
+                    start_fill_idx: start_fill_idx + (1024 - rel_start_fill_idx), nn
+                    ] = in_buffer[0, rel_start_fill_idx:, nn]
+                del in_buffer
+            gen_fine_arr = in_arr.detach().cpu().numpy().squeeze().T
+            del in_arr
+        # if OFFLOAD_CPU:
+        #     model.to("cpu")
+        gen_fine_arr = gen_fine_arr[:, n_history:]
+        if n_remove_from_end > 0:
+            gen_fine_arr = gen_fine_arr[:, :-n_remove_from_end]
+        assert gen_fine_arr.shape[-1] == x_coarse_gen.shape[-1]
+        # _clear_cuda_cache()
+        return gen_fine_arr
+
+    def codec_decode(self, fine_tokens):
+        """Turn quantized audio codes into audio array using encodec."""
+
+        model = self._encodec
+        device = next(model.parameters()).device
+        arr = torch.from_numpy(fine_tokens)[None]
+        arr = arr.to(device)
+        arr = arr.transpose(0, 1)
+        emb = model.quantizer.decode(arr)
+        out = model.decoder(emb)
+        audio_arr = out.detach().cpu().numpy().squeeze()
+        del arr, emb, out
+
+        return audio_arr
+
+
+if __name__ == '__main__':
+    bark_load = BarkModelLoader(tokenizer_path='/media/checkpoint/bark/bert-base-multilingual-cased/',
+                                text_path='/media/checkpoint/bark/suno/bark_v0/text_2.pt',
+                                coarse_path='/media/checkpoint/bark/suno/bark_v0/coarse_2.pt',
+                                fine_path='/media/checkpoint/bark/suno/bark_v0/fine_2.pt')
+
+    print(bark_load)
diff --git a/bark/model.py b/bark/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..457b49e749f396c47c6b35f44955fd512d233d79
--- /dev/null
+++ b/bark/model.py
@@ -0,0 +1,218 @@
+"""
+Much of this code is adapted from Andrej Karpathy's NanoGPT
+(https://github.com/karpathy/nanoGPT)
+"""
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+class LayerNorm(nn.Module):
+    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
+
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+
+    def forward(self, input):
+        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        if not self.flash:
+            # print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
+            # causal mask to ensure that attention is only applied to the left in the input sequence
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                        .view(1, 1, config.block_size, config.block_size))
+
+    def forward(self, x, past_kv=None, use_cache=False):
+        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k ,v  = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+
+        if past_kv is not None:
+            past_key = past_kv[0]
+            past_value = past_kv[1]
+            k = torch.cat((past_key, k), dim=-2)
+            v = torch.cat((past_value, v), dim=-2)
+
+        FULL_T = k.shape[-2]
+
+        if use_cache is True:
+            present = (k, v)
+        else:
+            present = None
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        if self.flash:
+            # efficient attention using Flash Attention CUDA kernels
+            if past_kv is not None:
+                # When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
+                # the query for the last token. scaled_dot_product_attention interprets this as the first token in the
+                # sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so 
+                # to work around this we set is_causal=False.
+                is_causal = False
+            else:
+                is_causal = True
+
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, is_causal=is_causal)
+        else:
+            # manual implementation of attention
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = att.masked_fill(self.bias[:,:,FULL_T-T:FULL_T,:FULL_T] == 0, float('-inf'))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return (y, present)
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+        self.gelu = nn.GELU()
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+class Block(nn.Module):
+
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
+        self.mlp = MLP(config)
+        self.layer_idx = layer_idx
+
+    def forward(self, x, past_kv=None, use_cache=False):
+        attn_output, prev_kvs = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
+        x = x + attn_output
+        x = x + self.mlp(self.ln_2(x))
+        return (x, prev_kvs)
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024
+    input_vocab_size: int = 10_048
+    output_vocab_size: int = 10_048
+    n_layer: int = 12
+    n_head: int = 12
+    n_embd: int = 768
+    dropout: float = 0.0
+    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.input_vocab_size is not None
+        assert config.output_vocab_size is not None
+        assert config.block_size is not None
+        self.config = config
+
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.input_vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            drop = nn.Dropout(config.dropout),
+            h = nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
+            ln_f = LayerNorm(config.n_embd, bias=config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding:
+            n_params -= self.transformer.wte.weight.numel()
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+
+    def forward(self, idx, merge_context=False, past_kv=None, position_ids=None, use_cache=False):
+        device = idx.device
+        b, t = idx.size()
+        if past_kv is not None:
+            assert t == 1
+            tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
+        else:
+            if merge_context:
+                assert(idx.shape[1] >= 256+256+1)
+                t = idx.shape[1] - 256
+            else:
+                assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+
+            # forward the GPT model itself
+            if merge_context:
+                tok_emb = torch.cat([
+                    self.transformer.wte(idx[:,:256]) + self.transformer.wte(idx[:,256:256+256]),
+                    self.transformer.wte(idx[:,256+256:])
+                ], dim=1)
+            else:
+                tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
+
+        if past_kv is None:
+            past_length = 0
+            past_kv = tuple([None] * len(self.transformer.h))
+        else:
+            past_length = past_kv[0][0].size(-2)
+
+        if position_ids is None:
+            position_ids = torch.arange(past_length, t + past_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0) # shape (1, t)
+            assert position_ids.shape == (1, t)
+
+        pos_emb = self.transformer.wpe(position_ids) # position embeddings of shape (1, t, n_embd)
+
+        x = self.transformer.drop(tok_emb + pos_emb)
+
+        new_kv = () if use_cache else None
+
+        for i, (block, past_layer_kv) in enumerate(zip(self.transformer.h, past_kv)):
+            x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
+
+            if use_cache:
+                new_kv = new_kv + (kv,)
+
+        x = self.transformer.ln_f(x)
+
+        # inference-time mini-optimization: only forward the lm_head on the very last position
+        logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
+
+        return (logits, new_kv)
diff --git a/bark/model_fine.py b/bark/model_fine.py
new file mode 100644
index 0000000000000000000000000000000000000000..e51d3e6a9de947b6ceb6b8441f72a6b3fe1037f0
--- /dev/null
+++ b/bark/model_fine.py
@@ -0,0 +1,149 @@
+"""
+Much of this code is adapted from Andrej Karpathy's NanoGPT
+(https://github.com/karpathy/nanoGPT)
+"""
+from dataclasses import dataclass
+import math
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from .model import GPT, GPTConfig, MLP
+
+
+class NonCausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
+        self.flash = (
+            hasattr(torch.nn.functional, "scaled_dot_product_attention")
+        )
+
+    def forward(self, x):
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)  # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        if self.flash:
+            # efficient attention using Flash Attention CUDA kernels
+            y = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=False
+            )
+        else:
+            # manual implementation of attention
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = (
+            y.transpose(1, 2).contiguous().view(B, T, C)
+        )  # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+
+class FineBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = NonCausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class FineGPT(GPT):
+    def __init__(self, config):
+        super().__init__(config)
+        del self.lm_head
+        self.config = config
+        self.n_codes_total = config.n_codes_total
+        self.transformer = nn.ModuleDict(
+            dict(
+                wtes=nn.ModuleList(
+                    [
+                        nn.Embedding(config.input_vocab_size, config.n_embd)
+                        for _ in range(config.n_codes_total)
+                    ]
+                ),
+                wpe=nn.Embedding(config.block_size, config.n_embd),
+                drop=nn.Dropout(config.dropout),
+                h=nn.ModuleList([FineBlock(config) for _ in range(config.n_layer)]),
+                ln_f=nn.LayerNorm(config.n_embd),
+            )
+        )
+        self.lm_heads = nn.ModuleList(
+            [
+                nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
+                for _ in range(config.n_codes_given, self.n_codes_total)
+            ]
+        )
+        for i in range(self.n_codes_total - config.n_codes_given):
+            self.transformer.wtes[i + 1].weight = self.lm_heads[i].weight
+
+    def forward(self, pred_idx, idx):
+        device = idx.device
+        b, t, codes = idx.size()
+        assert (
+            t <= self.config.block_size
+        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+        assert pred_idx > 0, "cannot predict 0th codebook"
+        assert codes == self.n_codes_total, (b, t, codes)
+        pos = torch.arange(0, t, dtype=torch.long, device=device).unsqueeze(0)  # shape (1, t)
+
+        # forward the GPT model itself
+        tok_embs = [
+            wte(idx[:, :, i]).unsqueeze(-1) for i, wte in enumerate(self.transformer.wtes)
+        ]  # token embeddings of shape (b, t, n_embd)
+        tok_emb = torch.cat(tok_embs, dim=-1)
+        pos_emb = self.transformer.wpe(pos)  # position embeddings of shape (1, t, n_embd)
+        x = tok_emb[:, :, :, : pred_idx + 1].sum(dim=-1)
+        x = self.transformer.drop(x + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        logits = self.lm_heads[pred_idx - self.config.n_codes_given](x)
+        return logits
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding:
+            for wte in self.transformer.wtes:
+                n_params -= wte.weight.numel()
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+
+
+@dataclass
+class FineGPTConfig(GPTConfig):
+    n_codes_total: int = 8
+    n_codes_given: int = 1
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..570458d4aaf3b2b1c7e0f01586166079cbf21c47
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf270229a4141b962555902a0276d40c9229952a46510c51a0debdeb1bad297b
+size 533
diff --git a/rvc/__init__.py b/rvc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..829f9a80eea18e845a7bccf315486b0e6d177b71
--- /dev/null
+++ b/rvc/__init__.py
@@ -0,0 +1,6 @@
+from speakers.common.registry import registry
+import os
+
+root_dir = os.path.dirname(os.path.abspath(__file__))
+registry.register_path("rvc_library_root", root_dir)
+
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diff --git a/rvc/infer_pack/attentions.py b/rvc/infer_pack/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a0b19616f0049178c0b890a5897db57d59c5e5a
--- /dev/null
+++ b/rvc/infer_pack/attentions.py
@@ -0,0 +1,414 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from rvc.infer_pack import commons
+from rvc.infer_pack.modules import LayerNorm
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        window_size=10,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    window_size=window_size,
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size=1,
+        p_dropout=0.0,
+        proximal_bias=False,
+        proximal_init=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+
+        self.drop = nn.Dropout(p_dropout)
+        self.self_attn_layers = nn.ModuleList()
+        self.norm_layers_0 = nn.ModuleList()
+        self.encdec_attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.self_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels,
+                    hidden_channels,
+                    n_heads,
+                    p_dropout=p_dropout,
+                    proximal_bias=proximal_bias,
+                    proximal_init=proximal_init,
+                )
+            )
+            self.norm_layers_0.append(LayerNorm(hidden_channels))
+            self.encdec_attn_layers.append(
+                MultiHeadAttention(
+                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
+                )
+            )
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(
+                    hidden_channels,
+                    hidden_channels,
+                    filter_channels,
+                    kernel_size,
+                    p_dropout=p_dropout,
+                    causal=True,
+                )
+            )
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+    def forward(self, x, x_mask, h, h_mask):
+        """
+        x: decoder input
+        h: encoder output
+        """
+        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
+            device=x.device, dtype=x.dtype
+        )
+        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        x = x * x_mask
+        for i in range(self.n_layers):
+            y = self.self_attn_layers[i](x, x, self_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_0[i](x + y)
+
+            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+            y = self.drop(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        n_heads,
+        p_dropout=0.0,
+        window_size=None,
+        heads_share=True,
+        block_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+    ):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.proximal_init = proximal_init
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels**-0.5
+            self.emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+            self.emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
+                * rel_stddev
+            )
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+        if proximal_init:
+            with torch.no_grad():
+                self.conv_k.weight.copy_(self.conv_q.weight)
+                self.conv_k.bias.copy_(self.conv_q.bias)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+        if self.window_size is not None:
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(
+                query / math.sqrt(self.k_channels), key_relative_embeddings
+            )
+            scores_local = self._relative_position_to_absolute_position(rel_logits)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(
+                device=scores.device, dtype=scores.dtype
+            )
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
+                block_mask = (
+                    torch.ones_like(scores)
+                    .triu(-self.block_length)
+                    .tril(self.block_length)
+                )
+                scores = scores.masked_fill(block_mask == 0, -1e4)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
+            output = output + self._matmul_with_relative_values(
+                relative_weights, value_relative_embeddings
+            )
+        output = (
+            output.transpose(2, 3).contiguous().view(b, d, t_t)
+        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
+            )
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[
+            :, slice_start_position:slice_end_position
+        ]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(
+            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
+        )
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
+            :, :, :length, length - 1 :
+        ]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(
+            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
+        )
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        filter_channels,
+        kernel_size,
+        p_dropout=0.0,
+        activation=None,
+        causal=False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+        self.causal = causal
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(self.padding(x * x_mask))
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(self.padding(x * x_mask))
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, commons.convert_pad_shape(padding))
+        return x
diff --git a/rvc/infer_pack/commons.py b/rvc/infer_pack/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..54470986f37825b35d90d7efa7437d1c26b87215
--- /dev/null
+++ b/rvc/infer_pack/commons.py
@@ -0,0 +1,166 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+    """KL(P||Q)"""
+    kl = (logs_q - logs_p) - 0.5
+    kl += (
+        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    )
+    return kl
+
+
+def rand_gumbel(shape):
+    """Sample from the Gumbel distribution, protect from overflows."""
+    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+    return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+    return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, :, idx_str:idx_end]
+    return ret
+
+
+def slice_segments2(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, idx_str:idx_end]
+    return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = x_lengths - segment_size + 1
+    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
+    position = torch.arange(length, dtype=torch.float)
+    num_timescales = channels // 2
+    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
+        num_timescales - 1
+    )
+    inv_timescales = min_timescale * torch.exp(
+        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
+    )
+    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+    signal = F.pad(signal, [0, 0, 0, channels % 2])
+    signal = signal.view(1, channels, length)
+    return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+    b, channels, length = x.size()
+    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+    return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+    """
+    duration: [b, 1, t_x]
+    mask: [b, 1, t_y, t_x]
+    """
+    device = duration.device
+
+    b, _, t_y, t_x = mask.shape
+    cum_duration = torch.cumsum(duration, -1)
+
+    cum_duration_flat = cum_duration.view(b * t_x)
+    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+    path = path.view(b, t_x, t_y)
+    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+    path = path.unsqueeze(1).transpose(2, 3) * mask
+    return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = list(filter(lambda p: p.grad is not None, parameters))
+    norm_type = float(norm_type)
+    if clip_value is not None:
+        clip_value = float(clip_value)
+
+    total_norm = 0
+    for p in parameters:
+        param_norm = p.grad.data.norm(norm_type)
+        total_norm += param_norm.item() ** norm_type
+        if clip_value is not None:
+            p.grad.data.clamp_(min=-clip_value, max=clip_value)
+    total_norm = total_norm ** (1.0 / norm_type)
+    return total_norm
diff --git a/rvc/infer_pack/models.py b/rvc/infer_pack/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac994caaa179ef7fb603463b61e3330a0dcfa992
--- /dev/null
+++ b/rvc/infer_pack/models.py
@@ -0,0 +1,1112 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from rvc.infer_pack import modules, commons, attentions
+from rvc.infer_pack.commons import get_padding
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from rvc.infer_pack.commons import init_weights
+import numpy as np
+
+
+class TextEncoder256(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        f0=True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.emb_phone = nn.Linear(256, hidden_channels)
+        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
+        if f0 == True:
+            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
+        self.encoder = attentions.Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, phone, pitch, lengths):
+        if pitch == None:
+            x = self.emb_phone(phone)
+        else:
+            x = self.emb_phone(phone) + self.emb_pitch(pitch)
+        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = self.lrelu(x)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.encoder(x * x_mask, x_mask)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return m, logs, x_mask
+class TextEncoder768(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        f0=True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.emb_phone = nn.Linear(768, hidden_channels)
+        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
+        if f0 == True:
+            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
+        self.encoder = attentions.Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, phone, pitch, lengths):
+        if pitch == None:
+            x = self.emb_phone(phone)
+        else:
+            x = self.emb_phone(phone) + self.emb_pitch(pitch)
+        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = self.lrelu(x)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.encoder(x * x_mask, x_mask)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return m, logs, x_mask
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+    def remove_weight_norm(self):
+        for i in range(self.n_flows):
+            self.flows[i * 2].remove_weight_norm()
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+    def remove_weight_norm(self):
+        self.enc.remove_weight_norm()
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class SineGen(torch.nn.Module):
+    """Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(
+        self,
+        samp_rate,
+        harmonic_num=0,
+        sine_amp=0.1,
+        noise_std=0.003,
+        voiced_threshold=0,
+        flag_for_pulse=False,
+    ):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def forward(self, f0, upp):
+        """sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0 = f0[:, None].transpose(1, 2)
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
+                    idx + 2
+                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
+            rand_ini = torch.rand(
+                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
+            )
+            rand_ini[:, 0] = 0
+            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
+            tmp_over_one *= upp
+            tmp_over_one = F.interpolate(
+                tmp_over_one.transpose(2, 1),
+                scale_factor=upp,
+                mode="linear",
+                align_corners=True,
+            ).transpose(2, 1)
+            rad_values = F.interpolate(
+                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
+            ).transpose(
+                2, 1
+            )  #######
+            tmp_over_one %= 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+            sine_waves = torch.sin(
+                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
+            )
+            sine_waves = sine_waves * self.sine_amp
+            uv = self._f02uv(f0)
+            uv = F.interpolate(
+                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
+            ).transpose(2, 1)
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(
+        self,
+        sampling_rate,
+        harmonic_num=0,
+        sine_amp=0.1,
+        add_noise_std=0.003,
+        voiced_threshod=0,
+        is_half=True,
+    ):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+        self.is_half = is_half
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(
+            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
+        )
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x, upp=None):
+        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
+        if self.is_half:
+            sine_wavs = sine_wavs.half()
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+        return sine_merge, None, None  # noise, uv
+
+
+class GeneratorNSF(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels,
+        sr,
+        is_half=False,
+    ):
+        super(GeneratorNSF, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=sr, harmonic_num=0, is_half=is_half
+        )
+        self.noise_convs = nn.ModuleList()
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            c_cur = upsample_initial_channel // (2 ** (i + 1))
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+            if i + 1 < len(upsample_rates):
+                stride_f0 = np.prod(upsample_rates[i + 1 :])
+                self.noise_convs.append(
+                    Conv1d(
+                        1,
+                        c_cur,
+                        kernel_size=stride_f0 * 2,
+                        stride=stride_f0,
+                        padding=stride_f0 // 2,
+                    )
+                )
+            else:
+                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+        self.upp = np.prod(upsample_rates)
+
+    def forward(self, x, f0, g=None):
+        har_source, noi_source, uv = self.m_source(f0, self.upp)
+        har_source = har_source.transpose(1, 2)
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            x_source = self.noise_convs[i](har_source)
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+sr2sr = {
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+
+class SynthesizerTrnMs256NSFsid(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        if type(sr) == type("strr"):
+            sr = sr2sr[sr]
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            sr=sr,
+            is_half=kwargs["is_half"],
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(
+        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
+    ):  # 这里ds是id，[bs,1]
+        # print(1,pitch.shape)#[bs,t]
+        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+        z_p = self.flow(z, y_mask, g=g)
+        z_slice, ids_slice = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
+        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
+        # print(-2,pitchf.shape,z_slice.shape)
+        o = self.dec(z_slice, pitchf, g=g)
+        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
+        z = self.flow(z_p, x_mask, g=g, reverse=True)
+        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)
+class SynthesizerTrnMs768NSFsid(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        if type(sr) == type("strr"):
+            sr = sr2sr[sr]
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder768(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            sr=sr,
+            is_half=kwargs["is_half"],
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(
+        self, phone, phone_lengths, pitch, pitchf, y, y_lengths, ds
+    ):  # 这里ds是id，[bs,1]
+        # print(1,pitch.shape)#[bs,t]
+        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+        z_p = self.flow(z, y_mask, g=g)
+        z_slice, ids_slice = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
+        pitchf = commons.slice_segments2(pitchf, ids_slice, self.segment_size)
+        # print(-2,pitchf.shape,z_slice.shape)
+        o = self.dec(z_slice, pitchf, g=g)
+        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+    def infer(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
+        z = self.flow(z_p, x_mask, g=g, reverse=True)
+        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)
+
+
+class SynthesizerTrnMs256NSFsid_nono(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr=None,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            f0=False,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
+        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
+        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+        z_p = self.flow(z, y_mask, g=g)
+        z_slice, ids_slice = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        o = self.dec(z_slice, g=g)
+        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+    def infer(self, phone, phone_lengths, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
+        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
+        z = self.flow(z_p, x_mask, g=g, reverse=True)
+        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)
+class SynthesizerTrnMs768NSFsid_nono(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr=None,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder768(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            f0=False,
+        )
+        self.dec = Generator(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(self, phone, phone_lengths, y, y_lengths, ds):  # 这里ds是id，[bs,1]
+        g = self.emb_g(ds).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
+        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
+        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+        z_p = self.flow(z, y_mask, g=g)
+        z_slice, ids_slice = commons.rand_slice_segments(
+            z, y_lengths, self.segment_size
+        )
+        o = self.dec(z_slice, g=g)
+        return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+    def infer(self, phone, phone_lengths, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
+        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
+        z = self.flow(z_p, x_mask, g=g, reverse=True)
+        o = self.dec((z * x_mask)[:, :, :max_len], g=g)
+        return o, x_mask, (z, z_p, m_p, logs_p)
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11, 17]
+        # periods = [3, 5, 7, 11, 17, 23, 37]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []  #
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            # for j in range(len(fmap_r)):
+            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+class MultiPeriodDiscriminatorV2(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminatorV2, self).__init__()
+        # periods = [2, 3, 5, 7, 11, 17]
+        periods = [2,3, 5, 7, 11, 17, 23, 37]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []  #
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            # for j in range(len(fmap_r)):
+            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
diff --git a/rvc/infer_pack/models_onnx.py b/rvc/infer_pack/models_onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..c42227e7a6de4c18539224c1063d7053c5d5236f
--- /dev/null
+++ b/rvc/infer_pack/models_onnx.py
@@ -0,0 +1,756 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from rvc.infer_pack import modules, commons, attentions
+from rvc.infer_pack.commons import get_padding
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from rvc.infer_pack.commons import init_weights
+import numpy as np
+
+
+class TextEncoder256(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        f0=True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.emb_phone = nn.Linear(256, hidden_channels)
+        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
+        if f0 == True:
+            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
+        self.encoder = attentions.Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, phone, pitch, lengths):
+        if pitch == None:
+            x = self.emb_phone(phone)
+        else:
+            x = self.emb_phone(phone) + self.emb_pitch(pitch)
+        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = self.lrelu(x)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.encoder(x * x_mask, x_mask)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return m, logs, x_mask
+
+
+class TextEncoder256Sim(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        f0=True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.emb_phone = nn.Linear(256, hidden_channels)
+        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
+        if f0 == True:
+            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
+        self.encoder = attentions.Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+
+    def forward(self, phone, pitch, lengths):
+        if pitch == None:
+            x = self.emb_phone(phone)
+        else:
+            x = self.emb_phone(phone) + self.emb_pitch(pitch)
+        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = self.lrelu(x)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.encoder(x * x_mask, x_mask)
+        x = self.proj(x) * x_mask
+        return x, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+    def remove_weight_norm(self):
+        for i in range(self.n_flows):
+            self.flows[i * 2].remove_weight_norm()
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+    def remove_weight_norm(self):
+        self.enc.remove_weight_norm()
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class SineGen(torch.nn.Module):
+    """Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(
+        self,
+        samp_rate,
+        harmonic_num=0,
+        sine_amp=0.1,
+        noise_std=0.003,
+        voiced_threshold=0,
+        flag_for_pulse=False,
+    ):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def forward(self, f0, upp):
+        """sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0 = f0[:, None].transpose(1, 2)
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
+                    idx + 2
+                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
+            rand_ini = torch.rand(
+                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
+            )
+            rand_ini[:, 0] = 0
+            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
+            tmp_over_one *= upp
+            tmp_over_one = F.interpolate(
+                tmp_over_one.transpose(2, 1),
+                scale_factor=upp,
+                mode="linear",
+                align_corners=True,
+            ).transpose(2, 1)
+            rad_values = F.interpolate(
+                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
+            ).transpose(
+                2, 1
+            )  #######
+            tmp_over_one %= 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+            sine_waves = torch.sin(
+                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
+            )
+            sine_waves = sine_waves * self.sine_amp
+            uv = self._f02uv(f0)
+            uv = F.interpolate(
+                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
+            ).transpose(2, 1)
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(
+        self,
+        sampling_rate,
+        harmonic_num=0,
+        sine_amp=0.1,
+        add_noise_std=0.003,
+        voiced_threshod=0,
+        is_half=True,
+    ):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+        self.is_half = is_half
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(
+            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
+        )
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x, upp=None):
+        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
+        if self.is_half:
+            sine_wavs = sine_wavs.half()
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+        return sine_merge, None, None  # noise, uv
+
+
+class GeneratorNSF(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels,
+        sr,
+        is_half=False,
+    ):
+        super(GeneratorNSF, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=sr, harmonic_num=0, is_half=is_half
+        )
+        self.noise_convs = nn.ModuleList()
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            c_cur = upsample_initial_channel // (2 ** (i + 1))
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+            if i + 1 < len(upsample_rates):
+                stride_f0 = np.prod(upsample_rates[i + 1 :])
+                self.noise_convs.append(
+                    Conv1d(
+                        1,
+                        c_cur,
+                        kernel_size=stride_f0 * 2,
+                        stride=stride_f0,
+                        padding=stride_f0 // 2,
+                    )
+                )
+            else:
+                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+        self.upp = np.prod(upsample_rates)
+
+    def forward(self, x, f0, g=None):
+        har_source, noi_source, uv = self.m_source(f0, self.upp)
+        har_source = har_source.transpose(1, 2)
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            x_source = self.noise_convs[i](har_source)
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+sr2sr = {
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+
+class SynthesizerTrnMs256NSFsidO(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        if type(sr) == type("strr"):
+            sr = sr2sr[sr]
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            sr=sr,
+            is_half=kwargs["is_half"],
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask
+        z = self.flow(z_p, x_mask, g=g, reverse=True)
+        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
+        return o
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11, 17]
+        # periods = [3, 5, 7, 11, 17, 23, 37]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []  #
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            # for j in range(len(fmap_r)):
+            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
diff --git a/rvc/infer_pack/models_onnx_moess.py b/rvc/infer_pack/models_onnx_moess.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fe3d2ba4deffd608d79f2892a3040320c773787
--- /dev/null
+++ b/rvc/infer_pack/models_onnx_moess.py
@@ -0,0 +1,845 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from rvc.infer_pack import modules, commons, attentions
+from rvc.infer_pack.commons import get_padding
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from rvc.infer_pack.commons import init_weights
+import numpy as np
+
+
+class TextEncoder256(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        f0=True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.emb_phone = nn.Linear(256, hidden_channels)
+        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
+        if f0 == True:
+            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
+        self.encoder = attentions.Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, phone, pitch, lengths):
+        if pitch == None:
+            x = self.emb_phone(phone)
+        else:
+            x = self.emb_phone(phone) + self.emb_pitch(pitch)
+        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = self.lrelu(x)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.encoder(x * x_mask, x_mask)
+        stats = self.proj(x) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return m, logs, x_mask
+
+
+class TextEncoder256Sim(nn.Module):
+    def __init__(
+        self,
+        out_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        f0=True,
+    ):
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.emb_phone = nn.Linear(256, hidden_channels)
+        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
+        if f0 == True:
+            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
+        self.encoder = attentions.Encoder(
+            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+
+    def forward(self, phone, pitch, lengths):
+        if pitch == None:
+            x = self.emb_phone(phone)
+        else:
+            x = self.emb_phone(phone) + self.emb_pitch(pitch)
+        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
+        x = self.lrelu(x)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.encoder(x * x_mask, x_mask)
+        x = self.proj(x) * x_mask
+        return x, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        n_flows=4,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.n_flows = n_flows
+        self.gin_channels = gin_channels
+
+        self.flows = nn.ModuleList()
+        for i in range(n_flows):
+            self.flows.append(
+                modules.ResidualCouplingLayer(
+                    channels,
+                    hidden_channels,
+                    kernel_size,
+                    dilation_rate,
+                    n_layers,
+                    gin_channels=gin_channels,
+                    mean_only=True,
+                )
+            )
+            self.flows.append(modules.Flip())
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        if not reverse:
+            for flow in self.flows:
+                x, _ = flow(x, x_mask, g=g, reverse=reverse)
+        else:
+            for flow in reversed(self.flows):
+                x = flow(x, x_mask, g=g, reverse=reverse)
+        return x
+
+    def remove_weight_norm(self):
+        for i in range(self.n_flows):
+            self.flows[i * 2].remove_weight_norm()
+
+
+class PosteriorEncoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+
+        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+        self.enc = modules.WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=gin_channels,
+        )
+        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+    def forward(self, x, x_lengths, g=None):
+        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
+            x.dtype
+        )
+        x = self.pre(x) * x_mask
+        x = self.enc(x, x_mask, g=g)
+        stats = self.proj(x) * x_mask
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+        return z, m, logs, x_mask
+
+    def remove_weight_norm(self):
+        self.enc.remove_weight_norm()
+
+
+class Generator(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels=0,
+    ):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class SineGen(torch.nn.Module):
+    """Definition of sine generator
+    SineGen(samp_rate, harmonic_num = 0,
+            sine_amp = 0.1, noise_std = 0.003,
+            voiced_threshold = 0,
+            flag_for_pulse=False)
+    samp_rate: sampling rate in Hz
+    harmonic_num: number of harmonic overtones (default 0)
+    sine_amp: amplitude of sine-wavefrom (default 0.1)
+    noise_std: std of Gaussian noise (default 0.003)
+    voiced_thoreshold: F0 threshold for U/V classification (default 0)
+    flag_for_pulse: this SinGen is used inside PulseGen (default False)
+    Note: when flag_for_pulse is True, the first time step of a voiced
+        segment is always sin(np.pi) or cos(0)
+    """
+
+    def __init__(
+        self,
+        samp_rate,
+        harmonic_num=0,
+        sine_amp=0.1,
+        noise_std=0.003,
+        voiced_threshold=0,
+        flag_for_pulse=False,
+    ):
+        super(SineGen, self).__init__()
+        self.sine_amp = sine_amp
+        self.noise_std = noise_std
+        self.harmonic_num = harmonic_num
+        self.dim = self.harmonic_num + 1
+        self.sampling_rate = samp_rate
+        self.voiced_threshold = voiced_threshold
+
+    def _f02uv(self, f0):
+        # generate uv signal
+        uv = torch.ones_like(f0)
+        uv = uv * (f0 > self.voiced_threshold)
+        return uv
+
+    def forward(self, f0, upp):
+        """sine_tensor, uv = forward(f0)
+        input F0: tensor(batchsize=1, length, dim=1)
+                  f0 for unvoiced steps should be 0
+        output sine_tensor: tensor(batchsize=1, length, dim)
+        output uv: tensor(batchsize=1, length, 1)
+        """
+        with torch.no_grad():
+            f0 = f0[:, None].transpose(1, 2)
+            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
+            # fundamental component
+            f0_buf[:, :, 0] = f0[:, :, 0]
+            for idx in np.arange(self.harmonic_num):
+                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
+                    idx + 2
+                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
+            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
+            rand_ini = torch.rand(
+                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
+            )
+            rand_ini[:, 0] = 0
+            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
+            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
+            tmp_over_one *= upp
+            tmp_over_one = F.interpolate(
+                tmp_over_one.transpose(2, 1),
+                scale_factor=upp,
+                mode="linear",
+                align_corners=True,
+            ).transpose(2, 1)
+            rad_values = F.interpolate(
+                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
+            ).transpose(
+                2, 1
+            )  #######
+            tmp_over_one %= 1
+            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
+            cumsum_shift = torch.zeros_like(rad_values)
+            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
+            sine_waves = torch.sin(
+                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
+            )
+            sine_waves = sine_waves * self.sine_amp
+            uv = self._f02uv(f0)
+            uv = F.interpolate(
+                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
+            ).transpose(2, 1)
+            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
+            noise = noise_amp * torch.randn_like(sine_waves)
+            sine_waves = sine_waves * uv + noise
+        return sine_waves, uv, noise
+
+
+class SourceModuleHnNSF(torch.nn.Module):
+    """SourceModule for hn-nsf
+    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
+                 add_noise_std=0.003, voiced_threshod=0)
+    sampling_rate: sampling_rate in Hz
+    harmonic_num: number of harmonic above F0 (default: 0)
+    sine_amp: amplitude of sine source signal (default: 0.1)
+    add_noise_std: std of additive Gaussian noise (default: 0.003)
+        note that amplitude of noise in unvoiced is decided
+        by sine_amp
+    voiced_threshold: threhold to set U/V given F0 (default: 0)
+    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
+    F0_sampled (batchsize, length, 1)
+    Sine_source (batchsize, length, 1)
+    noise_source (batchsize, length 1)
+    uv (batchsize, length, 1)
+    """
+
+    def __init__(
+        self,
+        sampling_rate,
+        harmonic_num=0,
+        sine_amp=0.1,
+        add_noise_std=0.003,
+        voiced_threshod=0,
+        is_half=True,
+    ):
+        super(SourceModuleHnNSF, self).__init__()
+
+        self.sine_amp = sine_amp
+        self.noise_std = add_noise_std
+        self.is_half = is_half
+        # to produce sine waveforms
+        self.l_sin_gen = SineGen(
+            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
+        )
+
+        # to merge source harmonics into a single excitation
+        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
+        self.l_tanh = torch.nn.Tanh()
+
+    def forward(self, x, upp=None):
+        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
+        if self.is_half:
+            sine_wavs = sine_wavs.half()
+        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
+        return sine_merge, None, None  # noise, uv
+
+
+class GeneratorNSF(torch.nn.Module):
+    def __init__(
+        self,
+        initial_channel,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        gin_channels,
+        sr,
+        is_half=False,
+    ):
+        super(GeneratorNSF, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+
+        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
+        self.m_source = SourceModuleHnNSF(
+            sampling_rate=sr, harmonic_num=0, is_half=is_half
+        )
+        self.noise_convs = nn.ModuleList()
+        self.conv_pre = Conv1d(
+            initial_channel, upsample_initial_channel, 7, 1, padding=3
+        )
+        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            c_cur = upsample_initial_channel // (2 ** (i + 1))
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+            if i + 1 < len(upsample_rates):
+                stride_f0 = np.prod(upsample_rates[i + 1 :])
+                self.noise_convs.append(
+                    Conv1d(
+                        1,
+                        c_cur,
+                        kernel_size=stride_f0 * 2,
+                        stride=stride_f0,
+                        padding=stride_f0 // 2,
+                    )
+                )
+            else:
+                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for j, (k, d) in enumerate(
+                zip(resblock_kernel_sizes, resblock_dilation_sizes)
+            ):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+        self.upp = np.prod(upsample_rates)
+
+    def forward(self, x, f0, g=None):
+        har_source, noi_source, uv = self.m_source(f0, self.upp)
+        har_source = har_source.transpose(1, 2)
+        x = self.conv_pre(x)
+        if g is not None:
+            x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            x_source = self.noise_convs[i](har_source)
+            x = x + x_source
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+sr2sr = {
+    "32k": 32000,
+    "40k": 40000,
+    "48k": 48000,
+}
+
+
+class SynthesizerTrnMs256NSFsidM(nn.Module):
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        gin_channels,
+        sr,
+        **kwargs
+    ):
+        super().__init__()
+        if type(sr) == type("strr"):
+            sr = sr2sr[sr]
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            sr=sr,
+            is_half=kwargs["is_half"],
+        )
+        self.enc_q = PosteriorEncoder(
+            spec_channels,
+            inter_channels,
+            hidden_channels,
+            5,
+            1,
+            16,
+            gin_channels=gin_channels,
+        )
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(self, phone, phone_lengths, pitch, nsff0, sid, rnd, max_len=None):
+        g = self.emb_g(sid).unsqueeze(-1)
+        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
+        z = self.flow(z_p, x_mask, g=g, reverse=True)
+        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
+        return o
+
+
+class SynthesizerTrnMs256NSFsid_sim(nn.Module):
+    """
+    Synthesizer for Training
+    """
+
+    def __init__(
+        self,
+        spec_channels,
+        segment_size,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout,
+        resblock,
+        resblock_kernel_sizes,
+        resblock_dilation_sizes,
+        upsample_rates,
+        upsample_initial_channel,
+        upsample_kernel_sizes,
+        spk_embed_dim,
+        # hop_length,
+        gin_channels=0,
+        use_sdp=True,
+        **kwargs
+    ):
+        super().__init__()
+        self.spec_channels = spec_channels
+        self.inter_channels = inter_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.resblock = resblock
+        self.resblock_kernel_sizes = resblock_kernel_sizes
+        self.resblock_dilation_sizes = resblock_dilation_sizes
+        self.upsample_rates = upsample_rates
+        self.upsample_initial_channel = upsample_initial_channel
+        self.upsample_kernel_sizes = upsample_kernel_sizes
+        self.segment_size = segment_size
+        self.gin_channels = gin_channels
+        # self.hop_length = hop_length#
+        self.spk_embed_dim = spk_embed_dim
+        self.enc_p = TextEncoder256Sim(
+            inter_channels,
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+        )
+        self.dec = GeneratorNSF(
+            inter_channels,
+            resblock,
+            resblock_kernel_sizes,
+            resblock_dilation_sizes,
+            upsample_rates,
+            upsample_initial_channel,
+            upsample_kernel_sizes,
+            gin_channels=gin_channels,
+            is_half=kwargs["is_half"],
+        )
+
+        self.flow = ResidualCouplingBlock(
+            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
+        )
+        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
+        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
+
+    def remove_weight_norm(self):
+        self.dec.remove_weight_norm()
+        self.flow.remove_weight_norm()
+        self.enc_q.remove_weight_norm()
+
+    def forward(
+        self, phone, phone_lengths, pitch, pitchf, ds, max_len=None
+    ):  # y是spec不需要了现在
+        g = self.emb_g(ds.unsqueeze(0)).unsqueeze(-1)  # [b, 256, 1]##1是t，广播的
+        x, x_mask = self.enc_p(phone, pitch, phone_lengths)
+        x = self.flow(x, x_mask, g=g, reverse=True)
+        o = self.dec((x * x_mask)[:, :, :max_len], pitchf, g=g)
+        return o
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2, 3, 5, 7, 11, 17]
+        # periods = [3, 5, 7, 11, 17, 23, 37]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [
+            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
+        ]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []  #
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            # for j in range(len(fmap_r)):
+            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+            ]
+        )
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList(
+            [
+                norm_f(
+                    Conv2d(
+                        1,
+                        32,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        32,
+                        128,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        128,
+                        512,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        512,
+                        1024,
+                        (kernel_size, 1),
+                        (stride, 1),
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+                norm_f(
+                    Conv2d(
+                        1024,
+                        1024,
+                        (kernel_size, 1),
+                        1,
+                        padding=(get_padding(kernel_size, 1), 0),
+                    )
+                ),
+            ]
+        )
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0:  # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
diff --git a/rvc/infer_pack/modules.py b/rvc/infer_pack/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..39027e1d9fd2f62a68b4c14dbb5add7f05ba14ea
--- /dev/null
+++ b/rvc/infer_pack/modules.py
@@ -0,0 +1,519 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from rvc.infer_pack import commons
+from rvc.infer_pack.commons import init_weights, get_padding
+from rvc.infer_pack.transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        hidden_channels,
+        out_channels,
+        kernel_size,
+        n_layers,
+        p_dropout,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(
+            nn.Conv1d(
+                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
+            )
+        )
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(
+                nn.Conv1d(
+                    hidden_channels,
+                    hidden_channels,
+                    kernel_size,
+                    padding=kernel_size // 2,
+                )
+            )
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    groups=channels,
+                    dilation=dilation,
+                    padding=padding,
+                )
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(
+        self,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        gin_channels=0,
+        p_dropout=0,
+    ):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = (kernel_size,)
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(
+                gin_channels, 2 * hidden_channels * n_layers, 1
+            )
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(
+                hidden_channels,
+                2 * hidden_channels,
+                kernel_size,
+                dilation=dilation,
+                padding=padding,
+            )
+            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(
+        self,
+        channels,
+        hidden_channels,
+        kernel_size,
+        dilation_rate,
+        n_layers,
+        p_dropout=0,
+        gin_channels=0,
+        mean_only=False,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            p_dropout=p_dropout,
+            gin_channels=gin_channels,
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+    def remove_weight_norm(self):
+        self.enc.remove_weight_norm()
+
+
+class ConvFlow(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        filter_channels,
+        kernel_size,
+        n_layers,
+        num_bins=10,
+        tail_bound=5.0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
+        self.proj = nn.Conv1d(
+            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
+        )
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
+            self.filter_channels
+        )
+        unnormalized_derivatives = h[..., 2 * self.num_bins :]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(
+            x1,
+            unnormalized_widths,
+            unnormalized_heights,
+            unnormalized_derivatives,
+            inverse=reverse,
+            tails="linear",
+            tail_bound=self.tail_bound,
+        )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
diff --git a/rvc/infer_pack/modules/__init__.py b/rvc/infer_pack/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc34fc3e0993a3b5a59e286a39b8c29c71606179
--- /dev/null
+++ b/rvc/infer_pack/modules/__init__.py
@@ -0,0 +1,519 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from rvc.infer_pack import commons
+from rvc.infer_pack.commons import init_weights, get_padding
+from rvc.infer_pack.transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-5):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        x = x.transpose(1, -1)
+        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+        return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            hidden_channels,
+            out_channels,
+            kernel_size,
+            n_layers,
+            p_dropout,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(
+            nn.Conv1d(
+                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
+            )
+        )
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(
+                nn.Conv1d(
+                    hidden_channels,
+                    hidden_channels,
+                    kernel_size,
+                    padding=kernel_size // 2,
+                )
+            )
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class DDSConv(nn.Module):
+    """
+    Dialted and Depth-Separable Convolution
+    """
+
+    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+
+        self.drop = nn.Dropout(p_dropout)
+        self.convs_sep = nn.ModuleList()
+        self.convs_1x1 = nn.ModuleList()
+        self.norms_1 = nn.ModuleList()
+        self.norms_2 = nn.ModuleList()
+        for i in range(n_layers):
+            dilation = kernel_size**i
+            padding = (kernel_size * dilation - dilation) // 2
+            self.convs_sep.append(
+                nn.Conv1d(
+                    channels,
+                    channels,
+                    kernel_size,
+                    groups=channels,
+                    dilation=dilation,
+                    padding=padding,
+                )
+            )
+            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+            self.norms_1.append(LayerNorm(channels))
+            self.norms_2.append(LayerNorm(channels))
+
+    def forward(self, x, x_mask, g=None):
+        if g is not None:
+            x = x + g
+        for i in range(self.n_layers):
+            y = self.convs_sep[i](x * x_mask)
+            y = self.norms_1[i](y)
+            y = F.gelu(y)
+            y = self.convs_1x1[i](y)
+            y = self.norms_2[i](y)
+            y = F.gelu(y)
+            y = self.drop(y)
+            x = x + y
+        return x * x_mask
+
+
+class WN(torch.nn.Module):
+    def __init__(
+            self,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            gin_channels=0,
+            p_dropout=0,
+    ):
+        super(WN, self).__init__()
+        assert kernel_size % 2 == 1
+        self.hidden_channels = hidden_channels
+        self.kernel_size = (kernel_size,)
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = gin_channels
+        self.p_dropout = p_dropout
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if gin_channels != 0:
+            cond_layer = torch.nn.Conv1d(
+                gin_channels, 2 * hidden_channels * n_layers, 1
+            )
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
+
+        for i in range(n_layers):
+            dilation = dilation_rate**i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(
+                hidden_channels,
+                2 * hidden_channels,
+                kernel_size,
+                dilation=dilation,
+                padding=padding,
+                )
+            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_channels
+            else:
+                res_skip_channels = hidden_channels
+
+            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
+            self.res_skip_layers.append(res_skip_layer)
+
+    def forward(self, x, x_mask, g=None, **kwargs):
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+        if g is not None:
+            g = self.cond_layer(g)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            if g is not None:
+                cond_offset = i * 2 * self.hidden_channels
+                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
+            else:
+                g_l = torch.zeros_like(x_in)
+
+            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
+            acts = self.drop(acts)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                res_acts = res_skip_acts[:, : self.hidden_channels, :]
+                x = (x + res_acts) * x_mask
+                output = output + res_skip_acts[:, self.hidden_channels :, :]
+            else:
+                output = output + res_skip_acts
+        return output * x_mask
+
+    def remove_weight_norm(self):
+        if self.gin_channels != 0:
+            torch.nn.utils.remove_weight_norm(self.cond_layer)
+        for l in self.in_layers:
+            torch.nn.utils.remove_weight_norm(l)
+        for l in self.res_skip_layers:
+            torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=1,
+                        padding=get_padding(kernel_size, 1),
+                    )
+                ),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+            logdet = torch.sum(-y, [1, 2])
+            return y, logdet
+        else:
+            x = torch.exp(x) * x_mask
+            return x
+
+
+class Flip(nn.Module):
+    def forward(self, x, *args, reverse=False, **kwargs):
+        x = torch.flip(x, [1])
+        if not reverse:
+            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+            return x, logdet
+        else:
+            return x
+
+
+class ElementwiseAffine(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.channels = channels
+        self.m = nn.Parameter(torch.zeros(channels, 1))
+        self.logs = nn.Parameter(torch.zeros(channels, 1))
+
+    def forward(self, x, x_mask, reverse=False, **kwargs):
+        if not reverse:
+            y = self.m + torch.exp(self.logs) * x
+            y = y * x_mask
+            logdet = torch.sum(self.logs * x_mask, [1, 2])
+            return y, logdet
+        else:
+            x = (x - self.m) * torch.exp(-self.logs) * x_mask
+            return x
+
+
+class ResidualCouplingLayer(nn.Module):
+    def __init__(
+            self,
+            channels,
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            p_dropout=0,
+            gin_channels=0,
+            mean_only=False,
+    ):
+        assert channels % 2 == 0, "channels should be divisible by 2"
+        super().__init__()
+        self.channels = channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.half_channels = channels // 2
+        self.mean_only = mean_only
+
+        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+        self.enc = WN(
+            hidden_channels,
+            kernel_size,
+            dilation_rate,
+            n_layers,
+            p_dropout=p_dropout,
+            gin_channels=gin_channels,
+        )
+        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+        self.post.weight.data.zero_()
+        self.post.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0) * x_mask
+        h = self.enc(h, x_mask, g=g)
+        stats = self.post(h) * x_mask
+        if not self.mean_only:
+            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
+        else:
+            m = stats
+            logs = torch.zeros_like(m)
+
+        if not reverse:
+            x1 = m + x1 * torch.exp(logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            logdet = torch.sum(logs, [1, 2])
+            return x, logdet
+        else:
+            x1 = (x1 - m) * torch.exp(-logs) * x_mask
+            x = torch.cat([x0, x1], 1)
+            return x
+
+    def remove_weight_norm(self):
+        self.enc.remove_weight_norm()
+
+
+class ConvFlow(nn.Module):
+    def __init__(
+            self,
+            in_channels,
+            filter_channels,
+            kernel_size,
+            n_layers,
+            num_bins=10,
+            tail_bound=5.0,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.num_bins = num_bins
+        self.tail_bound = tail_bound
+        self.half_channels = in_channels // 2
+
+        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
+        self.proj = nn.Conv1d(
+            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
+        )
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask, g=None, reverse=False):
+        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
+        h = self.pre(x0)
+        h = self.convs(h, x_mask, g=g)
+        h = self.proj(h) * x_mask
+
+        b, c, t = x0.shape
+        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
+
+        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
+        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
+            self.filter_channels
+        )
+        unnormalized_derivatives = h[..., 2 * self.num_bins :]
+
+        x1, logabsdet = piecewise_rational_quadratic_transform(
+            x1,
+            unnormalized_widths,
+            unnormalized_heights,
+            unnormalized_derivatives,
+            inverse=reverse,
+            tails="linear",
+            tail_bound=self.tail_bound,
+        )
+
+        x = torch.cat([x0, x1], 1) * x_mask
+        logdet = torch.sum(logabsdet * x_mask, [1, 2])
+        if not reverse:
+            return x, logdet
+        else:
+            return x
diff --git a/rvc/infer_pack/modules/__pycache__/__init__.cpython-310.pyc b/rvc/infer_pack/modules/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c1e5163ad38dd7952747d3e288f60ee87ab2cf39
Binary files /dev/null and b/rvc/infer_pack/modules/__pycache__/__init__.cpython-310.pyc differ
diff --git a/rvc/infer_pack/transforms.py b/rvc/infer_pack/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..a11f799e023864ff7082c1f49c0cc18351a13b47
--- /dev/null
+++ b/rvc/infer_pack/transforms.py
@@ -0,0 +1,209 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails=None,
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
+
+    outputs, logabsdet = spline_fn(
+        inputs=inputs,
+        unnormalized_widths=unnormalized_widths,
+        unnormalized_heights=unnormalized_heights,
+        unnormalized_derivatives=unnormalized_derivatives,
+        inverse=inverse,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+        **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
+
+
+def unconstrained_rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    tails="linear",
+    tail_bound=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == "linear":
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError("{} tails are not implemented.".format(tails))
+
+    (
+        outputs[inside_interval_mask],
+        logabsdet[inside_interval_mask],
+    ) = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound,
+        right=tail_bound,
+        bottom=-tail_bound,
+        top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative,
+    )
+
+    return outputs, logabsdet
+
+
+def rational_quadratic_spline(
+    inputs,
+    unnormalized_widths,
+    unnormalized_heights,
+    unnormalized_derivatives,
+    inverse=False,
+    left=0.0,
+    right=1.0,
+    bottom=0.0,
+    top=1.0,
+    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+    min_derivative=DEFAULT_MIN_DERIVATIVE,
+):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError("Input to a transform is not within its domain")
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError("Minimal bin width too large for the number of bins")
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError("Minimal bin height too large for the number of bins")
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        ) + input_heights * (input_delta - input_derivatives)
+        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
+            input_derivatives + input_derivatives_plus_one - 2 * input_delta
+        )
+        c = -input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * root.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - root).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (
+            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
+        )
+        denominator = input_delta + (
+            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+            * theta_one_minus_theta
+        )
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (
+            input_derivatives_plus_one * theta.pow(2)
+            + 2 * input_delta * theta_one_minus_theta
+            + input_derivatives * (1 - theta).pow(2)
+        )
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet
diff --git a/rvc/lib/__init__.py b/rvc/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/rvc/lib/__pycache__/__init__.cpython-310.pyc b/rvc/lib/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7d8b7ecf6f041e32083c2b0cb42af5af0d960b81
Binary files /dev/null and b/rvc/lib/__pycache__/__init__.cpython-310.pyc differ
diff --git a/rvc/lib/__pycache__/rmvpe.cpython-310.pyc b/rvc/lib/__pycache__/rmvpe.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..8da0262c99fb3c00afba2aba4489923e7359afbb
Binary files /dev/null and b/rvc/lib/__pycache__/rmvpe.cpython-310.pyc differ
diff --git a/rvc/lib/rmvpe.py b/rvc/lib/rmvpe.py
new file mode 100644
index 0000000000000000000000000000000000000000..c42650d06fbf83fa191ed2e5e84a163f4a164cda
--- /dev/null
+++ b/rvc/lib/rmvpe.py
@@ -0,0 +1,431 @@
+import torch, numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BiGRU(nn.Module):
+    def __init__(self, input_features, hidden_features, num_layers):
+        super(BiGRU, self).__init__()
+        self.gru = nn.GRU(
+            input_features,
+            hidden_features,
+            num_layers=num_layers,
+            batch_first=True,
+            bidirectional=True,
+        )
+
+    def forward(self, x):
+        return self.gru(x)[0]
+
+
+class ConvBlockRes(nn.Module):
+    def __init__(self, in_channels, out_channels, momentum=0.01):
+        super(ConvBlockRes, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(3, 3),
+                stride=(1, 1),
+                padding=(1, 1),
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+            nn.Conv2d(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                kernel_size=(3, 3),
+                stride=(1, 1),
+                padding=(1, 1),
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+        )
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
+            self.is_shortcut = True
+        else:
+            self.is_shortcut = False
+
+    def forward(self, x):
+        if self.is_shortcut:
+            return self.conv(x) + self.shortcut(x)
+        else:
+            return self.conv(x) + x
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        in_size,
+        n_encoders,
+        kernel_size,
+        n_blocks,
+        out_channels=16,
+        momentum=0.01,
+    ):
+        super(Encoder, self).__init__()
+        self.n_encoders = n_encoders
+        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
+        self.layers = nn.ModuleList()
+        self.latent_channels = []
+        for i in range(self.n_encoders):
+            self.layers.append(
+                ResEncoderBlock(
+                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
+                )
+            )
+            self.latent_channels.append([out_channels, in_size])
+            in_channels = out_channels
+            out_channels *= 2
+            in_size //= 2
+        self.out_size = in_size
+        self.out_channel = out_channels
+
+    def forward(self, x):
+        concat_tensors = []
+        x = self.bn(x)
+        for i in range(self.n_encoders):
+            _, x = self.layers[i](x)
+            concat_tensors.append(_)
+        return x, concat_tensors
+
+
+class ResEncoderBlock(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
+    ):
+        super(ResEncoderBlock, self).__init__()
+        self.n_blocks = n_blocks
+        self.conv = nn.ModuleList()
+        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
+        for i in range(n_blocks - 1):
+            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
+        self.kernel_size = kernel_size
+        if self.kernel_size is not None:
+            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
+
+    def forward(self, x):
+        for i in range(self.n_blocks):
+            x = self.conv[i](x)
+        if self.kernel_size is not None:
+            return x, self.pool(x)
+        else:
+            return x
+
+
+class Intermediate(nn.Module):  #
+    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
+        super(Intermediate, self).__init__()
+        self.n_inters = n_inters
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
+        )
+        for i in range(self.n_inters - 1):
+            self.layers.append(
+                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
+            )
+
+    def forward(self, x):
+        for i in range(self.n_inters):
+            x = self.layers[i](x)
+        return x
+
+
+class ResDecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
+        super(ResDecoderBlock, self).__init__()
+        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
+        self.n_blocks = n_blocks
+        self.conv1 = nn.Sequential(
+            nn.ConvTranspose2d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=(3, 3),
+                stride=stride,
+                padding=(1, 1),
+                output_padding=out_padding,
+                bias=False,
+            ),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+        )
+        self.conv2 = nn.ModuleList()
+        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
+        for i in range(n_blocks - 1):
+            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
+
+    def forward(self, x, concat_tensor):
+        x = self.conv1(x)
+        x = torch.cat((x, concat_tensor), dim=1)
+        for i in range(self.n_blocks):
+            x = self.conv2[i](x)
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
+        super(Decoder, self).__init__()
+        self.layers = nn.ModuleList()
+        self.n_decoders = n_decoders
+        for i in range(self.n_decoders):
+            out_channels = in_channels // 2
+            self.layers.append(
+                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
+            )
+            in_channels = out_channels
+
+    def forward(self, x, concat_tensors):
+        for i in range(self.n_decoders):
+            x = self.layers[i](x, concat_tensors[-1 - i])
+        return x
+
+
+class DeepUnet(nn.Module):
+    def __init__(
+        self,
+        kernel_size,
+        n_blocks,
+        en_de_layers=5,
+        inter_layers=4,
+        in_channels=1,
+        en_out_channels=16,
+    ):
+        super(DeepUnet, self).__init__()
+        self.encoder = Encoder(
+            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
+        )
+        self.intermediate = Intermediate(
+            self.encoder.out_channel // 2,
+            self.encoder.out_channel,
+            inter_layers,
+            n_blocks,
+        )
+        self.decoder = Decoder(
+            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
+        )
+
+    def forward(self, x):
+        x, concat_tensors = self.encoder(x)
+        x = self.intermediate(x)
+        x = self.decoder(x, concat_tensors)
+        return x
+
+
+class E2E(nn.Module):
+    def __init__(
+        self,
+        n_blocks,
+        n_gru,
+        kernel_size,
+        en_de_layers=5,
+        inter_layers=4,
+        in_channels=1,
+        en_out_channels=16,
+    ):
+        super(E2E, self).__init__()
+        self.unet = DeepUnet(
+            kernel_size,
+            n_blocks,
+            en_de_layers,
+            inter_layers,
+            in_channels,
+            en_out_channels,
+        )
+        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
+        if n_gru:
+            self.fc = nn.Sequential(
+                BiGRU(3 * 128, 256, n_gru),
+                nn.Linear(512, 360),
+                nn.Dropout(0.25),
+                nn.Sigmoid(),
+            )
+        else:
+            self.fc = nn.Sequential(
+                nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
+            )
+
+    def forward(self, mel):
+        mel = mel.transpose(-1, -2).unsqueeze(1)
+        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
+        x = self.fc(x)
+        return x
+
+
+from librosa.filters import mel
+
+
+class MelSpectrogram(torch.nn.Module):
+    def __init__(
+        self,
+        is_half,
+        n_mel_channels,
+        sampling_rate,
+        win_length,
+        hop_length,
+        n_fft=None,
+        mel_fmin=0,
+        mel_fmax=None,
+        clamp=1e-5,
+    ):
+        super().__init__()
+        n_fft = win_length if n_fft is None else n_fft
+        self.hann_window = {}
+        mel_basis = mel(
+            sr=sampling_rate,
+            n_fft=n_fft,
+            n_mels=n_mel_channels,
+            fmin=mel_fmin,
+            fmax=mel_fmax,
+            htk=True,
+        )
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer("mel_basis", mel_basis)
+        self.n_fft = win_length if n_fft is None else n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.sampling_rate = sampling_rate
+        self.n_mel_channels = n_mel_channels
+        self.clamp = clamp
+        self.is_half = is_half
+
+    def forward(self, audio, keyshift=0, speed=1, center=True):
+        factor = 2 ** (keyshift / 12)
+        n_fft_new = int(np.round(self.n_fft * factor))
+        win_length_new = int(np.round(self.win_length * factor))
+        hop_length_new = int(np.round(self.hop_length * speed))
+        keyshift_key = str(keyshift) + "_" + str(audio.device)
+        if keyshift_key not in self.hann_window:
+            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
+                audio.device
+            )
+        fft = torch.stft(
+            audio,
+            n_fft=n_fft_new,
+            hop_length=hop_length_new,
+            win_length=win_length_new,
+            window=self.hann_window[keyshift_key],
+            center=center,
+            return_complex=True,
+        )
+        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
+        if keyshift != 0:
+            size = self.n_fft // 2 + 1
+            resize = magnitude.size(1)
+            if resize < size:
+                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
+            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
+        mel_output = torch.matmul(self.mel_basis, magnitude)
+        if self.is_half == True:
+            mel_output = mel_output.half()
+        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
+        return log_mel_spec
+
+
+class RMVPE:
+    def __init__(self, model_path, is_half, device=None):
+        self.resample_kernel = {}
+        model = E2E(4, 1, (2, 2))
+        ckpt = torch.load(model_path, map_location="cpu")
+        model.load_state_dict(ckpt)
+        model.eval()
+        if is_half == True:
+            model = model.half()
+        self.model = model
+        self.resample_kernel = {}
+        self.is_half = is_half
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device = device
+        self.mel_extractor = MelSpectrogram(
+            is_half, 128, 16000, 1024, 160, None, 30, 8000
+        ).to(device)
+        self.model = self.model.to(device)
+        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
+        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368
+
+    def mel2hidden(self, mel):
+        with torch.no_grad():
+            n_frames = mel.shape[-1]
+            mel = F.pad(
+                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
+            )
+            hidden = self.model(mel)
+            return hidden[:, :n_frames]
+
+    def decode(self, hidden, thred=0.03):
+        cents_pred = self.to_local_average_cents(hidden, thred=thred)
+        f0 = 10 * (2 ** (cents_pred / 1200))
+        f0[f0 == 10] = 0
+        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
+        return f0
+
+    def infer_from_audio(self, audio, thred=0.03):
+        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
+        # torch.cuda.synchronize()
+        # t0=ttime()
+        mel = self.mel_extractor(audio, center=True)
+        # torch.cuda.synchronize()
+        # t1=ttime()
+        hidden = self.mel2hidden(mel)
+        # torch.cuda.synchronize()
+        # t2=ttime()
+        hidden = hidden.squeeze(0).cpu().numpy()
+        if self.is_half == True:
+            hidden = hidden.astype("float32")
+        f0 = self.decode(hidden, thred=thred)
+        # torch.cuda.synchronize()
+        # t3=ttime()
+        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
+        return f0
+
+    def to_local_average_cents(self, salience, thred=0.05):
+        # t0 = ttime()
+        center = np.argmax(salience, axis=1)  # 帧长#index
+        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
+        # t1 = ttime()
+        center += 4
+        todo_salience = []
+        todo_cents_mapping = []
+        starts = center - 4
+        ends = center + 5
+        for idx in range(salience.shape[0]):
+            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
+            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
+        # t2 = ttime()
+        todo_salience = np.array(todo_salience)  # 帧长，9
+        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
+        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
+        weight_sum = np.sum(todo_salience, 1)  # 帧长
+        devided = product_sum / weight_sum  # 帧长
+        # t3 = ttime()
+        maxx = np.max(salience, axis=1)  # 帧长
+        devided[maxx <= thred] = 0
+        # t4 = ttime()
+        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
+        return devided
+
+
+# if __name__ == '__main__':
+#     audio, sampling_rate = sf.read("卢本伟语录~1.wav")
+#     if len(audio.shape) > 1:
+#         audio = librosa.to_mono(audio.transpose(1, 0))
+#     audio_bak = audio.copy()
+#     if sampling_rate != 16000:
+#         audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+#     model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
+#     thred = 0.03  # 0.01
+#     device = 'cuda' if torch.cuda.is_available() else 'cpu'
+#     rmvpe = RMVPE(model_path,is_half=False, device=device)
+#     t0=ttime()
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     f0 = rmvpe.infer_from_audio(audio, thred=thred)
+#     t1=ttime()
+#     print(f0.shape,t1-t0)
diff --git a/rvc/model/bob/Sponge.png b/rvc/model/bob/Sponge.png
new file mode 100644
index 0000000000000000000000000000000000000000..f8b1b0540b2c5bf0292286bf884900520d3d92cd
Binary files /dev/null and b/rvc/model/bob/Sponge.png differ
diff --git a/rvc/model/bob/added_IVF3536_Flat_nprobe_1_v2.index b/rvc/model/bob/added_IVF3536_Flat_nprobe_1_v2.index
new file mode 100644
index 0000000000000000000000000000000000000000..7afc74a1baa36eba263c7f67dc0c3d37c34598a7
--- /dev/null
+++ b/rvc/model/bob/added_IVF3536_Flat_nprobe_1_v2.index
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8483147b32a68613b9c72ca64dc79b3575fcd9d6d3e05fd109af4efeabd26259
+size 435669219
diff --git a/rvc/model/bob/bobsponge.pth b/rvc/model/bob/bobsponge.pth
new file mode 100644
index 0000000000000000000000000000000000000000..4639430815395b85a3cadf04495db0ccaa5acb9f
--- /dev/null
+++ b/rvc/model/bob/bobsponge.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3fa5a81d398f02225f19295e199c6508e8b788ed56ea6807d73433c91386483
+size 55229246
diff --git a/rvc/model/bob/config.json b/rvc/model/bob/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..86756252951ae13616b4b8e57c70355d5268d466
--- /dev/null
+++ b/rvc/model/bob/config.json
@@ -0,0 +1,11 @@
+{
+    "model": "bobsponge.pth",
+    "feat_index": "added_IVF3536_Flat_nprobe_1_v2.index",
+    "speaker_id": 0,
+
+    "name": "Bob Sponge",
+    "author": "Lüh Minion",
+    "source": "ALL",
+    "note": "This was trained on over 40 minutes of audio, using songs sang by SpongeBob as well as video game voice clips.  This was trained using the crepe method, 64 HOP, on the v2 model architecture ",
+    "icon": "Sponge.png"
+}
\ No newline at end of file
diff --git a/rvc/model/hubert_base.pt b/rvc/model/hubert_base.pt
new file mode 100644
index 0000000000000000000000000000000000000000..72f47ab58564f01d5cc8b05c63bdf96d944551ff
--- /dev/null
+++ b/rvc/model/hubert_base.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f54b40fd2802423a5643779c4861af1e9ee9c1564dc9d32f54f20b5ffba7db96
+size 189507909
diff --git a/rvc/model/lulu/config.json b/rvc/model/lulu/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a5661632de363cae41d01a24057bdbda95ac1f40
--- /dev/null
+++ b/rvc/model/lulu/config.json
@@ -0,0 +1,11 @@
+{
+    "model": "lulu-epoch.pth",
+    "feat_index": "added_IVF256_Flat_nprobe_1_lulu-epoch_v2.index",
+    "speaker_id": 0,
+
+    "name": "露露",
+    "author": "露露",
+    "source": "露露",
+    "note": "我是你的甜心陪玩，选我，选我~",
+    "icon": "cover.jpg"
+}
diff --git a/rvc/model/lulu/cover.png b/rvc/model/lulu/cover.png
new file mode 100644
index 0000000000000000000000000000000000000000..325b159248e82b97ea07fdc6205f3a1502a495b6
Binary files /dev/null and b/rvc/model/lulu/cover.png differ
diff --git a/rvc/model/lulu/lulu-epoch.pth b/rvc/model/lulu/lulu-epoch.pth
new file mode 100755
index 0000000000000000000000000000000000000000..c0413fd72e4835f8afa808f4043e9db6d0ef12b8
--- /dev/null
+++ b/rvc/model/lulu/lulu-epoch.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb020d92223738e3e0500e43fa55fb9788d1ae19ac21b0375d52d4bf035a790b
+size 55224656
diff --git a/rvc/model/rmvpe.pt b/rvc/model/rmvpe.pt
new file mode 100644
index 0000000000000000000000000000000000000000..bae4def4f226bb41cc24f800dc463cdf08940e6b
--- /dev/null
+++ b/rvc/model/rmvpe.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5ed4719f59085d1affc5d81354c70828c740584f2d24e782523345a6a278962
+size 181189687
diff --git a/rvc/model/yiqing/added_IVF1502_Flat_nprobe_1_yiqing_v2.index b/rvc/model/yiqing/added_IVF1502_Flat_nprobe_1_yiqing_v2.index
new file mode 100644
index 0000000000000000000000000000000000000000..66efca56a5e4ab37222bddd41bc62de619078437
--- /dev/null
+++ b/rvc/model/yiqing/added_IVF1502_Flat_nprobe_1_yiqing_v2.index
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:848b1d99ee7edad4abdcf68d5b3614d48a8f2293dac4d8df542f0a4a472ba0eb
+size 185058859
diff --git a/rvc/model/yiqing/config.json b/rvc/model/yiqing/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0a02b26bf579ae0cbbd9ec68f871a4ff5bb8c9bc
--- /dev/null
+++ b/rvc/model/yiqing/config.json
@@ -0,0 +1,11 @@
+{
+    "model": "yiqing.pth",
+    "feat_index": "added_IVF1502_Flat_nprobe_1_yiqing_v2.index",
+    "speaker_id": 0,
+
+    "name": "一清清清",
+    "author": "滔滔AI",
+    "source": "Bilibili",
+    "note": "大家好呀！我是音乐人一清清清，这是我的专属AI歌手，希望你们喜欢哦！",
+    "icon": "cover.png"
+}
\ No newline at end of file
diff --git a/rvc/model/yiqing/cover.png b/rvc/model/yiqing/cover.png
new file mode 100644
index 0000000000000000000000000000000000000000..5a1cc0f55044e3318a439304ac7b8115c7be8aae
Binary files /dev/null and b/rvc/model/yiqing/cover.png differ
diff --git a/rvc/model/yiqing/yiqing.pth b/rvc/model/yiqing/yiqing.pth
new file mode 100644
index 0000000000000000000000000000000000000000..28fa5b539a64227d5b92aecf232e74f0130d4c28
--- /dev/null
+++ b/rvc/model/yiqing/yiqing.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a833927ad538a960561434d05dc72faa6200ad311268ce320e40a2ef19de14b
+size 57581999
diff --git a/rvc/multi_config.json b/rvc/multi_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a6d4a54c4a724598bab03b2b825a54859b75f248
--- /dev/null
+++ b/rvc/multi_config.json
@@ -0,0 +1,9 @@
+{
+    "models": [
+        "yiqing", "bob", "arianagrande", "syz", "lulu", "maomao"
+    ],
+    "examples": {
+        "vc": [],
+        "tts_vc": []
+    }
+}
diff --git a/rvc/rvc.yaml b/rvc/rvc.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e05c2154ed6005ca56d545266a2e87c2a8252d3a
--- /dev/null
+++ b/rvc/rvc.yaml
@@ -0,0 +1,18 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+
+rmvpe_path: "model/rmvpe.pt"
+
+models:
+    - yiqing:
+        model_name: "yiqing"
+        path: "model/yiqing"
+    - bob:
+        model_name: "bob"
+        path: "model/bob" 
+    - lulu:
+        model_name: "lulu"
+        path: "model/lulu"
diff --git a/rvc/vc_infer_pipeline.py b/rvc/vc_infer_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..9859bff5de348f6ea48ec42a0a1ba83cb2a06690
--- /dev/null
+++ b/rvc/vc_infer_pipeline.py
@@ -0,0 +1,445 @@
+import numpy as np, parselmouth, torch, sys
+from time import time as ttime
+import torch.nn.functional as F
+import pyworld, os, traceback, faiss, librosa, torchcrepe
+from scipy import signal
+from functools import lru_cache
+
+now_dir = os.getcwd()
+sys.path.append(now_dir)
+
+bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
+
+input_audio_path2wav = {}
+
+
+@lru_cache
+def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
+    audio = input_audio_path2wav[input_audio_path]
+    f0, t = pyworld.harvest(
+        audio,
+        fs=fs,
+        f0_ceil=f0max,
+        f0_floor=f0min,
+        frame_period=frame_period,
+    )
+    f0 = pyworld.stonemask(audio, f0, t, fs)
+    return f0
+
+
+def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
+    # print(data1.max(),data2.max())
+    rms1 = librosa.feature.rms(
+        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
+    )  # 每半秒一个点
+    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
+    rms1 = torch.from_numpy(rms1)
+    rms1 = F.interpolate(
+        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.from_numpy(rms2)
+    rms2 = F.interpolate(
+        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
+    data2 *= (
+            torch.pow(rms1, torch.tensor(1 - rate))
+            * torch.pow(rms2, torch.tensor(rate - 1))
+    ).numpy()
+    return data2
+
+
+class VC(object):
+    def __init__(self, tgt_sr, x_pad, x_query, x_center, x_max, is_half, device,
+                 rmvpe_path: str = None):
+        self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
+            x_pad,
+            x_query,
+            x_center,
+            x_max,
+            is_half,
+        )
+        self.sr = 16000  # hubert输入采样率
+        self.window = 160  # 每帧点数
+        self.t_pad = self.sr * self.x_pad  # 每条前后pad时间
+        self.t_pad_tgt = tgt_sr * self.x_pad
+        self.t_pad2 = self.t_pad * 2
+        self.t_query = self.sr * self.x_query  # 查询切点前后查询时间
+        self.t_center = self.sr * self.x_center  # 查询切点位置
+        self.t_max = self.sr * self.x_max  # 免查询时长阈值
+        self.device = device
+        self.rmvpe_path = rmvpe_path
+
+    def get_f0(
+            self,
+            input_audio_path,
+            x,
+            p_len,
+            f0_up_key,
+            f0_method,
+            filter_radius,
+            inp_f0=None,
+    ):
+        global input_audio_path2wav
+        time_step = self.window / self.sr * 1000
+        f0_min = 50
+        f0_max = 1100
+        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+        if f0_method == "pm":
+            f0 = (
+                parselmouth.Sound(x, self.sr)
+                .to_pitch_ac(
+                    time_step=time_step / 1000,
+                    voicing_threshold=0.6,
+                    pitch_floor=f0_min,
+                    pitch_ceiling=f0_max,
+                )
+                .selected_array["frequency"]
+            )
+            pad_size = (p_len - len(f0) + 1) // 2
+            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
+                f0 = np.pad(
+                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
+                )
+        elif f0_method == "harvest":
+            input_audio_path2wav[input_audio_path] = x.astype(np.double)
+            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
+            if filter_radius > 2:
+                f0 = signal.medfilt(f0, 3)
+        elif f0_method == "crepe":
+            model = "full"
+            # Pick a batch size that doesn't cause memory errors on your gpu
+            batch_size = 512
+            # Compute pitch using first gpu
+            audio = torch.tensor(np.copy(x))[None].float()
+            f0, pd = torchcrepe.predict(
+                audio,
+                self.sr,
+                self.window,
+                f0_min,
+                f0_max,
+                model,
+                batch_size=batch_size,
+                device=self.device,
+                return_periodicity=True,
+            )
+            pd = torchcrepe.filter.median(pd, 3)
+            f0 = torchcrepe.filter.mean(f0, 3)
+            f0[pd < 0.1] = 0
+            f0 = f0[0].cpu().numpy()
+        elif f0_method == "rmvpe":
+            if hasattr(self, "model_rmvpe") == False and self.rmvpe_path is not None:
+                from rvc.lib.rmvpe import RMVPE
+
+                print("loading rmvpe model")
+
+                self.model_rmvpe = RMVPE(
+                    self.rmvpe_path, is_half=self.is_half, device=self.device
+                )
+            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
+        f0 *= pow(2, f0_up_key / 12)
+        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
+        tf0 = self.sr // self.window  # 每秒f0点数
+        if inp_f0 is not None:
+            delta_t = np.round(
+                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
+            ).astype("int16")
+            replace_f0 = np.interp(
+                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
+            )
+            shape = f0[self.x_pad * tf0: self.x_pad * tf0 + len(replace_f0)].shape[0]
+            f0[self.x_pad * tf0: self.x_pad * tf0 + len(replace_f0)] = replace_f0[
+                                                                       :shape
+                                                                       ]
+        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
+        f0bak = f0.copy()
+        f0_mel = 1127 * np.log(1 + f0 / 700)
+        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
+                f0_mel_max - f0_mel_min
+        ) + 1
+        f0_mel[f0_mel <= 1] = 1
+        f0_mel[f0_mel > 255] = 255
+        f0_coarse = np.rint(f0_mel).astype(np.int)
+        return f0_coarse, f0bak  # 1-0
+
+    def vc(
+            self,
+            model,
+            net_g,
+            sid,
+            audio0,
+            pitch,
+            pitchf,
+            times,
+            index,
+            big_npy,
+            index_rate,
+            version,
+            protect,
+    ):  # ,file_index,file_big_npy
+        feats = torch.from_numpy(audio0)
+        if self.is_half:
+            feats = feats.half()
+        else:
+            feats = feats.float()
+        if feats.dim() == 2:  # double channels
+            feats = feats.mean(-1)
+        assert feats.dim() == 1, feats.dim()
+        feats = feats.view(1, -1)
+        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
+
+        inputs = {
+            "source": feats.to(self.device),
+            "padding_mask": padding_mask,
+            "output_layer": 9 if version == "v1" else 12,
+        }
+        t0 = ttime()
+        with torch.no_grad():
+            logits = model.extract_features(**inputs)
+            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
+        if protect < 0.5 and pitch != None and pitchf != None:
+            feats0 = feats.clone()
+        if (
+                isinstance(index, type(None)) == False
+                and isinstance(big_npy, type(None)) == False
+                and index_rate != 0
+        ):
+            npy = feats[0].cpu().numpy()
+            if self.is_half:
+                npy = npy.astype("float32")
+
+            # _, I = index.search(npy, 1)
+            # npy = big_npy[I.squeeze()]
+
+            score, ix = index.search(npy, k=8)
+            weight = np.square(1 / score)
+            weight /= weight.sum(axis=1, keepdims=True)
+            npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
+
+            if self.is_half:
+                npy = npy.astype("float16")
+            feats = (
+                    torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
+                    + (1 - index_rate) * feats
+            )
+
+        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
+        if protect < 0.5 and pitch != None and pitchf != None:
+            feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
+                0, 2, 1
+            )
+        t1 = ttime()
+        p_len = audio0.shape[0] // self.window
+        if feats.shape[1] < p_len:
+            p_len = feats.shape[1]
+            if pitch != None and pitchf != None:
+                pitch = pitch[:, :p_len]
+                pitchf = pitchf[:, :p_len]
+
+        if protect < 0.5 and pitch != None and pitchf != None:
+            pitchff = pitchf.clone()
+            pitchff[pitchf > 0] = 1
+            pitchff[pitchf < 1] = protect
+            pitchff = pitchff.unsqueeze(-1)
+            feats = feats * pitchff + feats0 * (1 - pitchff)
+            feats = feats.to(feats0.dtype)
+        p_len = torch.tensor([p_len], device=self.device).long()
+        with torch.no_grad():
+            if pitch != None and pitchf != None:
+                audio1 = (
+                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
+                    .data.cpu()
+                    .float()
+                    .numpy()
+                )
+            else:
+                audio1 = (
+                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
+                )
+        del feats, p_len, padding_mask
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        t2 = ttime()
+        times[0] += t1 - t0
+        times[2] += t2 - t1
+        return audio1
+
+    def pipeline(
+            self,
+            model,
+            net_g,
+            sid,
+            audio,
+            input_audio_path,
+            times,
+            f0_up_key,
+            f0_method,
+            file_index,
+            # file_big_npy,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            f0_file=None,
+    ):
+        if (
+                file_index != ""
+                # and file_big_npy != ""
+                # and os.path.exists(file_big_npy) == True
+                and os.path.exists(file_index) == True
+                and index_rate != 0
+        ):
+            try:
+                index = faiss.read_index(file_index)
+                # big_npy = np.load(file_big_npy)
+                big_npy = index.reconstruct_n(0, index.ntotal)
+            except:
+                traceback.print_exc()
+                index = big_npy = None
+        else:
+            index = big_npy = None
+        audio = signal.filtfilt(bh, ah, audio)
+        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
+        opt_ts = []
+        if audio_pad.shape[0] > self.t_max:
+            audio_sum = np.zeros_like(audio)
+            for i in range(self.window):
+                audio_sum += audio_pad[i: i - self.window]
+            for t in range(self.t_center, audio.shape[0], self.t_center):
+                opt_ts.append(
+                    t
+                    - self.t_query
+                    + np.where(
+                        np.abs(audio_sum[t - self.t_query: t + self.t_query])
+                        == np.abs(audio_sum[t - self.t_query: t + self.t_query]).min()
+                    )[0][0]
+                )
+        s = 0
+        audio_opt = []
+        t = None
+        t1 = ttime()
+        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
+        p_len = audio_pad.shape[0] // self.window
+        inp_f0 = None
+        if hasattr(f0_file, "name") == True:
+            try:
+                with open(f0_file.name, "r") as f:
+                    lines = f.read().strip("\n").split("\n")
+                inp_f0 = []
+                for line in lines:
+                    inp_f0.append([float(i) for i in line.split(",")])
+                inp_f0 = np.array(inp_f0, dtype="float32")
+            except:
+                traceback.print_exc()
+        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
+        pitch, pitchf = None, None
+        if if_f0 == 1:
+            pitch, pitchf = self.get_f0(
+                input_audio_path,
+                audio_pad,
+                p_len,
+                f0_up_key,
+                f0_method,
+                filter_radius,
+                inp_f0,
+            )
+            pitch = pitch[:p_len]
+            pitchf = pitchf[:p_len]
+            if self.device == "mps":
+                pitchf = pitchf.astype(np.float32)
+            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
+            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
+        t2 = ttime()
+        times[1] += t2 - t1
+        for t in opt_ts:
+            t = t // self.window * self.window
+            if if_f0 == 1:
+                audio_opt.append(
+                    self.vc(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s: t + self.t_pad2 + self.window],
+                        pitch[:, s // self.window: (t + self.t_pad2) // self.window],
+                        pitchf[:, s // self.window: (t + self.t_pad2) // self.window],
+                        times,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt: -self.t_pad_tgt]
+                )
+            else:
+                audio_opt.append(
+                    self.vc(
+                        model,
+                        net_g,
+                        sid,
+                        audio_pad[s: t + self.t_pad2 + self.window],
+                        None,
+                        None,
+                        times,
+                        index,
+                        big_npy,
+                        index_rate,
+                        version,
+                        protect,
+                    )[self.t_pad_tgt: -self.t_pad_tgt]
+                )
+            s = t
+        if if_f0 == 1:
+            audio_opt.append(
+                self.vc(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    pitch[:, t // self.window:] if t is not None else pitch,
+                    pitchf[:, t // self.window:] if t is not None else pitchf,
+                    times,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt: -self.t_pad_tgt]
+            )
+        else:
+            audio_opt.append(
+                self.vc(
+                    model,
+                    net_g,
+                    sid,
+                    audio_pad[t:],
+                    None,
+                    None,
+                    times,
+                    index,
+                    big_npy,
+                    index_rate,
+                    version,
+                    protect,
+                )[self.t_pad_tgt: -self.t_pad_tgt]
+            )
+        audio_opt = np.concatenate(audio_opt)
+        if rms_mix_rate != 1:
+            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
+            audio_opt = librosa.resample(
+                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
+            )
+        audio_max = np.abs(audio_opt).max() / 0.99
+        max_int16 = 32768
+        if audio_max > 1:
+            max_int16 /= audio_max
+        audio_opt = (audio_opt * max_int16).astype(np.int16)
+        del pitch, pitchf, sid
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        return audio_opt
diff --git a/server.jpg b/server.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..430924ce9fa29671e96c7cd087c8165337267fa6
--- /dev/null
+++ b/server.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9738c97cb7aaf84a7389de121ab5f789abd9b1addde58e18574e61f55b544f0f
+size 562145
diff --git a/server.kra b/server.kra
new file mode 100644
index 0000000000000000000000000000000000000000..694a0ea74d0e12c9a1096cc3e96c2bcaac8e5419
--- /dev/null
+++ b/server.kra
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62e825945155da823436b43b144120babff745dcdb17b3efb5f2cb0dd277ba2f
+size 2770952
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e78c3e4fd4a470943736f8931cea9fd9e3d7312
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fc0b59913ec446199ad9f8da0deffb35e753bf9c0cb27c0e20df7947ab0b889
+size 1203
diff --git a/speakers/__init__.py b/speakers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..94790d37cc847847eb0b2d23e7e7ff127b1d81df
--- /dev/null
+++ b/speakers/__init__.py
@@ -0,0 +1,45 @@
+from speakers.common.registry import registry
+from speakers.speakers import set_main_logger, Speaker, WebSpeaker
+import torch
+import os
+import util
+from pathlib import Path
+import platform
+import tempfile
+
+__all__=[
+    "Speaker",
+    "WebSpeaker",
+    "set_main_logger",
+]
+
+root_dir = os.path.dirname(os.path.abspath(__file__))
+registry.register_path("library_root", root_dir)
+
+tempdir = Path("/tmp" if platform.system() == "Darwin" else tempfile.gettempdir())
+registry.register_path("tmp_root", str(tempdir))
+
+
+device = (
+    'cuda:0' if torch.cuda.is_available()
+    else (
+        'mps' if util.has_mps()
+        else 'cpu'
+    )
+)
+
+registry.register("device", device)
+
+is_half = util.is_half(device)
+
+registry.register("is_half", is_half)
+
+x_pad = 3 if is_half else 1
+x_query = 10 if is_half else 6
+x_center = 60 if is_half else 38
+x_max = 65 if is_half else 41
+
+registry.register("x_pad", x_pad)
+registry.register("x_query", x_query)
+registry.register("x_center", x_center)
+registry.register("x_max", x_max)
diff --git a/speakers/__main__.py b/speakers/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d2dc122fc38fa7a49e78885dd115be286b8e0ed
--- /dev/null
+++ b/speakers/__main__.py
@@ -0,0 +1,80 @@
+import os
+import asyncio
+import logging
+from argparse import Namespace
+
+from bark.mode_load import set_bark_model_load_logger
+from speakers.common.log import get_logger, set_log_level
+from speakers import set_main_logger, Speaker, WebSpeaker
+from speakers.processors.bark_to_voice import set_bark_to_voice_logger
+from speakers.processors.rvc_speakers_processor import set_rvc_speakers_logger
+from speakers.processors.vits_to_voice import set_vits_to_voice_logger
+from speakers.server.servlet.runner import set_server_runner_logger
+from speakers.server.model.flow_data import PayLoad
+from speakers.server import dispatch as dispatch_web
+import argparse
+
+
+logger = logging.getLogger('start_logger')
+
+
+def set_start_logger(l):
+    global logger
+    logger = l
+
+
+async def dispatch(args: Namespace):
+    args_dict = vars(args)
+
+    logger.info(f'Running in {args.mode} mode')
+
+    if args.mode in 'demo':
+
+        speaker = Speaker(speakers_config_file=args.speakers_config_file, verbose=args.verbose)
+        await speaker.preparation_runner(task_id="0", payload=PayLoad())
+    elif args.mode in 'web':
+
+        await dispatch_web(speakers_config_file=args.speakers_config_file)
+
+    elif args.mode in 'web_runner':
+
+        translator = WebSpeaker(speakers_config_file=args.speakers_config_file, verbose=args.verbose, nonce=args.nonce)
+        await translator.listen()
+
+
+def main():
+    parser = argparse.ArgumentParser(prog='speakers',
+                                     description='S')
+    parser.add_argument('-m', '--mode', default='demo', type=str, choices=['demo', 'web', 'web_runner'],
+                        help='Run ')
+
+    parser.add_argument('-v', '--verbose', action='store_true', help='Print debug info in result folder')
+    parser.add_argument("--speakers-config-file", type=str, default="speakers.yaml")
+    parser.add_argument('--nonce', default='', type=str, help='Used by web module to decide which secret for securing '
+                                                              'internal web server communication')
+
+    args = None
+    try:
+        args = parser.parse_args()
+        set_log_level(level=logging.DEBUG if args.verbose else logging.INFO)
+        set_start_logger(get_logger(args.mode))
+        set_main_logger(logger)
+        set_bark_model_load_logger(logger)
+        set_rvc_speakers_logger(logger)
+        set_vits_to_voice_logger(logger)
+        set_bark_to_voice_logger(logger)
+        set_server_runner_logger(logger)
+
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        loop.run_until_complete(dispatch(args))
+    except KeyboardInterrupt:
+        if not args or args.mode != 'web':
+            print()
+    except Exception as e:
+        logger.error(f'{e.__class__.__name__}: {e}',
+                     exc_info=e if args and args.verbose else None)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/speakers/__pycache__/__init__.cpython-310.pyc b/speakers/__pycache__/__init__.cpython-310.pyc
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diff --git a/speakers/common/__pycache__/registry.cpython-310.pyc b/speakers/common/__pycache__/registry.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..e207312e48d8c9405ed6e766a4d99f3e2af97bd3
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diff --git a/speakers/common/__pycache__/utils.cpython-310.pyc b/speakers/common/__pycache__/utils.cpython-310.pyc
new file mode 100644
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diff --git a/speakers/common/general.py b/speakers/common/general.py
new file mode 100644
index 0000000000000000000000000000000000000000..92b3c0c67bc63afcc3d086ec2750a0138bb38cd2
--- /dev/null
+++ b/speakers/common/general.py
@@ -0,0 +1,105 @@
+import os
+from typing import List, Callable, Tuple
+import numpy as np
+import hashlib
+import re
+
+MODULE_PATH = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+BASE_PATH = os.path.dirname(MODULE_PATH)
+
+
+# Adapted from argparse.Namespace
+class Context(dict):
+    __getattr__ = dict.get
+    __setattr__ = dict.__setitem__
+    __delattr__ = dict.__delitem__
+
+    def __init__(self, **kwargs):
+        for name in kwargs:
+            setattr(self, name, kwargs[name])
+
+    def __eq__(self, other):
+        if not isinstance(other, Context):
+            return NotImplemented
+        return vars(self) == vars(other)
+
+    def __contains__(self, key):
+        return key in self.keys()
+
+    def __repr__(self):
+        type_name = type(self).__name__
+        arg_strings = []
+        star_args = {}
+        for arg in self._get_args():
+            arg_strings.append(repr(arg))
+        for name, value in self._get_kwargs():
+            if name.isidentifier():
+                arg_strings.append('%s=%r' % (name, value))
+            else:
+                star_args[name] = value
+        if star_args:
+            arg_strings.append('**%s' % repr(star_args))
+        return '%s(%s)' % (type_name, ', '.join(arg_strings))
+
+    def _get_kwargs(self):
+        return list(self.items())
+
+    def _get_args(self):
+        return []
+
+
+def repeating_sequence(s: str):
+    """Extracts repeating sequence from string. Example: 'abcabca' -> 'abc'."""
+    for i in range(1, len(s) // 2 + 1):
+        seq = s[:i]
+        if seq * (len(s) // len(seq)) + seq[:len(s) % len(seq)] == s:
+            return seq
+    return s
+
+
+def replace_prefix(s: str, old: str, new: str):
+    if s.startswith(old):
+        s = new + s[len(old):]
+    return s
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i:i + n]
+
+
+def get_digest(file_path: str) -> str:
+    h = hashlib.sha256()
+    BUF_SIZE = 65536
+
+    with open(file_path, 'rb') as file:
+        while True:
+            # Reading is buffered, so we can read smaller chunks.
+            chunk = file.read(BUF_SIZE)
+            if not chunk:
+                break
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def get_filename_from_url(url: str, default: str = '') -> str:
+    m = re.search(r'/([^/?]+)[^/]*$', url)
+    if m:
+        return m.group(1)
+    return default
+
+
+def prompt_yes_no(query: str, default: bool = None) -> bool:
+    s = '%s (%s/%s): ' % (query, 'Y' if default == True else 'y', 'N' if default == False else 'n')
+    while True:
+        inp = input(s).lower()
+        if inp in ('yes', 'y'):
+            return True
+        elif inp in ('no', 'n'):
+            return False
+        elif default != None:
+            return default
+        if inp:
+            print('Error: Please answer with "y" or "n"')
+
diff --git a/speakers/common/log.py b/speakers/common/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..f13a2f0f475deb64e796a4296c151a2ee65ae86a
--- /dev/null
+++ b/speakers/common/log.py
@@ -0,0 +1,60 @@
+import logging
+import colorama
+
+from speakers.common.general import replace_prefix
+
+ROOT_TAG = 'speakers'
+
+
+class Formatter(logging.Formatter):
+    def formatMessage(self, record: logging.LogRecord) -> str:
+        if record.levelno >= logging.ERROR:
+            self._style._fmt = f'{colorama.Fore.RED}%(levelname)s:{colorama.Fore.RESET} [%(name)s] %(message)s'
+        elif record.levelno >= logging.WARN:
+            self._style._fmt = f'{colorama.Fore.YELLOW}%(levelname)s:{colorama.Fore.RESET} [%(name)s] %(message)s'
+        else:
+            self._style._fmt = '[%(name)s] %(message)s'
+        return super().formatMessage(record)
+
+
+class Filter(logging.Filter):
+    def filter(self, record: logging.LogRecord) -> bool:
+        # Try to filter out logs from imported modules
+        if not record.name.startswith(ROOT_TAG):
+            return False
+        # Shorten the name
+        record.name = replace_prefix(record.name, ROOT_TAG + '.', '')
+        return super().filter(record)
+
+
+logging.basicConfig(level=logging.INFO)
+root = logging.getLogger(ROOT_TAG)
+
+for h in logging.root.handlers:
+    h.setFormatter(Formatter())
+    h.addFilter(Filter())
+
+
+def set_log_level(level):
+    root.setLevel(level)
+
+
+def get_logger(name: str):
+    return root.getChild(name)
+
+
+file_handlers = {}
+
+
+def add_file_logger(path: str):
+    if path in file_handlers:
+        return
+    file_handlers[path] = logging.FileHandler(path, encoding='utf8')
+    logging.root.addHandler(file_handlers[path])
+
+
+def remove_file_logger(path: str):
+    if path in file_handlers:
+        logging.root.removeHandler(file_handlers[path])
+        file_handlers[path].close()
+        del file_handlers[path]
diff --git a/speakers/common/registry.py b/speakers/common/registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..d632c2268b51ce9b54d027c2a41f28bc157f0bf8
--- /dev/null
+++ b/speakers/common/registry.py
@@ -0,0 +1,187 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+
+class Registry:
+    """
+    注册管理器
+    """
+    mapping = {
+        "processor_name_mapping": {},
+        "task_name_mapping": {},
+        "state": {},
+        "paths": {},
+    }
+
+    @classmethod
+    def register_task(cls, name):
+        r"""Register a task to registry with key 'name'
+
+        Args:
+            name: Key with which the task will be registered.
+
+        Usage:
+
+            from lavis.common.registry import registry
+        """
+
+        print(f"from speakers.common.registry import registry {name}")
+
+        def wrap(task_cls):
+            from speakers.tasks.base_task import BaseTask
+            assert issubclass(
+                task_cls, BaseTask
+            ), "All tasks must inherit BaseTask class"
+            if name in cls.mapping["task_name_mapping"]:
+                raise KeyError(
+                    "Name '{}' already registered for {}.".format(
+                        name, cls.mapping["task_name_mapping"][name]
+                    )
+                )
+            cls.mapping["task_name_mapping"][name] = task_cls
+            return task_cls
+
+        return wrap
+
+    @classmethod
+    def register_processor(cls, name):
+        r"""Register a processor to registry with key 'name'
+
+        Args:
+            name: Key with which the task will be registered.
+
+        Usage:
+
+            from speakers.common.registry import registry
+        """
+        print(f"from speakers.common.registry import registry {name}")
+
+        def wrap(processor_cls):
+            from speakers.processors import BaseProcessor
+
+            assert issubclass(
+                processor_cls, BaseProcessor
+            ), "All processors must inherit BaseProcessor class"
+            if name in cls.mapping["processor_name_mapping"]:
+                raise KeyError(
+                    "Name '{}' already registered for {}.".format(
+                        name, cls.mapping["processor_name_mapping"][name]
+                    )
+                )
+            cls.mapping["processor_name_mapping"][name] = processor_cls
+            return processor_cls
+
+        return wrap
+
+    @classmethod
+    def register_path(cls, name, path):
+        r"""Register a path to registry with key 'name'
+
+        Args:
+            name: Key with which the path will be registered.
+            path: Key with which the path will be registered.
+
+        Usage:
+
+            from lavis.common.registry import registry
+        """
+        assert isinstance(path, str), "All path must be str."
+        if name in cls.mapping["paths"]:
+            raise KeyError("Name '{}' already registered.".format(name))
+        cls.mapping["paths"][name] = path
+
+    @classmethod
+    def register(cls, name, obj):
+        r"""Register an item to registry with key 'name'
+
+        Args:
+            name: Key with which the item will be registered.
+
+        Usage::
+
+            from lavis.common.registry import registry
+
+            registry.register("config", {})
+        """
+        path = name.split(".")
+        current = cls.mapping["state"]
+
+        for part in path[:-1]:
+            if part not in current:
+                current[part] = {}
+            current = current[part]
+
+        current[path[-1]] = obj
+        print(f" Key with which the item will be registered {current}")
+
+    @classmethod
+    def get_processor_class(cls, name):
+        return cls.mapping["processor_name_mapping"].get(name, None)
+
+    @classmethod
+    def get_task_class(cls, name):
+        return cls.mapping["task_name_mapping"].get(name, None)
+
+    @classmethod
+    def list_processors(cls):
+        return sorted(cls.mapping["processor_name_mapping"].keys())
+
+    @classmethod
+    def list_tasks(cls):
+        return sorted(cls.mapping["task_name_mapping"].keys())
+
+    @classmethod
+    def get_path(cls, name):
+        return cls.mapping["paths"].get(name, None)
+
+    @classmethod
+    def get(cls, name, default=None, no_warning=False):
+        r"""Get an item from registry with key 'name'
+
+        Args:
+            name (string): Key whose value needs to be retrieved.
+            default: If passed and key is not in registry, default value will
+                     be returned with a warning. Default: None
+            no_warning (bool): If passed as True, warning when key doesn't exist
+                               will not be generated. Useful for MMF's
+                               internal operations. Default: False
+        """
+        original_name = name
+        name = name.split(".")
+        value = cls.mapping["state"]
+        for subname in name:
+            value = value.get(subname, default)
+            if value is default:
+                break
+
+        if (
+                "writer" in cls.mapping["state"]
+                and value == default
+                and no_warning is False
+        ):
+            cls.mapping["state"]["writer"].warning(
+                "Key {} is not present in registry, returning default value "
+                "of {}".format(original_name, default)
+            )
+        return value
+
+    @classmethod
+    def unregister(cls, name):
+        r"""Remove an item from registry with key 'name'
+
+        Args:
+            name: Key which needs to be removed.
+        Usage::
+
+            from mmf.common.registry import registry
+
+            config = registry.unregister("config")
+        """
+        return cls.mapping["state"].pop(name, None)
+
+
+registry = Registry()
diff --git a/speakers/common/utils.py b/speakers/common/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd01619374f946591863300f9608067855246ba7
--- /dev/null
+++ b/speakers/common/utils.py
@@ -0,0 +1,10 @@
+from speakers.common.registry import registry
+import os
+
+
+def get_abs_path(rel_path):
+    return os.path.join(registry.get_path("library_root"), rel_path)
+
+
+def get_tmp_path(rel_path):
+    return os.path.join(registry.get_path("tmp_root"), rel_path)
diff --git a/speakers/load/__init__.py b/speakers/load/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1c042ba376692a50f00cf5e1ae1a1481d5d08c7
--- /dev/null
+++ b/speakers/load/__init__.py
@@ -0,0 +1 @@
+"""Serialization and deserialization."""
diff --git a/speakers/load/__pycache__/__init__.cpython-310.pyc b/speakers/load/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..69210b83a7ab504ab878f42f13da5eab772b2298
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diff --git a/speakers/load/__pycache__/serializable.cpython-310.pyc b/speakers/load/__pycache__/serializable.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4b86181fd9dd39346ac58d6a7422628397756d7b
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diff --git a/speakers/load/serializable.py b/speakers/load/serializable.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f0e5ccf8dd0f6d95e569f8b3b0965b4557f519b
--- /dev/null
+++ b/speakers/load/serializable.py
@@ -0,0 +1,163 @@
+from abc import ABC
+from typing import Any, Dict, List, Literal, TypedDict, Union, cast
+
+from pydantic import BaseModel, PrivateAttr
+
+
+class BaseSerialized(TypedDict):
+    """Base class for serialized objects."""
+
+    lc: int
+    id: List[str]
+
+
+class SerializedConstructor(BaseSerialized):
+    """Serialized constructor."""
+
+    type: Literal["constructor"]
+    kwargs: Dict[str, Any]
+
+
+class SerializedSecret(BaseSerialized):
+    """Serialized secret."""
+
+    type: Literal["secret"]
+
+
+class SerializedNotImplemented(BaseSerialized):
+    """Serialized not implemented."""
+
+    type: Literal["not_implemented"]
+
+
+class Serializable(BaseModel, ABC):
+    """Serializable base class."""
+
+    @property
+    def lc_serializable(self) -> bool:
+        """
+        Return whether or not the class is serializable.
+        """
+        return False
+
+    @property
+    def lc_namespace(self) -> List[str]:
+        """
+        Return the namespace of the langchain object.
+        eg. ["langchain", "llms", "openai"]
+        """
+        return self.__class__.__module__.split(".")
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        """
+        Return a map of constructor argument names to secret ids.
+        eg. {"openai_api_key": "OPENAI_API_KEY"}
+        """
+        return dict()
+
+    @property
+    def lc_attributes(self) -> Dict:
+        """
+        Return a list of attribute names that should be included in the
+        serialized kwargs. These attributes must be accepted by the
+        constructor.
+        """
+        return {}
+
+    class Config:
+        extra = "ignore"
+
+    _lc_kwargs = PrivateAttr(default_factory=dict)
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self._lc_kwargs = kwargs
+
+    def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]:
+        if not self.lc_serializable:
+            return self.to_json_not_implemented()
+
+        secrets = dict()
+        # Get latest values for kwargs if there is an attribute with same name
+        lc_kwargs = {
+            k: getattr(self, k, v)
+            for k, v in self._lc_kwargs.items()
+            if not (self.__exclude_fields__ or {}).get(k, False)  # type: ignore
+        }
+
+        # Merge the lc_secrets and lc_attributes from every class in the MRO
+        for cls in [None, *self.__class__.mro()]:
+            # Once we get to Serializable, we're done
+            if cls is Serializable:
+                break
+
+            # Get a reference to self bound to each class in the MRO
+            this = cast(Serializable, self if cls is None else super(cls, self))
+
+            secrets.update(this.lc_secrets)
+            lc_kwargs.update(this.lc_attributes)
+
+        # include all secrets, even if not specified in kwargs
+        # as these secrets may be passed as an environment variable instead
+        for key in secrets.keys():
+            secret_value = getattr(self, key, None) or lc_kwargs.get(key)
+            if secret_value is not None:
+                lc_kwargs.update({key: secret_value})
+
+        return {
+            "lc": 1,
+            "type": "constructor",
+            "id": [*self.lc_namespace, self.__class__.__name__],
+            "kwargs": lc_kwargs
+            if not secrets
+            else _replace_secrets(lc_kwargs, secrets),
+        }
+
+    def to_json_not_implemented(self) -> SerializedNotImplemented:
+        return to_json_not_implemented(self)
+
+
+def _replace_secrets(
+    root: Dict[Any, Any], secrets_map: Dict[str, str]
+) -> Dict[Any, Any]:
+    result = root.copy()
+    for path, secret_id in secrets_map.items():
+        [*parts, last] = path.split(".")
+        current = result
+        for part in parts:
+            if part not in current:
+                break
+            current[part] = current[part].copy()
+            current = current[part]
+        if last in current:
+            current[last] = {
+                "lc": 1,
+                "type": "secret",
+                "id": [secret_id],
+            }
+    return result
+
+
+def to_json_not_implemented(obj: object) -> SerializedNotImplemented:
+    """Serialize a "not implemented" object.
+
+    Args:
+        obj: object to serialize
+
+    Returns:
+        SerializedNotImplemented
+    """
+    _id: List[str] = []
+    try:
+        if hasattr(obj, "__name__"):
+            _id = [*obj.__module__.split("."), obj.__name__]
+        elif hasattr(obj, "__class__"):
+            _id = [*obj.__class__.__module__.split("."), obj.__class__.__name__]
+    except Exception:
+        pass
+    return {
+        "lc": 1,
+        "type": "not_implemented",
+        "id": _id,
+    }
diff --git a/speakers/processors/__init__.py b/speakers/processors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b13508dad0b19205db92b3cd569e55a1219eb1e1
--- /dev/null
+++ b/speakers/processors/__init__.py
@@ -0,0 +1,66 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+from typing import List
+
+from speakers.common.registry import registry
+from speakers.processors.base_processor import BaseProcessor
+from speakers.processors.base_processor import ProcessorData
+from speakers.processors.rvc_speakers_processor import RvcProcessorData
+from speakers.processors.vits_to_voice import VitsProcessorData
+from speakers.processors.bark_to_voice import BarkProcessorData
+
+__all__ = [
+    "BaseProcessor",
+    "ProcessorData",
+    "RvcProcessorData",
+    "VitsProcessorData",
+    "BarkProcessorData",
+    "get_processors",
+    "load_preprocess",
+]
+
+processors_cache = {}
+
+
+def load_preprocess(config: List[dict] = None):
+    """
+    Load preprocessor configs and construct preprocessors.
+
+    If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.
+
+    Args:
+        config (List[dict]): preprocessor configs.
+
+    Returns:
+        vits_processors (dict): preprocessors for vits inputs.
+        rvc_processors (dict): preprocessors for rvc inputs.
+
+    """
+
+    if config is None:
+        raise RuntimeError("Load preprocessor configs is None.")
+
+    def _build_proc_from_cfg(cfg):
+        print(cfg)
+        return (
+            registry.get_processor_class(cfg.name).from_config(cfg)
+            if cfg is not None
+            else BaseProcessor()
+        )
+    for process_cfg in config:  # 使用 .items() 方法获取键值对
+        for key, processor_cfg in process_cfg.items():  # 使用 .items() 方法获取键值对
+            processors = _build_proc_from_cfg(processor_cfg)
+            processors_cache[key] = processors
+
+
+def get_processors(key: str) -> BaseProcessor:
+
+    if not processors_cache.get(key):
+        raise ValueError(f'Could not find processors for: "{key}". '
+                         f'Choose from the following: %s' % ','.join(processors_cache))
+
+    return processors_cache[key]
diff --git a/speakers/processors/__pycache__/__init__.cpython-310.pyc b/speakers/processors/__pycache__/__init__.cpython-310.pyc
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diff --git a/speakers/processors/__pycache__/bark_to_voice.cpython-310.pyc b/speakers/processors/__pycache__/bark_to_voice.cpython-310.pyc
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diff --git a/speakers/processors/__pycache__/base_processor.cpython-310.pyc b/speakers/processors/__pycache__/base_processor.cpython-310.pyc
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diff --git a/speakers/processors/__pycache__/rvc_speakers_processor.cpython-310.pyc b/speakers/processors/__pycache__/rvc_speakers_processor.cpython-310.pyc
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index 0000000000000000000000000000000000000000..7036537a3657bf897e49253f259eb6c58b1b37f6
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diff --git a/speakers/processors/__pycache__/vits_to_voice.cpython-310.pyc b/speakers/processors/__pycache__/vits_to_voice.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1899a0f818be2fc352437fd4df5078d1098cd2b2
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diff --git a/speakers/processors/bark_to_voice.py b/speakers/processors/bark_to_voice.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed6a0399b50862815bbdba6540171a57a6fe174b
--- /dev/null
+++ b/speakers/processors/bark_to_voice.py
@@ -0,0 +1,244 @@
+from typing import Optional, Union, Dict
+from bark.mode_load import BarkModelLoader, SAMPLE_RATE
+from vits.modules import commons
+from vits.text import text_to_sequence
+from torch import LongTensor
+from speakers.common.registry import registry
+from speakers.processors import BaseProcessor, ProcessorData
+import os
+import logging
+import numpy as np
+import nltk  # we'll use this to split into sentences
+from nltk.tokenize import RegexpTokenizer
+
+logger = logging.getLogger('bark_to_voice')
+
+
+def set_bark_to_voice_logger(l):
+    global logger
+    logger = l
+
+
+silence = np.zeros(int(0.25 * SAMPLE_RATE))  # quarter second of silence
+
+
+def get_text(text, hps):
+    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm, clean_text
+
+
+class BarkProcessorData(ProcessorData):
+    """
+        :param text: 生成文本
+        :param speaker_history_prompt: 音频预设npz文件
+        :param text_temp: 提示特殊标记程序，趋近于1，提示词特殊标记越明显
+        :param waveform_temp: 提示隐藏空间转音频参数比例
+
+    """
+    """生成文本"""
+    text: str
+    """音频预设npz文件"""
+    speaker_history_prompt: str
+    """提示特殊标记程序，趋近于1，提示词特殊标记越明显"""
+    text_temp: float
+    """提示隐藏空间转音频参数比例"""
+    waveform_temp: float
+
+    @property
+    def type(self) -> str:
+        """Type of the Message, used for serialization."""
+        return "BARK"
+
+
+@registry.register_processor("bark_to_voice")
+class BarkToVoice(BaseProcessor):
+
+    def __init__(self, tokenizer_path: str, text_path: str, coarse_path: str, fine_path: str):
+        super().__init__()
+        self._load_bark_mode(tokenizer_path=tokenizer_path,
+                             text_path=text_path,
+                             coarse_path=coarse_path,
+                             fine_path=fine_path)
+
+    def __call__(
+            self,
+            data: BarkProcessorData
+    ):
+        # 分词，适配长句子
+        script = data.text.replace("\n", "。").strip()
+        tokenizer = RegexpTokenizer(r'[^，。！？]+[，。！？]?')
+        sentences = tokenizer.tokenize(script)
+
+        pieces = []
+        logger.info(f"sentences:{sentences}")
+        for sentence in sentences:
+            audio_array = self._generate_audio(text=sentence,
+                                               history_prompt_dir=registry.get_path('bark_library_root'),
+                                               history_prompt=data.speaker_history_prompt,
+                                               text_temp=data.text_temp,
+                                               waveform_temp=data.waveform_temp)
+
+            pieces += [audio_array, silence.copy()]
+
+        audio_array_out = np.concatenate(pieces)
+        del pieces
+        return audio_array_out
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        if cfg is None:
+            raise RuntimeError("from_config cfg is None.")
+
+        tokenizer_path = cfg.get("tokenizer_path", "")
+        text_model_path = cfg.get("text_model_path", "")
+        coarse_model_path = cfg.get("coarse_model_path", "")
+        fine_model_path = cfg.get("fine_model_path", "")
+
+        return cls(tokenizer_path=os.path.join(registry.get_path("vits_library_root"),
+                                               tokenizer_path),
+                   text_path=os.path.join(registry.get_path("vits_library_root"),
+                                          text_model_path),
+                   coarse_path=os.path.join(registry.get_path("vits_library_root"),
+                                            coarse_model_path),
+                   fine_path=os.path.join(registry.get_path("vits_library_root"),
+                                          fine_model_path)
+                   )
+
+    def match(self, data: ProcessorData):
+        return "BARK" in data.type
+
+    def _load_bark_mode(self, tokenizer_path: str, text_path: str, coarse_path: str, fine_path: str):
+
+        logger.info(f'Bark model loading')
+        self.bark_load = BarkModelLoader(tokenizer_path=tokenizer_path,
+                                         text_path=text_path,
+                                         coarse_path=coarse_path,
+                                         fine_path=fine_path,
+                                         device=registry.get("device"))
+        logger.info(f'Models loaded bark')
+
+    def _generate_audio(
+            self,
+            text: str,
+            history_prompt: Optional[str] = None,
+            history_prompt_dir: str = None,
+            text_temp: float = 0.7,
+            waveform_temp: float = 0.7,
+            fine_temp: float = 0.5,
+            silent: bool = False,
+            output_full: bool = False):
+        """Generate audio array from input text.
+
+        Args:
+            text: text to be turned into audio
+            history_prompt: history choice for audio cloning
+            text_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+            waveform_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+            fine_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+            silent: disable progress bar
+            output_full: return full generation to be used as a history prompt
+
+        Returns:
+            numpy audio array at sample frequency 24khz
+        """
+        semantic_tokens = self._text_to_semantic(
+            text,
+            history_prompt=history_prompt,
+            history_prompt_dir=history_prompt_dir,
+            temp=text_temp,
+            silent=silent,
+        )
+        out = self._semantic_to_waveform(
+            semantic_tokens,
+            history_prompt=history_prompt,
+            history_prompt_dir=history_prompt_dir,
+            temp=waveform_temp,
+            fine_temp=fine_temp,
+            silent=silent,
+            output_full=output_full,
+        )
+        if output_full:
+            full_generation, audio_arr = out
+            return full_generation, audio_arr
+        else:
+            audio_arr = out
+        return audio_arr
+
+    def _text_to_semantic(
+            self,
+            text: str,
+            history_prompt: Optional[str] = None,
+            history_prompt_dir=None,
+            temp: float = 0.7,
+            silent: bool = False,
+    ):
+        """Generate semantic array from text.
+
+        Args:
+            text: text to be turned into audio
+            history_prompt: history choice for audio cloning
+            temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+            silent: disable progress bar
+
+        Returns:
+            numpy semantic array to be fed into `semantic_to_waveform`
+        """
+        x_semantic = self.bark_load.generate_text_semantic(
+            text,
+            history_prompt=history_prompt,
+            history_prompt_dir=history_prompt_dir,
+            temp=temp,
+            silent=silent,
+            use_kv_caching=True
+        )
+        return x_semantic
+
+    def _semantic_to_waveform(
+            self,
+            semantic_tokens: np.ndarray,
+            history_prompt: Optional[Union[Dict, str]] = None,
+            history_prompt_dir: str = None,
+            temp: float = 0.7,
+            fine_temp: float = 0.5,
+            silent: bool = False,
+            output_full: bool = False,
+    ):
+        """Generate audio array from semantic input.
+
+        Args:
+            semantic_tokens: semantic token output from `text_to_semantic`
+            history_prompt: history choice for audio cloning
+            fine_temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+            temp: generation temperature (1.0 more diverse, 0.0 more conservative)
+            silent: disable progress bar
+            output_full: return full generation to be used as a history prompt
+
+        Returns:
+            numpy audio array at sample frequency 24khz
+        """
+        coarse_tokens = self.bark_load.generate_coarse(
+            semantic_tokens,
+            history_prompt=history_prompt,
+            history_prompt_dir=history_prompt_dir,
+            temp=temp,
+            silent=silent,
+            use_kv_caching=True
+        )
+        fine_tokens = self.bark_load.generate_fine(
+            coarse_tokens,
+            history_prompt=history_prompt,
+            history_prompt_dir=history_prompt_dir,
+            temp=fine_temp,
+        )
+        audio_arr = self.bark_load.codec_decode(fine_tokens)
+        if output_full:
+            full_generation = {
+                "semantic_prompt": semantic_tokens,
+                "coarse_prompt": coarse_tokens,
+                "fine_prompt": fine_tokens,
+            }
+            return full_generation, audio_arr
+        return audio_arr
diff --git a/speakers/processors/base_processor.py b/speakers/processors/base_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddfe0a533634e9cce3442211b7071c8c4179cad8
--- /dev/null
+++ b/speakers/processors/base_processor.py
@@ -0,0 +1,58 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from omegaconf import OmegaConf
+from abc import abstractmethod
+from speakers.load.serializable import Serializable
+
+
+class ProcessorData(Serializable):
+    """
+    The base abstract ProcessorData class.
+    """
+
+    @property
+    @abstractmethod
+    def type(self) -> str:
+        """Type of the Message, used for serialization."""
+
+    @property
+    def lc_serializable(self) -> bool:
+        """Whether this class is Processor serializable."""
+        return True
+
+
+class BaseProcessor:
+    """
+    音频处理器有抽象处理器Processor，通过单独的Processor配置，
+    通过from_config工厂方法预加载音频处理器
+    """
+    def __init__(self):
+        self.transform = lambda x: x
+        return
+
+    def __call__(self, data: ProcessorData):
+        return self.transform(data)
+
+    @classmethod
+    def match(cls, data: ProcessorData):
+        """
+        匹配处理器
+        :param data:
+        :return:
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        return cls()
+
+    def build(self, **kwargs):
+        cfg = OmegaConf.create(kwargs)
+
+        return self.from_config(cfg)
+
diff --git a/speakers/processors/rvc_speakers_processor.py b/speakers/processors/rvc_speakers_processor.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c271e59f7095eb9a950ae43dc43a07a38c31357
--- /dev/null
+++ b/speakers/processors/rvc_speakers_processor.py
@@ -0,0 +1,311 @@
+import util
+import numpy as np
+import librosa
+import hashlib
+import json
+import os
+import torch
+import logging
+from rvc.infer_pack.models import (
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono
+)
+from os import getenv
+from typing import Union, Tuple, List
+from rvc.vc_infer_pipeline import VC
+from speakers.processors import BaseProcessor, ProcessorData
+from speakers.common.utils import get_abs_path
+from omegaconf import OmegaConf
+from speakers.common.registry import registry
+from pydantic import Field
+
+logger = logging.getLogger('speaker_runner')
+
+
+def set_rvc_speakers_logger(l):
+    global logger
+    logger = l
+
+
+class RvcProcessorData(ProcessorData):
+    """
+        # https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118  # noqa
+    :param f0_up_key:  变调(整数, 半音数量, 升八度12降八度-12)
+    :param input_audio:
+    :param f0_file:  F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调
+    :param protect: 保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果
+    :param model_index:
+    :param f0_method:
+    :param index_rate: 检索特征占比
+    :param filter_radius: >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音
+    :param rms_mix_rate: 输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络
+    :param resample_sr: 后处理重采样至最终采样率，0为不进行重采样
+    :return:
+    """
+    sample_rate: int = Field(
+        default=0
+    )
+    audio_samples: List[float] = Field(
+        default=[]
+    )
+
+    model_index: int
+
+    """ 变调(整数, 半音数量, 升八度12降八度-12)"""
+    f0_up_key: int
+
+    """ F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"""
+    f0_method: str
+
+    """检索特征占比"""
+    index_rate: float
+    """ >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"""
+    filter_radius: int
+    """输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"""
+    rms_mix_rate: float
+    """后处理重采样至最终采样率，0为不进行重采样"""
+    resample_sr: int
+    """保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"""
+    protect: float = Field(
+        default=0.33
+    )
+    f0_file: str = Field(
+        default=None
+    )
+
+    @property
+    def type(self) -> str:
+        """Type of the Message, used for serialization."""
+        return "RVC"
+
+
+@registry.register_processor("rvc_speakers")
+class RVCSpeakers(BaseProcessor):
+    """
+    音频处理器有抽象处理器Processor，通过单独的Processor配置，预加载音频处理器，
+    不同的处理器有着特定人物的说话风格与配置参数
+    """
+
+    def __init__(self, hubert_model_path: str, rvc_config_file: str):
+        # Reference: https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L21  # noqa
+        self.in_hf_space = getenv('SYSTEM') == 'spaces'
+        self._loaded_models = []
+        self._load_hubert(hubert_model_path=hubert_model_path)
+        self._load_rvc_mode(rvc_config_file=rvc_config_file)
+
+    def __call__(
+            self,
+            data: RvcProcessorData
+    ):
+        # 将 Python 列表转换为 NumPy 数组
+        audio_samples_np = np.array(data.audio_samples, dtype=np.float32)
+        input_audio = (data.sample_rate, audio_samples_np)
+
+        return self.vc_func(input_audio=input_audio,
+                            model_index=data.model_index,
+                            f0_up_key=data.f0_up_key,
+                            f0_method=data.f0_method,
+                            index_rate=data.index_rate,
+                            filter_radius=data.filter_radius,
+                            rms_mix_rate=data.rms_mix_rate,
+                            resample_sr=data.resample_sr,
+                            protect=data.protect,
+                            f0_file=data.f0_file)
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        if cfg is None:
+            raise RuntimeError("from_config cfg is None.")
+
+        hubert_model_path = cfg.get("hubert_model_path", "")
+        rvc_config_file = cfg.get("rvc_config_file", "")
+
+        return cls(hubert_model_path=os.path.join(registry.get_path("rvc_library_root"),
+                                                  hubert_model_path),
+                   rvc_config_file=os.path.join(registry.get_path("rvc_library_root"),
+                                                rvc_config_file))
+
+    def match(self, data: ProcessorData):
+        return "RVC" in data.type
+
+    @property
+    def loaded_models(self):
+        return self._loaded_models
+
+    def _load_hubert(self, hubert_model_path: str):
+
+        # Load hubert model
+        logger.info(f'Load hubert model{hubert_model_path}')
+        self.hubert_model = util.load_hubert_model(registry.get("device"), model_path=hubert_model_path)
+        self.hubert_model.eval()
+        logger.info('Loaded hubert model')
+
+    def _load_rvc_mode(self, rvc_config_file: str):
+        """
+        模型配置加载
+        :param rvc_config_file:
+        :return:
+        """
+
+        # Load models
+        logger.info(f'Models Load:rvc_speakers')
+        multi_cfg = OmegaConf.load(get_abs_path(rvc_config_file))
+        rmvpe_path = os.path.join(registry.get_path("rvc_library_root"), multi_cfg.get("rmvpe_path"))
+        logger.info(f'rmvpe_path:{rmvpe_path}')
+        for item in multi_cfg.get('models'):
+            for key, model_info in item.items():  # 使用 .items() 方法获取键值对
+
+                logger.info(f'Loading model: {key}')
+                model_name = model_info.get("model_name")
+                # Load model info
+                model_info_config_file = os.path.join(registry.get_path("rvc_library_root"),
+                                                      model_info.get("path"),
+                                                      'config.json')
+
+                logger.info(f'Loading model model_info_config_file: {model_info_config_file}')
+                model_info_config = json.load(open(model_info_config_file, 'r'))
+
+                # Load RVC checkpoint
+                torch_file = os.path.join(registry.get_path("rvc_library_root"),
+                                          model_info.get("path"),
+                                          model_info_config['model'])
+                cpt = torch.load(
+                    torch_file,
+                    map_location='cpu'
+                )
+                tgt_sr = cpt['config'][-1]
+                cpt['config'][-3] = cpt['weight']['emb_g.weight'].shape[0]  # n_spk
+
+                if_f0 = cpt.get('f0', 1)
+                net_g: Union[SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono]
+                if if_f0 == 1:
+                    net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt['config'],
+                        is_half=util.is_half(registry.get("device"))
+                    )
+                else:
+                    net_g = SynthesizerTrnMs768NSFsid_nono(*cpt['config'])
+
+                del net_g.enc_q
+
+                # According to original code, this thing seems necessary.
+                logger.info(net_g.load_state_dict(cpt['weight'], strict=False))
+
+                net_g.eval().to(registry.get("device"))
+                net_g = net_g.half() if util.is_half(registry.get("device")) else net_g.float()
+
+                vc = VC(tgt_sr,
+                        registry.get("x_pad"),
+                        registry.get("x_query"),
+                        registry.get("x_center"),
+                        registry.get("x_max"),
+                        registry.get("is_half"),
+                        registry.get("device"),
+                        rmvpe_path=rmvpe_path
+                        )
+
+                self._loaded_models.append(dict(
+                    name=model_name,
+                    metadata=model_info_config,
+                    vc=vc,
+                    net_g=net_g,
+                    if_f0=if_f0,
+                    target_sr=tgt_sr
+                ))
+
+        logger.info(f'Models loaded:rvc_speakers, len:{len(self._loaded_models)}')
+
+    def vc_func(
+            self,
+            input_audio: Tuple[int, np.ndarray], model_index, f0_up_key, f0_method: str, index_rate,
+            filter_radius, rms_mix_rate, resample_sr, protect: float = 0.33, f0_file: str = None
+    ) -> Tuple[int, np.ndarray]:
+        """
+            # https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/infer-web.py#L118  # noqa
+        :param f0_up_key:  变调(整数, 半音数量, 升八度12降八度-12)
+        :param input_audio:
+        :param f0_file:  F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调
+        :param protect: 保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果
+        :param model_index:
+        :param f0_method:
+        :param index_rate: 检索特征占比
+        :param filter_radius: >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音
+        :param rms_mix_rate: 输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络
+        :param resample_sr: 后处理重采样至最终采样率，0为不进行重采样
+        :return:
+        """
+        if input_audio is None:
+            raise RuntimeError("Please provide input audio.")
+
+        if model_index is None:
+            raise RuntimeError("Please select a model.")
+
+        model = self._loaded_models[model_index]
+
+        # Reference: so-vits
+        (audio_samp, audio_npy) = input_audio
+
+        # https://huggingface.co/spaces/zomehwh/rvc-models/blob/main/app.py#L49
+        # Can be change well, we will see
+        if (audio_npy.shape[0] / audio_samp) > 600 and self.in_hf_space:
+            raise RuntimeError("Input audio is longer than 600 secs.")
+
+        # Bloody hell: https://stackoverflow.com/questions/26921836/
+        if audio_npy.dtype != np.float32:  # :thonk:
+            audio_npy = (
+                    audio_npy / np.iinfo(audio_npy.dtype).max
+            ).astype(np.float32)
+
+        if len(audio_npy.shape) > 1:
+            audio_npy = librosa.to_mono(audio_npy.transpose(1, 0))
+
+        if audio_samp != 16000:
+            audio_npy = librosa.resample(
+                audio_npy,
+                orig_sr=audio_samp,
+                target_sr=16000
+            )
+
+        f0_up_key = int(f0_up_key)
+        times = [0, 0, 0]
+
+        checksum = hashlib.sha512()
+        checksum.update(audio_npy.tobytes())
+        feat_file_index = ''
+        if (
+                model['metadata']['feat_index'] != ""
+                # and file_big_npy != ""
+                # and os.path.exists(file_big_npy) == True
+                and os.path.exists(model['metadata']['feat_index']) == True
+                and index_rate != 0
+        ):
+            feat_file_index = model['metadata']['feat_index']
+
+        output_audio = model['vc'].pipeline(
+            self.hubert_model,
+            model['net_g'],
+            model['metadata'].get('speaker_id', 0),
+            audio_npy,
+            checksum.hexdigest(),
+            times,
+            f0_up_key,
+            f0_method,
+            feat_file_index,
+            index_rate,
+            model['if_f0'],
+            filter_radius,
+            model['target_sr'],
+            resample_sr,
+            rms_mix_rate,
+            'v2',
+            protect,
+            f0_file=f0_file
+        )
+
+        out_sr = (
+            resample_sr if 16000 <= resample_sr != model['target_sr']
+            else model['target_sr']
+        )
+
+        logger.info(f'npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s')
+        return out_sr, output_audio
diff --git a/speakers/processors/vits_to_voice.py b/speakers/processors/vits_to_voice.py
new file mode 100644
index 0000000000000000000000000000000000000000..7daace9e40fd9d6df4f87ef871860574b6b917d8
--- /dev/null
+++ b/speakers/processors/vits_to_voice.py
@@ -0,0 +1,172 @@
+from vits import utils
+from vits.modules import commons
+from vits.models import SynthesizerTrn
+from vits.text import text_to_sequence
+from torch import no_grad, LongTensor
+import torch
+import os
+from speakers.common.registry import registry
+from speakers.processors import BaseProcessor, ProcessorData
+import logging
+
+logger = logging.getLogger('speaker_runner')
+
+
+def set_vits_to_voice_logger(l):
+    global logger
+    logger = l
+
+
+def get_text(text, hps):
+    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = LongTensor(text_norm)
+    return text_norm, clean_text
+
+
+class VitsProcessorData(ProcessorData):
+    """
+        :param text: 生成文本
+        :param language: 序号  ['日本語', '简体中文', 'English', 'Mix']
+        :param speaker_id: 讲话人id
+        :param noise_scale: noise_scale(控制感情变化程度)
+        :param speed: length_scale(控制整体语速)
+        :param noise_scale_w: noise_scale_w(控制音素发音长度)
+
+    """
+    """生成文本"""
+    text: str
+    """语言- 序号  ['日本語', '简体中文', 'English', 'Mix'] """
+    language: int
+    """讲话人id"""
+    speaker_id: int
+    """ noise_scale(控制感情变化程度)"""
+    noise_scale: float
+    """length_scale(控制整体语速)"""
+    speed: int
+    """noise_scale_w(控制音素发音长度)"""
+    noise_scale_w: float
+
+    @property
+    def type(self) -> str:
+        """Type of the Message, used for serialization."""
+        return "VITS"
+
+
+@registry.register_processor("vits_to_voice")
+class VitsToVoice(BaseProcessor):
+
+    def __init__(self, vits_model_path: str, voice_config_file: str):
+        super().__init__()
+        import nest_asyncio
+        nest_asyncio.apply()
+        self.limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
+        logger.info(f'limit text and audio length in huggingface spaces: {self.limitation}')
+        self._load_voice_mode(vits_model=vits_model_path, voice_config_file=voice_config_file)
+
+        self._language_marks = {
+            "Japanese": "",
+            "日本語": "[JA]",
+            "简体中文": "[ZH]",
+            "English": "[EN]",
+            "Mix": "",
+        }
+        self._lang = ['日本語', '简体中文', 'English', 'Mix']
+
+    def __call__(
+            self,
+            data: VitsProcessorData
+    ):
+
+        return self.vits_func(text=data.text,
+                              language=data.language,
+                              speaker_id=data.speaker_id,
+                              noise_scale=data.noise_scale,
+                              noise_scale_w=data.noise_scale_w,
+                              speed=data.speed)
+
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        if cfg is None:
+            raise RuntimeError("from_config cfg is None.")
+
+        vits_model_path = cfg.get("vits_model_path", "")
+        voice_config_file = cfg.get("voice_config_file", "")
+
+        return cls(vits_model_path=os.path.join(registry.get_path("vits_library_root"),
+                                                vits_model_path),
+                   voice_config_file=os.path.join(registry.get_path("vits_library_root"),
+                                                  voice_config_file))
+
+    def match(self, data: ProcessorData):
+        return "VITS" in data.type
+
+    @property
+    def speakers(self):
+        return self._speakers
+
+    @property
+    def lang(self):
+        return self._lang
+
+    @property
+    def language_marks(self):
+        return self._language_marks
+
+    def _load_voice_mode(self, vits_model: str, voice_config_file: str):
+
+        logger.info(f'_load_voice_mode: {voice_config_file}')
+        device = torch.device(registry.get("device"))
+        self.hps_ms = utils.get_hparams_from_file(voice_config_file)
+        self.net_g_ms = SynthesizerTrn(
+            len(self.hps_ms.symbols),
+            self.hps_ms.data.filter_length // 2 + 1,
+            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
+            n_speakers=self.hps_ms.data.n_speakers, **self.hps_ms.model)
+
+        _ = self.net_g_ms.eval().to(device)
+
+        self._speakers = self.hps_ms.speakers
+        self.model, self.optimizer, self.learning_rate, self.epochs = utils.load_checkpoint(vits_model, self.net_g_ms,
+                                                                                            None)
+        logger.info(f'Models loaded vits_to_voice')
+
+    def search_speaker(self, search_value):
+        """
+        检索讲话人
+        :return:
+        """
+        for s in self._speakers:
+            if search_value == s or search_value in s:
+                return s
+
+    def vits_func(
+            self,
+            text: str, language: int, speaker_id: int,
+            noise_scale: float = 1, noise_scale_w: float = 1, speed=1):
+        """
+
+        :param text: 生成文本
+        :param language: 默认自动判断语言- 0中文， 1 日文
+        :param speaker_id: 讲话人id
+        :param noise_scale: noise_scale(控制感情变化程度)
+        :param speed: length_scale(控制整体语速)
+        :param noise_scale_w: noise_scale_w(控制音素发音长度)
+        :return:
+        """
+        if language is not None:
+            text = self._language_marks[self._lang[language]] + text + self._language_marks[self._lang[language]]
+
+        stn_tst, clean_text = get_text(text, self.hps_ms)
+        with no_grad():
+            x_tst = stn_tst.unsqueeze(0).to(registry.get("device"))
+            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(registry.get("device"))
+            sid = LongTensor([speaker_id]).to(registry.get("device"))
+            audio = self.model.infer(x_tst, x_tst_lengths, sid=sid,
+                                     noise_scale=noise_scale,
+                                     noise_scale_w=noise_scale_w,
+                                     length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy()
+        del stn_tst, x_tst, x_tst_lengths, sid
+        return audio
diff --git a/speakers/server/__init__.py b/speakers/server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0591109378f8c017c4a65acab9f496dfb28454c0
--- /dev/null
+++ b/speakers/server/__init__.py
@@ -0,0 +1,109 @@
+from speakers.common.registry import registry
+from speakers.server.bootstrap.bootstrap_register import load_bootstrap, get_bootstrap
+
+from omegaconf import OmegaConf
+
+from speakers.common.utils import get_abs_path
+from oscrypto import util as crypto_utils
+
+import asyncio
+import time
+import os
+import sys
+import traceback
+
+import subprocess
+
+root_dir = os.path.dirname(os.path.abspath(__file__))
+registry.register_path("server_library_root", root_dir)
+# Time to wait for web client to send a request to /task-state request
+# before that web clients task gets removed from the queue
+WEB_CLIENT_TIMEOUT = -1
+# Time before finished tasks get removed from memory
+FINISHED_TASK_REMOVE_TIMEOUT = 1800
+
+
+def generate_nonce():
+    return crypto_utils.rand_bytes(16).hex()
+
+
+def start_translator_client_proc(speakers_config_file: str, nonce: str = None):
+    cmds = [
+        sys.executable,
+        '-m', 'speakers',
+        '--mode', 'web_runner',
+        '--speakers-config-file', speakers_config_file,
+        '--nonce', nonce,
+    ]
+
+    proc = subprocess.Popen(cmds, cwd=f"{registry.get_path('library_root')}/../")
+    return proc
+
+
+async def start_async_app(speakers_config_file: str, nonce: str = None):
+    config = OmegaConf.load(get_abs_path(speakers_config_file))
+    load_bootstrap(config=config.get("bootstrap"))
+
+    runner_bootstrap_web = get_bootstrap("runner_bootstrap_web")
+
+    runner_bootstrap_web.set_nonce(nonce=nonce)
+    await runner_bootstrap_web.run()
+    return runner_bootstrap_web
+
+
+async def dispatch(speakers_config_file: str, nonce: str = None):
+
+    if nonce is None:
+        nonce = os.getenv('MT_WEB_NONCE', generate_nonce())
+
+    runner = await start_async_app(speakers_config_file=speakers_config_file, nonce=nonce)
+    # Create client process
+    print()
+    client_process = start_translator_client_proc(speakers_config_file, nonce=nonce)
+
+    try:
+        while True:
+            """任务队列状态维护"""
+            await asyncio.sleep(1)
+
+            # Restart client if OOM or similar errors occured
+            if client_process.poll() is not None:
+                print('Restarting translator process')
+                if len(runner.ongoing_tasks) > 0:
+                    task_id = runner.ongoing_tasks.pop(0)
+                    state = runner.task_states[task_id]
+                    state['info'] = 'error'
+                    state['finished'] = True
+                client_process = start_translator_client_proc(speakers_config_file=speakers_config_file)
+
+            # Filter queued and finished tasks
+            now = time.time()
+            to_del_task_ids = set()
+            for tid, s in runner.task_states.items():
+                payload = runner.task_data[tid]
+                # Remove finished tasks after 30 minutes
+                if s['finished'] and now - payload.created_at > FINISHED_TASK_REMOVE_TIMEOUT:
+                    to_del_task_ids.add(tid)
+
+                # Remove queued tasks without web client
+                elif WEB_CLIENT_TIMEOUT >= 0:
+                    if tid not in runner.ongoing_tasks and not s['finished'] \
+                            and now - payload.requested_at > WEB_CLIENT_TIMEOUT:
+                        print('REMOVING TASK', tid)
+                        to_del_task_ids.add(tid)
+                        try:
+                            runner.queue.remove(tid)
+                        except Exception:
+                            pass
+
+            for tid in to_del_task_ids:
+                del runner.task_states[tid]
+                del runner.task_data[tid]
+
+    except:
+        if client_process.poll() is None:
+            # client_process.terminate()
+            client_process.kill()
+        await runner.destroy()
+        traceback.print_exc()
+        raise
diff --git a/speakers/server/__pycache__/__init__.cpython-310.pyc b/speakers/server/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d14c590a57b546dd781dd5155213e8cc169984e9
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diff --git a/speakers/server/__pycache__/utils.cpython-310.pyc b/speakers/server/__pycache__/utils.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1ace267444436d1cf112b84ef8a6090bd42f1993
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diff --git a/speakers/server/bootstrap/__init__.py b/speakers/server/bootstrap/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccaf0c7cc17700a7521df36fcc8bf6f38642baa8
--- /dev/null
+++ b/speakers/server/bootstrap/__init__.py
@@ -0,0 +1,8 @@
+from speakers.server.bootstrap.base import Bootstrap
+from speakers.server.bootstrap.runner_bootstrap import RunnerBootstrapBaseWeb
+from speakers.server.bootstrap.bootstrap_register import bootstrap_register
+
+__all__ = [
+    "Bootstrap",
+    "RunnerBootstrapBaseWeb"
+]
diff --git a/speakers/server/bootstrap/__pycache__/__init__.cpython-310.pyc b/speakers/server/bootstrap/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..79ced5a5baf65614570adc6f577cc4eb127e5360
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diff --git a/speakers/server/bootstrap/__pycache__/base.cpython-310.pyc b/speakers/server/bootstrap/__pycache__/base.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6e3f951c27920330ec484d5357422435e0ab6dc4
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diff --git a/speakers/server/bootstrap/__pycache__/bootstrap_register.cpython-310.pyc b/speakers/server/bootstrap/__pycache__/bootstrap_register.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a68e97cf6034675906c552111536e0c4ddfdbd1e
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diff --git a/speakers/server/bootstrap/__pycache__/runner_bootstrap.cpython-310.pyc b/speakers/server/bootstrap/__pycache__/runner_bootstrap.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..2eba99cfbbd660d11a6c055511203bd179c71e84
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diff --git a/speakers/server/bootstrap/base.py b/speakers/server/bootstrap/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ed38f5aefe14c405c952c1a1a2ac3557e14c7c9
--- /dev/null
+++ b/speakers/server/bootstrap/base.py
@@ -0,0 +1,62 @@
+from collections import deque
+from typing import Dict, List
+from speakers.server.model.flow_data import BaseFlowData
+
+
+class Bootstrap:
+
+    """Used by web module to decide which secret for securing"""
+    _NONCE: str = ''
+    """最大的任务队列"""
+    _MAX_ONGOING_TASKS: int = 1
+
+    """任务队列"""
+    _QUEUE: deque = deque()
+    """进行的任务数据"""
+    _TASK_DATA: Dict[str, BaseFlowData] = {}
+    """进行的任务状态"""
+    _TASK_STATES = {}
+    """正在进行的任务"""
+    _ONGOING_TASKS: List[str] = []
+
+    def __init__(self):
+        self._version = "v0.0.1"
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        return cls()
+
+    @property
+    def version(self):
+        return self._version
+
+    @property
+    def max_ongoing_tasks(self) -> int:
+        return self._MAX_ONGOING_TASKS
+
+    @property
+    def ongoing_tasks(self) -> List[str]:
+        return self._ONGOING_TASKS
+
+    @property
+    def queue(self) -> deque:
+        return self._QUEUE
+
+    @property
+    def task_data(self) -> Dict[str, BaseFlowData]:
+        return self._TASK_DATA
+
+    @property
+    def task_states(self) -> dict:
+        return self._TASK_STATES
+
+    @property
+    def nonce(self) -> str:
+        return self._NONCE
+
+    def set_nonce(self, nonce: str):
+        self._NONCE = nonce
+
+    @classmethod
+    async def run(cls):
+        raise NotImplementedError
diff --git a/speakers/server/bootstrap/bootstrap_register.py b/speakers/server/bootstrap/bootstrap_register.py
new file mode 100644
index 0000000000000000000000000000000000000000..304832a465078af67bfb443ad736b40345cf1dda
--- /dev/null
+++ b/speakers/server/bootstrap/bootstrap_register.py
@@ -0,0 +1,76 @@
+from speakers.server.bootstrap import Bootstrap
+
+
+class BootstrapRegister:
+    """
+    注册管理器
+    """
+    mapping = {
+        "bootstrap": {},
+    }
+
+    @classmethod
+    def register_bootstrap(cls, name):
+        r"""Register system bootstrap to registry with key 'name'
+
+        Args:
+            name: Key with which the task will be registered.
+
+        Usage:
+
+            from lavis.common.registry import registry
+        """
+
+        print(f"register_bootstrap {name}")
+
+        def wrap(task_cls):
+            from speakers.server.bootstrap.base import Bootstrap
+            assert issubclass(
+                task_cls, Bootstrap
+            ), "All tasks must inherit bootstrap class"
+            if name in cls.mapping["bootstrap"]:
+                raise KeyError(
+                    "Name '{}' already registered for {}.".format(
+                        name, cls.mapping["bootstrap"][name]
+                    )
+                )
+            cls.mapping["bootstrap"][name] = task_cls
+            return task_cls
+
+        return wrap
+
+    @classmethod
+    def get_bootstrap_class(cls, name):
+        return cls.mapping["bootstrap"].get(name, None)
+
+    @classmethod
+    def list_bootstrap(cls):
+        return sorted(cls.mapping["bootstrap"].keys())
+
+
+bootstrap_register = BootstrapRegister()
+
+
+bootstrap_cache = {}
+
+
+def load_bootstrap(config: dict = None):
+
+    def _build_task_from_cfg(cfg):
+        return (
+            bootstrap_register.get_bootstrap_class(cfg.name).from_config(cfg)
+            if cfg is not None
+            else Bootstrap()
+        )
+    for bootstraps in config:
+        for key, bootstrap_cfg in bootstraps.items():  # 使用 .items() 方法获取键值对
+            bootstrap = _build_task_from_cfg(bootstrap_cfg)
+            bootstrap_cache[key] = bootstrap
+
+
+def get_bootstrap(key: str) -> Bootstrap:
+    if not bootstrap_cache.get(key):
+        raise ValueError(f'Could not find bootstrap_cache for: "{key}". '
+                         f'Choose from the following: %s' % ','.join(bootstrap_cache))
+
+    return bootstrap_cache[key]
diff --git a/speakers/server/bootstrap/runner_bootstrap.py b/speakers/server/bootstrap/runner_bootstrap.py
new file mode 100644
index 0000000000000000000000000000000000000000..28a36a31a0933cd43fd6744855b21502bae8ebd7
--- /dev/null
+++ b/speakers/server/bootstrap/runner_bootstrap.py
@@ -0,0 +1,86 @@
+from fastapi import FastAPI
+from starlette.responses import HTMLResponse
+from fastapi.middleware.cors import CORSMiddleware
+from speakers.server.utils import MakeFastAPIOffline
+from speakers.server.model.result import BaseResponse
+from speakers.server.servlet.document import page_index, document
+from speakers.server.servlet.runner import (submit_async,
+                                            get_task_async,
+                                            post_task_update_async,
+                                            result_async)
+from speakers.server.bootstrap.bootstrap_register import bootstrap_register
+from speakers.server.bootstrap.base import Bootstrap
+import uvicorn
+import threading
+
+
+@bootstrap_register.register_bootstrap("runner_bootstrap_web")
+class RunnerBootstrapBaseWeb(Bootstrap):
+    """
+    Bootstrap Server Lifecycle
+    """
+    app: FastAPI
+    server_thread: threading
+
+    def __init__(self, host: str, port: int):
+        super().__init__()
+
+        self.host = host
+        self.port = port
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        host = cfg.get("host")
+        port = cfg.get("port")
+        return cls(host=host, port=port)
+
+    async def run(self):
+        self.app = FastAPI(
+            title="API Server",
+            version=self.version
+        )
+        MakeFastAPIOffline(self.app)
+        # Add CORS middleware to allow all origins
+        # 在config.py中设置OPEN_DOMAIN=True，允许跨域
+        # set OPEN_DOMAIN=True in config.py to allow cross-domain
+        self.app.add_middleware(
+            CORSMiddleware,
+            allow_origins=["*"],
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+
+        self.app.get("/",
+                     response_model=BaseResponse,
+                     summary="演示首页")(page_index)
+        self.app.get("/docs",
+                     response_model=BaseResponse,
+                     summary="swagger 文档")(document)
+        self.app.post("/runner/submit",
+                      tags=["Runner"],
+                      summary="提交调度Runner")(submit_async)
+        self.app.get("/runner/task-internal",
+                     tags=["Runner"],
+                     summary="内部获取调度Runner")(get_task_async)
+        self.app.post("/runner/task-update-internal",
+                      tags=["Runner"],
+                      summary="内部同步调度RunnerStat")(post_task_update_async)
+        self.app.get("/runner/result",
+                     tags=["Runner"],
+                     summary="获取任务结果")(result_async)
+        app = self.app
+
+        def run_server():
+            uvicorn.run(app, host=self.host, port=self.port)
+
+        self.server_thread = threading.Thread(target=run_server)
+        self.server_thread.start()
+
+    async def destroy(self):
+        server_thread = self.server_thread
+        app = self.app
+
+        @app.on_event("shutdown")
+        def shutdown_event():
+            server_thread.join()  # 等待服务器线程结束
diff --git a/speakers/server/model/__pycache__/flow_data.cpython-310.pyc b/speakers/server/model/__pycache__/flow_data.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..eb4835fd32a34218137ab75a1d60004b3a9b281d
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diff --git a/speakers/server/model/__pycache__/result.cpython-310.pyc b/speakers/server/model/__pycache__/result.cpython-310.pyc
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index 0000000000000000000000000000000000000000..d88db8b1778d1256b8feb8545b70e1ceb53b5bae
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diff --git a/speakers/server/model/flow_data.py b/speakers/server/model/flow_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1c831c75ce3c6511e84a4c5865a1ddcb19deb3b
--- /dev/null
+++ b/speakers/server/model/flow_data.py
@@ -0,0 +1,88 @@
+from pydantic import BaseModel, Field
+from typing import Any, Dict, List, Optional, Union
+
+
+class BarkProcessorData(BaseModel):
+    """
+        :param text: 生成文本
+        :param speaker_history_prompt: 音频预设npz文件
+        :param text_temp: 提示特殊标记程序，趋近于1，提示词特殊标记越明显
+        :param waveform_temp: 提示隐藏空间转音频参数比例
+
+    """
+    """生成文本"""
+    text: str = Field(default="你好")
+    """音频预设npz文件"""
+    speaker_history_prompt: str = Field(default='zh_speaker_2')
+    """提示特殊标记程序，趋近于1，提示词特殊标记越明显"""
+    text_temp: float = Field(default=1)
+    """提示隐藏空间转音频参数比例"""
+    waveform_temp: float = Field(default=0.9)
+
+
+class VitsProcessorData(BaseModel):
+    """生成文本"""
+    text: str = Field(default="你好")
+    """语言- 序号  ['日本語', '简体中文', 'English', 'Mix'] """
+    language: int = Field(default=1)
+    """讲话人id"""
+    speaker_id: int = Field(default=0)
+    """ noise_scale(控制感情变化程度)"""
+    noise_scale: float = Field(default=0.5)
+    """length_scale(控制整体语速)"""
+    speed: int = Field(default=1)
+    """noise_scale_w(控制音素发音长度)"""
+    noise_scale_w: float = Field(default=1)
+
+
+class RvcProcessorData(BaseModel):
+    model_index: int = Field(default=0)
+
+    """ 变调(整数, 半音数量, 升八度12降八度-12)"""
+    f0_up_key: int = Field(default=0)
+
+    """ F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"""
+    f0_method: str = Field(default="rmvpe")
+
+    """检索特征占比"""
+    index_rate: float = Field(default=0.9)
+    """ >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"""
+    filter_radius: int = Field(default=1)
+    """输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"""
+    rms_mix_rate: int = Field(default=1)
+    """后处理重采样至最终采样率，0为不进行重采样"""
+    resample_sr: float = Field(default=0)
+    """保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果"""
+    protect: float = Field(
+        default=0.33
+    )
+    f0_file: str = Field(
+        default=None
+    )
+
+
+class BaseFlowData(BaseModel):
+    """任务创建时间"""
+    created_at: float
+    """任务请求时间"""
+    requested_at: float
+
+
+class VitsVoiceFlowData(BaseModel):
+    vits: VitsProcessorData
+    rvc: RvcProcessorData
+
+
+class RunnerParameter(BaseModel):
+    task_name: str = Field(default="vits_voice_task")
+
+
+class BarkVoiceFlowData(BaseModel):
+    bark: BarkProcessorData
+    rvc: RvcProcessorData
+
+
+
+class PayLoad(BaseFlowData):
+    parameter: RunnerParameter
+    payload: Union[Dict, BarkVoiceFlowData, VitsVoiceFlowData]
diff --git a/speakers/server/model/result.py b/speakers/server/model/result.py
new file mode 100644
index 0000000000000000000000000000000000000000..f47fc1974e769b91290a04344c959b7e599372f5
--- /dev/null
+++ b/speakers/server/model/result.py
@@ -0,0 +1,47 @@
+from pydantic import BaseModel, Field
+
+from speakers.server.model.flow_data import PayLoad
+
+
+class BaseResponse(BaseModel):
+    code: int = Field(200, description="HTTP status code")
+    msg: str = Field("success", description="HTTP status message")
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "code": 200,
+                "msg": "success",
+            }
+        }
+
+
+class TaskRunnerResponse(BaseResponse):
+    data: dict
+
+
+class TaskVoiceFlowInfo(BaseModel):
+    task_id: str
+    data: PayLoad
+
+
+class TaskInfoResponse(BaseResponse):
+    data: TaskVoiceFlowInfo
+
+    class Config:
+        schema_extra = {
+            "example": {
+                "code": 200,
+                "msg": "success",
+                "data": None,
+            }
+        }
+
+
+class RunnerState(BaseModel):
+    """RunnerState"""
+    task_id: str
+    runner_stat: str
+    nonce: str
+    state: str
+    finished: bool = Field(default=False)
diff --git a/speakers/server/servlet/__pycache__/document.cpython-310.pyc b/speakers/server/servlet/__pycache__/document.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bba37513ba230cd9b3e46d058d4203b0f7ddd236
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diff --git a/speakers/server/servlet/__pycache__/runner.cpython-310.pyc b/speakers/server/servlet/__pycache__/runner.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..956620bfb5a5eb52e43126d5d227485eefc3a1b7
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diff --git a/speakers/server/servlet/document.py b/speakers/server/servlet/document.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f3bc4a310d804d6c4b4501153b55bf2657b04f0
--- /dev/null
+++ b/speakers/server/servlet/document.py
@@ -0,0 +1,12 @@
+from starlette.responses import RedirectResponse
+from starlette.responses import HTMLResponse
+from speakers.common.registry import registry
+
+
+async def page_index():
+    html_file = open(f"{registry.get_path('server_library_root')}/static/index.html", 'r').read()
+    return HTMLResponse(html_file)
+
+
+async def document():
+    return RedirectResponse(url="/docs")
diff --git a/speakers/server/servlet/runner.py b/speakers/server/servlet/runner.py
new file mode 100644
index 0000000000000000000000000000000000000000..edb2b5d4077d3cf1dcafc099b1bc453af90bb228
--- /dev/null
+++ b/speakers/server/servlet/runner.py
@@ -0,0 +1,149 @@
+from speakers.server.model.flow_data import PayLoad
+from speakers.server.model.result import (BaseResponse,
+                                          TaskInfoResponse,
+                                          TaskVoiceFlowInfo,
+                                          RunnerState,
+                                          TaskRunnerResponse)
+from speakers.server.bootstrap.bootstrap_register import get_bootstrap
+from speakers.common.utils import get_tmp_path
+from fastapi import File, Form, Body, Query
+from fastapi.responses import FileResponse
+from speakers.common.registry import registry
+import os
+import time
+import logging
+
+logger = logging.getLogger('server_runner')
+
+
+def set_server_runner_logger(l):
+    global logger
+    logger = l
+
+
+def constant_compare(a, b):
+    if isinstance(a, str):
+        a = a.encode('utf-8')
+    if isinstance(b, str):
+        b = b.encode('utf-8')
+    if not isinstance(a, bytes) or not isinstance(b, bytes):
+        return False
+    if len(a) != len(b):
+        return False
+
+    result = 0
+    for x, y in zip(a, b):
+        result |= x ^ y
+    return result == 0
+
+
+async def submit_async(payload: PayLoad):
+    """
+        Adds new task to the queue
+        task_id = f'{calculate_md5(flowData.vits.text)}-{flowData.vits.speaker_id}-{flowData.vits.language}' \
+              f'-{flowData.vits.noise_scale}-{flowData.vits.speed}-{flowData.vits.noise_scale_w}' \
+              f'-{flowData.rvc.model_index}-{flowData.rvc.f0_up_key}'
+    """
+
+    runner_bootstrap_web = get_bootstrap("runner_bootstrap_web")
+    task = registry.get_task_class(payload.parameter.task_name)
+
+    runner = task.prepare(payload=payload)
+    task_id = runner.task_id
+    now = time.time()
+    payload.created_at = now
+    payload.requested_at = now
+
+    task_state = {}
+    if os.path.exists(get_tmp_path(f'result/{task_id}.wav')):
+        task_state = {
+            'task_id': task_id,
+            'info': 'saved',
+            'finished': True,
+        }
+        if task_id not in runner_bootstrap_web.task_data or task_id not in runner_bootstrap_web.task_states:
+
+            logger.info(f'New `submit` task {task_id}')
+            runner_bootstrap_web.task_data[task_id] = payload
+            runner_bootstrap_web.queue.append(task_id)
+            runner_bootstrap_web.task_states[task_id] = task_state
+
+    elif task_id not in runner_bootstrap_web.task_data or task_id not in runner_bootstrap_web.task_states:
+        os.makedirs(get_tmp_path('result'), exist_ok=True)
+        task_state = {
+            'task_id': task_id,
+            'info': 'pending',
+            'finished': False,
+        }
+
+        logger.info(f'New `submit` task {task_id}')
+        runner_bootstrap_web.task_data[task_id] = payload
+        runner_bootstrap_web.queue.append(task_id)
+
+        runner_bootstrap_web.task_states[task_id] = task_state
+    else:
+        task_state = runner_bootstrap_web.task_states[task_id]
+
+    return TaskRunnerResponse(code=200, msg="提交任务成功", data=task_state)
+
+
+async def get_task_async(nonce: str = Query(..., examples=["samples"])):
+    """
+    Called by the translator to get a translation task.
+    """
+
+    runner_bootstrap_web = get_bootstrap("runner_bootstrap_web")
+
+    if constant_compare(nonce, runner_bootstrap_web.nonce):
+        if len(runner_bootstrap_web.ongoing_tasks) < runner_bootstrap_web.max_ongoing_tasks:
+            if len(runner_bootstrap_web.queue) > 0:
+                task_id = runner_bootstrap_web.queue.popleft()
+                if task_id in runner_bootstrap_web.task_data:
+                    data = runner_bootstrap_web.task_data[task_id]
+                    runner_bootstrap_web.ongoing_tasks.append(task_id)
+                    info = TaskVoiceFlowInfo(task_id=task_id, data=data)
+                    return TaskInfoResponse(code=200, msg="成功", data=info)
+
+            return BaseResponse(code=200, msg="成功")
+
+        else:
+            return BaseResponse(code=200, msg="max_ongoing_tasks")
+    return BaseResponse(code=401, msg="无法获取任务")
+
+
+async def post_task_update_async(runner_state: RunnerState):
+    """
+    Lets the translator update the task state it is working on.
+    """
+
+    runner_bootstrap_web = get_bootstrap("runner_bootstrap_web")
+
+    if constant_compare(runner_state.nonce, runner_bootstrap_web.nonce):
+        task_id = runner_state.task_id
+        if task_id in runner_bootstrap_web.task_states and task_id in runner_bootstrap_web.task_data:
+            runner_bootstrap_web.task_states[task_id] = {
+                'info': runner_state.state,
+                'finished': runner_state.finished,
+            }
+            if runner_state.finished:
+                try:
+                    i = runner_bootstrap_web.ongoing_tasks.index(task_id)
+                    runner_bootstrap_web.ongoing_tasks.pop(i)
+                except ValueError:
+                    pass
+
+            logger.info(f'Task state {task_id} to {runner_bootstrap_web.task_states[task_id]}')
+
+    return BaseResponse(code=200, msg="成功")
+
+
+async def result_async(task_id: str = Query(..., examples=["task_id"])):
+    filepath = get_tmp_path(f'result/{task_id}.wav')
+    logger.info(f'Task  {task_id} result_async {filepath}')
+    if os.path.exists(filepath):
+        return FileResponse(
+            path=filepath,
+            filename=f"{task_id}.wav",
+            media_type="multipart/form-data")
+    else:
+        return BaseResponse(code=500, msg=f"{task_id}.wav 读取文件失败")
diff --git a/speakers/server/static/favicon.png b/speakers/server/static/favicon.png
new file mode 100644
index 0000000000000000000000000000000000000000..5de8ee80b5f219484c90b0b4604cfd1e85b58e3d
Binary files /dev/null and b/speakers/server/static/favicon.png differ
diff --git a/speakers/server/static/index.html b/speakers/server/static/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..60cb4f6dde812b5a50d9134d76fe063bebbdb741
--- /dev/null
+++ b/speakers/server/static/index.html
@@ -0,0 +1,296 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>API Server Documentation</title>
+    <style>
+        /* CSS for the progress bar */
+        #loading {
+            width: 0;
+            height: 20px;
+            background-color: #4CAF50;
+            transition: width 0.5s;
+        }
+        .container {
+            display: flex;
+            justify-content: flex-start;
+            align-items: flex-start;
+            width: 80%; 
+            gap: 20px;
+            flex-direction: row;
+            flex-wrap: nowrap;
+            align-content: stretch;
+        }
+        .container > div {
+            flex-basis: 45%; /* 设置子元素占比，以确保在大屏幕上有足够的间距 */
+        }
+        .container > div:last-child {
+            flex-basis: 100%; /* 最后一个子元素占满整行 */
+        }
+        .container > div span {
+            display: inline-block;
+            min-width: 150px; /* 设置最小宽度以对齐标签 */
+        }
+        .container input, .container select {
+            width: 100%;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <!-- BarkProcessorData Parameters -->
+
+        <div>
+            <div>BarkProcessorData Parameters</div>
+            <div>
+                <span>生成文本 (text):</span>
+                <input id="text" type="text" value="青春的时间转瞬即逝，秋水于海又岂有一只浮游"> <!-- 默认值填入输入框 -->
+            </div>
+
+            <div>
+                <span>音频预设npz文件 (speaker_history_prompt):</span>
+                <input id="speaker_history_prompt" type="text" value="zh_speaker_2"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>text_temp (提示特殊标记程序，趋近于1，提示词特殊标记越明显):</span>
+                <input id="text_temp" type="text" value="1" placeholder="0.0-1"> <!-- 默认值填入输入框 -->
+            </div>
+
+            <div>
+                <span>waveform_temp (提示隐藏空间转音频参数比例):</span>
+                <input id="waveform_temp" type="text" value="0.9" placeholder="0.0-1"> <!-- 默认值填入输入框 -->
+            </div>
+
+<!--            <div>-->
+<!--                <span>语言 (language):</span>-->
+<!--                <select id="language">-->
+<!--                    <option value="0">日本語</option>-->
+<!--                    <option value="1" selected>简体中文</option> &lt;!&ndash; 默认选择中文 &ndash;&gt;-->
+<!--                    <option value="2">English</option>-->
+<!--                    <option value="3">Mix</option>-->
+<!--                </select>-->
+<!--            </div>-->
+<!--            <div>-->
+<!--                <span>讲话人ID (speaker_id):</span>-->
+<!--                <input id="speaker_id" type="text" value="486"> &lt;!&ndash; 默认值填入输入框 &ndash;&gt;-->
+<!--            </div>-->
+<!--            <div>-->
+<!--                <span>noise_scale (控制感情变化程度):</span>-->
+<!--                <input id="noise_scale" type="text" value="0.5" placeholder="0.0-2.0"> &lt;!&ndash; 默认值填入输入框 &ndash;&gt;-->
+<!--            </div>-->
+<!--            <div>-->
+<!--                <span>speed (控制整体语速):</span>-->
+<!--                <input id="speed" type="text" value="1"   placeholder="0.0-2.0"> &lt;!&ndash; 默认值填入输入框 &ndash;&gt;-->
+<!--            </div>-->
+<!--            <div>-->
+<!--                <span>noise_scale_w (控制音素发音长度):</span>-->
+<!--                <input id="noise_scale_w" type="text" value="1"  placeholder="0.0-2.0"> &lt;!&ndash; 默认值填入输入框 &ndash;&gt;-->
+<!--            </div>-->
+        </div>
+
+        <!-- RvcProcessorData Parameters -->
+
+        <div>
+            <div>RvcProcessorData Parameters </div>
+
+            <div>
+                <span>变调 (f0_up_key):</span>
+                <input id="f0_up_key" type="text" value="4"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>F0曲线文件 (f0_file, 可选):</span>
+                <input id="f0_file" type="text" value=""> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果 (protect):</span>
+                <input id="protect" type="text" value="0.33"  placeholder="0.0-0.5"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>模型索引 (model_index):</span>
+                <input id="model_index" type="text" value="0"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>F0方法 (f0_method):</span>
+                <input id="f0_method" type="text" value="rmvpe"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>检索特征占比 (index_rate):</span>
+                <input id="index_rate" type="text" value="0.9"  placeholder="0.0-1"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>滤波半径 (filter_radius):</span>
+                <input id="filter_radius" type="text" value="1"   placeholder="1-9"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络 (rms_mix_rate):</span>
+                <input id="rms_mix_rate" type="text" value="1" placeholder="0.0-1"> <!-- 默认值填入输入框 -->
+            </div>
+            <div>
+                <span>后处理重采样至最终采样率，0为不进行重采样 (resample_sr):</span>
+                <input id="resample_sr" type="text" value="0"> <!-- 默认值填入输入框 -->
+            </div>
+        </div>
+    </div>
+
+    <div>
+        <button id="submit">提交</button>
+    </div>
+
+    <a href="/docs">查看文档</a>
+    <a target="_blank"  href="https://github.com/glide-the/RVC-Speakers">项目地址</a>
+    <div id="loading"></div>
+    <div id="audio-player" style="display:none;">
+        <h2>音频播放器</h2>
+        <audio controls id="audio-element">
+            <!-- 音频内容将在此处显示 -->
+        </audio>
+    </div>
+
+    <script>
+        let continueSubmitting = true; // Flag to control whether to continue submitting tasks
+ 
+
+        // Function to submit the runner and wait for it to finish
+        async function submitRunnerAndWait() {
+            // Retrieve parameter values from the input fields
+            const text = document.getElementById('text').value;
+            // const language = document.getElementById('language').value;
+            // const speaker_id = document.getElementById('speaker_id').value;
+            // const noise_scale = document.getElementById('noise_scale').value;
+            // const speed = document.getElementById('speed').value;
+            // const noise_scale_w = document.getElementById('noise_scale_w').value;
+            const speaker_history_prompt = document.getElementById('speaker_history_prompt').value;
+            const text_temp = document.getElementById('text_temp').value;
+            const waveform_temp = document.getElementById('waveform_temp').value;
+            const f0_up_key = document.getElementById('f0_up_key').value;
+            const f0_file = document.getElementById('f0_file').value;
+            const protect = document.getElementById('protect').value;
+            const model_index = document.getElementById('model_index').value;
+            const f0_method = document.getElementById('f0_method').value;
+            const index_rate = document.getElementById('index_rate').value;
+            const filter_radius = document.getElementById('filter_radius').value;
+            const rms_mix_rate = document.getElementById('rms_mix_rate').value;
+            const resample_sr = document.getElementById('resample_sr').value;
+
+            // Define the request body for the POST request
+            // const requestBody = {
+            //     "created_at": 0,
+            //     "requested_at": 0,
+            //     "parameter": {
+            //         "task_name": "vits_voice_task"
+            //     },
+            //     "payload": {
+            //         "vits": {
+            //             "text": text,
+            //             "language": parseInt(language),
+            //             "speaker_id": parseInt(speaker_id),
+            //             "noise_scale": parseFloat(noise_scale),
+            //             "speed": parseFloat(speed),
+            //             "noise_scale_w": parseFloat(noise_scale_w)
+            //         },
+            //         "rvc": {
+            //             "model_index": parseInt(model_index),
+            //             "f0_up_key": parseInt(f0_up_key),
+            //             "f0_method": f0_method,
+            //             "index_rate": parseFloat(index_rate),
+            //             "filter_radius": parseInt(filter_radius),
+            //             "rms_mix_rate": parseFloat(rms_mix_rate),
+            //             "resample_sr": parseFloat(resample_sr),
+            //             "protect": parseFloat(protect),
+            //             "f0_file": f0_file
+            //         }
+            //     }
+            // };
+            const requestBody = {
+                "created_at": 0,
+                "requested_at": 0,
+                "parameter": {
+                    "task_name": "bark_voice_task"
+                },
+                "payload": {
+                    "bark": {
+                        "text": text,
+                        "speaker_history_prompt": speaker_history_prompt,
+                        "text_temp": parseFloat(text_temp),
+                        "waveform_temp": parseFloat(waveform_temp),
+                    },
+                    "rvc": {
+                        "model_index": parseInt(model_index),
+                        "f0_up_key": parseInt(f0_up_key),
+                        "f0_method": f0_method,
+                        "index_rate": parseFloat(index_rate),
+                        "filter_radius": parseInt(filter_radius),
+                        "rms_mix_rate": parseFloat(rms_mix_rate),
+                        "resample_sr": parseFloat(resample_sr),
+                        "protect": parseFloat(protect),
+                        "f0_file": f0_file
+                    }
+                }
+            };
+
+            // Function to submit the runner and handle the result
+            async function submitRunner() {
+                if (!continueSubmitting) {
+                    return; // Stop submitting if the flag is set to false
+                }
+
+                const submitResponse = await fetch('/runner/submit', {
+                    method: 'POST',
+                    headers: {
+                        'Content-Type': 'application/json'
+                    },
+                    body: JSON.stringify(requestBody)
+                });
+
+                const submitData = await submitResponse.json();
+
+                if (submitData.code === 200 && submitData.data.finished === true) {
+                    // Task is already finished, get the result
+                    getResultAndDisplayAudio(submitData.data.task_id);
+                } else {
+                    // Task is not finished, continue submitting
+                    setTimeout(submitRunner, 5000); // Submit every 5 seconds (adjust as needed)
+                    
+                    // Update the progress bar
+                    const loadingDiv = document.getElementById('loading');
+                    loadingDiv.style.width = '50%'; // Update the width accordingly
+                }
+            }
+
+            // Start submitting the runner
+            submitRunner();
+        }
+
+        // Function to get the result and display audio
+        async function getResultAndDisplayAudio(taskId) {
+            continueSubmitting = false; // Stop further submissions
+            const resultResponse = await fetch(`/runner/result?task_id=${taskId}`);
+            const resultBlob = await resultResponse.blob();
+
+            // Create a URL for the audio file
+            const audioUrl = URL.createObjectURL(resultBlob);
+
+            // Display the audio player
+            const audioElement = document.getElementById('audio-element');
+            audioElement.src = audioUrl;
+            document.getElementById('audio-player').style.display = 'block';
+
+            // Hide the progress bar
+            const loadingDiv = document.getElementById('loading');
+            loadingDiv.style.display = 'none';
+        }
+
+        // Attach a click event listener to the submit button
+        document.getElementById('submit').addEventListener('click', function() {
+            const text = document.getElementById('text').value;
+            continueSubmitting = true; // Reset the flag
+            submitRunnerAndWait(text); // Start the submission process with the entered text
+            
+            // Display the progress bar
+            const loadingDiv = document.getElementById('loading');
+            loadingDiv.style.display = 'block';
+        });
+    </script>
+</body>
+</html>
diff --git a/speakers/server/static/redoc.standalone.js b/speakers/server/static/redoc.standalone.js
new file mode 100644
index 0000000000000000000000000000000000000000..16e7a423d259876df09269576b68864f1890a3f0
--- /dev/null
+++ b/speakers/server/static/redoc.standalone.js
@@ -0,0 +1,1782 @@
+/*! For license information please see redoc.standalone.js.LICENSE.txt */
+!function(e,t){"object"==typeof exports&&"object"==typeof module?module.exports=t(require("null")):"function"==typeof define&&define.amd?define(["null"],t):"object"==typeof exports?exports.Redoc=t(require("null")):e.Redoc=t(e.null)}(this,(function(e){return function(){var t={5499:function(e,t,n){"use strict";Object.defineProperty(t,"__esModule",{value:!0}),t.CodeGen=t.Name=t.nil=t.stringify=t.str=t._=t.KeywordCxt=void 0;const r=n(3325),o=n(6479),i=n(5522),a=n(1603),s=["/properties"],l="http://json-schema.org/draft-07/schema";class c extends r.default{_addVocabularies(){super._addVocabularies(),o.default.forEach((e=>this.addVocabulary(e))),this.opts.discriminator&&this.addKeyword(i.default)}_addDefaultMetaSchema(){if(super._addDefaultMetaSchema(),!this.opts.meta)return;const e=this.opts.$data?this.$dataMetaSchema(a,s):a;this.addMetaSchema(e,l,!1),this.refs["http://json-schema.org/schema"]=l}defaultMeta(){return this.opts.defaultMeta=super.defaultMeta()||(this.getSchema(l)?l:void 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n=u.str`${r}/$comment`,o=e.scopeValue("root",{ref:t.root});e.code(u._`${p.default.self}.opts.$comment(${i}, ${n}, ${o}.schema)`)}}function k(e,t,n,r){const{gen:o,schema:s,data:l,allErrors:c,opts:d,self:h}=e,{RULES:m}=h;function g(f){i.shouldUseGroup(s,f)&&(f.type?(o.if(a.checkDataType(f.type,l,d.strictNumbers)),_(e,f),1===t.length&&t[0]===f.type&&n&&(o.else(),a.reportTypeError(e)),o.endIf()):_(e,f),c||o.if(u._`${p.default.errors} === ${r||0}`))}!s.$ref||!d.ignoreKeywordsWithRef&&f.schemaHasRulesButRef(s,m)?(d.jtd||function(e,t){!e.schemaEnv.meta&&e.opts.strictTypes&&(function(e,t){t.length&&(e.dataTypes.length?(t.forEach((t=>{O(e.dataTypes,t)||S(e,`type "${t}" not allowed by context "${e.dataTypes.join(",")}"`)})),e.dataTypes=e.dataTypes.filter((e=>O(t,e)))):e.dataTypes=t)}(e,t),e.opts.allowUnionTypes||function(e,t){t.length>1&&(2!==t.length||!t.includes("null"))&&S(e,"use allowUnionTypes to allow union type keyword")}(e,t),function(e,t){const n=e.self.RULES.all;for(const r in n){const 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E{constructor(e,t,n){if(l.validateKeywordUsage(e,t,n),this.gen=e.gen,this.allErrors=e.allErrors,this.keyword=n,this.data=e.data,this.schema=e.schema[n],this.$data=t.$data&&e.opts.$data&&this.schema&&this.schema.$data,this.schemaValue=f.schemaRefOrVal(e,this.schema,n,this.$data),this.schemaType=t.schemaType,this.parentSchema=e.schema,this.params={},this.it=e,this.def=t,this.$data)this.schemaCode=e.gen.const("vSchema",C(this.$data,e));else if(this.schemaCode=this.schemaValue,!l.validSchemaType(this.schema,t.schemaType,t.allowUndefined))throw new Error(`${n} value must be ${JSON.stringify(t.schemaType)}`);("code"in t?t.trackErrors:!1!==t.errors)&&(this.errsCount=e.gen.const("_errs",p.default.errors))}result(e,t,n){this.gen.if(u.not(e)),n?n():this.error(),t?(this.gen.else(),t(),this.allErrors&&this.gen.endIf()):this.allErrors?this.gen.endIf():this.gen.else()}pass(e,t){this.result(e,void 0,t)}fail(e){if(void 0===e)return 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t={pattern:/((?:^|[^\\])(?:\\{2})*)#\{(?:[^{}]|\{[^{}]*\})*\}/,lookbehind:!0,inside:{content:{pattern:/^(#\{)[\s\S]+(?=\}$)/,lookbehind:!0,inside:e.languages.ruby},delimiter:{pattern:/^#\{|\}$/,alias:"punctuation"}}};delete e.languages.ruby.function;var 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e.replace(/^\\W+/,"").replace(/\\W+$/,"")}))},R.Pipeline.registerFunction(R.trimmer,"trimmer"),R.TokenSet=function(){this.final=!1,this.edges={},this.id=R.TokenSet._nextId,R.TokenSet._nextId+=1},R.TokenSet._nextId=1,R.TokenSet.fromArray=function(e){for(var t=new R.TokenSet.Builder,r=0,n=e.length;r<n;r++)t.insert(e[r]);return t.finish(),t.root},R.TokenSet.fromClause=function(e){return"editDistance"in e?R.TokenSet.fromFuzzyString(e.term,e.editDistance):R.TokenSet.fromString(e.term)},R.TokenSet.fromFuzzyString=function(e,t){for(var r=new R.TokenSet,n=[{node:r,editsRemaining:t,str:e}];n.length;){var i=n.pop();if(i.str.length>0){var s,o=i.str.charAt(0);o in i.node.edges?s=i.node.edges[o]:(s=new R.TokenSet,i.node.edges[o]=s),1==i.str.length&&(s.final=!0),n.push({node:s,editsRemaining:i.editsRemaining,str:i.str.slice(1)})}if(0!=i.editsRemaining){if("*"in i.node.edges)var a=i.node.edges["*"];else a=new R.TokenSet,i.node.edges["*"]=a;if(0==i.str.length&&(a.final=!0),n.push({node:a,editsRemaining:i.editsRemaining-1,str:i.str}),i.str.length>1&&n.push({node:i.node,editsRemaining:i.editsRemaining-1,str:i.str.slice(1)}),1==i.str.length&&(i.node.final=!0),i.str.length>=1){if("*"in i.node.edges)var u=i.node.edges["*"];else u=new R.TokenSet,i.node.edges["*"]=u;1==i.str.length&&(u.final=!0),n.push({node:u,editsRemaining:i.editsRemaining-1,str:i.str.slice(1)})}if(i.str.length>1){var l,c=i.str.charAt(0),h=i.str.charAt(1);h in i.node.edges?l=i.node.edges[h]:(l=new R.TokenSet,i.node.edges[h]=l),1==i.str.length&&(l.final=!0),n.push({node:l,editsRemaining:i.editsRemaining-1,str:c+i.str.slice(2)})}}}return r},R.TokenSet.fromString=function(e){for(var t=new R.TokenSet,r=t,n=0,i=e.length;n<i;n++){var s=e[n],o=n==i-1;if("*"==s)t.edges[s]=t,t.final=o;else{var a=new R.TokenSet;a.final=o,t.edges[s]=a,t=a}}return r},R.TokenSet.prototype.toArray=function(){for(var e=[],t=[{prefix:"",node:this}];t.length;){var r=t.pop(),n=Object.keys(r.node.edges),i=n.length;r.node.final&&(r.prefix.charAt(0),e.push(r.prefix));for(var s=0;s<i;s++){var o=n[s];t.push({prefix:r.prefix.concat(o),node:r.node.edges[o]})}}return e},R.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",t=Object.keys(this.edges).sort(),r=t.length,n=0;n<r;n++){var i=t[n];e=e+i+this.edges[i].id}return e},R.TokenSet.prototype.intersect=function(e){for(var t=new R.TokenSet,r=void 0,n=[{qNode:e,output:t,node:this}];n.length;){r=n.pop();for(var i=Object.keys(r.qNode.edges),s=i.length,o=Object.keys(r.node.edges),a=o.length,u=0;u<s;u++)for(var l=i[u],c=0;c<a;c++){var h=o[c];if(h==l||"*"==l){var d=r.node.edges[h],f=r.qNode.edges[l],p=d.final&&f.final,y=void 0;h in r.output.edges?(y=r.output.edges[h]).final=y.final||p:((y=new R.TokenSet).final=p,r.output.edges[h]=y),n.push({qNode:f,output:y,node:d})}}}return t},R.TokenSet.Builder=function(){this.previousWord="",this.root=new R.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},R.TokenSet.Builder.prototype.insert=function(e){var t,r=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var n=0;n<e.length&&n<this.previousWord.length&&e[n]==this.previousWord[n];n++)r++;for(this.minimize(r),t=0==this.uncheckedNodes.length?this.root:this.uncheckedNodes[this.uncheckedNodes.length-1].child,n=r;n<e.length;n++){var i=new R.TokenSet,s=e[n];t.edges[s]=i,this.uncheckedNodes.push({parent:t,char:s,child:i}),t=i}t.final=!0,this.previousWord=e},R.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},R.TokenSet.Builder.prototype.minimize=function(e){for(var t=this.uncheckedNodes.length-1;t>=e;t--){var r=this.uncheckedNodes[t],n=r.child.toString();n in this.minimizedNodes?r.parent.edges[r.char]=this.minimizedNodes[n]:(r.child._str=n,this.minimizedNodes[n]=r.child),this.uncheckedNodes.pop()}},R.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},R.Index.prototype.search=function(e){return this.query((function(t){new R.QueryParser(e,t).parse()}))},R.Index.prototype.query=function(e){for(var t=new R.Query(this.fields),r=Object.create(null),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=0;a<this.fields.length;a++)n[this.fields[a]]=new R.Vector;for(e.call(t,t),a=0;a<t.clauses.length;a++){var u,l=t.clauses[a],c=R.Set.empty;u=l.usePipeline?this.pipeline.runString(l.term,{fields:l.fields}):[l.term];for(var h=0;h<u.length;h++){var d=u[h];l.term=d;var f=R.TokenSet.fromClause(l),p=this.tokenSet.intersect(f).toArray();if(0===p.length&&l.presence===R.Query.presence.REQUIRED){for(var y=0;y<l.fields.length;y++)s[F=l.fields[y]]=R.Set.empty;break}for(var m=0;m<p.length;m++){var g=p[m],x=this.invertedIndex[g],v=x._index;for(y=0;y<l.fields.length;y++){var w=x[F=l.fields[y]],Q=Object.keys(w),k=g+"/"+F,S=new R.Set(Q);if(l.presence==R.Query.presence.REQUIRED&&(c=c.union(S),void 0===s[F]&&(s[F]=R.Set.complete)),l.presence!=R.Query.presence.PROHIBITED){if(n[F].upsert(v,l.boost,(function(e,t){return e+t})),!i[k]){for(var E=0;E<Q.length;E++){var L,P=Q[E],b=new R.FieldRef(P,F),T=w[P];void 0===(L=r[b])?r[b]=new R.MatchData(g,F,T):L.add(g,F,T)}i[k]=!0}}else void 0===o[F]&&(o[F]=R.Set.empty),o[F]=o[F].union(S)}}}if(l.presence===R.Query.presence.REQUIRED)for(y=0;y<l.fields.length;y++)s[F=l.fields[y]]=s[F].intersect(c)}var O=R.Set.complete,I=R.Set.empty;for(a=0;a<this.fields.length;a++){var F;s[F=this.fields[a]]&&(O=O.intersect(s[F])),o[F]&&(I=I.union(o[F]))}var C=Object.keys(r),N=[],j=Object.create(null);if(t.isNegated())for(C=Object.keys(this.fieldVectors),a=0;a<C.length;a++){b=C[a];var _=R.FieldRef.fromString(b);r[b]=new R.MatchData}for(a=0;a<C.length;a++){var D=(_=R.FieldRef.fromString(C[a])).docRef;if(O.contains(D)&&!I.contains(D)){var A,B=this.fieldVectors[_],z=n[_.fieldName].similarity(B);if(void 0!==(A=j[D]))A.score+=z,A.matchData.combine(r[_]);else{var V={ref:D,score:z,matchData:r[_]};j[D]=V,N.push(V)}}}return N.sort((function(e,t){return t.score-e.score}))},R.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map((function(e){return[e,this.invertedIndex[e]]}),this),t=Object.keys(this.fieldVectors).map((function(e){return[e,this.fieldVectors[e].toJSON()]}),this);return{version:R.version,fields:this.fields,fieldVectors:t,invertedIndex:e,pipeline:this.pipeline.toJSON()}},R.Index.load=function(e){var t={},r={},n=e.fieldVectors,i=Object.create(null),s=e.invertedIndex,o=new R.TokenSet.Builder,a=R.Pipeline.load(e.pipeline);e.version!=R.version&&R.utils.warn("Version mismatch when loading serialised index. Current version of lunr \'"+R.version+"\' does not match serialized index \'"+e.version+"\'");for(var u=0;u<n.length;u++){var l=(h=n[u])[0],c=h[1];r[l]=new R.Vector(c)}for(u=0;u<s.length;u++){var h,d=(h=s[u])[0],f=h[1];o.insert(d),i[d]=f}return o.finish(),t.fields=e.fields,t.fieldVectors=r,t.invertedIndex=i,t.tokenSet=o.root,t.pipeline=a,new R.Index(t)},R.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=R.tokenizer,this.pipeline=new R.Pipeline,this.searchPipeline=new R.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},R.Builder.prototype.ref=function(e){this._ref=e},R.Builder.prototype.field=function(e,t){if(/\\//.test(e))throw new RangeError("Field \'"+e+"\' contains illegal character \'/\'");this._fields[e]=t||{}},R.Builder.prototype.b=function(e){this._b=e<0?0:e>1?1:e},R.Builder.prototype.k1=function(e){this._k1=e},R.Builder.prototype.add=function(e,t){var r=e[this._ref],n=Object.keys(this._fields);this._documents[r]=t||{},this.documentCount+=1;for(var i=0;i<n.length;i++){var s=n[i],o=this._fields[s].extractor,a=o?o(e):e[s],u=this.tokenizer(a,{fields:[s]}),l=this.pipeline.run(u),c=new R.FieldRef(r,s),h=Object.create(null);this.fieldTermFrequencies[c]=h,this.fieldLengths[c]=0,this.fieldLengths[c]+=l.length;for(var d=0;d<l.length;d++){var f=l[d];if(null==h[f]&&(h[f]=0),h[f]+=1,null==this.invertedIndex[f]){var p=Object.create(null);p._index=this.termIndex,this.termIndex+=1;for(var y=0;y<n.length;y++)p[n[y]]=Object.create(null);this.invertedIndex[f]=p}null==this.invertedIndex[f][s][r]&&(this.invertedIndex[f][s][r]=Object.create(null));for(var m=0;m<this.metadataWhitelist.length;m++){var g=this.metadataWhitelist[m],x=f.metadata[g];null==this.invertedIndex[f][s][r][g]&&(this.invertedIndex[f][s][r][g]=[]),this.invertedIndex[f][s][r][g].push(x)}}}},R.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),t=e.length,r={},n={},i=0;i<t;i++){var s=R.FieldRef.fromString(e[i]),o=s.fieldName;n[o]||(n[o]=0),n[o]+=1,r[o]||(r[o]=0),r[o]+=this.fieldLengths[s]}var a=Object.keys(this._fields);for(i=0;i<a.length;i++){var u=a[i];r[u]=r[u]/n[u]}this.averageFieldLength=r},R.Builder.prototype.createFieldVectors=function(){for(var e={},t=Object.keys(this.fieldTermFrequencies),r=t.length,n=Object.create(null),i=0;i<r;i++){for(var s=R.FieldRef.fromString(t[i]),o=s.fieldName,a=this.fieldLengths[s],u=new R.Vector,l=this.fieldTermFrequencies[s],c=Object.keys(l),h=c.length,d=this._fields[o].boost||1,f=this._documents[s.docRef].boost||1,p=0;p<h;p++){var y,m,g,x=c[p],v=l[x],w=this.invertedIndex[x]._index;void 0===n[x]?(y=R.idf(this.invertedIndex[x],this.documentCount),n[x]=y):y=n[x],m=y*((this._k1+1)*v)/(this._k1*(1-this._b+this._b*(a/this.averageFieldLength[o]))+v),m*=d,m*=f,g=Math.round(1e3*m)/1e3,u.insert(w,g)}e[s]=u}this.fieldVectors=e},R.Builder.prototype.createTokenSet=function(){this.tokenSet=R.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},R.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new R.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},R.Builder.prototype.use=function(e){var t=Array.prototype.slice.call(arguments,1);t.unshift(this),e.apply(this,t)},R.MatchData=function(e,t,r){for(var n=Object.create(null),i=Object.keys(r||{}),s=0;s<i.length;s++){var o=i[s];n[o]=r[o].slice()}this.metadata=Object.create(null),void 0!==e&&(this.metadata[e]=Object.create(null),this.metadata[e][t]=n)},R.MatchData.prototype.combine=function(e){for(var t=Object.keys(e.metadata),r=0;r<t.length;r++){var n=t[r],i=Object.keys(e.metadata[n]);null==this.metadata[n]&&(this.metadata[n]=Object.create(null));for(var s=0;s<i.length;s++){var o=i[s],a=Object.keys(e.metadata[n][o]);null==this.metadata[n][o]&&(this.metadata[n][o]=Object.create(null));for(var u=0;u<a.length;u++){var l=a[u];null==this.metadata[n][o][l]?this.metadata[n][o][l]=e.metadata[n][o][l]:this.metadata[n][o][l]=this.metadata[n][o][l].concat(e.metadata[n][o][l])}}}},R.MatchData.prototype.add=function(e,t,r){if(!(e in this.metadata))return this.metadata[e]=Object.create(null),void(this.metadata[e][t]=r);if(t in this.metadata[e])for(var n=Object.keys(r),i=0;i<n.length;i++){var s=n[i];s in this.metadata[e][t]?this.metadata[e][t][s]=this.metadata[e][t][s].concat(r[s]):this.metadata[e][t][s]=r[s]}else this.metadata[e][t]=r},R.Query=function(e){this.clauses=[],this.allFields=e},R.Query.wildcard=new String("*"),R.Query.wildcard.NONE=0,R.Query.wildcard.LEADING=1,R.Query.wildcard.TRAILING=2,R.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},R.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=R.Query.wildcard.NONE),e.wildcard&R.Query.wildcard.LEADING&&e.term.charAt(0)!=R.Query.wildcard&&(e.term="*"+e.term),e.wildcard&R.Query.wildcard.TRAILING&&e.term.slice(-1)!=R.Query.wildcard&&(e.term=e.term+"*"),"presence"in e||(e.presence=R.Query.presence.OPTIONAL),this.clauses.push(e),this},R.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=R.Query.presence.PROHIBITED)return!1;return!0},R.Query.prototype.term=function(e,t){if(Array.isArray(e))return e.forEach((function(e){this.term(e,R.utils.clone(t))}),this),this;var r=t||{};return r.term=e.toString(),this.clause(r),this},R.QueryParseError=function(e,t,r){this.name="QueryParseError",this.message=e,this.start=t,this.end=r},R.QueryParseError.prototype=new Error,R.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},R.QueryLexer.prototype.run=function(){for(var e=R.QueryLexer.lexText;e;)e=e(this)},R.QueryLexer.prototype.sliceString=function(){for(var e=[],t=this.start,r=this.pos,n=0;n<this.escapeCharPositions.length;n++)r=this.escapeCharPositions[n],e.push(this.str.slice(t,r)),t=r+1;return e.push(this.str.slice(t,this.pos)),this.escapeCharPositions.length=0,e.join("")},R.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},R.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},R.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return R.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},R.QueryLexer.prototype.width=function(){return this.pos-this.start},R.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},R.QueryLexer.prototype.backup=function(){this.pos-=1},R.QueryLexer.prototype.acceptDigitRun=function(){var e,t;do{t=(e=this.next()).charCodeAt(0)}while(t>47&&t<58);e!=R.QueryLexer.EOS&&this.backup()},R.QueryLexer.prototype.more=function(){return this.pos<this.length},R.QueryLexer.EOS="EOS",R.QueryLexer.FIELD="FIELD",R.QueryLexer.TERM="TERM",R.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",R.QueryLexer.BOOST="BOOST",R.QueryLexer.PRESENCE="PRESENCE",R.QueryLexer.lexField=function(e){return e.backup(),e.emit(R.QueryLexer.FIELD),e.ignore(),R.QueryLexer.lexText},R.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(R.QueryLexer.TERM)),e.ignore(),e.more())return R.QueryLexer.lexText},R.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(R.QueryLexer.EDIT_DISTANCE),R.QueryLexer.lexText},R.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(R.QueryLexer.BOOST),R.QueryLexer.lexText},R.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(R.QueryLexer.TERM)},R.QueryLexer.termSeparator=R.tokenizer.separator,R.QueryLexer.lexText=function(e){for(;;){var t=e.next();if(t==R.QueryLexer.EOS)return R.QueryLexer.lexEOS;if(92!=t.charCodeAt(0)){if(":"==t)return R.QueryLexer.lexField;if("~"==t)return e.backup(),e.width()>0&&e.emit(R.QueryLexer.TERM),R.QueryLexer.lexEditDistance;if("^"==t)return e.backup(),e.width()>0&&e.emit(R.QueryLexer.TERM),R.QueryLexer.lexBoost;if("+"==t&&1===e.width())return e.emit(R.QueryLexer.PRESENCE),R.QueryLexer.lexText;if("-"==t&&1===e.width())return e.emit(R.QueryLexer.PRESENCE),R.QueryLexer.lexText;if(t.match(R.QueryLexer.termSeparator))return R.QueryLexer.lexTerm}else e.escapeCharacter()}},R.QueryParser=function(e,t){this.lexer=new R.QueryLexer(e),this.query=t,this.currentClause={},this.lexemeIdx=0},R.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=R.QueryParser.parseClause;e;)e=e(this);return this.query},R.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},R.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},R.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},R.QueryParser.parseClause=function(e){var t=e.peekLexeme();if(null!=t)switch(t.type){case R.QueryLexer.PRESENCE:return R.QueryParser.parsePresence;case R.QueryLexer.FIELD:return R.QueryParser.parseField;case R.QueryLexer.TERM:return R.QueryParser.parseTerm;default:var r="expected either a field or a term, found "+t.type;throw t.str.length>=1&&(r+=" with value \'"+t.str+"\'"),new R.QueryParseError(r,t.start,t.end)}},R.QueryParser.parsePresence=function(e){var t=e.consumeLexeme();if(null!=t){switch(t.str){case"-":e.currentClause.presence=R.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=R.Query.presence.REQUIRED;break;default:var r="unrecognised presence operator\'"+t.str+"\'";throw new R.QueryParseError(r,t.start,t.end)}var n=e.peekLexeme();if(null==n)throw r="expecting term or field, found nothing",new R.QueryParseError(r,t.start,t.end);switch(n.type){case R.QueryLexer.FIELD:return R.QueryParser.parseField;case R.QueryLexer.TERM:return R.QueryParser.parseTerm;default:throw r="expecting term or field, found \'"+n.type+"\'",new R.QueryParseError(r,n.start,n.end)}}},R.QueryParser.parseField=function(e){var t=e.consumeLexeme();if(null!=t){if(-1==e.query.allFields.indexOf(t.str)){var r=e.query.allFields.map((function(e){return"\'"+e+"\'"})).join(", "),n="unrecognised field \'"+t.str+"\', possible fields: "+r;throw new R.QueryParseError(n,t.start,t.end)}e.currentClause.fields=[t.str];var i=e.peekLexeme();if(null==i)throw 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dl={escape:/^\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/,autolink:/^<(scheme:[^\s\x00-\x1f<>]*|email)>/,url:rl,tag:"^comment|^</[a-zA-Z][\\w:-]*\\s*>|^<[a-zA-Z][\\w-]*(?:attribute)*?\\s*/?>|^<\\?[\\s\\S]*?\\?>|^<![a-zA-Z]+\\s[\\s\\S]*?>|^<!\\[CDATA\\[[\\s\\S]*?\\]\\]>",link:/^!?\[(label)\]\(\s*(href)(?:\s+(title))?\s*\)/,reflink:/^!?\[(label)\]\[(ref)\]/,nolink:/^!?\[(ref)\](?:\[\])?/,reflinkSearch:"reflink|nolink(?!\\()",emStrong:{lDelim:/^(?:\*+(?:([punct_])|[^\s*]))|^_+(?:([punct*])|([^\s_]))/,rDelimAst:/^[^_*]*?\_\_[^_*]*?\*[^_*]*?(?=\_\_)|[^*]+(?=[^*])|[punct_](\*+)(?=[\s]|$)|[^punct*_\s](\*+)(?=[punct_\s]|$)|[punct_\s](\*+)(?=[^punct*_\s])|[\s](\*+)(?=[punct_])|[punct_](\*+)(?=[punct_])|[^punct*_\s](\*+)(?=[^punct*_\s])/,rDelimUnd:/^[^_*]*?\*\*[^_*]*?\_[^_*]*?(?=\*\*)|[^_]+(?=[^_])|[punct*](\_+)(?=[\s]|$)|[^punct*_\s](\_+)(?=[punct*\s]|$)|[punct*\s](\_+)(?=[^punct*_\s])|[\s](\_+)(?=[punct*])|[punct*](\_+)(?=[punct*])/},code:/^(`+)([^`]|[^`][\s\S]*?[^`])\1(?!`)/,br:/^( {2,}|\\)\n(?!\s*$)/,del:rl,text:/^(`+|[^`])(?:(?= {2,}\n)|[\s\S]*?(?:(?=[\\<!\[`*_]|\b_|$)|[^ ](?= {2,}\n)))/,punctuation:/^([\spunctuation])/};function fl(e){return e.replace(/---/g,"—").replace(/--/g,"–").replace(/(^|[-\u2014/(\[{"\s])'/g,"$1‘").replace(/'/g,"’").replace(/(^|[-\u2014/(\[{\u2018\s])"/g,"$1“").replace(/"/g,"”").replace(/\.{3}/g,"…")}function hl(e){let t,n,r="";const o=e.length;for(t=0;t<o;t++)n=e.charCodeAt(t),Math.random()>.5&&(n="x"+n.toString(16)),r+="&#"+n+";";return r}dl._punctuation="!\"#$%&'()+\\-.,/:;<=>?@\\[\\]`^{|}~",dl.punctuation=Gs(dl.punctuation).replace(/punctuation/g,dl._punctuation).getRegex(),dl.blockSkip=/\[[^\]]*?\]\([^\)]*?\)|`[^`]*?`|<[^>]*?>/g,dl.escapedEmSt=/\\\*|\\_/g,dl._comment=Gs(pl._comment).replace("(?:--\x3e|$)","--\x3e").getRegex(),dl.emStrong.lDelim=Gs(dl.emStrong.lDelim).replace(/punct/g,dl._punctuation).getRegex(),dl.emStrong.rDelimAst=Gs(dl.emStrong.rDelimAst,"g").replace(/punct/g,dl._punctuation).getRegex(),dl.emStrong.rDelimUnd=Gs(dl.emStrong.rDelimUnd,"g").replace(/punct/g,dl._punctuation).getRegex(),dl._escapes=/\\([!"#$%&'()*+,\-./:;<=>?@\[\]\\^_`{|}~])/g,dl._scheme=/[a-zA-Z][a-zA-Z0-9+.-]{1,31}/,dl._email=/[a-zA-Z0-9.!#$%&'*+/=?^_`{|}~-]+(@)[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)+(?![-_])/,dl.autolink=Gs(dl.autolink).replace("scheme",dl._scheme).replace("email",dl._email).getRegex(),dl._attribute=/\s+[a-zA-Z:_][\w.:-]*(?:\s*=\s*"[^"]*"|\s*=\s*'[^']*'|\s*=\s*[^\s"'=<>`]+)?/,dl.tag=Gs(dl.tag).replace("comment",dl._comment).replace("attribute",dl._attribute).getRegex(),dl._label=/(?:\[(?:\\.|[^\[\]\\])*\]|\\.|`[^`]*`|[^\[\]\\`])*?/,dl._href=/<(?:\\.|[^\n<>\\])+>|[^\s\x00-\x1f]*/,dl._title=/"(?:\\"?|[^"\\])*"|'(?:\\'?|[^'\\])*'|\((?:\\\)?|[^)\\])*\)/,dl.link=Gs(dl.link).replace("label",dl._label).replace("href",dl._href).replace("title",dl._title).getRegex(),dl.reflink=Gs(dl.reflink).replace("label",dl._label).replace("ref",pl._label).getRegex(),dl.nolink=Gs(dl.nolink).replace("ref",pl._label).getRegex(),dl.reflinkSearch=Gs(dl.reflinkSearch,"g").replace("reflink",dl.reflink).replace("nolink",dl.nolink).getRegex(),dl.normal=ol({},dl),dl.pedantic=ol({},dl.normal,{strong:{start:/^__|\*\*/,middle:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,endAst:/\*\*(?!\*)/g,endUnd:/__(?!_)/g},em:{start:/^_|\*/,middle:/^()\*(?=\S)([\s\S]*?\S)\*(?!\*)|^_(?=\S)([\s\S]*?\S)_(?!_)/,endAst:/\*(?!\*)/g,endUnd:/_(?!_)/g},link:Gs(/^!?\[(label)\]\((.*?)\)/).replace("label",dl._label).getRegex(),reflink:Gs(/^!?\[(label)\]\s*\[([^\]]*)\]/).replace("label",dl._label).getRegex()}),dl.gfm=ol({},dl.normal,{escape:Gs(dl.escape).replace("])","~|])").getRegex(),_extended_email:/[A-Za-z0-9._+-]+(@)[a-zA-Z0-9-_]+(?:\.[a-zA-Z0-9-_]*[a-zA-Z0-9])+(?![-_])/,url:/^((?:ftp|https?):\/\/|www\.)(?:[a-zA-Z0-9\-]+\.?)+[^\s<]*|^email/,_backpedal:/(?:[^?!.,:;*_~()&]+|\([^)]*\)|&(?![a-zA-Z0-9]+;$)|[?!.,:;*_~)]+(?!$))+/,del:/^(~~?)(?=[^\s~])([\s\S]*?[^\s~])\1(?=[^~]|$)/,text:/^([`~]+|[^`~])(?:(?= {2,}\n)|(?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)|[\s\S]*?(?:(?=[\\<!\[`*~_]|\b_|https?:\/\/|ftp:\/\/|www\.|$)|[^ ](?= {2,}\n)|[^a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-](?=[a-zA-Z0-9.!#$%&'*+\/=?_`{\|}~-]+@)))/}),dl.gfm.url=Gs(dl.gfm.url,"i").replace("email",dl.gfm._extended_email).getRegex(),dl.breaks=ol({},dl.gfm,{br:Gs(dl.br).replace("{2,}","*").getRegex(),text:Gs(dl.gfm.text).replace("\\b_","\\b_| {2,}\\n").replace(/\{2,\}/g,"*").getRegex()});class ml{constructor(e){this.tokens=[],this.tokens.links=Object.create(null),this.options=e||Ms,this.options.tokenizer=this.options.tokenizer||new ul,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};const t={block:pl.normal,inline:dl.normal};this.options.pedantic?(t.block=pl.pedantic,t.inline=dl.pedantic):this.options.gfm&&(t.block=pl.gfm,this.options.breaks?t.inline=dl.breaks:t.inline=dl.gfm),this.tokenizer.rules=t}static get rules(){return{block:pl,inline:dl}}static lex(e,t){return new ml(t).lex(e)}static lexInline(e,t){return new ml(t).inlineTokens(e)}lex(e){let t;for(e=e.replace(/\r\n|\r/g,"\n"),this.blockTokens(e,this.tokens);t=this.inlineQueue.shift();)this.inlineTokens(t.src,t.tokens);return this.tokens}blockTokens(e,t=[]){let n,r,o,i;for(e=this.options.pedantic?e.replace(/\t/g,"    ").replace(/^ +$/gm,""):e.replace(/^( *)(\t+)/gm,((e,t,n)=>t+"    ".repeat(n.length)));e;)if(!(this.options.extensions&&this.options.extensions.block&&this.options.extensions.block.some((r=>!!(n=r.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.space(e))e=e.substring(n.raw.length),1===n.raw.length&&t.length>0?t[t.length-1].raw+="\n":t.push(n);else if(n=this.tokenizer.code(e))e=e.substring(n.raw.length),r=t[t.length-1],!r||"paragraph"!==r.type&&"text"!==r.type?t.push(n):(r.raw+="\n"+n.raw,r.text+="\n"+n.text,this.inlineQueue[this.inlineQueue.length-1].src=r.text);else if(n=this.tokenizer.fences(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.heading(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.hr(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.blockquote(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.list(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.html(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.def(e))e=e.substring(n.raw.length),r=t[t.length-1],!r||"paragraph"!==r.type&&"text"!==r.type?this.tokens.links[n.tag]||(this.tokens.links[n.tag]={href:n.href,title:n.title}):(r.raw+="\n"+n.raw,r.text+="\n"+n.raw,this.inlineQueue[this.inlineQueue.length-1].src=r.text);else if(n=this.tokenizer.table(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.lheading(e))e=e.substring(n.raw.length),t.push(n);else{if(o=e,this.options.extensions&&this.options.extensions.startBlock){let t=1/0;const n=e.slice(1);let r;this.options.extensions.startBlock.forEach((function(e){r=e.call({lexer:this},n),"number"==typeof r&&r>=0&&(t=Math.min(t,r))})),t<1/0&&t>=0&&(o=e.substring(0,t+1))}if(this.state.top&&(n=this.tokenizer.paragraph(o)))r=t[t.length-1],i&&"paragraph"===r.type?(r.raw+="\n"+n.raw,r.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=r.text):t.push(n),i=o.length!==e.length,e=e.substring(n.raw.length);else if(n=this.tokenizer.text(e))e=e.substring(n.raw.length),r=t[t.length-1],r&&"text"===r.type?(r.raw+="\n"+n.raw,r.text+="\n"+n.text,this.inlineQueue.pop(),this.inlineQueue[this.inlineQueue.length-1].src=r.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}return this.state.top=!0,t}inline(e,t){this.inlineQueue.push({src:e,tokens:t})}inlineTokens(e,t=[]){let n,r,o,i,a,s,l=e;if(this.tokens.links){const e=Object.keys(this.tokens.links);if(e.length>0)for(;null!=(i=this.tokenizer.rules.inline.reflinkSearch.exec(l));)e.includes(i[0].slice(i[0].lastIndexOf("[")+1,-1))&&(l=l.slice(0,i.index)+"["+ll("a",i[0].length-2)+"]"+l.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;null!=(i=this.tokenizer.rules.inline.blockSkip.exec(l));)l=l.slice(0,i.index)+"["+ll("a",i[0].length-2)+"]"+l.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);for(;null!=(i=this.tokenizer.rules.inline.escapedEmSt.exec(l));)l=l.slice(0,i.index)+"++"+l.slice(this.tokenizer.rules.inline.escapedEmSt.lastIndex);for(;e;)if(a||(s=""),a=!1,!(this.options.extensions&&this.options.extensions.inline&&this.options.extensions.inline.some((r=>!!(n=r.call({lexer:this},e,t))&&(e=e.substring(n.raw.length),t.push(n),!0)))))if(n=this.tokenizer.escape(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.tag(e))e=e.substring(n.raw.length),r=t[t.length-1],r&&"text"===n.type&&"text"===r.type?(r.raw+=n.raw,r.text+=n.text):t.push(n);else if(n=this.tokenizer.link(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.reflink(e,this.tokens.links))e=e.substring(n.raw.length),r=t[t.length-1],r&&"text"===n.type&&"text"===r.type?(r.raw+=n.raw,r.text+=n.text):t.push(n);else if(n=this.tokenizer.emStrong(e,l,s))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.codespan(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.br(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.del(e))e=e.substring(n.raw.length),t.push(n);else if(n=this.tokenizer.autolink(e,hl))e=e.substring(n.raw.length),t.push(n);else if(this.state.inLink||!(n=this.tokenizer.url(e,hl))){if(o=e,this.options.extensions&&this.options.extensions.startInline){let t=1/0;const n=e.slice(1);let r;this.options.extensions.startInline.forEach((function(e){r=e.call({lexer:this},n),"number"==typeof r&&r>=0&&(t=Math.min(t,r))})),t<1/0&&t>=0&&(o=e.substring(0,t+1))}if(n=this.tokenizer.inlineText(o,fl))e=e.substring(n.raw.length),"_"!==n.raw.slice(-1)&&(s=n.raw.slice(-1)),a=!0,r=t[t.length-1],r&&"text"===r.type?(r.raw+=n.raw,r.text+=n.text):t.push(n);else if(e){const t="Infinite loop on byte: "+e.charCodeAt(0);if(this.options.silent){console.error(t);break}throw new Error(t)}}else e=e.substring(n.raw.length),t.push(n);return t}}class gl{constructor(e){this.options=e||Ms}code(e,t,n){const r=(t||"").match(/\S*/)[0];if(this.options.highlight){const t=this.options.highlight(e,r);null!=t&&t!==e&&(n=!0,e=t)}return e=e.replace(/\n$/,"")+"\n",r?'<pre><code class="'+this.options.langPrefix+Ws(r,!0)+'">'+(n?e:Ws(e,!0))+"</code></pre>\n":"<pre><code>"+(n?e:Ws(e,!0))+"</code></pre>\n"}blockquote(e){return`<blockquote>\n${e}</blockquote>\n`}html(e){return e}heading(e,t,n,r){return this.options.headerIds?`<h${t} id="${this.options.headerPrefix+r.slug(n)}">${e}</h${t}>\n`:`<h${t}>${e}</h${t}>\n`}hr(){return this.options.xhtml?"<hr/>\n":"<hr>\n"}list(e,t,n){const r=t?"ol":"ul";return"<"+r+(t&&1!==n?' start="'+n+'"':"")+">\n"+e+"</"+r+">\n"}listitem(e){return`<li>${e}</li>\n`}checkbox(e){return"<input "+(e?'checked="" ':"")+'disabled="" type="checkbox"'+(this.options.xhtml?" /":"")+"> "}paragraph(e){return`<p>${e}</p>\n`}table(e,t){return t&&(t=`<tbody>${t}</tbody>`),"<table>\n<thead>\n"+e+"</thead>\n"+t+"</table>\n"}tablerow(e){return`<tr>\n${e}</tr>\n`}tablecell(e,t){const n=t.header?"th":"td";return(t.align?`<${n} align="${t.align}">`:`<${n}>`)+e+`</${n}>\n`}strong(e){return`<strong>${e}</strong>`}em(e){return`<em>${e}</em>`}codespan(e){return`<code>${e}</code>`}br(){return this.options.xhtml?"<br/>":"<br>"}del(e){return`<del>${e}</del>`}link(e,t,n){if(null===(e=Js(this.options.sanitize,this.options.baseUrl,e)))return n;let r='<a href="'+Ws(e)+'"';return t&&(r+=' title="'+t+'"'),r+=">"+n+"</a>",r}image(e,t,n){if(null===(e=Js(this.options.sanitize,this.options.baseUrl,e)))return n;let r=`<img src="${e}" alt="${n}"`;return t&&(r+=` title="${t}"`),r+=this.options.xhtml?"/>":">",r}text(e){return e}}class yl{strong(e){return e}em(e){return e}codespan(e){return e}del(e){return e}html(e){return e}text(e){return e}link(e,t,n){return""+n}image(e,t,n){return""+n}br(){return""}}class vl{constructor(){this.seen={}}serialize(e){return e.toLowerCase().trim().replace(/<[!\/a-z].*?>/gi,"").replace(/[\u2000-\u206F\u2E00-\u2E7F\\'!"#$%&()*+,./:;<=>?@[\]^`{|}~]/g,"").replace(/\s/g,"-")}getNextSafeSlug(e,t){let n=e,r=0;if(this.seen.hasOwnProperty(n)){r=this.seen[e];do{r++,n=e+"-"+r}while(this.seen.hasOwnProperty(n))}return t||(this.seen[e]=r,this.seen[n]=0),n}slug(e,t={}){const n=this.serialize(e);return this.getNextSafeSlug(n,t.dryrun)}}class bl{constructor(e){this.options=e||Ms,this.options.renderer=this.options.renderer||new gl,this.renderer=this.options.renderer,this.renderer.options=this.options,this.textRenderer=new yl,this.slugger=new vl}static parse(e,t){return new bl(t).parse(e)}static parseInline(e,t){return new bl(t).parseInline(e)}parse(e,t=!0){let n,r,o,i,a,s,l,c,u,p,d,f,h,m,g,y,v,b,w,x="";const k=e.length;for(n=0;n<k;n++)if(p=e[n],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[p.type]&&(w=this.options.extensions.renderers[p.type].call({parser:this},p),!1!==w||!["space","hr","heading","code","table","blockquote","list","html","paragraph","text"].includes(p.type)))x+=w||"";else switch(p.type){case"space":continue;case"hr":x+=this.renderer.hr();continue;case"heading":x+=this.renderer.heading(this.parseInline(p.tokens),p.depth,Ys(this.parseInline(p.tokens,this.textRenderer)),this.slugger);continue;case"code":x+=this.renderer.code(p.text,p.lang,p.escaped);continue;case"table":for(c="",l="",i=p.header.length,r=0;r<i;r++)l+=this.renderer.tablecell(this.parseInline(p.header[r].tokens),{header:!0,align:p.align[r]});for(c+=this.renderer.tablerow(l),u="",i=p.rows.length,r=0;r<i;r++){for(s=p.rows[r],l="",a=s.length,o=0;o<a;o++)l+=this.renderer.tablecell(this.parseInline(s[o].tokens),{header:!1,align:p.align[o]});u+=this.renderer.tablerow(l)}x+=this.renderer.table(c,u);continue;case"blockquote":u=this.parse(p.tokens),x+=this.renderer.blockquote(u);continue;case"list":for(d=p.ordered,f=p.start,h=p.loose,i=p.items.length,u="",r=0;r<i;r++)g=p.items[r],y=g.checked,v=g.task,m="",g.task&&(b=this.renderer.checkbox(y),h?g.tokens.length>0&&"paragraph"===g.tokens[0].type?(g.tokens[0].text=b+" "+g.tokens[0].text,g.tokens[0].tokens&&g.tokens[0].tokens.length>0&&"text"===g.tokens[0].tokens[0].type&&(g.tokens[0].tokens[0].text=b+" "+g.tokens[0].tokens[0].text)):g.tokens.unshift({type:"text",text:b}):m+=b),m+=this.parse(g.tokens,h),u+=this.renderer.listitem(m,v,y);x+=this.renderer.list(u,d,f);continue;case"html":x+=this.renderer.html(p.text);continue;case"paragraph":x+=this.renderer.paragraph(this.parseInline(p.tokens));continue;case"text":for(u=p.tokens?this.parseInline(p.tokens):p.text;n+1<k&&"text"===e[n+1].type;)p=e[++n],u+="\n"+(p.tokens?this.parseInline(p.tokens):p.text);x+=t?this.renderer.paragraph(u):u;continue;default:{const e='Token with "'+p.type+'" type was not found.';if(this.options.silent)return void console.error(e);throw new Error(e)}}return x}parseInline(e,t){t=t||this.renderer;let n,r,o,i="";const a=e.length;for(n=0;n<a;n++)if(r=e[n],this.options.extensions&&this.options.extensions.renderers&&this.options.extensions.renderers[r.type]&&(o=this.options.extensions.renderers[r.type].call({parser:this},r),!1!==o||!["escape","html","link","image","strong","em","codespan","br","del","text"].includes(r.type)))i+=o||"";else switch(r.type){case"escape":case"text":i+=t.text(r.text);break;case"html":i+=t.html(r.text);break;case"link":i+=t.link(r.href,r.title,this.parseInline(r.tokens,t));break;case"image":i+=t.image(r.href,r.title,r.text);break;case"strong":i+=t.strong(this.parseInline(r.tokens,t));break;case"em":i+=t.em(this.parseInline(r.tokens,t));break;case"codespan":i+=t.codespan(r.text);break;case"br":i+=t.br();break;case"del":i+=t.del(this.parseInline(r.tokens,t));break;default:{const e='Token with "'+r.type+'" type was not found.';if(this.options.silent)return void console.error(e);throw new Error(e)}}return i}}function wl(e,t,n){if(null==e)throw new Error("marked(): input parameter is undefined or null");if("string"!=typeof e)throw new Error("marked(): input parameter is of type "+Object.prototype.toString.call(e)+", string expected");if("function"==typeof t&&(n=t,t=null),sl(t=ol({},wl.defaults,t||{})),n){const r=t.highlight;let o;try{o=ml.lex(e,t)}catch(e){return n(e)}const i=function(e){let i;if(!e)try{t.walkTokens&&wl.walkTokens(o,t.walkTokens),i=bl.parse(o,t)}catch(t){e=t}return t.highlight=r,e?n(e):n(null,i)};if(!r||r.length<3)return i();if(delete t.highlight,!o.length)return i();let a=0;return wl.walkTokens(o,(function(e){"code"===e.type&&(a++,setTimeout((()=>{r(e.text,e.lang,(function(t,n){if(t)return i(t);null!=n&&n!==e.text&&(e.text=n,e.escaped=!0),a--,0===a&&i()}))}),0))})),void(0===a&&i())}try{const n=ml.lex(e,t);return t.walkTokens&&wl.walkTokens(n,t.walkTokens),bl.parse(n,t)}catch(e){if(e.message+="\nPlease report this to https://github.com/markedjs/marked.",t.silent)return"<p>An error occurred:</p><pre>"+Ws(e.message+"",!0)+"</pre>";throw 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t}return"any"}(u),this.format=u.format,this.enum=u.enum||[],this.example=u.example,this.examples=u.examples,this.deprecated=!!u.deprecated,this.pattern=u.pattern,this.externalDocs=u.externalDocs,this.constraints=us(u),this.displayFormat=this.format,this.isPrimitive=Za(u,this.type),this.default=u.default,this.readOnly=!!u.readOnly,this.writeOnly=!!u.writeOnly,this.const=u.const||"",this.contentEncoding=u.contentEncoding,this.contentMediaType=u.contentMediaType,this.minItems=u.minItems,this.maxItems=u.maxItems,(u.nullable||u["x-nullable"])&&(io(this.type)&&!this.type.some((e=>null===e||"null"===e))?this.type=[...this.type,"null"]:io(this.type)||null===this.type&&"null"===this.type||(this.type=[this.type,"null"])),this.displayType=io(this.type)?this.type.map((e=>null===e?"null":e)).join(" or "):this.type,!this.isCircular)if(u.if&&u.then||u.if&&u.else)this.initConditionalOperators(u,e);else if(t||void 0===Ac(u)){if(t&&io(u.oneOf)&&u.oneOf.find((e=>e.$ref===this.pointer))&&delete u.oneOf,void 0!==u.oneOf)return this.initOneOf(u.oneOf,e),this.oneOfType="One of",void(void 0!==u.anyOf&&console.warn(`oneOf and anyOf are not supported on the same level. 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0===this.example&&void 0!==(null==(l=this.items)?void 0:l.example)&&(this.example=[this.items.example]),(null==(c=this.items)?void 0:c.isPrimitive)&&(this.enum=this.items.enum),io(this.type))){const e=this.type.filter((e=>"array"!==e));e.length&&(this.displayType+=` or ${e.join(" or ")}`)}this.enum.length&&this.options.sortEnumValuesAlphabetically&&this.enum.sort()}else this.initDiscriminator(u,e)}initOneOf(e,t){if(this.oneOf=e.map(((e,n)=>{const{resolved:r,refsStack:o}=t.deref(e,this.refsStack,!0),i=t.mergeAllOf(r,this.pointer+"/oneOf/"+n,o),a=ss(e.$ref)&&!i.title?Da.baseName(e.$ref):`${i.title||""}${i.const&&JSON.stringify(i.const)||""}`;return new Sc(t,_c(kc({},i),{title:a,allOf:[_c(kc({},this.schema),{oneOf:void 0,anyOf:void 0})]}),e.$ref||this.pointer+"/oneOf/"+n,this.options,!1,o)})),this.options.simpleOneOfTypeLabel){const e=function(e){const t=new Set;return function e(n){for(const r of n.oneOf||[])r.oneOf?e(r):r.type&&t.add(r.type)}(e),Array.from(t.values())}(this);this.displayType=e.join(" or ")}else this.displayType=this.oneOf.map((e=>{let t=e.typePrefix+(e.title?`${e.title} (${e.displayType})`:e.displayType);return t.indexOf(" or ")>-1&&(t=`(${t})`),t})).join(" or ")}initDiscriminator(e,t){const n=Ac(e);this.discriminatorProp=n.propertyName;const r=t.findDerived([...this.schema["x-parentRefs"]||[],this.pointer]);if(e.oneOf)for(const t of e.oneOf){if(void 0===t.$ref)continue;const e=Da.baseName(t.$ref);r[t.$ref]=e}const o=n.mapping||{};let i=n["x-explicitMappingOnly"]||!1;0===Object.keys(o).length&&(i=!1);const a={};for(const e in o){const t=o[e];io(a[t])?a[t].push(e):a[t]=[e]}const s=kc(i?{}:kc({},r),a);let l=[];for(const e of Object.keys(s)){const t=s[e];if(io(t))for(const n of t)l.push({$ref:e,name:n});else l.push({$ref:e,name:t})}const c=Object.keys(o);0!==c.length&&(l=l.sort(((e,t)=>{const n=c.indexOf(e.name),r=c.indexOf(t.name);return n<0&&r<0?e.name.localeCompare(t.name):n<0?1:r<0?-1:n-r}))),this.oneOf=l.map((({$ref:e,name:n})=>{const r=new Sc(t,{$ref:e},e,this.options,!0,this.refsStack.slice(0,-1));return r.title=n,r}))}initConditionalOperators(e,t){const n=e,{if:r,else:o={},then:i={}}=n,a=((e,t)=>{var n={};for(var r in e)bc.call(e,r)&&t.indexOf(r)<0&&(n[r]=e[r]);if(null!=e&&vc)for(var r of vc(e))t.indexOf(r)<0&&wc.call(e,r)&&(n[r]=e[r]);return n})(n,["if","else","then"]),s=[{allOf:[a,i,r],title:r&&r["x-displayName"]||(null==r?void 0:r.title)||"case 1"},{allOf:[a,o],title:o&&o["x-displayName"]||(null==o?void 0:o.title)||"case 2"}];this.oneOf=s.map(((e,n)=>new Sc(t,kc({},e),this.pointer+"/oneOf/"+n,this.options,!1,this.refsStack))),this.oneOfType="One of"}};let Ec=Sc;function Pc(e,t,n,r,o){const i=t.properties||t.prefixItems||t.items||{},a=t.patternProperties||{},s=t.additionalProperties||t.unevaluatedProperties,l=t.prefixItems?t.items:t.additionalItems,c=t.default;let u=Object.keys(i||[]).map((a=>{let s=i[a];s||(console.warn(`Field "${a}" is invalid, skipping.\n Field must be an object but got ${typeof s} at "${n}"`),s={});const l=void 0!==t.required&&t.required.indexOf(a)>-1;return new Nc(e,{name:t.properties?a:`[${a}]`,required:l,schema:_c(kc({},s),{default:void 0===s.default&&c?c[a]:s.default})},n+"/properties/"+a,r,o)}));return r.sortPropsAlphabetically&&(u=ds(u,"name")),r.requiredPropsFirst&&(u=ps(u,r.sortPropsAlphabetically?void 0:t.required)),u.push(...Object.keys(a).map((t=>{let i=a[t];return i||(console.warn(`Field "${t}" is invalid, skipping.\n Field must be an object but got ${typeof i} at "${n}"`),i={}),new Nc(e,{name:t,required:!1,schema:i,kind:"patternProperties"},`${n}/patternProperties/${t}`,r,o)}))),"object"!=typeof s&&!0!==s||u.push(new Nc(e,{name:("object"==typeof s&&s["x-additionalPropertiesName"]||"property 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this.value&&(this.value=function(e,t={}){if(io(e))throw new Error("Payload must have fields: "+e.toString());return Object.keys(e).map((n=>{const r=e[n],{style:o="form",explode:i=!0}=t[n]||{};switch(o){case"form":return rs(n,i,r);case"spaceDelimited":return ts(r,n,"%20");case"pipeDelimited":return ts(r,n,"|");case"deepObject":return ns(r,n);default:return console.warn("Incorrect or unsupported encoding style: "+o),""}})).join("&")}(this.value,r))}getExternalValue(e){return this.externalValueUrl?(this.externalValueUrl in $c||($c[this.externalValueUrl]=fetch(this.externalValueUrl).then((t=>t.text().then((n=>{if(!t.ok)return Promise.reject(new Error(n));if(!es(e))return n;try{return JSON.parse(n)}catch(e){return n}}))))),$c[this.externalValueUrl]):Promise.resolve(void 0)}}var Rc=Object.defineProperty,jc=Object.getOwnPropertyDescriptor,Tc=(e,t,n,r)=>{for(var o,i=r>1?void 0:r?jc(t,n):t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=(r?o(t,n,i):o(i))||i);return r&&i&&Rc(t,n,i),i};const Ic={path:{style:"simple",explode:!1},query:{style:"form",explode:!0},header:{style:"simple",explode:!1},cookie:{style:"form",explode:!0}};class Nc{constructor(e,t,n,r,o){var i,a,s,l,c;this.expanded=void 0,tn(this);const{resolved:u}=e.deref(t);this.kind=t.kind||"field",this.name=t.name||u.name,this.in=u.in,this.required=!!u.required;let p=u.schema,d="";if(!p&&u.in&&u.content&&(d=Object.keys(u.content)[0],p=u.content[d]&&u.content[d].schema),this.schema=new Ec(e,p||{},n,r,!1,o),this.description=void 0===u.description?this.schema.description||"":u.description,this.example=u.example||this.schema.example,void 0!==u.examples||void 0!==this.schema.examples){const t=u.examples||this.schema.examples;this.examples=io(t)?t:Qr(t,((t,n)=>new Cc(e,t,n,u.encoding)))}d?this.serializationMime=d:u.style?this.style=u.style:this.in&&(this.style=null!=(a=null==(i=Ic[this.in])?void 0:i.style)?a:"form"),void 0===u.explode&&this.in?this.explode=null==(l=null==(s=Ic[this.in])?void 0:s.explode)||l:this.explode=!!u.explode,this.deprecated=void 0===u.deprecated?!!this.schema.deprecated:u.deprecated,r.showExtensions&&(this.extensions=gs(u,r.showExtensions)),this.const=(null==(c=this.schema)?void 0:c.const)||(null==u?void 0:u.const)||""}toggle(){this.expanded=!this.expanded}collapse(){this.expanded=!1}expand(){this.expanded=!0}}function Dc(e){return e<10?"0"+e:e}function Lc(e,t){return t>e.length?e.repeat(Math.trunc(t/e.length)+1).substring(0,t):e}function Mc(...e){const t=e=>e&&"object"==typeof e;return e.reduce(((e,n)=>(Object.keys(n||{}).forEach((r=>{const o=e[r],i=n[r];t(o)&&t(i)?e[r]=Mc(o,i):e[r]=i})),e)),Array.isArray(e[e.length-1])?[]:{})}function Fc(e){return{value:"object"===e?{}:"array"===e?[]:void 0}}function zc(e,t){t&&e.pop()}Tc([Ae],Nc.prototype,"expanded",2),Tc([Pt],Nc.prototype,"toggle",1),Tc([Pt],Nc.prototype,"collapse",1),Tc([Pt],Nc.prototype,"expand",1);const Uc={multipleOf:"number",maximum:"number",exclusiveMaximum:"number",minimum:"number",exclusiveMinimum:"number",maxLength:"string",minLength:"string",pattern:"string",items:"array",maxItems:"array",minItems:"array",uniqueItems:"array",additionalItems:"array",maxProperties:"object",minProperties:"object",required:"object",additionalProperties:"object",properties:"object",patternProperties:"object",dependencies:"object"};function Vc(e){if(void 0!==e.type)return Array.isArray(e.type)?0===e.type.length?null:e.type[0]:e.type;const t=Object.keys(Uc);for(var n=0;n<t.length;n++){let r=t[n],o=Uc[r];if(void 0!==e[r])return o}return null}let Bc={},qc=[];function Wc(e){let t;return void 0!==e.const?t=e.const:void 0!==e.examples&&e.examples.length?t=e.examples[0]:void 0!==e.enum&&e.enum.length?t=e.enum[0]:void 0!==e.default&&(t=e.default),t}function Hc(e){const t=Wc(e);if(void 0!==t)return{value:t,readOnly:e.readOnly,writeOnly:e.writeOnly,type:null}}function Yc(e,t,n,r){if(r){if(qc.includes(e))return Fc(Vc(e));qc.push(e)}if(r&&r.depth>t.maxSampleDepth)return zc(qc,r),Fc(Vc(e));if(e.$ref){if(!n)throw new Error("Your schema contains $ref. 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t=e.exclusiveMinimum?Math.floor(e.minimum)+1:e.minimum,(e.exclusiveMaximum&&t>=e.maximum||!e.exclusiveMaximum&&t>e.maximum)&&(t=(e.maximum+e.minimum)/2),t;if(e.minimum)return e.exclusiveMinimum?Math.floor(e.minimum)+1:e.minimum;if(e.maximum)return e.exclusiveMaximum?e.maximum>0?0:Math.floor(e.maximum)-1:e.maximum>0?0:e.maximum}else{if(e.minimum)return e.minimum;e.exclusiveMinimum?(t=Math.floor(e.exclusiveMinimum)+1,t===e.exclusiveMaximum&&(t=(t+Math.floor(e.exclusiveMaximum)-1)/2)):e.exclusiveMaximum?t=Math.floor(e.exclusiveMaximum)-1:e.maximum&&(t=e.maximum)}return t}function Gc({min:e,max:t,omitTime:n,omitDate:r}){let o=function(e,t,n,r){var o=n?"":e.getUTCFullYear()+"-"+Dc(e.getUTCMonth()+1)+"-"+Dc(e.getUTCDate());return t||(o+="T"+Dc(e.getUTCHours())+":"+Dc(e.getUTCMinutes())+":"+Dc(e.getUTCSeconds())+"Z"),o}(new Date("2019-08-24T14:15:22.123Z"),n,r);return o.length<e&&console.warn(`Using minLength = ${e} is incorrect with format "date-time"`),t&&o.length>t&&console.warn(`Using 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r,o},default:Qc,"json-pointer":function(){return"/json/pointer"},"relative-json-pointer":function(){return"1/relative/json/pointer"},regex:function(){return"/regex/"}};var Jc={};const Zc={skipReadOnly:!1,maxSampleDepth:15};function eu(e,t,n){let r=Object.assign({},Zc,t);return Bc={},qc=[],Yc(e,r,n).value}function tu(e,t){Jc[e]=t}tu("array",(function(e,t={},n,r){const o=r&&r.depth||1;let i=Math.min(null!=e.maxItems?e.maxItems:1/0,e.minItems||1);const a=e.prefixItems||e.items||e.contains;Array.isArray(a)&&(i=Math.max(i,a.length));let s=[];if(!a)return s;for(let e=0;e<i;e++){let r=(l=e,Array.isArray(a)?a[l]||{}:a||{}),{value:i}=Yc(r,t,n,{depth:o+1});s.push(i)}var l;return s})),tu("boolean",(function(e){return!0})),tu("integer",Kc),tu("number",Kc),tu("object",(function(e,t={},n,r){let o={};const i=r&&r.depth||1;if(e&&"object"==typeof e.properties){let r=(Array.isArray(e.required)?e.required:[]).reduce(((e,t)=>(e[t]=!0,e)),{});Object.keys(e.properties).forEach((a=>{if(t.skipNonRequired&&!r.hasOwnProperty(a))return;const s=Yc(e.properties[a],t,n,{propertyName:a,depth:i+1});t.skipReadOnly&&s.readOnly||t.skipWriteOnly&&s.writeOnly||(o[a]=s.value)}))}if(e&&"object"==typeof e.additionalProperties){const r=e.additionalProperties["x-additionalPropertiesName"]||"property";o[`${String(r)}1`]=Yc(e.additionalProperties,t,n,{depth:i+1}).value,o[`${String(r)}2`]=Yc(e.additionalProperties,t,n,{depth:i+1}).value}return o})),tu("string",(function(e,t,n,r){let o=e.format||"default",i=Xc[o]||Qc,a=r&&r.propertyName;return i(0|e.minLength,e.maxLength,a)}));class nu{constructor(e,t,n,r,o){this.name=t,this.isRequestType=n,this.schema=r.schema&&new Ec(e,r.schema,"",o),this.onlyRequiredInSamples=o.onlyRequiredInSamples,this.generatedPayloadSamplesMaxDepth=o.generatedPayloadSamplesMaxDepth,void 0!==r.examples?this.examples=Qr(r.examples,(n=>new 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+  padding: 0 ${e=>e.theme.spacing.sectionHorizontal}px;
+
+  ${({compact:e,theme:t})=>ma("medium",!0)`
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+    padding: ${`${e?0:t.spacing.sectionVertical}px ${t.spacing.sectionHorizontal}px`};
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+`,Su=ga.div.attrs((e=>({[gf]:e.id})))`
+  padding: ${e=>e.theme.spacing.sectionVertical}px 0;
+
+  &:last-child {
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+
+  & > &:last-child {
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+  }
+
+  ${ma("medium",!0)`
+    padding: 0;
+  `}
+  ${e=>e.underlined?"\n    position: relative;\n\n    &:not(:last-of-type):after {\n      position: absolute;\n      bottom: 0;\n      width: 100%;\n      display: block;\n      content: '';\n      border-bottom: 1px solid rgba(0, 0, 0, 0.2);\n    }\n  ":""}
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+  color: ${({theme:e})=>e.rightPanel.textColor};
+  background-color: ${e=>e.theme.rightPanel.backgroundColor};
+  padding: 0 ${e=>e.theme.spacing.sectionHorizontal}px;
+
+  ${ma("medium",!0)`
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+    padding: ${e=>`${e.theme.spacing.sectionVertical}px ${e.theme.spacing.sectionHorizontal}px`};
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+
+  ${ma("medium",!0)`
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+  font-weight: ${({theme:e})=>e.typography.headings.fontWeight};
+  font-size: ${$u[e]};
+  line-height: ${({theme:e})=>e.typography.headings.lineHeight};
+`,Ru=ga.h1`
+  ${Cu(1)};
+  color: ${({theme:e})=>e.colors.text.primary};
+
+  ${ya("H1")};
+`,ju=ga.h2`
+  ${Cu(2)};
+  color: ${({theme:e})=>e.colors.text.primary};
+  margin: 0 0 20px;
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+  ${ya("H2")};
+`,Tu=(ga.h2`
+  ${Cu(3)};
+  color: ${({theme:e})=>e.colors.text.primary};
+
+  ${ya("H3")};
+`,ga.h3`
+  color: ${({theme:e})=>e.rightPanel.textColor};
+
+  ${ya("RightPanelHeader")};
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+  margin: 1em 0 1em 0;
+  color: rgba(38, 50, 56, 0.5);
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+  text-transform: uppercase;
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+  ${ya("UnderlinedHeader")};
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+    visibility: hidden;
+    display: inline-block;
+    vertical-align: middle;
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+
+  h1:hover > ${e}::before, h2:hover > ${e}::before, ${e}:hover::before {
+    visibility: visible;
+  }
+`,zu=ga((function(e){const t=n.useContext(Nu),r=n.useCallback((n=>{t&&function(e,t,n){t.defaultPrevented||0!==t.button||(e=>!!(e.metaKey||e.altKey||e.ctrlKey||e.shiftKey))(t)||(t.preventDefault(),e.replace(encodeURI(n)))}(t.menu.history,n,e.to)}),[t,e.to]);return t?n.createElement("a",{className:e.className,href:t.menu.history.linkForId(e.to),onClick:r,"aria-label":e.to},e.children):null}))`
+  ${Fu("&")};
+`;function Uu(e){return n.createElement(zu,{to:e.to})}const Vu={left:"90deg",right:"-90deg",up:"-180deg",down:"0"},Bu=ga((e=>n.createElement("svg",{className:e.className,style:e.style,version:"1.1",viewBox:"0 0 24 24",x:"0",xmlns:"http://www.w3.org/2000/svg",y:"0","aria-hidden":"true"},n.createElement("polygon",{points:"17.3 8.3 12 13.6 6.7 8.3 5.3 9.7 12 16.4 18.7 9.7 "}))))`
+  height: ${e=>e.size||"18px"};
+  width: ${e=>e.size||"18px"};
+  min-width: ${e=>e.size||"18px"};
+  vertical-align: middle;
+  float: ${e=>e.float||""};
+  transition: transform 0.2s ease-out;
+  transform: rotateZ(${e=>Vu[e.direction||"down"]});
+
+  polygon {
+    fill: ${({color:e,theme:t})=>e&&t.colors.responses[e]&&t.colors.responses[e].color||e};
+  }
+`,qu=ga.span`
+  display: inline-block;
+  padding: 2px 8px;
+  margin: 0;
+  background-color: ${e=>e.theme.colors[e.type].main};
+  color: ${e=>e.theme.colors[e.type].contrastText};
+  font-size: ${e=>e.theme.typography.code.fontSize};
+  vertical-align: middle;
+  line-height: 1.6;
+  border-radius: 4px;
+  font-weight: ${({theme:e})=>e.typography.fontWeightBold};
+  font-size: 12px;
+  + span[type] {
+    margin-left: 4px;
+  }
+`,Wu=pa`
+  text-decoration: line-through;
+  color: #707070;
+`,Hu=ga.caption`
+  text-align: right;
+  font-size: 0.9em;
+  font-weight: normal;
+  color: ${e=>e.theme.colors.text.secondary};
+`,Yu=ga.td`
+  border-left: 1px solid ${e=>e.theme.schema.linesColor};
+  box-sizing: border-box;
+  position: relative;
+  padding: 10px 10px 10px 0;
+
+  ${ma("small")`
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+    overflow: hidden;
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+
+  tr:first-of-type > &,
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+    background-repeat: no-repeat;
+    background-size: 1px 100%;
+  }
+
+  tr:first-of-type > & {
+    background-image: linear-gradient(
+      to bottom,
+      transparent 0%,
+      transparent 22px,
+      ${e=>e.theme.schema.linesColor} 22px,
+      ${e=>e.theme.schema.linesColor} 100%
+    );
+  }
+
+  tr.last > & {
+    background-image: linear-gradient(
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+      ${e=>e.theme.schema.linesColor} 0%,
+      ${e=>e.theme.schema.linesColor} 22px,
+      transparent 22px,
+      transparent 100%
+    );
+  }
+
+  tr.last + tr > & {
+    border-left-color: transparent;
+  }
+
+  tr.last:first-child > & {
+    background: none;
+    border-left-color: transparent;
+  }
+`,Ku=ga(Yu)`
+  padding: 0;
+`,Gu=ga(Yu)`
+  vertical-align: top;
+  line-height: 20px;
+  white-space: nowrap;
+  font-size: 13px;
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+
+  &.deprecated {
+    ${Wu};
+  }
+
+  ${({kind:e})=>"patternProperties"===e&&pa`
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+        display: inline-table;
+        white-space: break-spaces;
+        margin-right: 20px;
+
+        ::before,
+        ::after {
+          content: '/';
+          filter: opacity(0.2);
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+      }
+    `}
+
+  ${({kind:e=""})=>["field","additionalProperties","patternProperties"].includes(e)?"":"font-style: italic"};
+
+  ${ya("PropertyNameCell")};
+`,Qu=ga.td`
+  border-bottom: 1px solid #9fb4be;
+  padding: 10px 0;
+  width: ${e=>e.theme.schema.defaultDetailsWidth};
+  box-sizing: border-box;
+
+  tr.expanded & {
+    border-bottom: none;
+  }
+
+  ${ma("small")`
+    padding: 0 20px;
+    border-bottom: none;
+    border-left: 1px solid ${e=>e.theme.schema.linesColor};
+
+    tr.last > & {
+      border-left: none;
+    }
+  `}
+
+  ${ya("PropertyDetailsCell")};
+`,Xu=ga.span`
+  color: ${e=>e.theme.schema.linesColor};
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  margin-right: 10px;
+
+  &::before {
+    content: '';
+    display: inline-block;
+    vertical-align: middle;
+    width: 10px;
+    height: 1px;
+    background: ${e=>e.theme.schema.linesColor};
+  }
+
+  &::after {
+    content: '';
+    display: inline-block;
+    vertical-align: middle;
+    width: 1px;
+    background: ${e=>e.theme.schema.linesColor};
+    height: 7px;
+  }
+`,Ju=ga.div`
+  padding: ${({theme:e})=>e.schema.nestingSpacing};
+`,Zu=ga.table`
+  border-collapse: separate;
+  border-radius: 3px;
+  font-size: ${e=>e.theme.typography.fontSize};
+
+  border-spacing: 0;
+  width: 100%;
+
+  > tr {
+    vertical-align: middle;
+  }
+
+  ${ma("small")`
+    display: block;
+    > tr, > tbody > tr {
+      display: block;
+    }
+  `}
+
+  ${ma("small",!1," and (-ms-high-contrast:none)")`
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+      float: left;
+      width: 100%;
+    }
+  `}
+
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+    ${Ju},
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+    ${Ju}
+    ${Ju}
+    ${Ju},
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+    ${Ju}
+    ${Ju}
+    ${Ju}
+    ${Ju}
+    ${Ju} {
+    margin: ${({theme:e})=>e.schema.nestingSpacing};
+    margin-right: 0;
+    background: ${({theme:e})=>e.schema.nestedBackground};
+  }
+
+  &
+    ${Ju}
+    ${Ju},
+    &
+    ${Ju}
+    ${Ju}
+    ${Ju}
+    ${Ju},
+    &
+    ${Ju}
+    ${Ju}
+    ${Ju}
+    ${Ju}
+    ${Ju}
+    ${Ju} {
+    background: #ffffff;
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+`,ep=ga.div`
+  margin: 0 0 3px 0;
+  display: inline-block;
+`,tp=ga.span`
+  font-size: 0.9em;
+  margin-right: 10px;
+  color: ${e=>e.theme.colors.primary.main};
+  font-family: ${e=>e.theme.typography.headings.fontFamily};
+}
+`,np=ga.button`
+  display: inline-block;
+  margin-right: 10px;
+  margin-bottom: 5px;
+  font-size: 0.8em;
+  cursor: pointer;
+  border: 1px solid ${e=>e.theme.colors.primary.main};
+  padding: 2px 10px;
+  line-height: 1.5em;
+  outline: none;
+  &:focus {
+    box-shadow: 0 0 0 1px ${e=>e.theme.colors.primary.main};
+  }
+
+  ${({deprecated:e})=>e&&Wu||""};
+
+  ${e=>e.active?`\n      color: white;\n      background-color: ${e.theme.colors.primary.main};\n      &:focus {\n        box-shadow: none;\n        background-color: ${Rr(.15,e.theme.colors.primary.main)};\n      }\n      `:`\n        color: ${e.theme.colors.primary.main};\n        background-color: white;\n      `}
+`,rp=ga.div`
+  font-size: 0.9em;
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  &::after {
+    content: ' [';
+  }
+`,op=ga.div`
+  font-size: 0.9em;
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  &::after {
+    content: ']';
+  }
+`;function ip(e){return function(t){return!!t.type&&t.type.tabsRole===e}}var ap=ip("Tab"),sp=ip("TabList"),lp=ip("TabPanel");function cp(){return cp=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},cp.apply(this,arguments)}function up(e,t){return n.Children.map(e,(function(e){return null===e?null:function(e){return ap(e)||sp(e)||lp(e)}(e)?t(e):e.props&&e.props.children&&"object"==typeof e.props.children?(0,n.cloneElement)(e,cp({},e.props,{children:up(e.props.children,t)})):e}))}function pp(e,t){return n.Children.forEach(e,(function(e){null!==e&&(ap(e)||lp(e)?t(e):e.props&&e.props.children&&"object"==typeof e.props.children&&(sp(e)&&t(e),pp(e.props.children,t)))}))}function dp(e){var t,n,r="";if("string"==typeof e||"number"==typeof e)r+=e;else if("object"==typeof e)if(Array.isArray(e))for(t=0;t<e.length;t++)e[t]&&(n=dp(e[t]))&&(r&&(r+=" "),r+=n);else for(t in e)e[t]&&(r&&(r+=" "),r+=t);return r}function fp(){for(var e,t,n=0,r="";n<arguments.length;)(e=arguments[n++])&&(t=dp(e))&&(r&&(r+=" "),r+=t);return r}var hp,mp=0;function gp(){return"react-tabs-"+mp++}function yp(e){var t=0;return pp(e,(function(e){ap(e)&&t++})),t}function vp(){return vp=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},vp.apply(this,arguments)}function bp(e,t){return bp=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e},bp(e,t)}function wp(e){return e&&"getAttribute"in e}function xp(e){return wp(e)&&"tab"===e.getAttribute("role")}function kp(e){return wp(e)&&"true"===e.getAttribute("aria-disabled")}var _p=function(e){var t,r;function o(){for(var t,n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return(t=e.call.apply(e,[this].concat(r))||this).tabNodes=[],t.handleKeyDown=function(e){var n=t.props,r=n.direction,o=n.disableUpDownKeys;if(t.isTabFromContainer(e.target)){var i=t.props.selectedIndex,a=!1,s=!1;32!==e.keyCode&&13!==e.keyCode||(a=!0,s=!1,t.handleClick(e)),37===e.keyCode||!o&&38===e.keyCode?(i="rtl"===r?t.getNextTab(i):t.getPrevTab(i),a=!0,s=!0):39===e.keyCode||!o&&40===e.keyCode?(i="rtl"===r?t.getPrevTab(i):t.getNextTab(i),a=!0,s=!0):35===e.keyCode?(i=t.getLastTab(),a=!0,s=!0):36===e.keyCode&&(i=t.getFirstTab(),a=!0,s=!0),a&&e.preventDefault(),s&&t.setSelected(i,e)}},t.handleClick=function(e){var n=e.target;do{if(t.isTabFromContainer(n)){if(kp(n))return;var r=[].slice.call(n.parentNode.children).filter(xp).indexOf(n);return void t.setSelected(r,e)}}while(null!=(n=n.parentNode))},t}r=e,(t=o).prototype=Object.create(r.prototype),t.prototype.constructor=t,bp(t,r);var i=o.prototype;return i.setSelected=function(e,t){if(!(e<0||e>=this.getTabsCount())){var n=this.props;(0,n.onSelect)(e,n.selectedIndex,t)}},i.getNextTab=function(e){for(var t=this.getTabsCount(),n=e+1;n<t;n++)if(!kp(this.getTab(n)))return n;for(var r=0;r<e;r++)if(!kp(this.getTab(r)))return r;return e},i.getPrevTab=function(e){for(var t=e;t--;)if(!kp(this.getTab(t)))return t;for(t=this.getTabsCount();t-- >e;)if(!kp(this.getTab(t)))return t;return e},i.getFirstTab=function(){for(var e=this.getTabsCount(),t=0;t<e;t++)if(!kp(this.getTab(t)))return t;return null},i.getLastTab=function(){for(var e=this.getTabsCount();e--;)if(!kp(this.getTab(e)))return e;return null},i.getTabsCount=function(){return yp(this.props.children)},i.getPanelsCount=function(){return e=this.props.children,t=0,pp(e,(function(e){lp(e)&&t++})),t;var e,t},i.getTab=function(e){return this.tabNodes["tabs-"+e]},i.getChildren=function(){var e=this,t=0,r=this.props,o=r.children,i=r.disabledTabClassName,a=r.focus,s=r.forceRenderTabPanel,l=r.selectedIndex,c=r.selectedTabClassName,u=r.selectedTabPanelClassName,p=r.environment;this.tabIds=this.tabIds||[],this.panelIds=this.panelIds||[];for(var d=this.tabIds.length-this.getTabsCount();d++<0;)this.tabIds.push(gp()),this.panelIds.push(gp());return up(o,(function(r){var o=r;if(sp(r)){var d=0,f=!1;null==hp&&function(e){var t=e||("undefined"!=typeof window?window:void 0);try{hp=!(void 0===t||!t.document||!t.document.activeElement)}catch(e){hp=!1}}(p),hp&&(f=n.Children.toArray(r.props.children).filter(ap).some((function(t,n){var r=p||("undefined"!=typeof window?window:void 0);return r&&r.document.activeElement===e.getTab(n)}))),o=(0,n.cloneElement)(r,{children:up(r.props.children,(function(t){var r="tabs-"+d,o=l===d,s={tabRef:function(t){e.tabNodes[r]=t},id:e.tabIds[d],panelId:e.panelIds[d],selected:o,focus:o&&(a||f)};return c&&(s.selectedClassName=c),i&&(s.disabledClassName=i),d++,(0,n.cloneElement)(t,s)}))})}else if(lp(r)){var h={id:e.panelIds[t],tabId:e.tabIds[t],selected:l===t};s&&(h.forceRender=s),u&&(h.selectedClassName=u),t++,o=(0,n.cloneElement)(r,h)}return o}))},i.isTabFromContainer=function(e){if(!xp(e))return!1;var t=e.parentElement;do{if(t===this.node)return!0;if(t.getAttribute("data-tabs"))break;t=t.parentElement}while(t);return!1},i.render=function(){var e=this,t=this.props,r=(t.children,t.className),o=(t.disabledTabClassName,t.domRef),i=(t.focus,t.forceRenderTabPanel,t.onSelect,t.selectedIndex,t.selectedTabClassName,t.selectedTabPanelClassName,t.environment,t.disableUpDownKeys,function(e,t){if(null==e)return{};var n,r,o={},i=Object.keys(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(t,["children","className","disabledTabClassName","domRef","focus","forceRenderTabPanel","onSelect","selectedIndex","selectedTabClassName","selectedTabPanelClassName","environment","disableUpDownKeys"]));return n.createElement("div",vp({},i,{className:fp(r),onClick:this.handleClick,onKeyDown:this.handleKeyDown,ref:function(t){e.node=t,o&&o(t)},"data-tabs":!0}),this.getChildren())},o}(n.Component);function Op(e,t){return Op=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e},Op(e,t)}_p.defaultProps={className:"react-tabs",focus:!1},_p.propTypes={};var Sp=function(e){var t,r;function o(t){var n;return(n=e.call(this,t)||this).handleSelected=function(e,t,r){var o=n.props.onSelect,i=n.state.mode;if("function"!=typeof o||!1!==o(e,t,r)){var a={focus:"keydown"===r.type};1===i&&(a.selectedIndex=e),n.setState(a)}},n.state=o.copyPropsToState(n.props,{},t.defaultFocus),n}return r=e,(t=o).prototype=Object.create(r.prototype),t.prototype.constructor=t,Op(t,r),o.getDerivedStateFromProps=function(e,t){return o.copyPropsToState(e,t)},o.getModeFromProps=function(e){return null===e.selectedIndex?1:0},o.copyPropsToState=function(e,t,n){void 0===n&&(n=!1);var r={focus:n,mode:o.getModeFromProps(e)};if(1===r.mode){var i,a=Math.max(0,yp(e.children)-1);i=null!=t.selectedIndex?Math.min(t.selectedIndex,a):e.defaultIndex||0,r.selectedIndex=i}return r},o.prototype.render=function(){var e=this.props,t=e.children,r=(e.defaultIndex,e.defaultFocus,function(e,t){if(null==e)return{};var n,r,o={},i=Object.keys(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(e,["children","defaultIndex","defaultFocus"])),o=this.state,i=o.focus,a=o.selectedIndex;return r.focus=i,r.onSelect=this.handleSelected,null!=a&&(r.selectedIndex=a),n.createElement(_p,r,t)},o}(n.Component);function Ep(){return Ep=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},Ep.apply(this,arguments)}function Pp(e,t){return Pp=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e},Pp(e,t)}Sp.defaultProps={defaultFocus:!1,forceRenderTabPanel:!1,selectedIndex:null,defaultIndex:null,environment:null,disableUpDownKeys:!1},Sp.propTypes={},Sp.tabsRole="Tabs";var Ap=function(e){var t,r;function o(){return e.apply(this,arguments)||this}return r=e,(t=o).prototype=Object.create(r.prototype),t.prototype.constructor=t,Pp(t,r),o.prototype.render=function(){var e=this.props,t=e.children,r=e.className,o=function(e,t){if(null==e)return{};var n,r,o={},i=Object.keys(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(e,["children","className"]);return n.createElement("ul",Ep({},o,{className:fp(r),role:"tablist"}),t)},o}(n.Component);function $p(){return $p=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},$p.apply(this,arguments)}function Cp(e,t){return Cp=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e},Cp(e,t)}Ap.defaultProps={className:"react-tabs__tab-list"},Ap.propTypes={},Ap.tabsRole="TabList";var Rp="react-tabs__tab",jp=function(e){var t,r;function o(){return e.apply(this,arguments)||this}r=e,(t=o).prototype=Object.create(r.prototype),t.prototype.constructor=t,Cp(t,r);var i=o.prototype;return i.componentDidMount=function(){this.checkFocus()},i.componentDidUpdate=function(){this.checkFocus()},i.checkFocus=function(){var e=this.props,t=e.selected,n=e.focus;t&&n&&this.node.focus()},i.render=function(){var e,t=this,r=this.props,o=r.children,i=r.className,a=r.disabled,s=r.disabledClassName,l=(r.focus,r.id),c=r.panelId,u=r.selected,p=r.selectedClassName,d=r.tabIndex,f=r.tabRef,h=function(e,t){if(null==e)return{};var n,r,o={},i=Object.keys(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(r,["children","className","disabled","disabledClassName","focus","id","panelId","selected","selectedClassName","tabIndex","tabRef"]);return n.createElement("li",$p({},h,{className:fp(i,(e={},e[p]=u,e[s]=a,e)),ref:function(e){t.node=e,f&&f(e)},role:"tab",id:l,"aria-selected":u?"true":"false","aria-disabled":a?"true":"false","aria-controls":c,tabIndex:d||(u?"0":null)}),o)},o}(n.Component);function Tp(){return Tp=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},Tp.apply(this,arguments)}function Ip(e,t){return Ip=Object.setPrototypeOf||function(e,t){return e.__proto__=t,e},Ip(e,t)}jp.defaultProps={className:Rp,disabledClassName:Rp+"--disabled",focus:!1,id:null,panelId:null,selected:!1,selectedClassName:Rp+"--selected"},jp.propTypes={},jp.tabsRole="Tab";var Np=function(e){var t,r;function o(){return e.apply(this,arguments)||this}return r=e,(t=o).prototype=Object.create(r.prototype),t.prototype.constructor=t,Ip(t,r),o.prototype.render=function(){var e,t=this.props,r=t.children,o=t.className,i=t.forceRender,a=t.id,s=t.selected,l=t.selectedClassName,c=t.tabId,u=function(e,t){if(null==e)return{};var n,r,o={},i=Object.keys(e);for(r=0;r<i.length;r++)n=i[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(t,["children","className","forceRender","id","selected","selectedClassName","tabId"]);return n.createElement("div",Tp({},u,{className:fp(o,(e={},e[l]=s,e)),role:"tabpanel",id:a,"aria-labelledby":c}),i||s?r:null)},o}(n.Component);Np.defaultProps={className:"react-tabs__tab-panel",forceRender:!1,selectedClassName:"react-tabs__tab-panel--selected"},Np.propTypes={},Np.tabsRole="TabPanel";const Dp=ga(Sp)`
+  > ul {
+    list-style: none;
+    padding: 0;
+    margin: 0;
+    margin: 0 -5px;
+
+    > li {
+      padding: 5px 10px;
+      display: inline-block;
+
+      background-color: ${({theme:e})=>e.codeBlock.backgroundColor};
+      border-bottom: 1px solid rgba(0, 0, 0, 0.5);
+      cursor: pointer;
+      text-align: center;
+      outline: none;
+      color: ${({theme:e})=>Rr(e.colors.tonalOffset,e.rightPanel.textColor)};
+      margin: 0
+        ${({theme:e})=>`${e.spacing.unit}px ${e.spacing.unit}px ${e.spacing.unit}px`};
+      border: 1px solid ${({theme:e})=>Rr(.05,e.codeBlock.backgroundColor)};
+      border-radius: 5px;
+      min-width: 60px;
+      font-size: 0.9em;
+      font-weight: bold;
+
+      &.react-tabs__tab--selected {
+        color: ${e=>e.theme.colors.text.primary};
+        background: ${({theme:e})=>e.rightPanel.textColor};
+        &:focus {
+          outline: auto;
+        }
+      }
+
+      &:only-child {
+        flex: none;
+        min-width: 100px;
+      }
+
+      &.tab-success {
+        color: ${e=>e.theme.colors.responses.success.tabTextColor};
+      }
+
+      &.tab-redirect {
+        color: ${e=>e.theme.colors.responses.redirect.tabTextColor};
+      }
+
+      &.tab-info {
+        color: ${e=>e.theme.colors.responses.info.tabTextColor};
+      }
+
+      &.tab-error {
+        color: ${e=>e.theme.colors.responses.error.tabTextColor};
+      }
+    }
+  }
+  > .react-tabs__tab-panel {
+    background: ${({theme:e})=>e.codeBlock.backgroundColor};
+    & > div,
+    & > pre {
+      padding: ${e=>4*e.theme.spacing.unit}px;
+      margin: 0;
+    }
+
+    & > div > pre {
+      padding: 0;
+    }
+  }
+`,Lp=(ga(Dp)`
+  > ul {
+    display: block;
+    > li {
+      padding: 2px 5px;
+      min-width: auto;
+      margin: 0 15px 0 0;
+      font-size: 13px;
+      font-weight: normal;
+      border-bottom: 1px dashed;
+      color: ${({theme:e})=>Rr(e.colors.tonalOffset,e.rightPanel.textColor)};
+      border-radius: 0;
+      background: none;
+
+      &:last-child {
+        margin-right: 0;
+      }
+
+      &.react-tabs__tab--selected {
+        color: ${({theme:e})=>e.rightPanel.textColor};
+        background: none;
+      }
+    }
+  }
+  > .react-tabs__tab-panel {
+    & > div,
+    & > pre {
+      padding: ${e=>2*e.theme.spacing.unit}px 0;
+    }
+  }
+`,ga.div`
+  /**
+  * Based on prism-dark.css
+  */
+
+  code[class*='language-'],
+  pre[class*='language-'] {
+    /* color: white;
+    background: none; */
+    text-shadow: 0 -0.1em 0.2em black;
+    text-align: left;
+    white-space: pre;
+    word-spacing: normal;
+    word-break: normal;
+    word-wrap: normal;
+    line-height: 1.5;
+
+    -moz-tab-size: 4;
+    -o-tab-size: 4;
+    tab-size: 4;
+
+    -webkit-hyphens: none;
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    hyphens: none;
+  }
+
+  @media print {
+    code[class*='language-'],
+    pre[class*='language-'] {
+      text-shadow: none;
+    }
+  }
+
+  /* Code blocks */
+  pre[class*='language-'] {
+    padding: 1em;
+    margin: 0.5em 0;
+    overflow: auto;
+  }
+
+  .token.comment,
+  .token.prolog,
+  .token.doctype,
+  .token.cdata {
+    color: hsl(30, 20%, 50%);
+  }
+
+  .token.punctuation {
+    opacity: 0.7;
+  }
+
+  .namespace {
+    opacity: 0.7;
+  }
+
+  .token.property,
+  .token.tag,
+  .token.number,
+  .token.constant,
+  .token.symbol {
+    color: #4a8bb3;
+  }
+
+  .token.boolean {
+    color: #e64441;
+  }
+
+  .token.selector,
+  .token.attr-name,
+  .token.string,
+  .token.char,
+  .token.builtin,
+  .token.inserted {
+    color: #a0fbaa;
+    & + a,
+    & + a:visited {
+      color: #4ed2ba;
+      text-decoration: underline;
+    }
+  }
+
+  .token.property.string {
+    color: white;
+  }
+
+  .token.operator,
+  .token.entity,
+  .token.url,
+  .token.variable {
+    color: hsl(40, 90%, 60%);
+  }
+
+  .token.atrule,
+  .token.attr-value,
+  .token.keyword {
+    color: hsl(350, 40%, 70%);
+  }
+
+  .token.regex,
+  .token.important {
+    color: #e90;
+  }
+
+  .token.important,
+  .token.bold {
+    font-weight: bold;
+  }
+  .token.italic {
+    font-style: italic;
+  }
+
+  .token.entity {
+    cursor: help;
+  }
+
+  .token.deleted {
+    color: red;
+  }
+
+  ${ya("Prism")};
+`),Mp=ga.div`
+  opacity: 0.7;
+  transition: opacity 0.3s ease;
+  text-align: right;
+  &:focus-within {
+    opacity: 1;
+  }
+  > button {
+    background-color: transparent;
+    border: 0;
+    color: inherit;
+    padding: 2px 10px;
+    font-family: ${({theme:e})=>e.typography.fontFamily};
+    font-size: ${({theme:e})=>e.typography.fontSize};
+    line-height: ${({theme:e})=>e.typography.lineHeight};
+    cursor: pointer;
+    outline: 0;
+
+    :hover,
+    :focus {
+      background: rgba(255, 255, 255, 0.1);
+    }
+  }
+`,Fp=ga.div`
+  &:hover ${Mp} {
+    opacity: 1;
+  }
+`,zp=ga(Lp.withComponent("pre"))`
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  font-size: ${e=>e.theme.typography.code.fontSize};
+  overflow-x: auto;
+  margin: 0;
+
+  white-space: ${({theme:e})=>e.typography.code.wrap?"pre-wrap":"pre"};
+`;function Up(e){return getComputedStyle(e)}function Vp(e,t){for(var n in t){var r=t[n];"number"==typeof r&&(r+="px"),e.style[n]=r}return e}function Bp(e){var t=document.createElement("div");return t.className=e,t}var qp="undefined"!=typeof Element&&(Element.prototype.matches||Element.prototype.webkitMatchesSelector||Element.prototype.mozMatchesSelector||Element.prototype.msMatchesSelector);function Wp(e,t){if(!qp)throw new Error("No element matching method supported");return qp.call(e,t)}function Hp(e){e.remove?e.remove():e.parentNode&&e.parentNode.removeChild(e)}function Yp(e,t){return Array.prototype.filter.call(e.children,(function(e){return Wp(e,t)}))}var Kp=function(e){return"ps__thumb-"+e},Gp=function(e){return"ps__rail-"+e},Qp="ps__child--consume",Xp="ps--focus",Jp="ps--clicking",Zp=function(e){return"ps--active-"+e},ed=function(e){return"ps--scrolling-"+e},td={x:null,y:null};function nd(e,t){var n=e.element.classList,r=ed(t);n.contains(r)?clearTimeout(td[t]):n.add(r)}function rd(e,t){td[t]=setTimeout((function(){return e.isAlive&&e.element.classList.remove(ed(t))}),e.settings.scrollingThreshold)}var od=function(e){this.element=e,this.handlers={}},id={isEmpty:{configurable:!0}};od.prototype.bind=function(e,t){void 0===this.handlers[e]&&(this.handlers[e]=[]),this.handlers[e].push(t),this.element.addEventListener(e,t,!1)},od.prototype.unbind=function(e,t){var n=this;this.handlers[e]=this.handlers[e].filter((function(r){return!(!t||r===t)||(n.element.removeEventListener(e,r,!1),!1)}))},od.prototype.unbindAll=function(){for(var e in this.handlers)this.unbind(e)},id.isEmpty.get=function(){var e=this;return Object.keys(this.handlers).every((function(t){return 0===e.handlers[t].length}))},Object.defineProperties(od.prototype,id);var ad=function(){this.eventElements=[]};function sd(e){if("function"==typeof window.CustomEvent)return new CustomEvent(e);var t=document.createEvent("CustomEvent");return t.initCustomEvent(e,!1,!1,void 0),t}function ld(e,t,n,r,o){var i;if(void 0===r&&(r=!0),void 0===o&&(o=!1),"top"===t)i=["contentHeight","containerHeight","scrollTop","y","up","down"];else{if("left"!==t)throw new Error("A proper axis should be provided");i=["contentWidth","containerWidth","scrollLeft","x","left","right"]}!function(e,t,n,r,o){var i=n[0],a=n[1],s=n[2],l=n[3],c=n[4],u=n[5];void 0===r&&(r=!0),void 0===o&&(o=!1);var p=e.element;e.reach[l]=null,p[s]<1&&(e.reach[l]="start"),p[s]>e[i]-e[a]-1&&(e.reach[l]="end"),t&&(p.dispatchEvent(sd("ps-scroll-"+l)),t<0?p.dispatchEvent(sd("ps-scroll-"+c)):t>0&&p.dispatchEvent(sd("ps-scroll-"+u)),r&&function(e,t){nd(e,t),rd(e,t)}(e,l)),e.reach[l]&&(t||o)&&p.dispatchEvent(sd("ps-"+l+"-reach-"+e.reach[l]))}(e,n,i,r,o)}function cd(e){return parseInt(e,10)||0}ad.prototype.eventElement=function(e){var t=this.eventElements.filter((function(t){return t.element===e}))[0];return t||(t=new od(e),this.eventElements.push(t)),t},ad.prototype.bind=function(e,t,n){this.eventElement(e).bind(t,n)},ad.prototype.unbind=function(e,t,n){var r=this.eventElement(e);r.unbind(t,n),r.isEmpty&&this.eventElements.splice(this.eventElements.indexOf(r),1)},ad.prototype.unbindAll=function(){this.eventElements.forEach((function(e){return e.unbindAll()})),this.eventElements=[]},ad.prototype.once=function(e,t,n){var r=this.eventElement(e),o=function(e){r.unbind(t,o),n(e)};r.bind(t,o)};var ud={isWebKit:"undefined"!=typeof document&&"WebkitAppearance"in document.documentElement.style,supportsTouch:"undefined"!=typeof window&&("ontouchstart"in window||"maxTouchPoints"in window.navigator&&window.navigator.maxTouchPoints>0||window.DocumentTouch&&document instanceof window.DocumentTouch),supportsIePointer:"undefined"!=typeof navigator&&navigator.msMaxTouchPoints,isChrome:"undefined"!=typeof navigator&&/Chrome/i.test(navigator&&navigator.userAgent)};function pd(e){var t=e.element,n=Math.floor(t.scrollTop),r=t.getBoundingClientRect();e.containerWidth=Math.round(r.width),e.containerHeight=Math.round(r.height),e.contentWidth=t.scrollWidth,e.contentHeight=t.scrollHeight,t.contains(e.scrollbarXRail)||(Yp(t,Gp("x")).forEach((function(e){return Hp(e)})),t.appendChild(e.scrollbarXRail)),t.contains(e.scrollbarYRail)||(Yp(t,Gp("y")).forEach((function(e){return Hp(e)})),t.appendChild(e.scrollbarYRail)),!e.settings.suppressScrollX&&e.containerWidth+e.settings.scrollXMarginOffset<e.contentWidth?(e.scrollbarXActive=!0,e.railXWidth=e.containerWidth-e.railXMarginWidth,e.railXRatio=e.containerWidth/e.railXWidth,e.scrollbarXWidth=dd(e,cd(e.railXWidth*e.containerWidth/e.contentWidth)),e.scrollbarXLeft=cd((e.negativeScrollAdjustment+t.scrollLeft)*(e.railXWidth-e.scrollbarXWidth)/(e.contentWidth-e.containerWidth))):e.scrollbarXActive=!1,!e.settings.suppressScrollY&&e.containerHeight+e.settings.scrollYMarginOffset<e.contentHeight?(e.scrollbarYActive=!0,e.railYHeight=e.containerHeight-e.railYMarginHeight,e.railYRatio=e.containerHeight/e.railYHeight,e.scrollbarYHeight=dd(e,cd(e.railYHeight*e.containerHeight/e.contentHeight)),e.scrollbarYTop=cd(n*(e.railYHeight-e.scrollbarYHeight)/(e.contentHeight-e.containerHeight))):e.scrollbarYActive=!1,e.scrollbarXLeft>=e.railXWidth-e.scrollbarXWidth&&(e.scrollbarXLeft=e.railXWidth-e.scrollbarXWidth),e.scrollbarYTop>=e.railYHeight-e.scrollbarYHeight&&(e.scrollbarYTop=e.railYHeight-e.scrollbarYHeight),function(e,t){var n={width:t.railXWidth},r=Math.floor(e.scrollTop);t.isRtl?n.left=t.negativeScrollAdjustment+e.scrollLeft+t.containerWidth-t.contentWidth:n.left=e.scrollLeft,t.isScrollbarXUsingBottom?n.bottom=t.scrollbarXBottom-r:n.top=t.scrollbarXTop+r,Vp(t.scrollbarXRail,n);var o={top:r,height:t.railYHeight};t.isScrollbarYUsingRight?t.isRtl?o.right=t.contentWidth-(t.negativeScrollAdjustment+e.scrollLeft)-t.scrollbarYRight-t.scrollbarYOuterWidth-9:o.right=t.scrollbarYRight-e.scrollLeft:t.isRtl?o.left=t.negativeScrollAdjustment+e.scrollLeft+2*t.containerWidth-t.contentWidth-t.scrollbarYLeft-t.scrollbarYOuterWidth:o.left=t.scrollbarYLeft+e.scrollLeft,Vp(t.scrollbarYRail,o),Vp(t.scrollbarX,{left:t.scrollbarXLeft,width:t.scrollbarXWidth-t.railBorderXWidth}),Vp(t.scrollbarY,{top:t.scrollbarYTop,height:t.scrollbarYHeight-t.railBorderYWidth})}(t,e),e.scrollbarXActive?t.classList.add(Zp("x")):(t.classList.remove(Zp("x")),e.scrollbarXWidth=0,e.scrollbarXLeft=0,t.scrollLeft=!0===e.isRtl?e.contentWidth:0),e.scrollbarYActive?t.classList.add(Zp("y")):(t.classList.remove(Zp("y")),e.scrollbarYHeight=0,e.scrollbarYTop=0,t.scrollTop=0)}function dd(e,t){return e.settings.minScrollbarLength&&(t=Math.max(t,e.settings.minScrollbarLength)),e.settings.maxScrollbarLength&&(t=Math.min(t,e.settings.maxScrollbarLength)),t}function fd(e,t){var n=t[0],r=t[1],o=t[2],i=t[3],a=t[4],s=t[5],l=t[6],c=t[7],u=t[8],p=e.element,d=null,f=null,h=null;function m(t){t.touches&&t.touches[0]&&(t[o]=t.touches[0].pageY),p[l]=d+h*(t[o]-f),nd(e,c),pd(e),t.stopPropagation(),t.type.startsWith("touch")&&t.changedTouches.length>1&&t.preventDefault()}function g(){rd(e,c),e[u].classList.remove(Jp),e.event.unbind(e.ownerDocument,"mousemove",m)}function y(t,a){d=p[l],a&&t.touches&&(t[o]=t.touches[0].pageY),f=t[o],h=(e[r]-e[n])/(e[i]-e[s]),a?e.event.bind(e.ownerDocument,"touchmove",m):(e.event.bind(e.ownerDocument,"mousemove",m),e.event.once(e.ownerDocument,"mouseup",g),t.preventDefault()),e[u].classList.add(Jp),t.stopPropagation()}e.event.bind(e[a],"mousedown",(function(e){y(e)})),e.event.bind(e[a],"touchstart",(function(e){y(e,!0)}))}var hd={"click-rail":function(e){e.element,e.event.bind(e.scrollbarY,"mousedown",(function(e){return e.stopPropagation()})),e.event.bind(e.scrollbarYRail,"mousedown",(function(t){var n=t.pageY-window.pageYOffset-e.scrollbarYRail.getBoundingClientRect().top>e.scrollbarYTop?1:-1;e.element.scrollTop+=n*e.containerHeight,pd(e),t.stopPropagation()})),e.event.bind(e.scrollbarX,"mousedown",(function(e){return e.stopPropagation()})),e.event.bind(e.scrollbarXRail,"mousedown",(function(t){var n=t.pageX-window.pageXOffset-e.scrollbarXRail.getBoundingClientRect().left>e.scrollbarXLeft?1:-1;e.element.scrollLeft+=n*e.containerWidth,pd(e),t.stopPropagation()}))},"drag-thumb":function(e){fd(e,["containerWidth","contentWidth","pageX","railXWidth","scrollbarX","scrollbarXWidth","scrollLeft","x","scrollbarXRail"]),fd(e,["containerHeight","contentHeight","pageY","railYHeight","scrollbarY","scrollbarYHeight","scrollTop","y","scrollbarYRail"])},keyboard:function(e){var t=e.element;e.event.bind(e.ownerDocument,"keydown",(function(n){if(!(n.isDefaultPrevented&&n.isDefaultPrevented()||n.defaultPrevented)&&(Wp(t,":hover")||Wp(e.scrollbarX,":focus")||Wp(e.scrollbarY,":focus"))){var r,o=document.activeElement?document.activeElement:e.ownerDocument.activeElement;if(o){if("IFRAME"===o.tagName)o=o.contentDocument.activeElement;else for(;o.shadowRoot;)o=o.shadowRoot.activeElement;if(Wp(r=o,"input,[contenteditable]")||Wp(r,"select,[contenteditable]")||Wp(r,"textarea,[contenteditable]")||Wp(r,"button,[contenteditable]"))return}var i=0,a=0;switch(n.which){case 37:i=n.metaKey?-e.contentWidth:n.altKey?-e.containerWidth:-30;break;case 38:a=n.metaKey?e.contentHeight:n.altKey?e.containerHeight:30;break;case 39:i=n.metaKey?e.contentWidth:n.altKey?e.containerWidth:30;break;case 40:a=n.metaKey?-e.contentHeight:n.altKey?-e.containerHeight:-30;break;case 32:a=n.shiftKey?e.containerHeight:-e.containerHeight;break;case 33:a=e.containerHeight;break;case 34:a=-e.containerHeight;break;case 36:a=e.contentHeight;break;case 35:a=-e.contentHeight;break;default:return}e.settings.suppressScrollX&&0!==i||e.settings.suppressScrollY&&0!==a||(t.scrollTop-=a,t.scrollLeft+=i,pd(e),function(n,r){var o=Math.floor(t.scrollTop);if(0===n){if(!e.scrollbarYActive)return!1;if(0===o&&r>0||o>=e.contentHeight-e.containerHeight&&r<0)return!e.settings.wheelPropagation}var i=t.scrollLeft;if(0===r){if(!e.scrollbarXActive)return!1;if(0===i&&n<0||i>=e.contentWidth-e.containerWidth&&n>0)return!e.settings.wheelPropagation}return!0}(i,a)&&n.preventDefault())}}))},wheel:function(e){var t=e.element;function n(n){var r=function(e){var t=e.deltaX,n=-1*e.deltaY;return void 0!==t&&void 0!==n||(t=-1*e.wheelDeltaX/6,n=e.wheelDeltaY/6),e.deltaMode&&1===e.deltaMode&&(t*=10,n*=10),t!=t&&n!=n&&(t=0,n=e.wheelDelta),e.shiftKey?[-n,-t]:[t,n]}(n),o=r[0],i=r[1];if(!function(e,n,r){if(!ud.isWebKit&&t.querySelector("select:focus"))return!0;if(!t.contains(e))return!1;for(var o=e;o&&o!==t;){if(o.classList.contains(Qp))return!0;var i=Up(o);if(r&&i.overflowY.match(/(scroll|auto)/)){var a=o.scrollHeight-o.clientHeight;if(a>0&&(o.scrollTop>0&&r<0||o.scrollTop<a&&r>0))return!0}if(n&&i.overflowX.match(/(scroll|auto)/)){var s=o.scrollWidth-o.clientWidth;if(s>0&&(o.scrollLeft>0&&n<0||o.scrollLeft<s&&n>0))return!0}o=o.parentNode}return!1}(n.target,o,i)){var a=!1;e.settings.useBothWheelAxes?e.scrollbarYActive&&!e.scrollbarXActive?(i?t.scrollTop-=i*e.settings.wheelSpeed:t.scrollTop+=o*e.settings.wheelSpeed,a=!0):e.scrollbarXActive&&!e.scrollbarYActive&&(o?t.scrollLeft+=o*e.settings.wheelSpeed:t.scrollLeft-=i*e.settings.wheelSpeed,a=!0):(t.scrollTop-=i*e.settings.wheelSpeed,t.scrollLeft+=o*e.settings.wheelSpeed),pd(e),a=a||function(n,r){var o=Math.floor(t.scrollTop),i=0===t.scrollTop,a=o+t.offsetHeight===t.scrollHeight,s=0===t.scrollLeft,l=t.scrollLeft+t.offsetWidth===t.scrollWidth;return!(Math.abs(r)>Math.abs(n)?i||a:s||l)||!e.settings.wheelPropagation}(o,i),a&&!n.ctrlKey&&(n.stopPropagation(),n.preventDefault())}}void 0!==window.onwheel?e.event.bind(t,"wheel",n):void 0!==window.onmousewheel&&e.event.bind(t,"mousewheel",n)},touch:function(e){if(ud.supportsTouch||ud.supportsIePointer){var t=e.element,n={},r=0,o={},i=null;ud.supportsTouch?(e.event.bind(t,"touchstart",c),e.event.bind(t,"touchmove",u),e.event.bind(t,"touchend",p)):ud.supportsIePointer&&(window.PointerEvent?(e.event.bind(t,"pointerdown",c),e.event.bind(t,"pointermove",u),e.event.bind(t,"pointerup",p)):window.MSPointerEvent&&(e.event.bind(t,"MSPointerDown",c),e.event.bind(t,"MSPointerMove",u),e.event.bind(t,"MSPointerUp",p)))}function a(n,r){t.scrollTop-=r,t.scrollLeft-=n,pd(e)}function s(e){return e.targetTouches?e.targetTouches[0]:e}function l(e){return!(e.pointerType&&"pen"===e.pointerType&&0===e.buttons||(!e.targetTouches||1!==e.targetTouches.length)&&(!e.pointerType||"mouse"===e.pointerType||e.pointerType===e.MSPOINTER_TYPE_MOUSE))}function c(e){if(l(e)){var t=s(e);n.pageX=t.pageX,n.pageY=t.pageY,r=(new Date).getTime(),null!==i&&clearInterval(i)}}function u(i){if(l(i)){var c=s(i),u={pageX:c.pageX,pageY:c.pageY},p=u.pageX-n.pageX,d=u.pageY-n.pageY;if(function(e,n,r){if(!t.contains(e))return!1;for(var o=e;o&&o!==t;){if(o.classList.contains(Qp))return!0;var i=Up(o);if(r&&i.overflowY.match(/(scroll|auto)/)){var a=o.scrollHeight-o.clientHeight;if(a>0&&(o.scrollTop>0&&r<0||o.scrollTop<a&&r>0))return!0}if(n&&i.overflowX.match(/(scroll|auto)/)){var s=o.scrollWidth-o.clientWidth;if(s>0&&(o.scrollLeft>0&&n<0||o.scrollLeft<s&&n>0))return!0}o=o.parentNode}return!1}(i.target,p,d))return;a(p,d),n=u;var f=(new Date).getTime(),h=f-r;h>0&&(o.x=p/h,o.y=d/h,r=f),function(n,r){var o=Math.floor(t.scrollTop),i=t.scrollLeft,a=Math.abs(n),s=Math.abs(r);if(s>a){if(r<0&&o===e.contentHeight-e.containerHeight||r>0&&0===o)return 0===window.scrollY&&r>0&&ud.isChrome}else if(a>s&&(n<0&&i===e.contentWidth-e.containerWidth||n>0&&0===i))return!0;return!0}(p,d)&&i.preventDefault()}}function p(){e.settings.swipeEasing&&(clearInterval(i),i=setInterval((function(){e.isInitialized?clearInterval(i):o.x||o.y?Math.abs(o.x)<.01&&Math.abs(o.y)<.01?clearInterval(i):e.element?(a(30*o.x,30*o.y),o.x*=.8,o.y*=.8):clearInterval(i):clearInterval(i)}),10))}}},md=function(e,t){var n=this;if(void 0===t&&(t={}),"string"==typeof e&&(e=document.querySelector(e)),!e||!e.nodeName)throw new Error("no element is specified to initialize PerfectScrollbar");for(var r in this.element=e,e.classList.add("ps"),this.settings={handlers:["click-rail","drag-thumb","keyboard","wheel","touch"],maxScrollbarLength:null,minScrollbarLength:null,scrollingThreshold:1e3,scrollXMarginOffset:0,scrollYMarginOffset:0,suppressScrollX:!1,suppressScrollY:!1,swipeEasing:!0,useBothWheelAxes:!1,wheelPropagation:!0,wheelSpeed:1},t)this.settings[r]=t[r];this.containerWidth=null,this.containerHeight=null,this.contentWidth=null,this.contentHeight=null;var o,i,a=function(){return e.classList.add(Xp)},s=function(){return e.classList.remove(Xp)};this.isRtl="rtl"===Up(e).direction,!0===this.isRtl&&e.classList.add("ps__rtl"),this.isNegativeScroll=(i=e.scrollLeft,e.scrollLeft=-1,o=e.scrollLeft<0,e.scrollLeft=i,o),this.negativeScrollAdjustment=this.isNegativeScroll?e.scrollWidth-e.clientWidth:0,this.event=new ad,this.ownerDocument=e.ownerDocument||document,this.scrollbarXRail=Bp(Gp("x")),e.appendChild(this.scrollbarXRail),this.scrollbarX=Bp(Kp("x")),this.scrollbarXRail.appendChild(this.scrollbarX),this.scrollbarX.setAttribute("tabindex",0),this.event.bind(this.scrollbarX,"focus",a),this.event.bind(this.scrollbarX,"blur",s),this.scrollbarXActive=null,this.scrollbarXWidth=null,this.scrollbarXLeft=null;var l=Up(this.scrollbarXRail);this.scrollbarXBottom=parseInt(l.bottom,10),isNaN(this.scrollbarXBottom)?(this.isScrollbarXUsingBottom=!1,this.scrollbarXTop=cd(l.top)):this.isScrollbarXUsingBottom=!0,this.railBorderXWidth=cd(l.borderLeftWidth)+cd(l.borderRightWidth),Vp(this.scrollbarXRail,{display:"block"}),this.railXMarginWidth=cd(l.marginLeft)+cd(l.marginRight),Vp(this.scrollbarXRail,{display:""}),this.railXWidth=null,this.railXRatio=null,this.scrollbarYRail=Bp(Gp("y")),e.appendChild(this.scrollbarYRail),this.scrollbarY=Bp(Kp("y")),this.scrollbarYRail.appendChild(this.scrollbarY),this.scrollbarY.setAttribute("tabindex",0),this.event.bind(this.scrollbarY,"focus",a),this.event.bind(this.scrollbarY,"blur",s),this.scrollbarYActive=null,this.scrollbarYHeight=null,this.scrollbarYTop=null;var c=Up(this.scrollbarYRail);this.scrollbarYRight=parseInt(c.right,10),isNaN(this.scrollbarYRight)?(this.isScrollbarYUsingRight=!1,this.scrollbarYLeft=cd(c.left)):this.isScrollbarYUsingRight=!0,this.scrollbarYOuterWidth=this.isRtl?function(e){var t=Up(e);return cd(t.width)+cd(t.paddingLeft)+cd(t.paddingRight)+cd(t.borderLeftWidth)+cd(t.borderRightWidth)}(this.scrollbarY):null,this.railBorderYWidth=cd(c.borderTopWidth)+cd(c.borderBottomWidth),Vp(this.scrollbarYRail,{display:"block"}),this.railYMarginHeight=cd(c.marginTop)+cd(c.marginBottom),Vp(this.scrollbarYRail,{display:""}),this.railYHeight=null,this.railYRatio=null,this.reach={x:e.scrollLeft<=0?"start":e.scrollLeft>=this.contentWidth-this.containerWidth?"end":null,y:e.scrollTop<=0?"start":e.scrollTop>=this.contentHeight-this.containerHeight?"end":null},this.isAlive=!0,this.settings.handlers.forEach((function(e){return hd[e](n)})),this.lastScrollTop=Math.floor(e.scrollTop),this.lastScrollLeft=e.scrollLeft,this.event.bind(this.element,"scroll",(function(e){return n.onScroll(e)})),pd(this)};md.prototype.update=function(){this.isAlive&&(this.negativeScrollAdjustment=this.isNegativeScroll?this.element.scrollWidth-this.element.clientWidth:0,Vp(this.scrollbarXRail,{display:"block"}),Vp(this.scrollbarYRail,{display:"block"}),this.railXMarginWidth=cd(Up(this.scrollbarXRail).marginLeft)+cd(Up(this.scrollbarXRail).marginRight),this.railYMarginHeight=cd(Up(this.scrollbarYRail).marginTop)+cd(Up(this.scrollbarYRail).marginBottom),Vp(this.scrollbarXRail,{display:"none"}),Vp(this.scrollbarYRail,{display:"none"}),pd(this),ld(this,"top",0,!1,!0),ld(this,"left",0,!1,!0),Vp(this.scrollbarXRail,{display:""}),Vp(this.scrollbarYRail,{display:""}))},md.prototype.onScroll=function(e){this.isAlive&&(pd(this),ld(this,"top",this.element.scrollTop-this.lastScrollTop),ld(this,"left",this.element.scrollLeft-this.lastScrollLeft),this.lastScrollTop=Math.floor(this.element.scrollTop),this.lastScrollLeft=this.element.scrollLeft)},md.prototype.destroy=function(){this.isAlive&&(this.event.unbindAll(),Hp(this.scrollbarX),Hp(this.scrollbarY),Hp(this.scrollbarXRail),Hp(this.scrollbarYRail),this.removePsClasses(),this.element=null,this.scrollbarX=null,this.scrollbarY=null,this.scrollbarXRail=null,this.scrollbarYRail=null,this.isAlive=!1)},md.prototype.removePsClasses=function(){this.element.className=this.element.className.split(" ").filter((function(e){return!e.match(/^ps([-_].+|)$/)})).join(" ")};var gd=md,yd=Object.defineProperty,vd=Object.getOwnPropertySymbols,bd=Object.prototype.hasOwnProperty,wd=Object.prototype.propertyIsEnumerable,xd=(e,t,n)=>t in e?yd(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;const kd=gd||t;let _d="";qr&&(_d=r(3433),_d="function"==typeof _d.toString&&_d.toString()||"",_d="[object Object]"===_d?"":_d);const Od=da`${_d}`,Sd=ga.div`
+  position: relative;
+`;class Ed extends n.Component{constructor(){super(...arguments),this.handleRef=e=>{this._container=e}}componentDidMount(){const e=this._container.parentElement&&this._container.parentElement.scrollTop||0;this.inst=new kd(this._container,this.props.options||{}),this._container.scrollTo&&this._container.scrollTo(0,e)}componentDidUpdate(){this.inst.update()}componentWillUnmount(){this.inst.destroy()}render(){const{children:e,className:t,updateFn:r}=this.props;return r&&r(this.componentDidUpdate.bind(this)),n.createElement(n.Fragment,null,_d&&n.createElement(Od,null),n.createElement(Sd,{className:`scrollbar-container ${t}`,ref:this.handleRef},e))}}function Pd(e){return n.createElement(Sa.Consumer,null,(t=>t.nativeScrollbars?n.createElement("div",{style:{overflow:"auto",overscrollBehavior:"contain",msOverflowStyle:"-ms-autohiding-scrollbar"}},e.children):n.createElement(Ed,((e,t)=>{for(var n in t||(t={}))bd.call(t,n)&&xd(e,n,t[n]);if(vd)for(var n of vd(t))wd.call(t,n)&&xd(e,n,t[n]);return e})({},e),e.children)))}const Ad=ga((({className:e,style:t})=>n.createElement("svg",{className:e,style:t,xmlns:"http://www.w3.org/2000/svg",width:"16",height:"16",viewBox:"0 0 24 24",fill:"none",stroke:"currentColor",strokeWidth:"2",strokeLinecap:"round",strokeLinejoin:"round"},n.createElement("polyline",{points:"6 9 12 15 18 9"}))))`
+  position: absolute;
+  pointer-events: none;
+  z-index: 1;
+  top: 50%;
+  -webkit-transform: translateY(-50%);
+  -ms-transform: translateY(-50%);
+  transform: translateY(-50%);
+  right: 8px;
+  margin: auto;
+  text-align: center;
+  polyline {
+    color: ${e=>"dark"===e.variant&&"white"};
+  }
+`,$d=n.memo((e=>{const{options:t,onChange:r,placeholder:o,value:i="",variant:a,className:s}=e;return n.createElement("div",{className:s},n.createElement(Ad,{variant:a}),n.createElement("select",{onChange:e=>{const{selectedIndex:n}=e.target;r(t[o?n-1:n])},value:i,className:"dropdown-select"},o&&n.createElement("option",{disabled:!0,hidden:!0,value:o},o),t.map((({idx:e,value:t,title:r},o)=>n.createElement("option",{key:e||t+o,value:t},r||t)))),n.createElement("label",null,i))})),Cd=ca($d)`
+  label {
+    box-sizing: border-box;
+    min-width: 100px;
+    outline: none;
+    display: inline-block;
+    font-family: ${e=>e.theme.typography.headings.fontFamily};
+    color: ${({theme:e})=>e.colors.text.primary};
+    vertical-align: bottom;
+    width: ${({fullWidth:e})=>e?"100%":"auto"};
+    text-transform: none;
+    padding: 0 22px 0 4px;
+
+    font-size: 0.929em;
+    line-height: 1.5em;
+    font-family: inherit;
+    text-overflow: ellipsis;
+    overflow: hidden;
+    white-space: nowrap;
+  }
+  .dropdown-select {
+    position: absolute;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    opacity: 0;
+    border: none;
+    appearance: none;
+    cursor: pointer;
+
+    color: ${({theme:e})=>e.colors.text.primary};
+    line-height: inherit;
+    font-family: inherit;
+  }
+  box-sizing: border-box;
+  min-width: 100px;
+  outline: none;
+  display: inline-block;
+  border-radius: 2px;
+  border: 1px solid rgba(38, 50, 56, 0.5);
+  vertical-align: bottom;
+  padding: 2px 0px 2px 6px;
+  position: relative;
+  width: auto;
+  background: white;
+  color: #263238;
+  font-family: ${e=>e.theme.typography.headings.fontFamily};
+  font-size: 0.929em;
+  line-height: 1.5em;
+  cursor: pointer;
+  transition: border 0.25s ease, color 0.25s ease, box-shadow 0.25s ease;
+
+  &:hover,
+  &:focus-within {
+    border: 1px solid ${e=>e.theme.colors.primary.main};
+    color: ${e=>e.theme.colors.primary.main};
+    box-shadow: 0px 0px 0px 1px ${e=>e.theme.colors.primary.main};
+  }
+`,Rd=ca(Cd)`
+  margin-left: 10px;
+  text-transform: none;
+  font-size: 0.969em;
+
+  font-size: 1em;
+  border: none;
+  padding: 0 1.2em 0 0;
+  background: transparent;
+
+  &:hover,
+  &:focus-within {
+    border: none;
+    box-shadow: none;
+    label {
+      color: ${e=>e.theme.colors.primary.main};
+      text-shadow: 0px 0px 0px ${e=>e.theme.colors.primary.main};
+    }
+  }
+`,jd=ca.span`
+  margin-left: 10px;
+  text-transform: none;
+  font-size: 0.929em;
+  color: black;
+`;var Td=Object.defineProperty,Id=Object.defineProperties,Nd=Object.getOwnPropertyDescriptors,Dd=Object.getOwnPropertySymbols,Ld=Object.prototype.hasOwnProperty,Md=Object.prototype.propertyIsEnumerable,Fd=(e,t,n)=>t in e?Td(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,zd=(e,t)=>{for(var n in t||(t={}))Ld.call(t,n)&&Fd(e,n,t[n]);if(Dd)for(var n of Dd(t))Md.call(t,n)&&Fd(e,n,t[n]);return e},Ud=(e,t)=>Id(e,Nd(t));class Vd{constructor(e,t,n){this.operations=[];const{resolved:r}=e.deref(n||{});this.initWebhooks(e,r,t)}initWebhooks(e,t,n){for(const r of Object.keys(t)){const o=t[r],i=Object.keys(o).filter(Xa);for(const t of i){const r=o[t];if(o.$ref){const r=e.deref(o||{});this.initWebhooks(e,{[t]:r},n)}if(!r)continue;const i=new _u(e,Ud(zd({},r),{httpVerb:t}),void 0,n,!1);this.operations.push(i)}}}}class Bd{constructor(e,t,n){const{resolved:r}=e.deref(n);this.id=t,this.sectionId=hs+t,this.type=r.type,this.displayName=r["x-displayName"]||t,this.description=r.description||"","apiKey"===r.type&&(this.apiKey={name:r.name,in:r.in}),"http"===r.type&&(this.http={scheme:r.scheme,bearerFormat:r.bearerFormat}),"openIdConnect"===r.type&&(this.openId={connectUrl:r.openIdConnectUrl}),"oauth2"===r.type&&r.flows&&(this.flows=r.flows)}}class qd{constructor(e){const t=e.spec.components&&e.spec.components.securitySchemes||{};this.schemes=Object.keys(t).map((n=>new Bd(e,n,t[n])))}}var Wd=Object.defineProperty,Hd=Object.getOwnPropertySymbols,Yd=Object.prototype.hasOwnProperty,Kd=Object.prototype.propertyIsEnumerable,Gd=(e,t,n)=>t in e?Wd(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,Qd=(e,t)=>{for(var n in t||(t={}))Yd.call(t,n)&&Gd(e,n,t[n]);if(Hd)for(var n of Hd(t))Kd.call(t,n)&&Gd(e,n,t[n]);return e};class Xd{constructor(e,t,n){var r,o,i;this.options=n,this.parser=new fc(e,t,n),this.info=new Il(this.parser,this.options),this.externalDocs=this.parser.spec.externalDocs,this.contentItems=df.buildStructure(this.parser,this.options),this.securitySchemes=new qd(this.parser);const a=Qd(Qd({},null==(o=null==(r=this.parser)?void 0:r.spec)?void 0:o["x-webhooks"]),null==(i=this.parser)?void 0:i.spec.webhooks);this.webhooks=new Vd(this.parser,n,a)}}var Jd=Object.defineProperty,Zd=Object.getOwnPropertyDescriptor,ef=(e,t,n,r)=>{for(var o,i=r>1?void 0:r?Zd(t,n):t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=(r?o(t,n,i):o(i))||i);return r&&i&&Jd(t,n,i),i};class tf{constructor(e,t,n){this.items=[],this.active=!1,this.expanded=!1,tn(this),this.id=t.id||e+"/"+no(t.name),this.type=e,this.name=t["x-displayName"]||t.name,this.level=t.level||1,this.sidebarLabel=this.name,this.description=t.description||"";const r=t.items;r&&r.length&&(this.description=jl.getTextBeforeHading(this.description,r[0].name)),this.parent=n,this.externalDocs=t.externalDocs,"group"===this.type&&(this.expanded=!0)}activate(){this.active=!0}expand(){this.parent&&this.parent.expand(),this.expanded=!0}collapse(){"group"!==this.type&&(this.expanded=!1)}deactivate(){this.active=!1}}ef([Ae],tf.prototype,"active",2),ef([Ae],tf.prototype,"expanded",2),ef([Pt],tf.prototype,"activate",1),ef([Pt],tf.prototype,"expand",1),ef([Pt],tf.prototype,"collapse",1),ef([Pt],tf.prototype,"deactivate",1);var nf=Object.defineProperty,rf=Object.defineProperties,of=Object.getOwnPropertyDescriptors,af=Object.getOwnPropertySymbols,sf=Object.prototype.hasOwnProperty,lf=Object.prototype.propertyIsEnumerable,cf=(e,t,n)=>t in e?nf(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,uf=(e,t)=>{for(var n in t||(t={}))sf.call(t,n)&&cf(e,n,t[n]);if(af)for(var n of af(t))lf.call(t,n)&&cf(e,n,t[n]);return e},pf=(e,t)=>rf(e,of(t));class df{static buildStructure(e,t){const n=e.spec,r=[],o=df.getTagsWithOperations(e,n);return r.push(...df.addMarkdownItems(n.info.description||"",void 0,1,t)),n["x-tagGroups"]&&n["x-tagGroups"].length>0?r.push(...df.getTagGroupsItems(e,void 0,n["x-tagGroups"],o,t)):r.push(...df.getTagsItems(e,o,void 0,void 0,t)),r}static addMarkdownItems(e,t,n,r){const o=new jl(r).extractHeadings(e||"");o.length&&t&&t.description&&(t.description=jl.getTextBeforeHading(t.description,o[0].name));const i=(e,t,n=1)=>t.map((t=>{const r=new tf("section",t,e);return r.depth=n,t.items&&(r.items=i(r,t.items,n+1)),r}));return i(t,o,n)}static getTagGroupsItems(e,t,n,r,o){const i=[];for(const a of n){const n=new tf("group",a,t);n.depth=0,n.items=df.getTagsItems(e,r,n,a,o),i.push(n)}return i}static getTagsItems(e,t,n,r,o){let i;i=void 0===r?Object.keys(t):r.tags;const a=i.map((e=>t[e]?(t[e].used=!0,t[e]):(console.warn(`Non-existing tag "${e}" is added to the group "${r.name}"`),null))),s=[];for(const t of a){if(!t)continue;const r=new tf("tag",t,n);if(r.depth=1,""!==t.name)r.items=[...df.addMarkdownItems(t.description||"",r,r.depth+1,o),...this.getOperationsItems(e,r,t,r.depth+1,o)],s.push(r);else{const n=[...df.addMarkdownItems(t.description||"",r,r.depth+1,o),...this.getOperationsItems(e,void 0,t,r.depth+1,o)];s.push(...n)}}return o.sortTagsAlphabetically&&s.sort(Cs("name")),s}static getOperationsItems(e,t,n,r,o){if(0===n.operations.length)return[];const i=[];for(const a of n.operations){const n=new _u(e,a,t,o);n.depth=r,i.push(n)}return o.sortOperationsAlphabetically&&i.sort(Cs("name")),i}static getTagsWithOperations(e,t){const n={},r=t["x-webhooks"]||t.webhooks;for(const e of t.tags||[])n[e.name]=pf(uf({},e),{operations:[]});function o(e,t,r){for(const i of Object.keys(t)){const a=t[i],s=Object.keys(a).filter(Xa);for(const t of s){const s=a[t];if(a.$ref){const{resolved:t}=e.deref(a);o(e,{[i]:t},r);continue}let l=null==s?void 0:s.tags;l&&l.length||(l=[""]);for(const e of l){let o=n[e];void 0===o&&(o={name:e,operations:[]},n[e]=o),o["x-traitTag"]||o.operations.push(pf(uf({},s),{pathName:i,pointer:Da.compile(["paths",i,t]),httpVerb:t,pathParameters:a.parameters||[],pathServers:a.servers,isWebhook:!!r}))}}}}return r&&o(e,r,!0),t.paths&&o(e,t.paths),n}}var ff=Object.defineProperty,hf=Object.getOwnPropertyDescriptor,mf=(e,t,n,r)=>{for(var o,i=r>1?void 0:r?hf(t,n):t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=(r?o(t,n,i):o(i))||i);return r&&i&&ff(t,n,i),i};const gf="data-section-id";class yf{constructor(e,t,n){this.scroll=t,this.history=n,this.activeItemIdx=-1,this.sideBarOpened=!1,this.updateOnScroll=e=>{const t=e?1:-1;let n=this.activeItemIdx;for(;(-1!==n||e)&&!(n>=this.flatItems.length-1&&e);){if(e){const e=this.getElementAtOrFirstChild(n+1);if(this.scroll.isElementBellow(e))break}else{const e=this.getElementAt(n);if(this.scroll.isElementAbove(e))break}n+=t}this.activate(this.flatItems[n],!0,!0)},this.updateOnHistory=(e=this.history.currentId)=>{if(!e)return;let t;t=this.flatItems.find((t=>t.id===e)),t?this.activateAndScroll(t,!1):(e.startsWith(hs)&&(t=this.flatItems.find((e=>hs.startsWith(e.id))),this.activateAndScroll(t,!1)),this.scroll.scrollIntoViewBySelector(`[${gf}="${oo(e)}"]`))},this.getItemById=e=>this.flatItems.find((t=>t.id===e)),tn(this),this.items=e.contentItems,this.flatItems=function(e,t){const n=[],r=e=>{for(const t of e)n.push(t),t.items&&r(t.items)};return r(e),n}(this.items||[]),this.flatItems.forEach(((e,t)=>e.absoluteIdx=t)),this.subscribe()}static updateOnHistory(e=Ns.currentId,t){e&&t.scrollIntoViewBySelector(`[${gf}="${oo(e)}"]`)}subscribe(){this._unsubscribe=this.scroll.subscribe(this.updateOnScroll),this._hashUnsubscribe=this.history.subscribe(this.updateOnHistory)}toggleSidebar(){this.sideBarOpened=!this.sideBarOpened}closeSidebar(){this.sideBarOpened=!1}getElementAt(e){const t=this.flatItems[e];return t&&Wr(`[${gf}="${oo(t.id)}"]`)||null}getElementAtOrFirstChild(e){let t=this.flatItems[e];return t&&"group"===t.type&&(t=t.items[0]),t&&Wr(`[${gf}="${oo(t.id)}"]`)||null}get activeItem(){return this.flatItems[this.activeItemIdx]||void 0}activate(e,t=!0,n=!1){if((this.activeItem&&this.activeItem.id)!==(e&&e.id)&&(!e||"group"!==e.type)){if(this.deactivate(this.activeItem),!e)return this.activeItemIdx=-1,void this.history.replace("",n);e.depth<=0||(this.activeItemIdx=e.absoluteIdx,t&&this.history.replace(encodeURI(e.id),n),e.activate(),e.expand())}}deactivate(e){if(void 0!==e)for(e.deactivate();void 0!==e;)e.collapse(),e=e.parent}activateAndScroll(e,t,n){const r=e&&this.getItemById(e.id)||e;this.activate(r,t,n),this.scrollToActive(),r&&r.items.length||this.closeSidebar()}scrollToActive(){this.scroll.scrollIntoView(this.getElementAt(this.activeItemIdx))}dispose(){this._unsubscribe(),this._hashUnsubscribe()}}mf([Ae],yf.prototype,"activeItemIdx",2),mf([Ae],yf.prototype,"sideBarOpened",2),mf([Pt],yf.prototype,"toggleSidebar",1),mf([Pt],yf.prototype,"closeSidebar",1),mf([Pt],yf.prototype,"activate",1),mf([Pt.bound],yf.prototype,"activateAndScroll",1);var vf=Object.defineProperty,bf=Object.getOwnPropertyDescriptor;const wf="scroll";class xf{constructor(e){this.options=e,this._prevOffsetY=0,this._scrollParent=qr?window:void 0,this._emiter=new ja,this.bind()}bind(){this._prevOffsetY=this.scrollY(),this._scrollParent&&this._scrollParent.addEventListener("scroll",this.handleScroll)}dispose(){this._scrollParent&&this._scrollParent.removeEventListener("scroll",this.handleScroll),this._emiter.removeAllListeners(wf)}scrollY(){return"undefined"!=typeof HTMLElement&&this._scrollParent instanceof HTMLElement?this._scrollParent.scrollTop:void 0!==this._scrollParent?this._scrollParent.pageYOffset:0}isElementBellow(e){if(null!==e)return e.getBoundingClientRect().top>this.options.scrollYOffset()}isElementAbove(e){if(null===e)return;const t=e.getBoundingClientRect().top;return(t>0?Math.floor(t):Math.ceil(t))<=this.options.scrollYOffset()}subscribe(e){const t=this._emiter.addListener(wf,e);return()=>t.removeListener(wf,e)}scrollIntoView(e){null!==e&&(e.scrollIntoView(),this._scrollParent&&this._scrollParent.scrollBy&&this._scrollParent.scrollBy(0,1-this.options.scrollYOffset()))}scrollIntoViewBySelector(e){const t=Wr(e);this.scrollIntoView(t)}handleScroll(){const e=this.scrollY()-this._prevOffsetY>0;this._prevOffsetY=this.scrollY(),this._emiter.emit(wf,e)}}((e,t,n,r)=>{for(var o,i=bf(t,n),a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(t,n,i)||i);i&&vf(t,n,i)})([Ra.bind,(100,(e,t,n)=>{n.value=function(e,t){let n,r,o,i=null,a=0;const s=()=>{a=(new Date).getTime(),i=null,o=e.apply(n,r),i||(n=r=null)};return function(){const l=(new Date).getTime(),c=t-(l-a);return n=this,r=arguments,c<=0||c>t?(i&&(clearTimeout(i),i=null),a=l,o=e.apply(n,r),i||(n=r=null)):i||(i=setTimeout(s,c)),o}}(n.value,100)})],xf.prototype,"handleScroll");class kf{constructor(){this.searchWorker=function(){let e;if(qr)try{e=r(6980)}catch(t){e=r(4798).default}else e=r(4798).default;return new e}()}indexItems(e){const t=e=>{e.forEach((e=>{"group"!==e.type&&this.add(e.name,e.description||"",e.id),t(e.items)}))};t(e),this.searchWorker.done()}add(e,t,n){this.searchWorker.add(e,t,n)}dispose(){this.searchWorker.terminate(),this.searchWorker.dispose()}search(e){return this.searchWorker.search(e)}toJS(){return e=this,null,t=function*(){return this.searchWorker.toJS()},new Promise(((n,r)=>{var o=e=>{try{a(t.next(e))}catch(e){r(e)}},i=e=>{try{a(t.throw(e))}catch(e){r(e)}},a=e=>e.done?n(e.value):Promise.resolve(e.value).then(o,i);a((t=t.apply(e,null)).next())}));var e,t}load(e){this.searchWorker.load(e)}fromExternalJS(e,t){e&&t&&this.searchWorker.fromExternalJS(e,t)}}var _f=Object.defineProperty,Of=Object.getOwnPropertySymbols,Sf=Object.prototype.hasOwnProperty,Ef=Object.prototype.propertyIsEnumerable,Pf=(e,t,n)=>t in e?_f(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;function Af(e){const{Label:t=jd,Dropdown:r=Rd}=e;return 1===e.options.length?n.createElement(t,null,e.options[0].value):n.createElement(r,((e,t)=>{for(var n in t||(t={}))Sf.call(t,n)&&Pf(e,n,t[n]);if(Of)for(var n of Of(t))Ef.call(t,n)&&Pf(e,n,t[n]);return e})({},e))}var $f=r(7856);const Cf=pa`
+  a {
+    text-decoration: ${e=>e.theme.typography.links.textDecoration};
+    color: ${e=>e.theme.typography.links.color};
+
+    &:visited {
+      color: ${e=>e.theme.typography.links.visited};
+    }
+
+    &:hover {
+      color: ${e=>e.theme.typography.links.hover};
+      text-decoration: ${e=>e.theme.typography.links.hoverTextDecoration};
+    }
+  }
+`,Rf=ga(Lp)`
+  font-family: ${e=>e.theme.typography.fontFamily};
+  font-weight: ${e=>e.theme.typography.fontWeightRegular};
+  line-height: ${e=>e.theme.typography.lineHeight};
+
+  p {
+    &:last-child {
+      margin-bottom: 0;
+    }
+  }
+
+  ${({compact:e})=>e&&"\n    p:first-child {\n      margin-top: 0;\n    }\n    p:last-child {\n      margin-bottom: 0;\n    }\n  "}
+
+  ${({inline:e})=>e&&" p {\n    display: inline-block;\n  }"}
+
+  h1 {
+    ${Cu(1)};
+    color: ${e=>e.theme.colors.primary.main};
+    margin-top: 0;
+  }
+
+  h2 {
+    ${Cu(2)};
+    color: ${e=>e.theme.colors.text.primary};
+  }
+
+  code {
+    color: ${({theme:e})=>e.typography.code.color};
+    background-color: ${({theme:e})=>e.typography.code.backgroundColor};
+
+    font-family: ${e=>e.theme.typography.code.fontFamily};
+    border-radius: 2px;
+    border: 1px solid rgba(38, 50, 56, 0.1);
+    padding: 0 ${({theme:e})=>e.spacing.unit}px;
+    font-size: ${e=>e.theme.typography.code.fontSize};
+    font-weight: ${({theme:e})=>e.typography.code.fontWeight};
+
+    word-break: break-word;
+  }
+
+  pre {
+    font-family: ${e=>e.theme.typography.code.fontFamily};
+    white-space: ${({theme:e})=>e.typography.code.wrap?"pre-wrap":"pre"};
+    background-color: ${({theme:e})=>e.codeBlock.backgroundColor};
+    color: white;
+    padding: ${e=>4*e.theme.spacing.unit}px;
+    overflow-x: auto;
+    line-height: normal;
+    border-radius: 0px;
+    border: 1px solid rgba(38, 50, 56, 0.1);
+
+    code {
+      background-color: transparent;
+      color: white;
+      padding: 0;
+
+      &:before,
+      &:after {
+        content: none;
+      }
+    }
+  }
+
+  blockquote {
+    margin: 0;
+    margin-bottom: 1em;
+    padding: 0 15px;
+    color: #777;
+    border-left: 4px solid #ddd;
+  }
+
+  img {
+    max-width: 100%;
+    box-sizing: content-box;
+  }
+
+  ul,
+  ol {
+    padding-left: 2em;
+    margin: 0;
+    margin-bottom: 1em;
+
+    ul,
+    ol {
+      margin-bottom: 0;
+      margin-top: 0;
+    }
+  }
+
+  table {
+    display: block;
+    width: 100%;
+    overflow: auto;
+    word-break: normal;
+    word-break: keep-all;
+    border-collapse: collapse;
+    border-spacing: 0;
+    margin-top: 1.5em;
+    margin-bottom: 1.5em;
+  }
+
+  table tr {
+    background-color: #fff;
+    border-top: 1px solid #ccc;
+
+    &:nth-child(2n) {
+      background-color: ${({theme:e})=>e.schema.nestedBackground};
+    }
+  }
+
+  table th,
+  table td {
+    padding: 6px 13px;
+    border: 1px solid #ddd;
+  }
+
+  table th {
+    text-align: left;
+    font-weight: bold;
+  }
+
+  ${Fu(".share-link")};
+
+  ${Cf}
+
+  ${ya("Markdown")};
+`;var jf=Object.defineProperty,Tf=Object.getOwnPropertySymbols,If=Object.prototype.hasOwnProperty,Nf=Object.prototype.propertyIsEnumerable,Df=(e,t,n)=>t in e?jf(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;const Lf=Rf.withComponent("span");function Mf(e){const t=e.inline?Lf:Rf;return n.createElement(Pa,null,(r=>{return n.createElement(t,((e,t)=>{for(var n in t||(t={}))If.call(t,n)&&Df(e,n,t[n]);if(Tf)for(var n of Tf(t))Nf.call(t,n)&&Df(e,n,t[n]);return e})({className:"redoc-markdown "+(e.className||""),dangerouslySetInnerHTML:{__html:(o=r.untrustedSpec,i=e.html,o?$f.sanitize(i):i)},"data-role":e["data-role"]},e));var o,i}))}class Ff extends n.Component{render(){const{source:e,inline:t,compact:r,className:o,"data-role":i}=this.props,a=new jl;return n.createElement(Mf,{html:a.renderMd(e),inline:t,compact:r,className:o,"data-role":i})}}const zf=ga.div`
+  position: relative;
+`,Uf=ga.div`
+  position: absolute;
+  min-width: 80px;
+  max-width: 500px;
+  background: #fff;
+  bottom: 100%;
+  left: 50%;
+  margin-bottom: 10px;
+  transform: translateX(-50%);
+
+  border-radius: 4px;
+  padding: 0.3em 0.6em;
+  text-align: center;
+  box-shadow: 0px 0px 5px 0px rgba(204, 204, 204, 1);
+`,Vf=ga.div`
+  background: #fff;
+  color: #000;
+  display: inline;
+  font-size: 0.85em;
+  white-space: nowrap;
+`,Bf=ga.div`
+  position: absolute;
+  width: 0;
+  height: 0;
+  bottom: -5px;
+  left: 50%;
+  margin-left: -5px;
+  border-left: solid transparent 5px;
+  border-right: solid transparent 5px;
+  border-top: solid #fff 5px;
+`,qf=ga.div`
+  position: absolute;
+  width: 100%;
+  height: 20px;
+  bottom: -20px;
+`;class Wf extends n.Component{render(){const{open:e,title:t,children:r}=this.props;return n.createElement(zf,null,r,e&&n.createElement(Uf,null,n.createElement(Vf,null,t),n.createElement(Bf,null),n.createElement(qf,null)))}}const Hf="undefined"!=typeof document&&document.queryCommandSupported&&document.queryCommandSupported("copy");class Yf{static isSupported(){return Hf}static selectElement(e){let t,n;document.body.createTextRange?(t=document.body.createTextRange(),t.moveToElementText(e),t.select()):document.createRange&&window.getSelection&&(n=window.getSelection(),t=document.createRange(),t.selectNodeContents(e),n.removeAllRanges(),n.addRange(t))}static deselect(){if(document.selection)document.selection.empty();else if(window.getSelection){const e=window.getSelection();e&&e.removeAllRanges()}}static copySelected(){let e;try{e=document.execCommand("copy")}catch(t){e=!1}return e}static copyElement(e){Yf.selectElement(e);const t=Yf.copySelected();return t&&Yf.deselect(),t}static copyCustom(e){const t=document.createElement("textarea");t.style.position="fixed",t.style.top="0",t.style.left="0",t.style.width="2em",t.style.height="2em",t.style.padding="0",t.style.border="none",t.style.outline="none",t.style.boxShadow="none",t.style.background="transparent",t.value=e,document.body.appendChild(t),t.select();const n=Yf.copySelected();return document.body.removeChild(t),n}}const Kf=e=>{const[t,r]=n.useState(!1),o=()=>{const t="string"==typeof e.data?e.data:JSON.stringify(e.data,null,2);Yf.copyCustom(t),i()},i=()=>{r(!0),setTimeout((()=>{r(!1)}),1500)};return e.children({renderCopyButton:()=>n.createElement("button",{onClick:o},n.createElement(Wf,{title:Yf.isSupported()?"Copied":"Not supported in your browser",open:t},"Copy"))})};let Gf=1;function Qf(e,t){Gf=1;let n="";return n+='<div class="redoc-json">',n+="<code>",n+=th(e,t),n+="</code>",n+="</div>",n}function Xf(e){return void 0!==e?e.toString().replace(/&/g,"&amp;").replace(/"/g,"&quot;").replace(/</g,"&lt;").replace(/>/g,"&gt;"):""}function Jf(e){return JSON.stringify(e).slice(1,-1)}function Zf(e,t){return'<span class="'+t+'">'+Xf(e)+"</span>"}function eh(e){return'<span class="token punctuation">'+e+"</span>"}function th(e,t){const n=typeof e;let r="";return null==e?r+=Zf("null","token keyword"):e&&e.constructor===Array?(Gf++,r+=function(e,t){const n=Gf>t?"collapsed":"";let r=`<button class="collapser" aria-label="${Gf>t+1?"expand":"collapse"}"></button>${eh("[")}<span class="ellipsis"></span><ul class="array collapsible">`,o=!1;const i=e.length;for(let a=0;a<i;a++)o=!0,r+='<li><div class="hoverable '+n+'">',r+=th(e[a],t),a<i-1&&(r+=","),r+="</div></li>";return r+=`</ul>${eh("]")}`,o||(r=eh("[ ]")),r}(e,t),Gf--):e&&e.constructor===Date?r+=Zf('"'+e.toISOString()+'"',"token string"):"object"===n?(Gf++,r+=function(e,t){const n=Gf>t?"collapsed":"",r=Object.keys(e),o=r.length;let i=`<button class="collapser" aria-label="${Gf>t+1?"expand":"collapse"}"></button>${eh("{")}<span class="ellipsis"></span><ul class="obj collapsible">`,a=!1;for(let s=0;s<o;s++){const l=r[s];a=!0,i+='<li><div class="hoverable '+n+'">',i+='<span class="property token string">"'+Xf(l)+'"</span>: ',i+=th(e[l],t),s<o-1&&(i+=eh(",")),i+="</div></li>"}return i+=`</ul>${eh("}")}`,a||(i=eh("{ }")),i}(e,t),Gf--):"number"===n?r+=Zf(e,"token number"):"string"===n?/^(http|https):\/\/[^\s]+$/.test(e)?r+=Zf('"',"token string")+'<a href="'+encodeURI(e)+'">'+Xf(Jf(e))+"</a>"+Zf('"',"token string"):r+=Zf('"'+Jf(e)+'"',"token string"):"boolean"===n&&(r+=Zf(e,"token boolean")),r}const nh=pa`
+  .redoc-json code > .collapser {
+    display: none;
+    pointer-events: none;
+  }
+
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  font-size: ${e=>e.theme.typography.code.fontSize};
+
+  white-space: ${({theme:e})=>e.typography.code.wrap?"pre-wrap":"pre"};
+  contain: content;
+  overflow-x: auto;
+
+  .callback-function {
+    color: gray;
+  }
+
+  .collapser:after {
+    content: '-';
+    cursor: pointer;
+  }
+
+  .collapsed > .collapser:after {
+    content: '+';
+    cursor: pointer;
+  }
+
+  .ellipsis:after {
+    content: ' … ';
+  }
+
+  .collapsible {
+    margin-left: 2em;
+  }
+
+  .hoverable {
+    padding-top: 1px;
+    padding-bottom: 1px;
+    padding-left: 2px;
+    padding-right: 2px;
+    border-radius: 2px;
+  }
+
+  .hovered {
+    background-color: rgba(235, 238, 249, 1);
+  }
+
+  .collapser {
+    background-color: transparent;
+    border: 0;
+    color: #fff;
+    font-family: ${e=>e.theme.typography.code.fontFamily};
+    font-size: ${e=>e.theme.typography.code.fontSize};
+    padding-right: 6px;
+    padding-left: 6px;
+    padding-top: 0;
+    padding-bottom: 0;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    width: 15px;
+    height: 15px;
+    position: absolute;
+    top: 4px;
+    left: -1.5em;
+    cursor: default;
+    user-select: none;
+    -webkit-user-select: none;
+    padding: 2px;
+    &:focus {
+      outline-color: #fff;
+      outline-style: dotted;
+      outline-width: 1px;
+    }
+  }
+
+  ul {
+    list-style-type: none;
+    padding: 0px;
+    margin: 0px 0px 0px 26px;
+  }
+
+  li {
+    position: relative;
+    display: block;
+  }
+
+  .hoverable {
+    display: inline-block;
+  }
+
+  .selected {
+    outline-style: solid;
+    outline-width: 1px;
+    outline-style: dotted;
+  }
+
+  .collapsed > .collapsible {
+    display: none;
+  }
+
+  .ellipsis {
+    display: none;
+  }
+
+  .collapsed > .ellipsis {
+    display: inherit;
+  }
+`,rh=ga.div`
+  &:hover > ${Mp} {
+    opacity: 1;
+  }
+`,oh=ga((e=>{const[t,r]=n.useState(),o=({renderCopyButton:t})=>{const o=e.data&&Object.values(e.data).some((e=>"object"==typeof e&&null!==e));return n.createElement(rh,null,n.createElement(Mp,null,t(),o&&n.createElement(n.Fragment,null,n.createElement("button",{onClick:i}," Expand all "),n.createElement("button",{onClick:a}," Collapse all "))),n.createElement(Sa.Consumer,null,(t=>n.createElement(Lp,{className:e.className,ref:e=>r(e),dangerouslySetInnerHTML:{__html:Qf(e.data,t.jsonSampleExpandLevel)}}))))},i=()=>{const e=null==t?void 0:t.getElementsByClassName("collapsible");for(const t of Array.prototype.slice.call(e)){const e=t.parentNode;e.classList.remove("collapsed"),e.querySelector(".collapser").setAttribute("aria-label","collapse")}},a=()=>{const e=null==t?void 0:t.getElementsByClassName("collapsible"),n=Array.prototype.slice.call(e,1);for(const e of n){const t=e.parentNode;t.classList.add("collapsed"),t.querySelector(".collapser").setAttribute("aria-label","expand")}},s=e=>{let t;"collapser"===e.className&&(t=e.parentElement.getElementsByClassName("collapsible")[0],t.parentElement.classList.contains("collapsed")?(t.parentElement.classList.remove("collapsed"),e.setAttribute("aria-label","collapse")):(t.parentElement.classList.add("collapsed"),e.setAttribute("aria-label","expand")))},l=n.useCallback((e=>{s(e.target)}),[]),c=n.useCallback((e=>{"Enter"===e.key&&s(e.target)}),[]);return n.useEffect((()=>(null==t||t.addEventListener("click",l),null==t||t.addEventListener("focus",c),()=>{null==t||t.removeEventListener("click",l),null==t||t.removeEventListener("focus",c)})),[l,c,t]),n.createElement(Kf,{data:e.data},o)}))`
+  ${nh};
+`,ih=e=>{const{source:t,lang:r}=e;return n.createElement(zp,{dangerouslySetInnerHTML:{__html:vs(t,r)}})},ah=e=>{const{source:t,lang:r}=e;return n.createElement(Kf,{data:t},(({renderCopyButton:e})=>n.createElement(Fp,null,n.createElement(Mp,null,e()),n.createElement(ih,{lang:r,source:t}))))};function sh({value:e,mimeType:t}){return es(t)?n.createElement(oh,{data:e}):("object"==typeof e&&(e=JSON.stringify(e,null,2)),n.createElement(ah,{lang:(r=t,-1!==r.search(/xml/i)?"xml":-1!==r.search(/csv/i)?"csv":-1!==r.search(/plain/i)?"tex":"clike"),source:e}));var r}function lh({example:e,mimeType:t}){return void 0===e.value&&e.externalValueUrl?n.createElement(ch,{example:e,mimeType:t}):n.createElement(sh,{value:e.value,mimeType:t})}function ch({example:e,mimeType:t}){const r=function(e,t){const[,r]=(0,n.useState)(!0),o=(0,n.useRef)(void 0),i=(0,n.useRef)(void 0);return i.current!==e&&(o.current=void 0),i.current=e,(0,n.useEffect)((()=>{(()=>{return n=this,i=function*(){r(!0);try{o.current=yield e.getExternalValue(t)}catch(e){o.current=e}r(!1)},new Promise(((e,t)=>{var r=e=>{try{a(i.next(e))}catch(e){t(e)}},o=e=>{try{a(i.throw(e))}catch(e){t(e)}},a=t=>t.done?e(t.value):Promise.resolve(t.value).then(r,o);a((i=i.apply(n,null)).next())}));var n,i})()}),[e,t]),o.current}(e,t);return void 0===r?n.createElement("span",null,"Loading..."):r instanceof Error?n.createElement(zp,null,"Error loading external example: ",n.createElement("br",null),n.createElement("a",{className:"token string",href:e.externalValueUrl,target:"_blank",rel:"noopener noreferrer"},e.externalValueUrl)):n.createElement(sh,{value:r,mimeType:t})}const uh=ga.div`
+  padding: 0.9em;
+  background-color: ${({theme:e})=>Ur(.6,e.rightPanel.backgroundColor)};
+  margin: 0 0 10px 0;
+  display: block;
+  font-family: ${({theme:e})=>e.typography.headings.fontFamily};
+  font-size: 0.929em;
+  line-height: 1.5em;
+`,ph=ga.span`
+  font-family: ${({theme:e})=>e.typography.headings.fontFamily};
+  font-size: 12px;
+  position: absolute;
+  z-index: 1;
+  top: -11px;
+  left: 12px;
+  font-weight: ${({theme:e})=>e.typography.fontWeightBold};
+  color: ${({theme:e})=>Ur(.3,e.rightPanel.textColor)};
+`,dh=ga.div`
+  position: relative;
+`,fh=ga(Cd)`
+  label {
+    color: ${({theme:e})=>e.rightPanel.textColor};
+    text-overflow: ellipsis;
+    white-space: nowrap;
+    overflow: hidden;
+    font-size: 1em;
+    text-transform: none;
+    border: none;
+  }
+  margin: 0 0 10px 0;
+  display: block;
+  background-color: ${({theme:e})=>Ur(.6,e.rightPanel.backgroundColor)};
+  border: none;
+  padding: 0.9em 1.6em 0.9em 0.9em;
+  box-shadow: none;
+  &:hover,
+  &:focus-within {
+    border: none;
+    box-shadow: none;
+    background-color: ${({theme:e})=>Ur(.3,e.rightPanel.backgroundColor)};
+  }
+`,hh=ga.div`
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  font-size: 12px;
+  color: #ee807f;
+`;class mh extends n.Component{constructor(){super(...arguments),this.state={activeIdx:0},this.switchMedia=({idx:e})=>{void 0!==e&&this.setState({activeIdx:e})}}render(){const{activeIdx:e}=this.state,t=this.props.mediaType.examples||{},r=this.props.mediaType.name,o=n.createElement(hh,null,"No sample"),i=Object.keys(t);if(0===i.length)return o;if(i.length>1){const o=i.map(((e,n)=>({value:t[e].summary||e,idx:n}))),a=t[i[e]],s=a.description;return n.createElement(gh,null,n.createElement(dh,null,n.createElement(ph,null,"Example"),this.props.renderDropdown({value:o[e].value,options:o,onChange:this.switchMedia,ariaLabel:"Example"})),n.createElement("div",null,s&&n.createElement(Ff,{source:s}),n.createElement(lh,{example:a,mimeType:r})))}{const e=t[i[0]];return n.createElement(gh,null,e.description&&n.createElement(Ff,{source:e.description}),n.createElement(lh,{example:e,mimeType:r}))}}}const gh=ga.div`
+  margin-top: 15px;
+`;if(!n.useState)throw new Error("mobx-react-lite requires React with Hooks support");if(!tn)throw new Error("mobx-react-lite@3 requires mobx at least version 6 to be available");function yh(e){e()}var vh=[];function bh(e){return Dt(Bn(e,t));var t}var wh="undefined"==typeof FinalizationRegistry?void 0:FinalizationRegistry;function xh(e){return{reaction:e,mounted:!1,changedBeforeMount:!1,cleanAt:Date.now()+kh}}var kh=1e4,_h=wh?function(e){var t=new Map,n=1,r=new e((function(e){var n=t.get(e);n&&(n.reaction.dispose(),t.delete(e))}));return{addReactionToTrack:function(e,o,i){var a=n++;return r.register(i,a,e),e.current=xh(o),e.current.finalizationRegistryCleanupToken=a,t.set(a,e.current),e.current},recordReactionAsCommitted:function(e){r.unregister(e),e.current&&e.current.finalizationRegistryCleanupToken&&t.delete(e.current.finalizationRegistryCleanupToken)},forceCleanupTimerToRunNowForTests:function(){},resetCleanupScheduleForTests:function(){}}}(wh):function(){var e,t=new Set;function 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o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],Pg);var Ag=Object.defineProperty,$g=Object.defineProperties,Cg=Object.getOwnPropertyDescriptors,Rg=Object.getOwnPropertySymbols,jg=Object.prototype.hasOwnProperty,Tg=Object.prototype.propertyIsEnumerable,Ig=(e,t,n)=>t in e?Ag(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;class Ng extends n.PureComponent{constructor(){super(...arguments),this.renderDropdown=e=>{return n.createElement(Af,(t=((e,t)=>{for(var n in t||(t={}))jg.call(t,n)&&Ig(e,n,t[n]);if(Rg)for(var n of Rg(t))Tg.call(t,n)&&Ig(e,n,t[n]);return e})({Label:jd,Dropdown:fh},e),$g(t,Cg({variant:"dark"}))));var t}}static getMediaType(e,t){if(!e)return{};const n={schema:{$ref:e}};return t&&(n.examples={example:{$ref:t}}),n}get mediaModel(){const{parser:e,schemaRef:t,exampleRef:n,options:r}=this.props;return this._mediaModel||(this._mediaModel=new nu(e,"json",!1,Ng.getMediaType(t,n),r)),this._mediaModel}render(){const{showReadOnly:e=!0,showWriteOnly:t=!1}=this.props;return n.createElement(Su,null,n.createElement(Au,null,n.createElement(Ou,null,n.createElement(Pg,{skipWriteOnly:!t,skipReadOnly:!e,schema:this.mediaModel.schema})),n.createElement(Pu,null,n.createElement(Dg,null,n.createElement(mh,{renderDropdown:this.renderDropdown,mediaType:this.mediaModel})))))}}const Dg=ga.div`
+  background: ${({theme:e})=>e.codeBlock.backgroundColor};
+  & > div,
+  & > pre {
+    padding: ${e=>4*e.theme.spacing.unit}px;
+    margin: 0;
+  }
+
+  & > div > pre {
+    padding: 0;
+  }
+`,Lg=(ca.div`
+  background-color: #e4e7eb;
+`,ca.ul`
+  display: inline;
+  list-style: none;
+  padding: 0;
+
+  li {
+    display: inherit;
+
+    &:after {
+      content: ',';
+    }
+    &:last-child:after {
+      content: none;
+    }
+  }
+`,ca.code`
+  font-size: ${e=>e.theme.typography.code.fontSize};
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  margin: 0 3px;
+  padding: 0.2em;
+  display: inline-block;
+  line-height: 1;
+
+  &:after {
+    content: ',';
+    font-weight: normal;
+  }
+
+  &:last-child:after {
+    content: none;
+  }
+`),Mg=ca.span`
+  &:after {
+    content: ' and ';
+    font-weight: normal;
+  }
+
+  &:last-child:after {
+    content: none;
+  }
+
+  ${Cf};
+`,Fg=ca.span`
+  ${e=>!e.expanded&&"white-space: nowrap;"}
+  &:after {
+    content: ' or ';
+    ${e=>e.expanded&&"content: ' or \\a';"}
+    white-space: pre;
+  }
+
+  &:last-child:after,
+  &:only-child:after {
+    content: none;
+  }
+
+  ${Cf};
+`,zg=ca.div`
+  flex: 1 1 auto;
+  cursor: pointer;
+`,Ug=ca.div`
+  width: ${e=>e.theme.schema.defaultDetailsWidth};
+  text-overflow: ellipsis;
+  border-radius: 4px;
+  overflow: hidden;
+  ${e=>e.expanded&&`background: ${e.theme.colors.gray[100]};\n     padding: 8px 9.6px;\n     margin: 20px 0;\n     width: 100%;\n    `};
+  ${ma("small")`
+    margin-top: 10px;
+  `}
+`,Vg=ca(Iu)`
+  display: inline-block;
+  margin: 0;
+`,Bg=ca.div`
+  width: 100%;
+  display: flex;
+  margin: 1em 0;
+  flex-direction: ${e=>e.expanded?"column":"row"};
+  ${ma("small")`
+    flex-direction: column;
+  `}
+`,qg=ca.div`
+  margin: 0.5em 0;
+`,Wg=ca.div`
+  border-bottom: 1px solid ${({theme:e})=>e.colors.border.dark};
+  margin-bottom: 1.5em;
+  padding-bottom: 0.7em;
+
+  h5 {
+    line-height: 1em;
+    margin: 0 0 0.6em;
+    font-size: ${({theme:e})=>e.typography.fontSize};
+  }
+
+  .redoc-markdown p:first-child {
+    display: inline;
+  }
+`;function Hg({children:e,height:t}){const r=n.createRef(),[o,i]=n.useState(!1),[a,s]=n.useState(!1);return n.useEffect((()=>{r.current&&r.current.clientHeight+20<r.current.scrollHeight&&s(!0)}),[r]),n.createElement(n.Fragment,null,n.createElement(Yg,{ref:r,className:o?"":"container",style:{height:o?"auto":t}},e),n.createElement(Kg,{dimmed:!o},a&&n.createElement(Gg,{onClick:()=>{i(!o)}},o?"See less":"See more")))}const Yg=ca.div`
+  overflow-y: hidden;
+`,Kg=ca.div`
+  text-align: center;
+  line-height: 1.5em;
+  ${({dimmed:e})=>e&&"background-image: linear-gradient(to bottom, transparent,rgb(255 255 255));\n     position: relative;\n     top: -0.5em;\n     padding-top: 0.5em;\n     background-position-y: -1em;\n    "}
+`,Gg=ca.a`
+  cursor: pointer;
+`,Qg=n.memo((function(e){const{type:t,flow:r,RequiredScopes:o}=e,i=Object.keys((null==r?void 0:r.scopes)||{});return n.createElement(n.Fragment,null,n.createElement(qg,null,n.createElement("b",null,"Flow type: "),n.createElement("code",null,t," ")),("implicit"===t||"authorizationCode"===t)&&n.createElement(qg,null,n.createElement("strong",null," Authorization URL: "),n.createElement("code",null,n.createElement("a",{target:"_blank",rel:"noopener noreferrer",href:r.authorizationUrl},r.authorizationUrl))),("password"===t||"clientCredentials"===t||"authorizationCode"===t)&&n.createElement(qg,null,n.createElement("b",null," Token URL: "),n.createElement("code",null,r.tokenUrl)),r.refreshUrl&&n.createElement(qg,null,n.createElement("strong",null," Refresh URL: "),r.refreshUrl),!!i.length&&n.createElement(n.Fragment,null,o||null,n.createElement(qg,null,n.createElement("b",null," Scopes: ")),n.createElement(Hg,{height:"4em"},n.createElement("ul",null,i.map((e=>n.createElement("li",{key:e},n.createElement("code",null,e)," -"," ",n.createElement(Ff,{className:"redoc-markdown",inline:!0,source:r.scopes[e]||""}))))))))}));function Xg(e){const{RequiredScopes:t,scheme:r}=e;return n.createElement(Rf,null,r.apiKey?n.createElement(n.Fragment,null,n.createElement(qg,null,n.createElement("b",null,(o=r.apiKey.in||"").charAt(0).toUpperCase()+o.slice(1)," parameter name: "),n.createElement("code",null,r.apiKey.name)),t):r.http?n.createElement(n.Fragment,null,n.createElement(qg,null,n.createElement("b",null,"HTTP Authorization Scheme: "),n.createElement("code",null,r.http.scheme)),n.createElement(qg,null,"bearer"===r.http.scheme&&r.http.bearerFormat&&n.createElement(n.Fragment,null,n.createElement("b",null,"Bearer format: "),n.createElement("code",null,r.http.bearerFormat))),t):r.openId?n.createElement(n.Fragment,null,n.createElement(qg,null,n.createElement("b",null,"Connect URL: "),n.createElement("code",null,n.createElement("a",{target:"_blank",rel:"noopener noreferrer",href:r.openId.connectUrl},r.openId.connectUrl))),t):r.flows?Object.keys(r.flows).map((e=>n.createElement(Qg,{key:e,type:e,RequiredScopes:t,flow:r.flows[e]}))):null);var o}const Jg={oauth2:"OAuth2",apiKey:"API Key",http:"HTTP",openIdConnect:"OpenID Connect"};class Zg extends n.PureComponent{render(){return this.props.securitySchemes.schemes.map((e=>n.createElement(Su,{id:e.sectionId,key:e.id},n.createElement(Au,null,n.createElement(Ou,null,n.createElement(ju,null,n.createElement(Uu,{to:e.sectionId}),e.displayName),n.createElement(Ff,{source:e.description||""}),n.createElement(Wg,null,n.createElement(qg,null,n.createElement("b",null,"Security Scheme Type: "),n.createElement("span",null,Jg[e.type]||e.type)),n.createElement(Xg,{scheme:e})))))))}}class ey{constructor(e,t,n={},r=!0){var o,i,a,s;this.marker=new Ls,this.disposer=null,this.rawOptions=n,this.options=new xo(n,ty),this.scroll=new xf(this.options),yf.updateOnHistory(Ns.currentId,this.scroll),this.spec=new Xd(e,t,this.options),this.menu=new yf(this.spec,this.scroll,Ns),this.options.disableSearch||(this.search=new kf,r&&this.search.indexItems(this.menu.items),this.disposer=(o=this.menu,i="activeItemIdx",w(a=e=>{this.updateMarkOnMenu(e.newValue)})?function(e,t,n,r){return qn(e,t).observe_(n,r)}(o,i,a,s):function(e,t,n){return qn(e).observe_(t,n)}(o,i,a)))}static fromJS(e){const t=new ey(e.spec.data,e.spec.url,e.options,!1);return t.menu.activeItemIdx=e.menu.activeItemIdx||0,t.menu.activate(t.menu.flatItems[t.menu.activeItemIdx]),t.options.disableSearch||t.search.load(e.searchIndex),t}onDidMount(){this.menu.updateOnHistory(),this.updateMarkOnMenu(this.menu.activeItemIdx)}dispose(){this.scroll.dispose(),this.menu.dispose(),this.search&&this.search.dispose(),null!=this.disposer&&this.disposer()}toJS(){return e=this,t=null,n=function*(){return{menu:{activeItemIdx:this.menu.activeItemIdx},spec:{url:this.spec.parser.specUrl,data:this.spec.parser.spec},searchIndex:this.search?yield this.search.toJS():void 0,options:this.rawOptions}},new Promise(((r,o)=>{var i=e=>{try{s(n.next(e))}catch(e){o(e)}},a=e=>{try{s(n.throw(e))}catch(e){o(e)}},s=e=>e.done?r(e.value):Promise.resolve(e.value).then(i,a);s((n=n.apply(e,t)).next())}));var e,t,n}updateMarkOnMenu(e){const t=Math.max(0,e),n=Math.min(this.menu.flatItems.length,t+5),r=[];for(let e=t;e<n;e++){const t=this.menu.getElementAt(e);t&&r.push(t)}if(-1===e&&qr){const e=document.querySelector('[data-role="redoc-description"]'),t=document.querySelector('[data-role="redoc-summary"]');e&&r.push(e),t&&r.push(t)}this.marker.addOnly(r),this.marker.mark()}}const ty={allowedMdComponents:{SecurityDefinitions:{component:Zg,propsSelector:e=>({securitySchemes:e.spec.securitySchemes})},"security-definitions":{component:Zg,propsSelector:e=>({securitySchemes:e.spec.securitySchemes})},SchemaDefinition:{component:Ng,propsSelector:e=>({parser:e.spec.parser,options:e.options})}}},ny=ga(Ru)`
+  margin-top: 0;
+  margin-bottom: 0.5em;
+
+  ${ya("ApiHeader")};
+`,ry=ga.a`
+  border: 1px solid ${e=>e.theme.colors.primary.main};
+  color: ${e=>e.theme.colors.primary.main};
+  font-weight: normal;
+  margin-left: 0.5em;
+  padding: 4px 8px 4px;
+  display: inline-block;
+  text-decoration: none;
+  cursor: pointer;
+
+  ${ya("DownloadButton")};
+`,oy=ga.span`
+  &::before {
+    content: '|';
+    display: inline-block;
+    opacity: 0.5;
+    width: ${15}px;
+    text-align: center;
+  }
+
+  &:last-child::after {
+    display: none;
+  }
+`,iy=ga.div`
+  overflow: hidden;
+`,ay=ga.div`
+  display: flex;
+  flex-wrap: wrap;
+  // hide separator on new lines: idea from https://stackoverflow.com/a/31732902/1749888
+  margin-left: -${15}px;
+`;Object.defineProperty,Object.getOwnPropertyDescriptor;let sy=class extends n.Component{constructor(){super(...arguments),this.handleDownloadClick=e=>{e.target.href||(e.target.href=this.props.store.spec.info.downloadLink)}}render(){const{store:e}=this.props,{info:t,externalDocs:r}=e.spec,o=e.options.hideDownloadButton,i=t.downloadFileName,a=t.downloadLink,s=t.license&&n.createElement(oy,null,"License:"," ",t.license.identifier?t.license.identifier:n.createElement("a",{href:t.license.url},t.license.name))||null,l=t.contact&&t.contact.url&&n.createElement(oy,null,"URL: ",n.createElement("a",{href:t.contact.url},t.contact.url))||null,c=t.contact&&t.contact.email&&n.createElement(oy,null,t.contact.name||"E-mail",":"," ",n.createElement("a",{href:"mailto:"+t.contact.email},t.contact.email))||null,u=t.termsOfService&&n.createElement(oy,null,n.createElement("a",{href:t.termsOfService},"Terms of Service"))||null,p=t.version&&n.createElement("span",null,"(",t.version,")")||null;return n.createElement(Su,null,n.createElement(Au,null,n.createElement(Ou,{className:"api-info"},n.createElement(ny,null,t.title," ",p),!o&&n.createElement("p",null,lo("downloadSpecification"),":",n.createElement(ry,{download:i||!0,target:"_blank",href:a,onClick:this.handleDownloadClick},lo("download"))),n.createElement(Rf,null,(t.license||t.contact||t.termsOfService)&&n.createElement(iy,null,n.createElement(ay,null,c," ",l," ",s," ",u))||null),n.createElement(Ff,{source:e.spec.info.summary,"data-role":"redoc-summary"}),n.createElement(Ff,{source:e.spec.info.description,"data-role":"redoc-description"}),r&&n.createElement(xm,{externalDocs:r}))))}};sy=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],sy);const ly=ga.img`
+  max-height: ${e=>e.theme.logo.maxHeight};
+  max-width: ${e=>e.theme.logo.maxWidth};
+  padding: ${e=>e.theme.logo.gutter};
+  width: 100%;
+  display: block;
+`,cy=ga.div`
+  text-align: center;
+`,uy=ga.a`
+  display: inline-block;
+`;Object.defineProperty,Object.getOwnPropertyDescriptor;let py=class extends n.Component{render(){const{info:e}=this.props,t=e["x-logo"];if(!t||!t.url)return null;const r=t.href||e.contact&&e.contact.url,o=t.altText?t.altText:"logo",i=n.createElement(ly,{src:t.url,alt:o});return n.createElement(cy,{style:{backgroundColor:t.backgroundColor}},r?(a=r,e=>n.createElement(uy,{href:a},e))(i):i);var a}};py=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],py);var dy=Object.defineProperty,fy=Object.getOwnPropertySymbols,hy=Object.prototype.hasOwnProperty,my=Object.prototype.propertyIsEnumerable,gy=(e,t,n)=>t in e?dy(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,yy=(e,t)=>{for(var n in t||(t={}))hy.call(t,n)&&gy(e,n,t[n]);if(fy)for(var n of fy(t))my.call(t,n)&&gy(e,n,t[n]);return e};class vy extends n.Component{render(){return n.createElement(Pa,null,(e=>n.createElement(Lu,null,(t=>this.renderWithOptionsAndStore(e,t)))))}renderWithOptionsAndStore(e,t){const{source:r,htmlWrap:o=(e=>e)}=this.props;if(!t)throw new Error("When using components in markdown, store prop must be provided");const i=new jl(e).renderMdWithComponents(r);return i.length?i.map(((e,r)=>{if("string"==typeof e)return n.cloneElement(o(n.createElement(Mf,{html:e,inline:!1,compact:!1})),{key:r});const i=e.component;return n.createElement(i,yy({key:r},yy(yy({},e.props),e.propsSelector(t))))})):null}}var by=r(4184),wy=r.n(by);const xy=ga.span.attrs((e=>({className:`operation-type ${e.type}`})))`
+  width: 9ex;
+  display: inline-block;
+  height: ${e=>e.theme.typography.code.fontSize};
+  line-height: ${e=>e.theme.typography.code.fontSize};
+  background-color: #333;
+  border-radius: 3px;
+  background-repeat: no-repeat;
+  background-position: 6px 4px;
+  font-size: 7px;
+  font-family: Verdana, sans-serif; // web-safe
+  color: white;
+  text-transform: uppercase;
+  text-align: center;
+  font-weight: bold;
+  vertical-align: middle;
+  margin-right: 6px;
+  margin-top: 2px;
+
+  &.get {
+    background-color: ${e=>e.theme.colors.http.get};
+  }
+
+  &.post {
+    background-color: ${e=>e.theme.colors.http.post};
+  }
+
+  &.put {
+    background-color: ${e=>e.theme.colors.http.put};
+  }
+
+  &.options {
+    background-color: ${e=>e.theme.colors.http.options};
+  }
+
+  &.patch {
+    background-color: ${e=>e.theme.colors.http.patch};
+  }
+
+  &.delete {
+    background-color: ${e=>e.theme.colors.http.delete};
+  }
+
+  &.basic {
+    background-color: ${e=>e.theme.colors.http.basic};
+  }
+
+  &.link {
+    background-color: ${e=>e.theme.colors.http.link};
+  }
+
+  &.head {
+    background-color: ${e=>e.theme.colors.http.head};
+  }
+
+  &.hook {
+    background-color: ${e=>e.theme.colors.primary.main};
+  }
+`;function ky(e,{theme:t},n){return e>1?t.sidebar.level1Items[n]:1===e?t.sidebar.groupItems[n]:""}const _y=ga.ul`
+  margin: 0;
+  padding: 0;
+
+  & & {
+    font-size: 0.929em;
+  }
+
+  ${e=>e.expanded?"":"display: none;"};
+`,Oy=ga.li`
+  list-style: none inside none;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  padding: 0;
+  ${e=>0===e.depth?"margin-top: 15px":""};
+`,Sy={0:pa`
+    opacity: 0.7;
+    text-transform: ${({theme:e})=>e.sidebar.groupItems.textTransform};
+    font-size: 0.8em;
+    padding-bottom: 0;
+    cursor: default;
+  `,1:pa`
+    font-size: 0.929em;
+    text-transform: ${({theme:e})=>e.sidebar.level1Items.textTransform};
+  `},Ey=ga.label.attrs((e=>({role:"menuitem",className:wy()("-depth"+e.depth,{active:e.active})})))`
+  cursor: pointer;
+  color: ${e=>e.active?ky(e.depth,e,"activeTextColor"):e.theme.sidebar.textColor};
+  margin: 0;
+  padding: 12.5px ${e=>4*e.theme.spacing.unit}px;
+  ${({depth:e,type:t,theme:n})=>"section"===t&&e>1&&"padding-left: "+8*n.spacing.unit+"px;"||""}
+  display: flex;
+  justify-content: space-between;
+  font-family: ${e=>e.theme.typography.headings.fontFamily};
+  ${e=>Sy[e.depth]};
+  background-color: ${e=>e.active?ky(e.depth,e,"activeBackgroundColor"):e.theme.sidebar.backgroundColor};
+
+  ${e=>e.deprecated&&Wu||""};
+
+  &:hover {
+    color: ${e=>ky(e.depth,e,"activeTextColor")};
+    background-color: ${e=>ky(e.depth,e,"activeBackgroundColor")};
+  }
+
+  ${Bu} {
+    height: ${({theme:e})=>e.sidebar.arrow.size};
+    width: ${({theme:e})=>e.sidebar.arrow.size};
+    polygon {
+      fill: ${({theme:e})=>e.sidebar.arrow.color};
+    }
+  }
+`,Py=ga.span`
+  display: inline-block;
+  vertical-align: middle;
+  width: ${e=>e.width?e.width:"auto"};
+  overflow: hidden;
+  text-overflow: ellipsis;
+`,Ay=ga.div`
+  ${({theme:e})=>`\n  font-size: 0.8em;\n  margin-top: ${2*e.spacing.unit}px;\n  text-align: center;\n  position: fixed;\n  width: ${e.sidebar.width};\n  bottom: 0px;\n  background: ${e.sidebar.backgroundColor};\n \n  a,\n  a:visited,\n  a:hover {\n    color: ${e.sidebar.textColor} !important;\n    padding: ${e.spacing.unit}px 0;\n    border-top: 1px solid ${Rr(.1,e.sidebar.backgroundColor)};\n    text-decoration: none;\n    display: flex;\n    align-items: center;\n    justify-content: center;\n  }\n`};
+  img {
+    width: 15px;
+    margin-right: 5px;
+  }
+
+  ${ma("small")`
+    width: 100%;
+  `};
+`,$y=ga.button`
+  border: 0;
+  width: 100%;
+  text-align: left;
+  & > * {
+    vertical-align: middle;
+  }
+
+  ${Bu} {
+    polygon {
+      fill: ${({theme:e})=>Rr(e.colors.tonalOffset,e.colors.gray[100])};
+    }
+  }
+`,Cy=ga.span`
+  text-decoration: ${e=>e.deprecated?"line-through":"none"};
+  margin-right: 8px;
+`,Ry=ga(xy)`
+  margin: 0 5px 0 0;
+`,jy=ga((e=>{const{name:t,opened:r,className:o,onClick:i,httpVerb:a,deprecated:s}=e;return n.createElement($y,{className:o,onClick:i||void 0},n.createElement(Ry,{type:a},ms(a)),n.createElement(Bu,{size:"1.5em",direction:r?"down":"right",float:"left"}),n.createElement(Cy,{deprecated:s},t),s?n.createElement(qu,{type:"warning"}," ",lo("deprecated")," "):null)}))`
+  padding: 10px;
+  border-radius: 2px;
+  margin-bottom: 4px;
+  line-height: 1.5em;
+  background-color: ${({theme:e})=>e.colors.gray[100]};
+  cursor: pointer;
+  outline-color: ${({theme:e})=>Rr(e.colors.tonalOffset,e.colors.gray[100])};
+`,Ty=ga.div`
+  padding: 10px 25px;
+  background-color: ${({theme:e})=>e.colors.gray[50]};
+  margin-bottom: 5px;
+  margin-top: 5px;
+`;class Iy extends n.PureComponent{constructor(){super(...arguments),this.selectElement=()=>{Yf.selectElement(this.child)}}render(){const{children:e}=this.props;return n.createElement("div",{ref:e=>this.child=e,onClick:this.selectElement,onFocus:this.selectElement,tabIndex:0,role:"button"},e)}}const Ny=ga.div`
+  cursor: pointer;
+  position: relative;
+  margin-bottom: 5px;
+`,Dy=ga.span`
+  font-family: ${e=>e.theme.typography.code.fontFamily};
+  margin-left: 10px;
+  flex: 1;
+  overflow-x: hidden;
+  text-overflow: ellipsis;
+`,Ly=ga.button`
+  outline: 0;
+  color: inherit;
+  width: 100%;
+  text-align: left;
+  cursor: pointer;
+  padding: 10px 30px 10px ${e=>e.inverted?"10px":"20px"};
+  border-radius: ${e=>e.inverted?"0":"4px 4px 0 0"};
+  background-color: ${e=>e.inverted?"transparent":e.theme.codeBlock.backgroundColor};
+  display: flex;
+  white-space: nowrap;
+  align-items: center;
+  border: ${e=>e.inverted?"0":"1px solid transparent"};
+  border-bottom: ${e=>e.inverted?"1px solid #ccc":"0"};
+  transition: border-color 0.25s ease;
+
+  ${e=>e.expanded&&!e.inverted&&`border-color: ${e.theme.colors.border.dark};`||""}
+
+  .${Dy} {
+    color: ${e=>e.inverted?e.theme.colors.text.primary:"#ffffff"};
+  }
+  &:focus {
+    box-shadow: inset 0 2px 2px rgba(0, 0, 0, 0.45), 0 2px 0 rgba(128, 128, 128, 0.25);
+  }
+`,My=ga.span.attrs((e=>({className:`http-verb ${e.type}`})))`
+  font-size: ${e=>e.compact?"0.8em":"0.929em"};
+  line-height: ${e=>e.compact?"18px":"20px"};
+  background-color: ${e=>e.theme.colors.http[e.type]||"#999999"};
+  color: #ffffff;
+  padding: ${e=>e.compact?"2px 8px":"3px 10px"};
+  text-transform: uppercase;
+  font-family: ${e=>e.theme.typography.headings.fontFamily};
+  margin: 0;
+`,Fy=ga.div`
+  position: absolute;
+  width: 100%;
+  z-index: 100;
+  background: ${e=>e.theme.rightPanel.servers.overlay.backgroundColor};
+  color: ${e=>e.theme.rightPanel.servers.overlay.textColor};
+  box-sizing: border-box;
+  box-shadow: 0px 0px 6px rgba(0, 0, 0, 0.33);
+  overflow: hidden;
+  border-bottom-left-radius: 4px;
+  border-bottom-right-radius: 4px;
+  transition: all 0.25s ease;
+  visibility: hidden;
+  ${e=>e.expanded?"visibility: visible;":"transform: translateY(-50%) scaleY(0);"}
+`,zy=ga.div`
+  padding: 10px;
+`,Uy=ga.div`
+  padding: 5px;
+  border: 1px solid #ccc;
+  background: ${e=>e.theme.rightPanel.servers.url.backgroundColor};
+  word-break: break-all;
+  color: ${e=>e.theme.colors.primary.main};
+  > span {
+    color: ${e=>e.theme.colors.text.primary};
+  }
+`;class Vy extends n.Component{constructor(e){super(e),this.toggle=()=>{this.setState({expanded:!this.state.expanded})},this.state={expanded:!1}}render(){const{operation:e,inverted:t,hideHostname:r}=this.props,{expanded:o}=this.state;return n.createElement(Sa.Consumer,null,(i=>n.createElement(Ny,null,n.createElement(Ly,{onClick:this.toggle,expanded:o,inverted:t},n.createElement(My,{type:e.httpVerb,compact:this.props.compact},e.httpVerb),n.createElement(Dy,null,e.path),n.createElement(Bu,{float:"right",color:t?"black":"white",size:"20px",direction:o?"up":"down",style:{marginRight:"-25px"}})),n.createElement(Fy,{expanded:o,"aria-hidden":!o},e.servers.map((t=>{const o=i.expandDefaultServerVariables?function(e,t={}){return e.replace(/(?:{)([\w-.]+)(?:})/g,((e,n)=>t[n]&&t[n].default||e))}(t.url,t.variables):t.url,a=function(e){try{return ro(e).pathname}catch(t){return e}}(o);return n.createElement(zy,{key:o},n.createElement(Ff,{source:t.description||"",compact:!0}),n.createElement(Iy,null,n.createElement(Uy,null,n.createElement("span",null,r||i.hideHostname?"/"===a?"":a:o),e.path)))}))))))}}class By extends n.PureComponent{render(){const{place:e,parameters:t}=this.props;return t&&t.length?n.createElement("div",{key:e},n.createElement(Iu,null,e," Parameters"),n.createElement(Zu,null,n.createElement("tbody",null,Gr(t,((e,t)=>n.createElement(Hm,{key:e.name,isLast:t,field:e,showExamples:!0})))))):null}}Object.defineProperty,Object.getOwnPropertyDescriptor;let qy=class extends n.Component{constructor(){super(...arguments),this.switchMedia=({idx:e})=>{this.props.content&&void 0!==e&&this.props.content.activate(e)}}render(){const{content:e}=this.props;if(!e||!e.mediaTypes||!e.mediaTypes.length)return null;const t=e.activeMimeIdx,r=e.mediaTypes.map(((e,t)=>({value:e.name,idx:t}))),o=({children:e})=>this.props.withLabel?n.createElement(dh,null,n.createElement(ph,null,"Content type"),e):e;return n.createElement(n.Fragment,null,n.createElement(o,null,this.props.renderDropdown({value:r[t].value,options:r,onChange:this.switchMedia,ariaLabel:"Content type"})),this.props.children(e.active))}};qy=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],qy);var Wy=Object.defineProperty,Hy=Object.getOwnPropertySymbols,Yy=Object.prototype.hasOwnProperty,Ky=Object.prototype.propertyIsEnumerable,Gy=(e,t,n)=>t in e?Wy(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;const Qy=["path","query","cookie","header"];class Xy extends n.PureComponent{orderParams(e){const t={};return e.forEach((e=>{var n,r,o;o=e,(n=t)[r=e.in]||(n[r]=[]),n[r].push(o)})),t}render(){const{body:e,parameters:t=[]}=this.props;if(void 0===e&&void 0===t)return null;const r=this.orderParams(t),o=t.length>0?Qy:[],i=e&&e.content,a=e&&e.description;return n.createElement(n.Fragment,null,o.map((e=>n.createElement(By,{key:e,place:e,parameters:r[e]}))),i&&n.createElement(Zy,{content:i,description:a}))}}function Jy(e){return n.createElement(Iu,{key:"header"},"Request Body schema: ",n.createElement(Af,((e,t)=>{for(var n in t||(t={}))Yy.call(t,n)&&Gy(e,n,t[n]);if(Hy)for(var n of Hy(t))Ky.call(t,n)&&Gy(e,n,t[n]);return e})({},e)))}function Zy(e){const{content:t,description:r}=e,{isRequestType:o}=t;return n.createElement(qy,{content:t,renderDropdown:Jy},(({schema:e})=>n.createElement(n.Fragment,null,void 0!==r&&n.createElement(Ff,{source:r}),"object"===(null==e?void 0:e.type)&&n.createElement(Am,{constraints:(null==e?void 0:e.constraints)||[]}),n.createElement(Pg,{skipReadOnly:o,skipWriteOnly:!o,key:"schema",schema:e}))))}const ev=ga(n.memo((function({title:e,type:t,empty:r,code:o,opened:i,className:a,onClick:s}){return n.createElement("button",{className:a,onClick:!r&&s||void 0,"aria-expanded":i,disabled:r},!r&&n.createElement(Bu,{size:"1.5em",color:t,direction:i?"down":"right",float:"left"}),n.createElement(rv,null,o," "),n.createElement(Ff,{compact:!0,inline:!0,source:e}))})))`
+  display: block;
+  border: 0;
+  width: 100%;
+  text-align: left;
+  padding: 10px;
+  border-radius: 2px;
+  margin-bottom: 4px;
+  line-height: 1.5em;
+  cursor: pointer;
+
+  color: ${e=>e.theme.colors.responses[e.type].color};
+  background-color: ${e=>e.theme.colors.responses[e.type].backgroundColor};
+  &:focus {
+    outline: auto ${e=>e.theme.colors.responses[e.type].color};
+  }
+  ${e=>e.empty?'\ncursor: default;\n&::before {\n  content: "—";\n  font-weight: bold;\n  width: 1.5em;\n  text-align: center;\n  display: inline-block;\n  vertical-align: top;\n}\n&:focus {\n  outline: 0;\n}\n':""};
+`,tv=ga.div`
+  padding: 10px;
+`,nv=ga(Iu.withComponent("caption"))`
+  text-align: left;
+  margin-top: 1em;
+  caption-side: top;
+`,rv=ga.strong`
+  vertical-align: top;
+`;class ov extends n.PureComponent{render(){const{headers:e}=this.props;return void 0===e||0===e.length?null:n.createElement(Zu,null,n.createElement(nv,null," Response Headers "),n.createElement("tbody",null,Gr(e,((e,t)=>n.createElement(Hm,{isLast:t,key:e.name,field:e,showExamples:!0})))))}}var iv=Object.defineProperty,av=Object.getOwnPropertySymbols,sv=Object.prototype.hasOwnProperty,lv=Object.prototype.propertyIsEnumerable,cv=(e,t,n)=>t in e?iv(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;class uv extends n.PureComponent{constructor(){super(...arguments),this.renderDropdown=e=>n.createElement(Iu,{key:"header"},"Response Schema: ",n.createElement(Af,((e,t)=>{for(var n in t||(t={}))sv.call(t,n)&&cv(e,n,t[n]);if(av)for(var n of av(t))lv.call(t,n)&&cv(e,n,t[n]);return e})({},e)))}render(){const{description:e,extensions:t,headers:r,content:o}=this.props.response;return n.createElement(n.Fragment,null,e&&n.createElement(Ff,{source:e}),n.createElement(Sm,{extensions:t}),n.createElement(ov,{headers:r}),n.createElement(qy,{content:o,renderDropdown:this.renderDropdown},(({schema:e})=>n.createElement(n.Fragment,null,"object"===(null==e?void 0:e.type)&&n.createElement(Am,{constraints:(null==e?void 0:e.constraints)||[]}),n.createElement(Pg,{skipWriteOnly:!0,key:"schema",schema:e})))))}}const pv=im((({response:e})=>{const{extensions:t,headers:r,type:o,summary:i,description:a,code:s,expanded:l,content:c}=e,u=n.useMemo((()=>void 0===c?[]:c.mediaTypes.filter((e=>void 0!==e.schema))),[c]),p=n.useMemo((()=>!(t&&0!==Object.keys(t).length||0!==r.length||0!==u.length||a)),[t,r,u,a]);return n.createElement("div",null,n.createElement(ev,{onClick:()=>e.toggle(),type:o,empty:p,title:i||"",code:s,opened:l}),l&&!p&&n.createElement(tv,null,n.createElement(uv,{response:e})))})),dv=ga.h3`
+  font-size: 1.3em;
+  padding: 0.2em 0;
+  margin: 3em 0 1.1em;
+  color: ${({theme:e})=>e.colors.text.primary};
+  font-weight: normal;
+`;class fv extends n.PureComponent{render(){const{responses:e,isCallback:t}=this.props;return e&&0!==e.length?n.createElement("div",null,n.createElement(dv,null,lo(t?"callbackResponses":"responses")),e.map((e=>n.createElement(pv,{key:e.code,response:e})))):null}}function hv(e){const{security:t,showSecuritySchemeType:r,expanded:o}=e,i=t.schemes.length>1;return n.createElement(Fg,{expanded:o},i&&"(",t.schemes.map((e=>n.createElement(Mg,{key:e.id},r&&`${Jg[e.type]||e.type}: `,n.createElement("i",null,e.displayName),o&&e.scopes.length?[" (",e.scopes.map((e=>n.createElement(Lg,{key:e},e))),") "]:null))),i&&") ")}const mv=({scopes:e})=>e.length?n.createElement("div",null,n.createElement("b",null,"Required scopes: "),e.map(((e,t)=>n.createElement(n.Fragment,{key:t},n.createElement("code",null,e)," ")))):null;function gv(e){const t=(0,n.useContext)(Nu),r=null==t?void 0:t.options.showSecuritySchemeType,[o,i]=(0,n.useState)(!1),{securities:a}=e;if(!(null==a?void 0:a.length)||(null==t?void 0:t.options.hideSecuritySection))return null;const s=null==t?void 0:t.spec.securitySchemes.schemes.filter((({id:e})=>a.find((t=>t.schemes.find((t=>t.id===e))))));return n.createElement(n.Fragment,null,n.createElement(Bg,{expanded:o},n.createElement(zg,{onClick:()=>i(!o)},n.createElement(Vg,null,"Authorizations:"),n.createElement(Bu,{size:"1.3em",direction:o?"down":"right"})),n.createElement(Ug,{expanded:o},a.map(((e,t)=>n.createElement(hv,{key:t,expanded:o,showSecuritySchemeType:r,security:e}))))),o&&(null==s?void 0:s.length)&&s.map(((e,t)=>n.createElement(Wg,{key:t},n.createElement("h5",null,n.createElement(yv,null)," ",Jg[e.type]||e.type,": ",e.id),n.createElement(Ff,{source:e.description||""}),n.createElement(Xg,{key:e.id,scheme:e,RequiredScopes:n.createElement(mv,{scopes:vv(e.id,a)})})))))}const yv=()=>n.createElement("svg",{xmlns:"http://www.w3.org/2000/svg",viewBox:"0 0 24 24",width:"11",height:"11"},n.createElement("path",{fill:"currentColor",d:"M18 10V6A6 6 0 0 0 6 6v4H3v14h18V10h-3zM8 6c0-2.206 1.794-4 4-4s4 1.794 4 4v4H8V6zm11 16H5V12h14v10z"}));function vv(e,t){const n=[];let r=t.length;for(;r--;){const o=t[r];let i=o.schemes.length;for(;i--;){const t=o.schemes[i];t.id===e&&Array.isArray(t.scopes)&&n.push(...t.scopes)}}return Array.from(new Set(n))}Object.defineProperty,Object.getOwnPropertyDescriptor;let bv=class extends n.Component{render(){const{operation:e}=this.props,{description:t,externalDocs:r}=e,o=!(!t&&!r);return n.createElement(Ty,null,o&&n.createElement(wv,null,void 0!==t&&n.createElement(Ff,{source:t}),r&&n.createElement(xm,{externalDocs:r})),n.createElement(Vy,{operation:this.props.operation,inverted:!0,compact:!0}),n.createElement(Sm,{extensions:e.extensions}),n.createElement(gv,{securities:e.security}),n.createElement(Xy,{parameters:e.parameters,body:e.requestBody}),n.createElement(fv,{responses:e.responses,isCallback:e.isCallback}))}};bv=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],bv);const wv=ga.div`
+  margin-bottom: ${({theme:e})=>3*e.spacing.unit}px;
+`;Object.defineProperty,Object.getOwnPropertyDescriptor;let xv=class extends n.Component{constructor(){super(...arguments),this.toggle=()=>{this.props.callbackOperation.toggle()}}render(){const{name:e,expanded:t,httpVerb:r,deprecated:o}=this.props.callbackOperation;return n.createElement(n.Fragment,null,n.createElement(jy,{onClick:this.toggle,name:e,opened:t,httpVerb:r,deprecated:o}),t&&n.createElement(bv,{operation:this.props.callbackOperation}))}};xv=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],xv);class kv extends n.PureComponent{render(){const{callbacks:e}=this.props;return e&&0!==e.length?n.createElement("div",null,n.createElement(_v,null," Callbacks "),e.map((e=>e.operations.map(((t,r)=>n.createElement(xv,{key:`${e.name}_${r}`,callbackOperation:t})))))):null}}const _v=ga.h3`
+  font-size: 1.3em;
+  padding: 0.2em 0;
+  margin: 3em 0 1.1em;
+  color: ${({theme:e})=>e.colors.text.primary};
+  font-weight: normal;
+`;Object.defineProperty,Object.getOwnPropertyDescriptor;let Ov=class extends n.Component{constructor(e){super(e),this.switchItem=({idx:e})=>{this.props.items&&void 0!==e&&this.setState({activeItemIdx:e})},this.state={activeItemIdx:0}}render(){const{items:e}=this.props;if(!e||!e.length)return null;const t=({children:e})=>this.props.label?n.createElement(dh,null,n.createElement(ph,null,this.props.label),e):e;return n.createElement(n.Fragment,null,n.createElement(t,null,this.props.renderDropdown({value:this.props.options[this.state.activeItemIdx].value,options:this.props.options,onChange:this.switchItem,ariaLabel:this.props.label||"Callback"})),this.props.children(e[this.state.activeItemIdx]))}};Ov=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],Ov);var Sv=Object.defineProperty,Ev=Object.defineProperties,Pv=(Object.getOwnPropertyDescriptor,Object.getOwnPropertyDescriptors),Av=Object.getOwnPropertySymbols,$v=Object.prototype.hasOwnProperty,Cv=Object.prototype.propertyIsEnumerable,Rv=(e,t,n)=>t in e?Sv(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;let jv=class extends n.Component{constructor(){super(...arguments),this.renderDropdown=e=>{return n.createElement(Af,(t=((e,t)=>{for(var n in t||(t={}))$v.call(t,n)&&Rv(e,n,t[n]);if(Av)for(var n of Av(t))Cv.call(t,n)&&Rv(e,n,t[n]);return e})({Label:uh,Dropdown:fh},e),Ev(t,Pv({variant:"dark"}))));var t}}render(){const e=this.props.content;return void 0===e?null:n.createElement(qy,{content:e,renderDropdown:this.renderDropdown,withLabel:!0},(e=>n.createElement(mh,{key:"samples",mediaType:e,renderDropdown:this.renderDropdown})))}};jv=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],jv);class Tv extends n.Component{render(){const e=this.props.callback.codeSamples.find((e=>xu(e)));return e?n.createElement(Iv,null,n.createElement(jv,{content:e.requestBodyContent})):null}}const Iv=ga.div`
+  margin-top: 15px;
+`;var Nv=Object.defineProperty,Dv=Object.defineProperties,Lv=(Object.getOwnPropertyDescriptor,Object.getOwnPropertyDescriptors),Mv=Object.getOwnPropertySymbols,Fv=Object.prototype.hasOwnProperty,zv=Object.prototype.propertyIsEnumerable,Uv=(e,t,n)=>t in e?Nv(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;let Vv=class extends n.Component{constructor(){super(...arguments),this.renderDropdown=e=>{return n.createElement(Af,(t=((e,t)=>{for(var n in t||(t={}))Fv.call(t,n)&&Uv(e,n,t[n]);if(Mv)for(var n of Mv(t))zv.call(t,n)&&Uv(e,n,t[n]);return e})({Label:uh,Dropdown:fh},e),Dv(t,Lv({variant:"dark"}))));var t}}render(){const{callbacks:e}=this.props;if(!e||0===e.length)return null;const t=e.map((e=>e.operations.map((e=>e)))).reduce(((e,t)=>e.concat(t)),[]);if(!t.some((e=>e.codeSamples.length>0)))return null;const r=t.map(((e,t)=>({value:`${e.httpVerb.toUpperCase()}: ${e.name}`,idx:t})));return n.createElement("div",null,n.createElement(Tu,null," Callback payload samples "),n.createElement(Bv,null,n.createElement(Ov,{items:t,renderDropdown:this.renderDropdown,label:"Callback",options:r},(e=>n.createElement(Tv,{key:"callbackPayloadSample",callback:e,renderDropdown:this.renderDropdown})))))}};Vv.contextType=Sa,Vv=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],Vv);const Bv=ga.div`
+  background: ${({theme:e})=>e.codeBlock.backgroundColor};
+  padding: ${e=>4*e.theme.spacing.unit}px;
+`;Object.defineProperty,Object.getOwnPropertyDescriptor;let qv=class extends n.Component{render(){const{operation:e}=this.props,t=e.codeSamples,r=t.length>0,o=1===t.length&&this.context.hideSingleRequestSampleTab;return r&&n.createElement("div",null,n.createElement(Tu,null," ",lo("requestSamples")," "),n.createElement(Dp,{defaultIndex:0},n.createElement(Ap,{hidden:o},t.map((e=>n.createElement(jp,{key:e.lang+"_"+(e.label||"")},void 0!==e.label?e.label:e.lang)))),t.map((e=>n.createElement(Np,{key:e.lang+"_"+(e.label||"")},xu(e)?n.createElement("div",null,n.createElement(jv,{content:e.requestBodyContent})):n.createElement(ah,{lang:e.lang,source:e.source}))))))||null}};qv.contextType=Sa,qv=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],qv);Object.defineProperty,Object.getOwnPropertyDescriptor;let Wv=class extends n.Component{render(){const{operation:e}=this.props,t=e.responses.filter((e=>e.content&&e.content.hasSample));return t.length>0&&n.createElement("div",null,n.createElement(Tu,null," ",lo("responseSamples")," "),n.createElement(Dp,{defaultIndex:0},n.createElement(Ap,null,t.map((e=>n.createElement(jp,{className:"tab-"+e.type,key:e.code},e.code)))),t.map((e=>n.createElement(Np,{key:e.code},n.createElement("div",null,n.createElement(jv,{content:e.content})))))))||null}};Wv=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],Wv);var Hv=Object.defineProperty,Yv=Object.defineProperties,Kv=Object.getOwnPropertyDescriptors,Gv=Object.getOwnPropertySymbols,Qv=Object.prototype.hasOwnProperty,Xv=Object.prototype.propertyIsEnumerable,Jv=(e,t,n)=>t in e?Hv(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;const Zv=ga.div`
+  margin-bottom: ${({theme:e})=>6*e.spacing.unit}px;
+`,eb=im((({operation:e})=>{const{name:t,description:r,deprecated:o,externalDocs:i,isWebhook:a,httpVerb:s}=e,l=!(!r&&!i),{showWebhookVerb:c}=n.useContext(Sa);return n.createElement(Sa.Consumer,null,(u=>n.createElement(Au,((e,t)=>Yv(e,Kv(t)))(((e,t)=>{for(var n in t||(t={}))Qv.call(t,n)&&Jv(e,n,t[n]);if(Gv)for(var n of Gv(t))Xv.call(t,n)&&Jv(e,n,t[n]);return e})({},{[gf]:e.operationHash}),{id:e.operationHash}),n.createElement(Ou,null,n.createElement(ju,null,n.createElement(Uu,{to:e.id}),t," ",o&&n.createElement(qu,{type:"warning"}," Deprecated "),a&&n.createElement(qu,{type:"primary"}," ","Webhook ",c&&s&&"| "+s.toUpperCase())),u.pathInMiddlePanel&&!a&&n.createElement(Vy,{operation:e,inverted:!0}),l&&n.createElement(Zv,null,void 0!==r&&n.createElement(Ff,{source:r}),i&&n.createElement(xm,{externalDocs:i})),n.createElement(Sm,{extensions:e.extensions}),n.createElement(gv,{securities:e.security}),n.createElement(Xy,{parameters:e.parameters,body:e.requestBody}),n.createElement(fv,{responses:e.responses}),n.createElement(kv,{callbacks:e.callbacks})),n.createElement(Pu,null,!u.pathInMiddlePanel&&!a&&n.createElement(Vy,{operation:e}),n.createElement(qv,{operation:e}),n.createElement(Wv,{operation:e}),n.createElement(Vv,{callbacks:e.callbacks})))))}));var tb=Object.defineProperty,nb=Object.getOwnPropertyDescriptor,rb=Object.getOwnPropertySymbols,ob=Object.prototype.hasOwnProperty,ib=Object.prototype.propertyIsEnumerable,ab=(e,t,n)=>t in e?tb(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n,sb=(e,t,n,r)=>{for(var o,i=r>1?void 0:r?nb(t,n):t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=(r?o(t,n,i):o(i))||i);return r&&i&&tb(t,n,i),i};let lb=class extends n.Component{render(){const e=this.props.items;return 0===e.length?null:e.map((e=>n.createElement(cb,{key:e.id,item:e})))}};lb=sb([im],lb);let cb=class extends n.Component{render(){const e=this.props.item;let t;const{type:r}=e;switch(r){case"group":t=null;break;case"tag":case"section":default:t=n.createElement(pb,((e,t)=>{for(var n in t||(t={}))ob.call(t,n)&&ab(e,n,t[n]);if(rb)for(var n of rb(t))ib.call(t,n)&&ab(e,n,t[n]);return e})({},this.props));break;case"operation":t=n.createElement(db,{item:e})}return n.createElement(n.Fragment,null,t&&n.createElement(Su,{id:e.id,underlined:"operation"===e.type},t),e.items&&n.createElement(lb,{items:e.items}))}};cb=sb([im],cb);const ub=e=>n.createElement(Ou,{compact:!0},e);let pb=class extends n.Component{render(){const{name:e,description:t,externalDocs:r,level:o}=this.props.item,i=2===o?ju:Ru;return n.createElement(n.Fragment,null,n.createElement(Au,null,n.createElement(Ou,{compact:!1},n.createElement(i,null,n.createElement(Uu,{to:this.props.item.id}),e))),n.createElement(vy,{source:t||"",htmlWrap:ub}),r&&n.createElement(Au,null,n.createElement(Ou,null,n.createElement(xm,{externalDocs:r}))))}};pb=sb([im],pb);let db=class extends n.Component{render(){return n.createElement(eb,{operation:this.props.item})}};db=sb([im],db);var fb=Object.defineProperty,hb=Object.defineProperties,mb=(Object.getOwnPropertyDescriptor,Object.getOwnPropertyDescriptors),gb=Object.getOwnPropertySymbols,yb=Object.prototype.hasOwnProperty,vb=Object.prototype.propertyIsEnumerable,bb=(e,t,n)=>t in e?fb(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;let wb=class extends n.Component{constructor(){super(...arguments),this.ref=n.createRef(),this.activate=e=>{this.props.onActivate(this.props.item),e.stopPropagation()}}componentDidMount(){this.scrollIntoViewIfActive()}componentDidUpdate(){this.scrollIntoViewIfActive()}scrollIntoViewIfActive(){this.props.item.active&&this.ref.current&&Hr(this.ref.current)}render(){const{item:e,withoutChildren:t}=this.props;return n.createElement(Oy,{onClick:this.activate,depth:e.depth,"data-item-id":e.id},"operation"===e.type?n.createElement(xb,((e,t)=>hb(e,mb(t)))(((e,t)=>{for(var n in t||(t={}))yb.call(t,n)&&bb(e,n,t[n]);if(gb)for(var n of gb(t))vb.call(t,n)&&bb(e,n,t[n]);return e})({},this.props),{item:e})):n.createElement(Ey,{depth:e.depth,active:e.active,type:e.type,ref:this.ref},n.createElement(Py,{title:e.sidebarLabel},e.sidebarLabel,this.props.children),e.depth>0&&e.items.length>0&&n.createElement(Bu,{float:"right",direction:e.expanded?"down":"right"})||null),!t&&e.items&&e.items.length>0&&n.createElement(Pb,{expanded:e.expanded,items:e.items,onActivate:this.props.onActivate}))}};wb=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],wb);const xb=im((e=>{const{item:t}=e,r=n.createRef(),{showWebhookVerb:o}=n.useContext(Sa);return n.useEffect((()=>{e.item.active&&r.current&&Hr(r.current)}),[e.item.active,r]),n.createElement(Ey,{depth:t.depth,active:t.active,deprecated:t.deprecated,ref:r},t.isWebhook?n.createElement(xy,{type:"hook"},o?t.httpVerb:lo("webhook")):n.createElement(xy,{type:t.httpVerb},ms(t.httpVerb)),n.createElement(Py,{width:"calc(100% - 38px)"},t.sidebarLabel,e.children))}));var kb=Object.defineProperty,_b=(Object.getOwnPropertyDescriptor,Object.getOwnPropertySymbols),Ob=Object.prototype.hasOwnProperty,Sb=Object.prototype.propertyIsEnumerable,Eb=(e,t,n)=>t in e?kb(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;let Pb=class extends n.Component{render(){const{items:e,root:t,className:r}=this.props,o=null==this.props.expanded||this.props.expanded;return n.createElement(_y,((e,t)=>{for(var n in t||(t={}))Ob.call(t,n)&&Eb(e,n,t[n]);if(_b)for(var n of _b(t))Sb.call(t,n)&&Eb(e,n,t[n]);return e})({className:r,style:this.props.style,expanded:o},t?{role:"menu"}:{}),e.map(((e,t)=>n.createElement(wb,{key:t,item:e,onActivate:this.props.onActivate}))))}};function Ab(){const[e,t]=(0,n.useState)(!1);return(0,n.useEffect)((()=>{t(!0)}),[]),e?n.createElement("img",{alt:"redocly logo",onError:()=>t(!1),src:"https://cdn.redoc.ly/redoc/logo-mini.svg"}):null}Pb=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],Pb);Object.defineProperty,Object.getOwnPropertyDescriptor;let $b=class extends n.Component{constructor(){super(...arguments),this.activate=e=>{if(e&&e.active&&this.context.menuToggle)return e.expanded?e.collapse():e.expand();this.props.menu.activateAndScroll(e,!0),setTimeout((()=>{this._updateScroll&&this._updateScroll()}))},this.saveScrollUpdate=e=>{this._updateScroll=e}}render(){const e=this.props.menu;return n.createElement(Pd,{updateFn:this.saveScrollUpdate,className:this.props.className,options:{wheelPropagation:!1}},n.createElement(Pb,{items:e.items,onActivate:this.activate,root:!0}),n.createElement(Ay,null,n.createElement("a",{target:"_blank",rel:"noopener noreferrer",href:"https://redocly.com/redoc/"},n.createElement(Ab,null),"API docs by Redocly")))}};$b.contextType=Sa,$b=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],$b);const Cb=({open:e})=>{const t=e?8:-4;return n.createElement(jb,null,n.createElement(Rb,{size:15,style:{transform:`translate(2px, ${t}px) rotate(180deg)`,transition:"transform 0.2s ease"}}),n.createElement(Rb,{size:15,style:{transform:`translate(2px, ${0-t}px)`,transition:"transform 0.2s ease"}}))},Rb=({size:e=10,className:t="",style:r})=>n.createElement("svg",{className:t,style:r||{},viewBox:"0 0 926.23699 573.74994",version:"1.1",x:"0px",y:"0px",width:e,height:e},n.createElement("g",{transform:"translate(904.92214,-879.1482)"},n.createElement("path",{d:"\n          m -673.67664,1221.6502 -231.2455,-231.24803 55.6165,\n          -55.627 c 30.5891,-30.59485 56.1806,-55.627 56.8701,-55.627 0.6894,\n          0 79.8637,78.60862 175.9427,174.68583 l 174.6892,174.6858 174.6892,\n          -174.6858 c 96.079,-96.07721 175.253196,-174.68583 175.942696,\n          -174.68583 0.6895,0 26.281,25.03215 56.8701,\n          55.627 l 55.6165,55.627 -231.245496,231.24803 c -127.185,127.1864\n          -231.5279,231.248 -231.873,231.248 -0.3451,0 -104.688,\n          -104.0616 -231.873,-231.248 z\n        ",fill:"currentColor"}))),jb=ga.div`
+  user-select: none;
+  width: 20px;
+  height: 20px;
+  align-self: center;
+  display: flex;
+  flex-direction: column;
+  color: ${e=>e.theme.colors.primary.main};
+`;Object.defineProperty,Object.getOwnPropertyDescriptor;let Tb;qr&&(Tb=r(5114));const Ib=Tb&&Tb(),Nb=ga.div`
+  width: ${e=>e.theme.sidebar.width};
+  background-color: ${e=>e.theme.sidebar.backgroundColor};
+  overflow: hidden;
+  display: flex;
+  flex-direction: column;
+
+  backface-visibility: hidden;
+  /* contain: strict; TODO: breaks layout since Chrome 80*/
+
+  height: 100vh;
+  position: sticky;
+  position: -webkit-sticky;
+  top: 0;
+
+  ${ma("small")`
+    position: fixed;
+    z-index: 20;
+    width: 100%;
+    background: ${({theme:e})=>e.sidebar.backgroundColor};
+    display: ${e=>e.open?"flex":"none"};
+  `};
+
+  @media print {
+    display: none;
+  }
+`,Db=ga.div`
+  outline: none;
+  user-select: none;
+  background-color: ${({theme:e})=>e.fab.backgroundColor};
+  color: ${e=>e.theme.colors.primary.main};
+  display: none;
+  cursor: pointer;
+  position: fixed;
+  right: 20px;
+  z-index: 100;
+  border-radius: 50%;
+  box-shadow: 0 0 20px rgba(0, 0, 0, 0.3);
+  ${ma("small")`
+    display: flex;
+  `};
+
+  bottom: 44px;
+
+  width: 60px;
+  height: 60px;
+  padding: 0 20px;
+  svg {
+    color: ${({theme:e})=>e.fab.color};
+  }
+
+  @media print {
+    display: none;
+  }
+`;let Lb=class extends n.Component{constructor(){super(...arguments),this.state={offsetTop:"0px"},this.toggleNavMenu=()=>{this.props.menu.toggleSidebar()}}componentDidMount(){Ib&&Ib.add(this.stickyElement),this.setState({offsetTop:this.getScrollYOffset(this.context)})}componentWillUnmount(){Ib&&Ib.remove(this.stickyElement)}getScrollYOffset(e){let t;return t=void 0!==this.props.scrollYOffset?xo.normalizeScrollYOffset(this.props.scrollYOffset)():e.scrollYOffset(),t+"px"}render(){const e=this.props.menu.sideBarOpened,t=this.state.offsetTop;return n.createElement(n.Fragment,null,n.createElement(Nb,{open:e,className:this.props.className,style:{top:t,height:`calc(100vh - ${t})`},ref:e=>{this.stickyElement=e}},this.props.children),!this.context.hideFab&&n.createElement(Db,{onClick:this.toggleNavMenu},n.createElement(Cb,{open:e})))}};Lb.contextType=Sa,Lb=((e,t,n,r)=>{for(var o,i=t,a=e.length-1;a>=0;a--)(o=e[a])&&(i=o(i)||i);return i})([im],Lb);const Mb=ga.div`
+  ${({theme:e})=>`\n  font-family: ${e.typography.fontFamily};\n  font-size: ${e.typography.fontSize};\n  font-weight: ${e.typography.fontWeightRegular};\n  line-height: ${e.typography.lineHeight};\n  color: ${e.colors.text.primary};\n  display: flex;\n  position: relative;\n  text-align: left;\n\n  -webkit-font-smoothing: ${e.typography.smoothing};\n  font-smoothing: ${e.typography.smoothing};\n  ${e.typography.optimizeSpeed?"text-rendering: optimizeSpeed !important":""};\n\n  tap-highlight-color: rgba(0, 0, 0, 0);\n  text-size-adjust: 100%;\n\n  * {\n    box-sizing: border-box;\n    -webkit-tap-highlight-color: rgba(255, 255, 255, 0);\n  }\n`};
+`,Fb=ga.div`
+  z-index: 1;
+  position: relative;
+  overflow: hidden;
+  width: calc(100% - ${e=>e.theme.sidebar.width});
+  ${ma("small",!0)`
+    width: 100%;
+  `};
+
+  contain: layout;
+`,zb=ga.div`
+  background: ${({theme:e})=>e.rightPanel.backgroundColor};
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  right: 0;
+  width: ${({theme:e})=>{if(e.rightPanel.width.endsWith("%")){const t=parseInt(e.rightPanel.width,10);return`calc((100% - ${e.sidebar.width}) * ${t/100})`}return e.rightPanel.width}};
+  ${ma("medium",!0)`
+    display: none;
+  `};
+`,Ub=ga.div`
+  padding: 5px 0;
+`,Vb=ga.input.attrs((()=>({className:"search-input"})))`
+  width: calc(100% - ${e=>8*e.theme.spacing.unit}px);
+  box-sizing: border-box;
+  margin: 0 ${e=>4*e.theme.spacing.unit}px;
+  padding: 5px ${e=>2*e.theme.spacing.unit}px 5px
+    ${e=>4*e.theme.spacing.unit}px;
+  border: 0;
+  border-bottom: 1px solid
+    ${({theme:e})=>(Ir(e.sidebar.backgroundColor)>.5?Rr:Dr)(.1,e.sidebar.backgroundColor)};
+  font-family: ${({theme:e})=>e.typography.fontFamily};
+  font-weight: bold;
+  font-size: 13px;
+  color: ${e=>e.theme.sidebar.textColor};
+  background-color: transparent;
+  outline: none;
+`,Bb=ga((e=>n.createElement("svg",{className:e.className,version:"1.1",viewBox:"0 0 1000 1000",x:"0px",xmlns:"http://www.w3.org/2000/svg",y:"0px"},n.createElement("path",{d:"M968.2,849.4L667.3,549c83.9-136.5,66.7-317.4-51.7-435.6C477.1-25,252.5-25,113.9,113.4c-138.5,138.3-138.5,362.6,0,501C219.2,730.1,413.2,743,547.6,666.5l301.9,301.4c43.6,43.6,76.9,14.9,104.2-12.4C981,928.3,1011.8,893,968.2,849.4z M524.5,522c-88.9,88.7-233,88.7-321.8,0c-88.9-88.7-88.9-232.6,0-321.3c88.9-88.7,233-88.7,321.8,0C613.4,289.4,613.4,433.3,524.5,522z"})))).attrs({className:"search-icon"})`
+  position: absolute;
+  left: ${e=>4*e.theme.spacing.unit}px;
+  height: 1.8em;
+  width: 0.9em;
+
+  path {
+    fill: ${e=>e.theme.sidebar.textColor};
+  }
+`,qb=ga.div`
+  padding: ${e=>e.theme.spacing.unit}px 0;
+  background-color: ${({theme:e})=>Rr(.05,e.sidebar.backgroundColor)}};
+  color: ${e=>e.theme.sidebar.textColor};
+  min-height: 150px;
+  max-height: 250px;
+  border-top: ${({theme:e})=>Rr(.1,e.sidebar.backgroundColor)}};
+  border-bottom: ${({theme:e})=>Rr(.1,e.sidebar.backgroundColor)}};
+  margin-top: 10px;
+  line-height: 1.4;
+  font-size: 0.9em;
+  
+  li {
+    background-color: inherit;
+  }
+
+  ${Ey} {
+    padding-top: 6px;
+    padding-bottom: 6px;
+
+    &:hover,
+    &.active {
+      background-color: ${({theme:e})=>Rr(.1,e.sidebar.backgroundColor)};
+    }
+
+    > svg {
+      display: none;
+    }
+  }
+`,Wb=ga.i`
+  position: absolute;
+  display: inline-block;
+  width: ${e=>2*e.theme.spacing.unit}px;
+  text-align: center;
+  right: ${e=>4*e.theme.spacing.unit}px;
+  line-height: 2em;
+  vertical-align: middle;
+  margin-right: 2px;
+  cursor: pointer;
+  font-style: normal;
+  color: '#666';
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diff --git a/speakers/server/static/swagger-ui-bundle.js b/speakers/server/static/swagger-ui-bundle.js
new file mode 100644
index 0000000000000000000000000000000000000000..b4e15abfa947dc18d60d735811dd38d60868e05d
--- /dev/null
+++ b/speakers/server/static/swagger-ui-bundle.js
@@ -0,0 +1,9 @@
+/**
+ * Skipped minification because the original files appears to be already minified.
+ * Original file: /npm/swagger-ui-dist@5.3.1/swagger-ui-bundle.js
+ *
+ * Do NOT use SRI with dynamically generated files! More information: https://www.jsdelivr.com/using-sri-with-dynamic-files
+ */
+/*! For license information please see swagger-ui-bundle.js.LICENSE.txt */
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Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="contact"}get name(){return this.get("name")}set name(e){this.set("name",e)}get url(){return this.get("url")}set url(e){this.set("url",e)}get email(){return this.get("email")}set email(e){this.set("email",e)}}const ea=Qi;class ta extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="discriminator"}get propertyName(){return this.get("propertyName")}set propertyName(e){this.set("propertyName",e)}get mapping(){return this.get("mapping")}set mapping(e){this.set("mapping",e)}}const na=ta;class ra extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="encoding"}get contentType(){return this.get("contentType")}set contentType(e){this.set("contentType",e)}get headers(){return this.get("headers")}set headers(e){this.set("headers",e)}get style(){return this.get("style")}set style(e){this.set("style",e)}get explode(){return this.get("explode")}set explode(e){this.set("explode",e)}get allowedReserved(){return this.get("allowedReserved")}set allowedReserved(e){this.set("allowedReserved",e)}}const oa=ra;class sa extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="example"}get summary(){return this.get("summary")}set summary(e){this.set("summary",e)}get description(){return this.get("description")}set description(e){this.set("description",e)}get value(){return this.get("value")}set value(e){this.set("value",e)}get externalValue(){return this.get("externalValue")}set externalValue(e){this.set("externalValue",e)}}const ia=sa;class aa extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="externalDocumentation"}get description(){return this.get("description")}set description(e){this.set("description",e)}get url(){return this.get("url")}set url(e){this.set("url",e)}}const la=aa;class ca extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="header"}get required(){return this.hasKey("required")?this.get("required"):new Nt.hh(!1)}set required(e){this.set("required",e)}get deprecated(){return this.hasKey("deprecated")?this.get("deprecated"):new Nt.hh(!1)}set deprecated(e){this.set("deprecated",e)}get allowEmptyValue(){return this.get("allowEmptyValue")}set allowEmptyValue(e){this.set("allowEmptyValue",e)}get style(){return this.get("style")}set style(e){this.set("style",e)}get explode(){return this.get("explode")}set explode(e){this.set("explode",e)}get allowReserved(){return this.get("allowReserved")}set allowReserved(e){this.set("allowReserved",e)}get schema(){return this.get("schema")}set schema(e){this.set("schema",e)}get example(){return this.get("example")}set example(e){this.set("example",e)}get examples(){return this.get("examples")}set examples(e){this.set("examples",e)}get contentProp(){return this.get("content")}set contentProp(e){this.set("content",e)}}Object.defineProperty(ca.prototype,"description",{get(){return this.get("description")},set(e){this.set("description",e)},enumerable:!0});const ua=ca;class pa extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="info",this.classes.push("info")}get title(){return this.get("title")}set title(e){this.set("title",e)}get description(){return this.get("description")}set description(e){this.set("description",e)}get termsOfService(){return this.get("termsOfService")}set termsOfService(e){this.set("termsOfService",e)}get contact(){return this.get("contact")}set contact(e){this.set("contact",e)}get license(){return this.get("license")}set license(e){this.set("license",e)}get version(){return this.get("version")}set version(e){this.set("version",e)}}const ha=pa;class fa extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="license"}get name(){return this.get("name")}set name(e){this.set("name",e)}get url(){return this.get("url")}set url(e){this.set("url",e)}}const da=fa;class ma extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="link"}get operationRef(){return this.get("operationRef")}set operationRef(e){this.set("operationRef",e)}get operationId(){return this.get("operationId")}set operationId(e){this.set("operationId",e)}get operation(){var e,t;return gs(this.operationRef)?null===(e=this.operationRef)||void 0===e?void 0:e.meta.get("operation"):gs(this.operationId)?null===(t=this.operationId)||void 0===t?void 0:t.meta.get("operation"):void 0}set operation(e){this.set("operation",e)}get parameters(){return this.get("parameters")}set parameters(e){this.set("parameters",e)}get requestBody(){return this.get("requestBody")}set requestBody(e){this.set("requestBody",e)}get description(){return this.get("description")}set description(e){this.set("description",e)}get server(){return this.get("server")}set server(e){this.set("server",e)}}const ga=ma;class ya extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="mediaType"}get schema(){return this.get("schema")}set schema(e){this.set("schema",e)}get example(){return this.get("example")}set example(e){this.set("example",e)}get examples(){return this.get("examples")}set examples(e){this.set("examples",e)}get encoding(){return this.get("encoding")}set encoding(e){this.set("encoding",e)}}const va=ya;class ba extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="oAuthFlow"}get authorizationUrl(){return this.get("authorizationUrl")}set authorizationUrl(e){this.set("authorizationUrl",e)}get tokenUrl(){return this.get("tokenUrl")}set tokenUrl(e){this.set("tokenUrl",e)}get refreshUrl(){return this.get("refreshUrl")}set refreshUrl(e){this.set("refreshUrl",e)}get scopes(){return this.get("scopes")}set scopes(e){this.set("scopes",e)}}const wa=ba;class Ea extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="oAuthFlows"}get implicit(){return this.get("implicit")}set implicit(e){this.set("implicit",e)}get password(){return this.get("password")}set password(e){this.set("password",e)}get clientCredentials(){return this.get("clientCredentials")}set clientCredentials(e){this.set("clientCredentials",e)}get authorizationCode(){return this.get("authorizationCode")}set authorizationCode(e){this.set("authorizationCode",e)}}const xa=Ea;class Sa extends Nt.RP{constructor(e,t,n){super(e,t,n),this.element="openapi",this.classes.push("spec-version"),this.classes.push("version")}}const _a=Sa;class ja extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="openApi3_0",this.classes.push("api")}get openapi(){return this.get("openapi")}set openapi(e){this.set("openapi",e)}get info(){return this.get("info")}set info(e){this.set("info",e)}get servers(){return this.get("servers")}set servers(e){this.set("servers",e)}get paths(){return this.get("paths")}set paths(e){this.set("paths",e)}get components(){return this.get("components")}set components(e){this.set("components",e)}get security(){return this.get("security")}set security(e){this.set("security",e)}get tags(){return this.get("tags")}set tags(e){this.set("tags",e)}get externalDocs(){return this.get("externalDocs")}set externalDocs(e){this.set("externalDocs",e)}}const Oa=ja;class ka extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="operation"}get tags(){return this.get("tags")}set tags(e){this.set("tags",e)}get summary(){return this.get("summary")}set summary(e){this.set("summary",e)}get description(){return this.get("description")}set description(e){this.set("description",e)}set externalDocs(e){this.set("externalDocs",e)}get externalDocs(){return this.get("externalDocs")}get operationId(){return this.get("operationId")}set operationId(e){this.set("operationId",e)}get parameters(){return this.get("parameters")}set parameters(e){this.set("parameters",e)}get requestBody(){return this.get("requestBody")}set requestBody(e){this.set("requestBody",e)}get responses(){return this.get("responses")}set responses(e){this.set("responses",e)}get callbacks(){return this.get("callbacks")}set callbacks(e){this.set("callbacks",e)}get deprecated(){return this.hasKey("deprecated")?this.get("deprecated"):new Nt.hh(!1)}set deprecated(e){this.set("deprecated",e)}get security(){return this.get("security")}set security(e){this.set("security",e)}get servers(){return this.get("severs")}set servers(e){this.set("servers",e)}}const Aa=ka;class Ca extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="parameter"}get name(){return this.get("name")}set name(e){this.set("name",e)}get in(){return this.get("in")}set in(e){this.set("in",e)}get required(){return this.hasKey("required")?this.get("required"):new Nt.hh(!1)}set required(e){this.set("required",e)}get deprecated(){return this.hasKey("deprecated")?this.get("deprecated"):new Nt.hh(!1)}set deprecated(e){this.set("deprecated",e)}get allowEmptyValue(){return this.get("allowEmptyValue")}set allowEmptyValue(e){this.set("allowEmptyValue",e)}get style(){return this.get("style")}set style(e){this.set("style",e)}get explode(){return this.get("explode")}set explode(e){this.set("explode",e)}get allowReserved(){return this.get("allowReserved")}set allowReserved(e){this.set("allowReserved",e)}get schema(){return this.get("schema")}set schema(e){this.set("schema",e)}get example(){return this.get("example")}set example(e){this.set("example",e)}get examples(){return this.get("examples")}set examples(e){this.set("examples",e)}get contentProp(){return this.get("content")}set contentProp(e){this.set("content",e)}}Object.defineProperty(Ca.prototype,"description",{get(){return this.get("description")},set(e){this.set("description",e)},enumerable:!0});const Pa=Ca;class Na extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="pathItem"}get $ref(){return this.get("$ref")}set $ref(e){this.set("$ref",e)}get summary(){return this.get("summary")}set summary(e){this.set("summary",e)}get description(){return this.get("description")}set description(e){this.set("description",e)}get GET(){return this.get("get")}set GET(e){this.set("GET",e)}get PUT(){return this.get("put")}set PUT(e){this.set("PUT",e)}get POST(){return this.get("post")}set POST(e){this.set("POST",e)}get DELETE(){return this.get("delete")}set DELETE(e){this.set("DELETE",e)}get OPTIONS(){return this.get("options")}set OPTIONS(e){this.set("OPTIONS",e)}get HEAD(){return this.get("head")}set HEAD(e){this.set("HEAD",e)}get PATCH(){return this.get("patch")}set PATCH(e){this.set("PATCH",e)}get TRACE(){return this.get("trace")}set TRACE(e){this.set("TRACE",e)}get servers(){return this.get("servers")}set servers(e){this.set("servers",e)}get parameters(){return this.get("parameters")}set parameters(e){this.set("parameters",e)}}const Ia=Na;class Ta extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="paths"}}const Ra=Ta;class Ma extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="reference",this.classes.push("openapi-reference")}get $ref(){return this.get("$ref")}set $ref(e){this.set("$ref",e)}}const Da=Ma;class Fa extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="requestBody"}get description(){return this.get("description")}set description(e){this.set("description",e)}get contentProp(){return this.get("content")}set contentProp(e){this.set("content",e)}get required(){return this.hasKey("required")?this.get("required"):new Nt.hh(!1)}set required(e){this.set("required",e)}}const La=Fa;class Ba extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="response"}get description(){return this.get("description")}set description(e){this.set("description",e)}get headers(){return this.get("headers")}set headers(e){this.set("headers",e)}get contentProp(){return this.get("content")}set contentProp(e){this.set("content",e)}get links(){return this.get("links")}set links(e){this.set("links",e)}}const $a=Ba;class qa extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="responses"}get default(){return this.get("default")}set default(e){this.set("default",e)}}const Ua=qa;class za extends Nt.Sb{constructor(e,t,n){super(e,t,n),this.element="JSONSchemaDraft4"}get idProp(){return this.get("id")}set idProp(e){this.set("id",e)}get $schema(){return this.get("$schema")}set $schema(e){this.set("idProp",e)}get multipleOf(){return this.get("multipleOf")}set multipleOf(e){this.set("multipleOf",e)}get maximum(){return this.get("maximum")}set maximum(e){this.set("maximum",e)}get exclusiveMaximum(){return this.get("exclusiveMaximum")}set exclusiveMaximum(e){this.set("exclusiveMaximum",e)}get minimum(){return this.get("minimum")}set minimum(e){this.set("minimum",e)}get exclusiveMinimum(){return this.get("exclusiveMinimum")}set exclusiveMinimum(e){this.set("exclusiveMinimum",e)}get maxLength(){return this.get("maxLength")}set maxLength(e){this.set("maxLength",e)}get minLength(){return this.get("minLength")}set minLength(e){this.set("minLength",e)}get pattern(){return this.get("pattern")}set pattern(e){this.set("pattern",e)}get additionalItems(){return this.get("additionalItems")}set additionalItems(e){this.set("additionalItems",e)}get items(){return this.get("items")}set items(e){this.set("items",e)}get maxItems(){return this.get("maxItems")}set maxItems(e){this.set("maxItems",e)}get minItems(){return this.get("minItems")}set minItems(e){this.set("minItems",e)}get uniqueItems(){return this.get("uniqueItems")}set uniqueItems(e){this.set("uniqueItems",e)}get maxProperties(){return this.get("maxProperties")}set maxProperties(e){this.set("maxProperties",e)}get minProperties(){return this.get("minProperties")}set minProperties(e){this.set("minProperties",e)}get required(){return this.get("required")}set required(e){this.set("required",e)}get properties(){return this.get("properties")}set properties(e){this.set("properties",e)}get additionalProperties(){return this.get("additionalProperties")}set additionalProperties(e){this.set("additionalProperties",e)}get patternProperties(){return this.get("patternProperties")}set patternProperties(e){this.set("patternProperties",e)}get dependencies(){return this.get("dependencies")}set dependencies(e){this.set("dependencies",e)}get enum(){return this.get("enum")}set enum(e){this.set("enum",e)}get type(){return this.get("type")}set type(e){this.set("type",e)}get allOf(){return this.get("allOf")}set allOf(e){this.set("allOf",e)}get anyOf(){return this.get("anyOf")}set anyOf(e){this.set("anyOf",e)}get oneOf(){return this.get("oneOf")}set oneOf(e){this.set("oneOf",e)}get not(){return this.get("not")}set not(e){this.set("not",e)}get definitions(){return this.get("definitions")}set definitions(e){this.set("definitions",e)}get title(){return this.get("title")}set title(e){this.set("title",e)}get description(){return this.get("description")}set description(e){this.set("description",e)}get default(){return this.get("default")}set default(e){this.set("default",e)}get format(){return this.get("format")}set format(e){this.set("format",e)}get base(){return this.get("base")}set base(e){this.set("base",e)}get links(){return this.get("links")}set links(e){this.set("links",e)}get 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Ub}}}),Rw=Xs(Tw,{props:{timeout:5e3,redirects:5,withCredentials:!1},init({timeout:e=this.timeout,redirects:t=this.redirects,withCredentials:n=this.withCredentials}={}){this.timeout=e,this.redirects=t,this.withCredentials=n},methods:{canRead:e=>gb(e.uri),async read(){throw new Ub},getHttpClient(){throw new Ub}}}).compose({props:{name:"http-swagger-client",swaggerHTTPClient:ct,swaggerHTTPClientConfig:{}},init(){let{swaggerHTTPClient:e=this.swaggerHTTPClient}=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};this.swaggerHTTPClient=e},methods:{getHttpClient(){return this.swaggerHTTPClient},async read(e){const t=this.getHttpClient(),n=new AbortController,{signal:r}=n,o=setTimeout((()=>{n.abort()}),this.timeout),s=this.getHttpClient().withCredentials||this.withCredentials?"include":"same-origin",i=0===this.redirects?"error":"follow",a=this.redirects>0?this.redirects:void 0;try{return(await t(f()({url:e.uri,signal:r,userFetch:async(e,t)=>{let n=await 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Fb(`Error parsing "${e.uri}"`,{cause:t})}}}}),Dw=Vb.compose({props:{name:"yaml-1-2-swagger-client",fileExtensions:[".yaml",".yml"],mediaTypes:["text/yaml","application/yaml"]},methods:{async canParse(e){const t=0===this.fileExtensions.length||this.fileExtensions.includes(e.extension),n=this.mediaTypes.includes(e.mediaType);if(!t)return!1;if(n)return!0;if(!n)try{return le.ZP.load(e.toString(),{schema:le.A8}),!0}catch(e){return!1}return!1},async parse(e){if(this.sourceMap)throw new Fb("yaml-1-2-swagger-client parser plugin doesn't support sourceMaps option");const t=new Vo,n=e.toString();try{const e=le.ZP.load(n,{schema:le.A8});if(this.allowEmpty&&void 0===e)return t;const r=Ti(e);return r.classes.push("result"),t.push(r),t}catch(t){throw new Fb(`Error parsing "${e.uri}"`,{cause:t})}}}}),Fw=Vb.compose({props:{name:"openapi-json-3-1-swagger-client",fileExtensions:[".json"],mediaTypes:new 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tb(...nb.filterByFormat("generic"),...nb.filterByFormat("yaml")),detectionRegExp:/(?<YAML>^(["']?)openapi\2\s*:\s*(["']?)(?<version_yaml>3\.1\.(?:[1-9]\d*|0))\3(?:\s+|$))|(?<JSON>"openapi"\s*:\s*"(?<version_json>3\.1\.(?:[1-9]\d*|0))")/m},methods:{async canParse(e){const t=0===this.fileExtensions.length||this.fileExtensions.includes(e.extension),n=this.mediaTypes.includes(e.mediaType);if(!t)return!1;if(n)return!0;if(!n)try{const t=e.toString();return le.ZP.load(t),this.detectionRegExp.test(t)}catch(e){return!1}return!1},async parse(e){if(this.sourceMap)throw new Fb("openapi-yaml-3-1-swagger-client parser plugin doesn't support sourceMaps option");const t=new Vo,n=e.toString();try{const e=le.ZP.load(n,{schema:le.A8});if(this.allowEmpty&&void 0===e)return t;const r=$m.refract(e,this.refractorOpts);return r.classes.push("result"),t.push(r),t}catch(t){throw new Fb(`Error parsing "${e.uri}"`,{cause:t})}}}}),Bw=Xs({props:{name:null},methods:{canDereference:()=>!1,async dereference(){throw new Ub}}});function $w(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function qw(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?$w(Object(n),!0).forEach((function(t){Qo(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):$w(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}const Uw=di[Symbol.for("nodejs.util.promisify.custom")],zw=Xs({props:{indirections:null,namespace:null,reference:null,options:null,ancestors:null},init({indirections:e=[],reference:t,namespace:n,options:r,ancestors:o=[]}){this.indirections=e,this.namespace=n,this.reference=t,this.options=r,this.ancestors=[...o]},methods:{toBaseURI(e){return Eb(this.reference.uri,xb(bb(e)))},toAncestorLineage(e){const t=new WeakSet(e.filter(ms));return[[...this.ancestors,t],t]},async toReference(e){if(this.reference.depth>=this.options.resolve.maxDepth)throw new Gb(`Maximum resolution depth of ${this.options.resolve.maxDepth} has been exceeded by file "${this.reference.uri}"`);const t=this.toBaseURI(e),{refSet:n}=this.reference;if(n.has(t))return n.find(So(t,"uri"));const r=await ew(Sb(t),qw(qw({},this.options),{},{parse:qw(qw({},this.options.parse),{},{mediaType:"text/plain"})})),o=jb({uri:t,value:r,depth:this.reference.depth+1});return n.add(o),o},async ReferenceElement(e,t,n,r,o){var s,i,a,l,c;const[u,p]=this.toAncestorLineage([...o,n]);if(u.some((t=>t.has(e))))return!1;if(!this.options.resolve.external&&ty(e))return!1;const h=await this.toReference(null===(s=e.$ref)||void 0===s?void 0:s.toValue()),{uri:f}=h,d=Eb(f,null===(i=e.$ref)||void 0===i?void 0:i.toValue());this.indirections.push(e);const m=Hi(d);let g=Ki(m,h.value.result);if(Cs(g)){const t=e.meta.get("referenced-element").toValue();if(zc(g))g=Jm.refract(g),g.setMetaProperty("referenced-element",t);else{g=this.namespace.getElementClass(t).refract(g)}}if(this.indirections.includes(g))throw new Error("Recursive Reference Object detected");if(this.indirections.length>this.options.dereference.maxDepth)throw new Zb(`Maximum dereference depth of "${this.options.dereference.maxDepth}" has been exceeded in file "${this.reference.uri}"`);p.add(e);const y=zw({reference:h,namespace:this.namespace,indirections:[...this.indirections],options:this.options,ancestors:u});g=await Uw(g,y,{keyMap:Jv,nodeTypeGetter:Wv}),p.delete(e),this.indirections.pop(),g=g.clone(),g.setMetaProperty("ref-fields",{$ref:null===(a=e.$ref)||void 0===a?void 0:a.toValue(),description:null===(l=e.description)||void 0===l?void 0:l.toValue(),summary:null===(c=e.summary)||void 0===c?void 0:c.toValue()}),g.setMetaProperty("ref-origin",h.uri);const v=Eo(ib,["description"],e),b=Eo(ib,["summary"],e);return v&&Zr("description",g)&&(g.description=e.description),b&&Zr("summary",g)&&(g.summary=e.summary),this.indirections.pop(),g},async PathItemElement(e,t,n,r,o){var s,i,a;const[l,c]=this.toAncestorLineage([...o,n]);if(!gs(e.$ref))return;if(l.some((t=>t.has(e))))return!1;if(!this.options.resolve.external&&Xg(e))return;const u=await this.toReference(null===(s=e.$ref)||void 0===s?void 0:s.toValue()),{uri:p}=u,h=Eb(p,null===(i=e.$ref)||void 0===i?void 0:i.toValue());this.indirections.push(e);const f=Hi(h);let d=Ki(f,u.value.result);if(Cs(d)&&(d=zm.refract(d)),this.indirections.includes(d))throw new Error("Recursive Path Item Object reference detected");if(this.indirections.length>this.options.dereference.maxDepth)throw new Zb(`Maximum dereference depth of "${this.options.dereference.maxDepth}" has been exceeded in file "${this.reference.uri}"`);c.add(e);const m=zw({reference:u,namespace:this.namespace,indirections:[...this.indirections],options:this.options,ancestors:l});d=await 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s;const[i,a]=this.toAncestorLineage([...o,n]);if(!gs(e.$ref))return;if(i.some((t=>t.has(e))))return!1;let l=await this.toReference(Sb(this.reference.uri)),{uri:c}=l;const u=uw(c,e),p=bb(u),h=Nb({uri:p}),f=yo((e=>e.canRead(h)),this.options.resolve.resolvers),d=!f,m=d&&c!==p;if(!this.options.resolve.external&&m)return;let g;this.indirections.push(e);try{if(f||d){g=fw(u,hw(l.value.result))}else{l=await this.toReference(Sb(u));const e=Hi(u);g=hw(Ki(e,l.value.result))}}catch(e){if(!(d&&e instanceof nw))throw e;if(aw(lw(u))){l=await this.toReference(Sb(u)),c=l.uri;const e=lw(u);g=cw(e,hw(l.value.result))}else{l=await this.toReference(Sb(u)),c=l.uri;const e=Hi(u);g=hw(Ki(e,l.value.result))}}if(this.indirections.includes(g))throw new Error("Recursive Schema Object reference detected");if(this.indirections.length>this.options.dereference.maxDepth)throw new Zb(`Maximum dereference depth of "${this.options.dereference.maxDepth}" has been exceeded in file "${this.reference.uri}"`);a.add(e);const 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ReferenceElement(e,t,n,r,o){try{const[t,r]=this.toAncestorLineage([...o,n]);if(Is(["cycle"],e.$ref))return!1;if(t.some((t=>t.has(e))))return!1;if(!this.options.resolve.external&&ty(e))return!1;const s=await this.toReference(e.$ref.toValue()),{uri:i}=s,a=Eb(i,e.$ref.toValue());this.indirections.push(e);const l=Hi(a);let c=Ki(l,s.value.result);if(Cs(c)){const t=e.meta.get("referenced-element").toValue();if(zc(c))c=Jm.refract(c),c.setMetaProperty("referenced-element",t);else{const e=this.namespace.getElementClass(t);c=e.refract(c)}}if(this.indirections.includes(c))throw new Error("Recursive JSON Pointer detected");if(this.indirections.length>this.options.dereference.maxDepth)throw new Zb(`Maximum dereference depth of "${this.options.dereference.maxDepth}" has been exceeded in file "${this.reference.uri}"`);if(!this.useCircularStructures){if(t.some((e=>e.has(c)))){if(gb(i)||mb(i)){const t=new Jm({$ref:a},e.meta.clone(),e.attributes.clone());return t.get("$ref").classes.push("cycle"),t}return!1}}r.add(e);const u=Qw({reference:s,namespace:this.namespace,indirections:[...this.indirections],options:this.options,ancestors:t,allowMetaPatches:this.allowMetaPatches,useCircularStructures:this.useCircularStructures,basePath:this.basePath??[...Hw([...o,n,e]),"$ref"]});c=await Xw(c,u,{keyMap:Jv,nodeTypeGetter:Wv}),r.delete(e),this.indirections.pop(),c=c.clone(),c.setMetaProperty("ref-fields",{$ref:e.$ref?.toValue(),description:e.description?.toValue(),summary:e.summary?.toValue()}),c.setMetaProperty("ref-origin",s.uri);const p=void 0!==e.description,h=void 0!==e.summary;if(p&&"description"in c&&(c.description=e.description),h&&"summary"in c&&(c.summary=e.summary),this.allowMetaPatches&&ws(c)){const e=c;if(void 0===e.get("$$ref")){const t=Eb(i,a);e.set("$$ref",t)}}return c}catch(t){const r=Gw(t),s=Yw(r,{baseDoc:this.reference.uri,$ref:e.$ref.toValue(),pointer:Hi(e.$ref.toValue()),fullPath:this.basePath??[...Hw([...o,n,e]),"$ref"]});return void this.options.dereference.dereferenceOpts?.errors?.push?.(s)}},async PathItemElement(e,t,n,r,o){try{const[t,r]=this.toAncestorLineage([...o,n]);if(!gs(e.$ref))return;if(Is(["cycle"],e.$ref))return!1;if(t.some((t=>t.has(e))))return!1;if(!this.options.resolve.external&&Xg(e))return;const s=await this.toReference(e.$ref.toValue()),{uri:i}=s,a=Eb(i,e.$ref.toValue());this.indirections.push(e);const l=Hi(a);let c=Ki(l,s.value.result);if(Cs(c)&&(c=zm.refract(c)),this.indirections.includes(c))throw new Error("Recursive JSON Pointer detected");if(this.indirections.length>this.options.dereference.maxDepth)throw new Zb(`Maximum dereference depth of "${this.options.dereference.maxDepth}" has been exceeded in file "${this.reference.uri}"`);if(!this.useCircularStructures){if(t.some((e=>e.has(c)))){if(gb(i)||mb(i)){const t=new zm({$ref:a},e.meta.clone(),e.attributes.clone());return t.get("$ref").classes.push("cycle"),t}return!1}}r.add(e);const 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o,s,i,a,l,c,u,p,h,f,d,m,g,y,v,b,w,E,x,S,_,j=!0;if((p=H(e,t))>=0)m=!0;else{if(!((p=K(e,t))>=0))return!1;m=!1}if(e.level>=e.options.maxNesting)return!1;if(d=e.src.charCodeAt(p-1),r)return!0;for(y=e.tokens.length,m?(u=e.bMarks[t]+e.tShift[t],f=Number(e.src.substr(u,p-u-1)),e.tokens.push({type:"ordered_list_open",order:f,lines:b=[t,0],level:e.level++})):e.tokens.push({type:"bullet_list_open",lines:b=[t,0],level:e.level++}),o=t,v=!1,E=e.parser.ruler.getRules("list");!(!(o<n)||((h=(g=e.skipSpaces(p))>=e.eMarks[o]?1:g-p)>4&&(h=1),h<1&&(h=1),s=p-e.bMarks[o]+h,e.tokens.push({type:"list_item_open",lines:w=[t,0],level:e.level++}),a=e.blkIndent,l=e.tight,i=e.tShift[t],c=e.parentType,e.tShift[t]=g-e.bMarks[t],e.blkIndent=s,e.tight=!0,e.parentType="list",e.parser.tokenize(e,t,n,!0),e.tight&&!v||(j=!1),v=e.line-t>1&&e.isEmpty(e.line-1),e.blkIndent=a,e.tShift[t]=i,e.tight=l,e.parentType=c,e.tokens.push({type:"list_item_close",level:--e.level}),o=t=e.line,w[1]=o,g=e.bMarks[t],o>=n)||e.isEmpty(o)||e.tShift[o]<e.blkIndent);){for(_=!1,x=0,S=E.length;x<S;x++)if(E[x](e,o,n,!0)){_=!0;break}if(_)break;if(m){if((p=H(e,o))<0)break}else 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r?e.requestResolvedSubtree(r.toJS()):e.requestResolvedSubtree(["paths",t,n])}));const{tryItOutEnabled:n}=e.getConfigs();this.state={tryItOutEnabled:!0===n||"true"===n,executeInProgress:!1}}mapStateToProps(e,t){const{op:n,layoutSelectors:r,getConfigs:o}=t,{docExpansion:s,deepLinking:i,displayOperationId:a,displayRequestDuration:l,supportedSubmitMethods:c}=o(),u=r.showSummary(),p=n.getIn(["operation","__originalOperationId"])||n.getIn(["operation","operationId"])||(0,he.Z)(n.get("operation"),t.path,t.method)||n.get("id"),h=["operations",t.tag,p],f=i&&"false"!==i,d=pe()(c).call(c,t.method)>=0&&(void 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0===r&&this.requestResolvedSubtree()}render(){let{op:e,tag:t,path:n,method:r,security:o,isAuthorized:s,operationId:i,showSummary:a,isShown:l,jumpToKey:c,allowTryItOut:u,response:p,request:h,displayOperationId:f,displayRequestDuration:d,isDeepLinkingEnabled:m,specPath:g,specSelectors:y,specActions:v,getComponent:b,getConfigs:w,layoutSelectors:E,layoutActions:x,authActions:S,authSelectors:_,oas3Actions:j,oas3Selectors:O,fn:k}=this.props;const C=b("operation"),N=this.getResolvedSubtree()||(0,P.Map)(),I=(0,P.fromJS)({op:N,tag:t,path:n,summary:e.getIn(["operation","summary"])||"",deprecated:N.get("deprecated")||e.getIn(["operation","deprecated"])||!1,method:r,security:o,isAuthorized:s,operationId:i,originalOperationId:N.getIn(["operation","__originalOperationId"]),showSummary:a,isShown:l,jumpToKey:c,allowTryItOut:u,request:h,displayOperationId:f,displayRequestDuration:d,isDeepLinkingEnabled:m,executeInProgress:this.state.executeInProgress,tryItOutEnabled:this.state.tryItOutEnabled});return A.createElement(C,{operation:I,response:p,request:h,isShown:l,toggleShown:this.toggleShown,onTryoutClick:this.onTryoutClick,onResetClick:this.onResetClick,onCancelClick:this.onCancelClick,onExecute:this.onExecute,specPath:g,specActions:v,specSelectors:y,oas3Actions:j,oas3Selectors:O,layoutActions:x,layoutSelectors:E,authActions:S,authSelectors:_,getComponent:b,getConfigs:w,fn:k})}}ce()(fe,"defaultProps",{showSummary:!0,response:null,allowTryItOut:!0,displayOperationId:!1,displayRequestDuration:!1});class de extends A.Component{getLayout(){let{getComponent:e,layoutSelectors:t}=this.props;const n=t.current(),r=e(n,!0);return r||(()=>A.createElement("h1",null,' No layout defined for "',n,'" '))}render(){const e=this.getLayout();return A.createElement(e,null)}}de.defaultProps={};class me extends A.Component{constructor(){super(...arguments),ce()(this,"close",(()=>{let{authActions:e}=this.props;e.showDefinitions(!1)}))}render(){var e;let{authSelectors:t,authActions:n,getComponent:r,errSelectors:o,specSelectors:s,fn:{AST:i={}}}=this.props,a=t.shownDefinitions();const l=r("auths"),c=r("CloseIcon");return A.createElement("div",{className:"dialog-ux"},A.createElement("div",{className:"backdrop-ux"}),A.createElement("div",{className:"modal-ux"},A.createElement("div",{className:"modal-dialog-ux"},A.createElement("div",{className:"modal-ux-inner"},A.createElement("div",{className:"modal-ux-header"},A.createElement("h3",null,"Available authorizations"),A.createElement("button",{type:"button",className:"close-modal",onClick:this.close},A.createElement(c,null))),A.createElement("div",{className:"modal-ux-content"},j()(e=a.valueSeq()).call(e,((e,a)=>A.createElement(l,{key:a,AST:i,definitions:e,getComponent:r,errSelectors:o,authSelectors:t,authActions:n,specSelectors:s}))))))))}}class ge extends A.Component{render(){let{isAuthorized:e,showPopup:t,onClick:n,getComponent:r}=this.props;const o=r("authorizationPopup",!0),s=r("LockAuthIcon",!0),i=r("UnlockAuthIcon",!0);return A.createElement("div",{className:"auth-wrapper"},A.createElement("button",{className:e?"btn authorize locked":"btn authorize unlocked",onClick:n},A.createElement("span",null,"Authorize"),e?A.createElement(s,null):A.createElement(i,null)),t&&A.createElement(o,null))}}class ye extends A.Component{render(){const{authActions:e,authSelectors:t,specSelectors:n,getComponent:r}=this.props,o=n.securityDefinitions(),s=t.definitionsToAuthorize(),i=r("authorizeBtn");return o?A.createElement(i,{onClick:()=>e.showDefinitions(s),isAuthorized:!!t.authorized().size,showPopup:!!t.shownDefinitions(),getComponent:r}):null}}class ve extends A.Component{constructor(){super(...arguments),ce()(this,"onClick",(e=>{e.stopPropagation();let{onClick:t}=this.props;t&&t()}))}render(){let{isAuthorized:e,getComponent:t}=this.props;const n=t("LockAuthOperationIcon",!0),r=t("UnlockAuthOperationIcon",!0);return A.createElement("button",{className:"authorization__btn","aria-label":e?"authorization button locked":"authorization button unlocked",onClick:this.onClick},e?A.createElement(n,{className:"locked"}):A.createElement(r,{className:"unlocked"}))}}class be extends A.Component{constructor(e,t){super(e,t),ce()(this,"onAuthChange",(e=>{let{name:t}=e;this.setState({[t]:e})})),ce()(this,"submitAuth",(e=>{e.preventDefault();let{authActions:t}=this.props;t.authorizeWithPersistOption(this.state)})),ce()(this,"logoutClick",(e=>{e.preventDefault();let{authActions:t,definitions:n}=this.props,r=j()(n).call(n,((e,t)=>t)).toArray();this.setState(S()(r).call(r,((e,t)=>(e[t]="",e)),{})),t.logoutWithPersistOption(r)})),ce()(this,"close",(e=>{e.preventDefault();let{authActions:t}=this.props;t.showDefinitions(!1)})),this.state={}}render(){var e;let{definitions:t,getComponent:n,authSelectors:r,errSelectors:o}=this.props;const s=n("AuthItem"),i=n("oauth2",!0),l=n("Button");let c=r.authorized(),u=a()(t).call(t,((e,t)=>!!c.get(t))),p=a()(t).call(t,(e=>"oauth2"!==e.get("type"))),h=a()(t).call(t,(e=>"oauth2"===e.get("type")));return A.createElement("div",{className:"auth-container"},!!p.size&&A.createElement("form",{onSubmit:this.submitAuth},j()(p).call(p,((e,t)=>A.createElement(s,{key:t,schema:e,name:t,getComponent:n,onAuthChange:this.onAuthChange,authorized:c,errSelectors:o}))).toArray(),A.createElement("div",{className:"auth-btn-wrapper"},p.size===u.size?A.createElement(l,{className:"btn modal-btn auth",onClick:this.logoutClick},"Logout"):A.createElement(l,{type:"submit",className:"btn modal-btn auth authorize"},"Authorize"),A.createElement(l,{className:"btn modal-btn auth btn-done",onClick:this.close},"Close"))),h&&h.size?A.createElement("div",null,A.createElement("div",{className:"scope-def"},A.createElement("p",null,"Scopes are used to grant an application different levels of access to data on behalf of the end user. Each API may declare one or more scopes."),A.createElement("p",null,"API requires the following scopes. Select which ones you want to grant to Swagger UI.")),j()(e=a()(t).call(t,(e=>"oauth2"===e.get("type")))).call(e,((e,t)=>A.createElement("div",{key:t},A.createElement(i,{authorized:c,schema:e,name:t})))).toArray()):null)}}class we extends A.Component{render(){let{schema:e,name:t,getComponent:n,onAuthChange:r,authorized:o,errSelectors:s}=this.props;const i=n("apiKeyAuth"),a=n("basicAuth");let l;const c=e.get("type");switch(c){case"apiKey":l=A.createElement(i,{key:t,schema:e,name:t,errSelectors:s,authorized:o,getComponent:n,onChange:r});break;case"basic":l=A.createElement(a,{key:t,schema:e,name:t,errSelectors:s,authorized:o,getComponent:n,onChange:r});break;default:l=A.createElement("div",{key:t},"Unknown security definition type ",c)}return A.createElement("div",{key:`${t}-jump`},l)}}class Ee extends A.Component{render(){let{error:e}=this.props,t=e.get("level"),n=e.get("message"),r=e.get("source");return A.createElement("div",{className:"errors"},A.createElement("b",null,r," ",t),A.createElement("span",null,n))}}class xe extends A.Component{constructor(e,t){super(e,t),ce()(this,"onChange",(e=>{let{onChange:t}=this.props,n=e.target.value,r=y()({},this.state,{value:n});this.setState(r),t(r)}));let{name:n,schema:r}=this.props,o=this.getValue();this.state={name:n,schema:r,value:o}}getValue(){let{name:e,authorized:t}=this.props;return t&&t.getIn([e,"value"])}render(){var e,t;let{schema:n,getComponent:r,errSelectors:o,name:s}=this.props;const i=r("Input"),l=r("Row"),c=r("Col"),u=r("authError"),p=r("Markdown",!0),h=r("JumpToPath",!0);let f=this.getValue(),d=a()(e=o.allErrors()).call(e,(e=>e.get("authId")===s));return A.createElement("div",null,A.createElement("h4",null,A.createElement("code",null,s||n.get("name"))," (apiKey)",A.createElement(h,{path:["securityDefinitions",s]})),f&&A.createElement("h6",null,"Authorized"),A.createElement(l,null,A.createElement(p,{source:n.get("description")})),A.createElement(l,null,A.createElement("p",null,"Name: ",A.createElement("code",null,n.get("name")))),A.createElement(l,null,A.createElement("p",null,"In: ",A.createElement("code",null,n.get("in")))),A.createElement(l,null,A.createElement("label",null,"Value:"),f?A.createElement("code",null," ****** "):A.createElement(c,null,A.createElement(i,{type:"text",onChange:this.onChange,autoFocus:!0}))),j()(t=d.valueSeq()).call(t,((e,t)=>A.createElement(u,{error:e,key:t}))))}}class Se extends A.Component{constructor(e,t){super(e,t),ce()(this,"onChange",(e=>{let{onChange:t}=this.props,{value:n,name:r}=e.target,o=this.state.value;o[r]=n,this.setState({value:o}),t(this.state)}));let{schema:n,name:r}=this.props,o=this.getValue().username;this.state={name:r,schema:n,value:o?{username:o}:{}}}getValue(){let{authorized:e,name:t}=this.props;return e&&e.getIn([t,"value"])||{}}render(){var e,t;let{schema:n,getComponent:r,name:o,errSelectors:s}=this.props;const i=r("Input"),l=r("Row"),c=r("Col"),u=r("authError"),p=r("JumpToPath",!0),h=r("Markdown",!0);let f=this.getValue().username,d=a()(e=s.allErrors()).call(e,(e=>e.get("authId")===o));return A.createElement("div",null,A.createElement("h4",null,"Basic authorization",A.createElement(p,{path:["securityDefinitions",o]})),f&&A.createElement("h6",null,"Authorized"),A.createElement(l,null,A.createElement(h,{source:n.get("description")})),A.createElement(l,null,A.createElement("label",null,"Username:"),f?A.createElement("code",null," ",f," "):A.createElement(c,null,A.createElement(i,{type:"text",required:"required",name:"username",onChange:this.onChange,autoFocus:!0}))),A.createElement(l,null,A.createElement("label",null,"Password:"),f?A.createElement("code",null," ****** "):A.createElement(c,null,A.createElement(i,{autoComplete:"new-password",name:"password",type:"password",onChange:this.onChange}))),j()(t=d.valueSeq()).call(t,((e,t)=>A.createElement(u,{error:e,key:t}))))}}function _e(e){const{example:t,showValue:n,getComponent:r,getConfigs:o}=e,s=r("Markdown",!0),i=r("highlightCode");return t?A.createElement("div",{className:"example"},t.get("description")?A.createElement("section",{className:"example__section"},A.createElement("div",{className:"example__section-header"},"Example Description"),A.createElement("p",null,A.createElement(s,{source:t.get("description")}))):null,n&&t.has("value")?A.createElement("section",{className:"example__section"},A.createElement("div",{className:"example__section-header"},"Example Value"),A.createElement(i,{getConfigs:o,value:(0,L.Pz)(t.get("value"))})):null):null}var je=n(18492),Oe=n.n(je);class ke extends A.PureComponent{constructor(){var e;super(...arguments),e=this,ce()(this,"_onSelect",(function(t){let{isSyntheticChange:n=!1}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};"function"==typeof e.props.onSelect&&e.props.onSelect(t,{isSyntheticChange:n})})),ce()(this,"_onDomSelect",(e=>{if("function"==typeof this.props.onSelect){const t=e.target.selectedOptions[0].getAttribute("value");this._onSelect(t,{isSyntheticChange:!1})}})),ce()(this,"getCurrentExample",(()=>{const{examples:e,currentExampleKey:t}=this.props,n=e.get(t),r=e.keySeq().first(),o=e.get(r);return n||o||Oe()({})}))}componentDidMount(){const{onSelect:e,examples:t}=this.props;if("function"==typeof e){const e=t.first(),n=t.keyOf(e);this._onSelect(n,{isSyntheticChange:!0})}}UNSAFE_componentWillReceiveProps(e){const{currentExampleKey:t,examples:n}=e;if(n!==this.props.examples&&!n.has(t)){const e=n.first(),t=n.keyOf(e);this._onSelect(t,{isSyntheticChange:!0})}}render(){const{examples:e,currentExampleKey:t,isValueModified:n,isModifiedValueAvailable:r,showLabels:o}=this.props;return A.createElement("div",{className:"examples-select"},o?A.createElement("span",{className:"examples-select__section-label"},"Examples: "):null,A.createElement("select",{className:"examples-select-element",onChange:this._onDomSelect,value:r&&n?"__MODIFIED__VALUE__":t||""},r?A.createElement("option",{value:"__MODIFIED__VALUE__"},"[Modified value]"):null,j()(e).call(e,((e,t)=>A.createElement("option",{key:t,value:t},e.get("summary")||t))).valueSeq()))}}ce()(ke,"defaultProps",{examples:N().Map({}),onSelect:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return console.log("DEBUG: ExamplesSelect was not given an onSelect callback",...t)},currentExampleKey:null,showLabels:!0});const Ae=e=>P.List.isList(e)?e:(0,L.Pz)(e);class Ce extends A.PureComponent{constructor(e){var t;super(e),t=this,ce()(this,"_getStateForCurrentNamespace",(()=>{const{currentNamespace:e}=this.props;return(this.state[e]||(0,P.Map)()).toObject()})),ce()(this,"_setStateForCurrentNamespace",(e=>{const{currentNamespace:t}=this.props;return this._setStateForNamespace(t,e)})),ce()(this,"_setStateForNamespace",((e,t)=>{const n=(this.state[e]||(0,P.Map)()).mergeDeep(t);return this.setState({[e]:n})})),ce()(this,"_isCurrentUserInputSameAsExampleValue",(()=>{const{currentUserInputValue:e}=this.props;return this._getCurrentExampleValue()===e})),ce()(this,"_getValueForExample",((e,t)=>{const{examples:n}=t||this.props;return Ae((n||(0,P.Map)({})).getIn([e,"value"]))})),ce()(this,"_getCurrentExampleValue",(e=>{const{currentKey:t}=e||this.props;return this._getValueForExample(t,e||this.props)})),ce()(this,"_onExamplesSelect",(function(e){let{isSyntheticChange:n}=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};const{onSelect:r,updateValue:o,currentUserInputValue:s,userHasEditedBody:i}=t.props,{lastUserEditedValue:a}=t._getStateForCurrentNamespace(),l=t._getValueForExample(e);if("__MODIFIED__VALUE__"===e)return o(Ae(a)),t._setStateForCurrentNamespace({isModifiedValueSelected:!0});if("function"==typeof r){for(var c=arguments.length,u=new Array(c>2?c-2:0),p=2;p<c;p++)u[p-2]=arguments[p];r(e,{isSyntheticChange:n},...u)}t._setStateForCurrentNamespace({lastDownstreamValue:l,isModifiedValueSelected:n&&i||!!s&&s!==l}),n||"function"==typeof o&&o(Ae(l))}));const n=this._getCurrentExampleValue();this.state={[e.currentNamespace]:(0,P.Map)({lastUserEditedValue:this.props.currentUserInputValue,lastDownstreamValue:n,isModifiedValueSelected:this.props.userHasEditedBody||this.props.currentUserInputValue!==n})}}componentWillUnmount(){this.props.setRetainRequestBodyValueFlag(!1)}UNSAFE_componentWillReceiveProps(e){const{currentUserInputValue:t,examples:n,onSelect:r,userHasEditedBody:o}=e,{lastUserEditedValue:s,lastDownstreamValue:i}=this._getStateForCurrentNamespace(),l=this._getValueForExample(e.currentKey,e),c=a()(n).call(n,(e=>e.get("value")===t||(0,L.Pz)(e.get("value"))===t));if(c.size){let t;t=c.has(e.currentKey)?e.currentKey:c.keySeq().first(),r(t,{isSyntheticChange:!0})}else t!==this.props.currentUserInputValue&&t!==s&&t!==i&&(this.props.setRetainRequestBodyValueFlag(!0),this._setStateForNamespace(e.currentNamespace,{lastUserEditedValue:e.currentUserInputValue,isModifiedValueSelected:o||t!==l}))}render(){const{currentUserInputValue:e,examples:t,currentKey:n,getComponent:r,userHasEditedBody:o}=this.props,{lastDownstreamValue:s,lastUserEditedValue:i,isModifiedValueSelected:a}=this._getStateForCurrentNamespace(),l=r("ExamplesSelect");return A.createElement(l,{examples:t,currentExampleKey:n,onSelect:this._onExamplesSelect,isModifiedValueAvailable:!!i&&i!==s,isValueModified:void 0!==e&&a&&e!==this._getCurrentExampleValue()||o})}}ce()(Ce,"defaultProps",{userHasEditedBody:!1,examples:(0,P.Map)({}),currentNamespace:"__DEFAULT__NAMESPACE__",setRetainRequestBodyValueFlag:()=>{},onSelect:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return console.log("ExamplesSelectValueRetainer: no `onSelect` function was provided",...t)},updateValue:function(){for(var e=arguments.length,t=new Array(e),n=0;n<e;n++)t[n]=arguments[n];return console.log("ExamplesSelectValueRetainer: no `updateValue` function was provided",...t)}});var Pe=n(25110),Ne=n.n(Pe),Ie=n(8712),Te=n.n(Ie),Re=n(58118),Me=n.n(Re),De=n(84564),Fe=n.n(De);class Le extends A.Component{constructor(e,t){super(e,t),ce()(this,"close",(e=>{e.preventDefault();let{authActions:t}=this.props;t.showDefinitions(!1)})),ce()(this,"authorize",(()=>{let{authActions:e,errActions:t,getConfigs:n,authSelectors:r,oas3Selectors:o}=this.props,s=n(),i=r.getConfigs();t.clear({authId:name,type:"auth",source:"auth"}),function(e){let{auth:t,authActions:n,errActions:r,configs:o,authConfigs:s={},currentServer:i}=e,{schema:a,scopes:l,name:c,clientId:u}=t,p=a.get("flow"),h=[];switch(p){case"password":return void n.authorizePassword(t);case"application":case"clientCredentials":case"client_credentials":return void n.authorizeApplication(t);case"accessCode":case"authorizationCode":case"authorization_code":h.push("response_type=code");break;case"implicit":h.push("response_type=token")}"string"==typeof u&&h.push("client_id="+encodeURIComponent(u));let f=o.oauth2RedirectUrl;if(void 0===f)return void r.newAuthErr({authId:c,source:"validation",level:"error",message:"oauth2RedirectUrl configuration is not passed. Oauth2 authorization cannot be performed."});h.push("redirect_uri="+encodeURIComponent(f));let d=[];if(E()(l)?d=l:N().List.isList(l)&&(d=l.toArray()),d.length>0){let e=s.scopeSeparator||" ";h.push("scope="+encodeURIComponent(d.join(e)))}let m=(0,L.r3)(new Date);if(h.push("state="+encodeURIComponent(m)),void 0!==s.realm&&h.push("realm="+encodeURIComponent(s.realm)),("authorizationCode"===p||"authorization_code"===p||"accessCode"===p)&&s.usePkceWithAuthorizationCodeGrant){const e=(0,L.Uj)(),n=(0,L.Xb)(e);h.push("code_challenge="+n),h.push("code_challenge_method=S256"),t.codeVerifier=e}let{additionalQueryStringParams:g}=s;for(let e in g){var y;void 0!==g[e]&&h.push(j()(y=[e,g[e]]).call(y,encodeURIComponent).join("="))}const v=a.get("authorizationUrl");let b;b=i?Fe()((0,L.Nm)(v),i,!0).toString():(0,L.Nm)(v);let w,x=[b,h.join("&")].join(-1===pe()(v).call(v,"?")?"?":"&");w="implicit"===p?n.preAuthorizeImplicit:s.useBasicAuthenticationWithAccessCodeGrant?n.authorizeAccessCodeWithBasicAuthentication:n.authorizeAccessCodeWithFormParams,n.authPopup(x,{auth:t,state:m,redirectUrl:f,callback:w,errCb:r.newAuthErr})}({auth:this.state,currentServer:o.serverEffectiveValue(o.selectedServer()),authActions:e,errActions:t,configs:s,authConfigs:i})})),ce()(this,"onScopeChange",(e=>{var t,n;let{target:r}=e,{checked:o}=r,s=r.dataset.value;if(o&&-1===pe()(t=this.state.scopes).call(t,s)){var i;let e=k()(i=this.state.scopes).call(i,[s]);this.setState({scopes:e})}else if(!o&&pe()(n=this.state.scopes).call(n,s)>-1){var l;this.setState({scopes:a()(l=this.state.scopes).call(l,(e=>e!==s))})}})),ce()(this,"onInputChange",(e=>{let{target:{dataset:{name:t},value:n}}=e,r={[t]:n};this.setState(r)})),ce()(this,"selectScopes",(e=>{var t;e.target.dataset.all?this.setState({scopes:Ne()(Te()(t=this.props.schema.get("allowedScopes")||this.props.schema.get("scopes")).call(t))}):this.setState({scopes:[]})})),ce()(this,"logout",(e=>{e.preventDefault();let{authActions:t,errActions:n,name:r}=this.props;n.clear({authId:r,type:"auth",source:"auth"}),t.logoutWithPersistOption([r])}));let{name:n,schema:r,authorized:o,authSelectors:s}=this.props,i=o&&o.get(n),l=s.getConfigs()||{},c=i&&i.get("username")||"",u=i&&i.get("clientId")||l.clientId||"",p=i&&i.get("clientSecret")||l.clientSecret||"",h=i&&i.get("passwordType")||"basic",f=i&&i.get("scopes")||l.scopes||[];"string"==typeof f&&(f=f.split(l.scopeSeparator||" ")),this.state={appName:l.appName,name:n,schema:r,scopes:f,clientId:u,clientSecret:p,username:c,password:"",passwordType:h}}render(){var e,t;let{schema:n,getComponent:r,authSelectors:o,errSelectors:s,name:i,specSelectors:l}=this.props;const c=r("Input"),u=r("Row"),p=r("Col"),h=r("Button"),f=r("authError"),d=r("JumpToPath",!0),m=r("Markdown",!0),g=r("InitializedInput"),{isOAS3:y}=l;let v=y()?n.get("openIdConnectUrl"):null;const b="implicit",w="password",E=y()?v?"authorization_code":"authorizationCode":"accessCode",x=y()?v?"client_credentials":"clientCredentials":"application";let S=!!(o.getConfigs()||{}).usePkceWithAuthorizationCodeGrant,_=n.get("flow"),O=_===E&&S?_+" with PKCE":_,k=n.get("allowedScopes")||n.get("scopes"),C=!!o.authorized().get(i),P=a()(e=s.allErrors()).call(e,(e=>e.get("authId")===i)),N=!a()(P).call(P,(e=>"validation"===e.get("source"))).size,I=n.get("description");return A.createElement("div",null,A.createElement("h4",null,i," (OAuth2, ",O,") ",A.createElement(d,{path:["securityDefinitions",i]})),this.state.appName?A.createElement("h5",null,"Application: ",this.state.appName," "):null,I&&A.createElement(m,{source:n.get("description")}),C&&A.createElement("h6",null,"Authorized"),v&&A.createElement("p",null,"OpenID Connect URL: ",A.createElement("code",null,v)),(_===b||_===E)&&A.createElement("p",null,"Authorization URL: ",A.createElement("code",null,n.get("authorizationUrl"))),(_===w||_===E||_===x)&&A.createElement("p",null,"Token URL:",A.createElement("code",null," ",n.get("tokenUrl"))),A.createElement("p",{className:"flow"},"Flow: ",A.createElement("code",null,O)),_!==w?null:A.createElement(u,null,A.createElement(u,null,A.createElement("label",{htmlFor:"oauth_username"},"username:"),C?A.createElement("code",null," ",this.state.username," "):A.createElement(p,{tablet:10,desktop:10},A.createElement("input",{id:"oauth_username",type:"text","data-name":"username",onChange:this.onInputChange,autoFocus:!0}))),A.createElement(u,null,A.createElement("label",{htmlFor:"oauth_password"},"password:"),C?A.createElement("code",null," ****** "):A.createElement(p,{tablet:10,desktop:10},A.createElement("input",{id:"oauth_password",type:"password","data-name":"password",onChange:this.onInputChange}))),A.createElement(u,null,A.createElement("label",{htmlFor:"password_type"},"Client credentials location:"),C?A.createElement("code",null," ",this.state.passwordType," "):A.createElement(p,{tablet:10,desktop:10},A.createElement("select",{id:"password_type","data-name":"passwordType",onChange:this.onInputChange},A.createElement("option",{value:"basic"},"Authorization header"),A.createElement("option",{value:"request-body"},"Request body"))))),(_===x||_===b||_===E||_===w)&&(!C||C&&this.state.clientId)&&A.createElement(u,null,A.createElement("label",{htmlFor:"client_id"},"client_id:"),C?A.createElement("code",null," ****** "):A.createElement(p,{tablet:10,desktop:10},A.createElement(g,{id:"client_id",type:"text",required:_===w,initialValue:this.state.clientId,"data-name":"clientId",onChange:this.onInputChange}))),(_===x||_===E||_===w)&&A.createElement(u,null,A.createElement("label",{htmlFor:"client_secret"},"client_secret:"),C?A.createElement("code",null," ****** "):A.createElement(p,{tablet:10,desktop:10},A.createElement(g,{id:"client_secret",initialValue:this.state.clientSecret,type:"password","data-name":"clientSecret",onChange:this.onInputChange}))),!C&&k&&k.size?A.createElement("div",{className:"scopes"},A.createElement("h2",null,"Scopes:",A.createElement("a",{onClick:this.selectScopes,"data-all":!0},"select all"),A.createElement("a",{onClick:this.selectScopes},"select none")),j()(k).call(k,((e,t)=>{var n;return A.createElement(u,{key:t},A.createElement("div",{className:"checkbox"},A.createElement(c,{"data-value":t,id:`${t}-${_}-checkbox-${this.state.name}`,disabled:C,checked:Me()(n=this.state.scopes).call(n,t),type:"checkbox",onChange:this.onScopeChange}),A.createElement("label",{htmlFor:`${t}-${_}-checkbox-${this.state.name}`},A.createElement("span",{className:"item"}),A.createElement("div",{className:"text"},A.createElement("p",{className:"name"},t),A.createElement("p",{className:"description"},e)))))})).toArray()):null,j()(t=P.valueSeq()).call(t,((e,t)=>A.createElement(f,{error:e,key:t}))),A.createElement("div",{className:"auth-btn-wrapper"},N&&(C?A.createElement(h,{className:"btn modal-btn auth authorize",onClick:this.logout},"Logout"):A.createElement(h,{className:"btn modal-btn auth authorize",onClick:this.authorize},"Authorize")),A.createElement(h,{className:"btn modal-btn auth btn-done",onClick:this.close},"Close")))}}class Be extends A.Component{constructor(){super(...arguments),ce()(this,"onClick",(()=>{let{specActions:e,path:t,method:n}=this.props;e.clearResponse(t,n),e.clearRequest(t,n)}))}render(){return A.createElement("button",{className:"btn btn-clear opblock-control__btn",onClick:this.onClick},"Clear")}}const $e=e=>{let{headers:t}=e;return A.createElement("div",null,A.createElement("h5",null,"Response headers"),A.createElement("pre",{className:"microlight"},t))},qe=e=>{let{duration:t}=e;return A.createElement("div",null,A.createElement("h5",null,"Request duration"),A.createElement("pre",{className:"microlight"},t," ms"))};class Ue extends A.Component{shouldComponentUpdate(e){return this.props.response!==e.response||this.props.path!==e.path||this.props.method!==e.method||this.props.displayRequestDuration!==e.displayRequestDuration}render(){const{response:e,getComponent:t,getConfigs:n,displayRequestDuration:r,specSelectors:o,path:s,method:i}=this.props,{showMutatedRequest:a,requestSnippetsEnabled:l}=n(),u=a?o.mutatedRequestFor(s,i):o.requestFor(s,i),p=e.get("status"),h=u.get("url"),f=e.get("headers").toJS(),d=e.get("notDocumented"),m=e.get("error"),g=e.get("text"),y=e.get("duration"),v=c()(f),b=f["content-type"]||f["Content-Type"],w=t("responseBody"),x=j()(v).call(v,(e=>{var t=E()(f[e])?f[e].join():f[e];return A.createElement("span",{className:"headerline",key:e}," ",e,": ",t," ")})),S=0!==x.length,_=t("Markdown",!0),O=t("RequestSnippets",!0),k=t("curl");return A.createElement("div",null,u&&(!0===l||"true"===l?A.createElement(O,{request:u}):A.createElement(k,{request:u,getConfigs:n})),h&&A.createElement("div",null,A.createElement("div",{className:"request-url"},A.createElement("h4",null,"Request URL"),A.createElement("pre",{className:"microlight"},h))),A.createElement("h4",null,"Server response"),A.createElement("table",{className:"responses-table live-responses-table"},A.createElement("thead",null,A.createElement("tr",{className:"responses-header"},A.createElement("td",{className:"col_header response-col_status"},"Code"),A.createElement("td",{className:"col_header response-col_description"},"Details"))),A.createElement("tbody",null,A.createElement("tr",{className:"response"},A.createElement("td",{className:"response-col_status"},p,d?A.createElement("div",{className:"response-undocumented"},A.createElement("i",null," Undocumented 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n=e.get("path"),r=e.get("method"),o=N().List(["paths",n,r]);return-1===pe()(l).call(l,r)?null:A.createElement(c,{key:`${n}-${r}`,specPath:o,op:e,path:n,method:r,tag:t})})).toArray()))}))}render(){let{specSelectors:e}=this.props;const t=e.taggedOperations();return 0===t.size?A.createElement("h3",null," No operations defined in spec!"):A.createElement("div",null,j()(t).call(t,this.renderOperationTag).toArray(),t.size<1?A.createElement("h3",null," No operations defined in spec! 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A.PureComponent{render(){let{specPath:e,response:t,request:r,toggleShown:o,onTryoutClick:s,onResetClick:i,onCancelClick:a,onExecute:l,fn:c,getComponent:u,getConfigs:p,specActions:h,specSelectors:f,authActions:d,authSelectors:m,oas3Actions:g,oas3Selectors:y}=this.props,v=this.props.operation,{deprecated:b,isShown:w,path:E,method:x,op:S,tag:_,operationId:O,allowTryItOut:k,displayRequestDuration:C,tryItOutEnabled:P,executeInProgress:N}=v.toJS(),{description:I,externalDocs:T,schemes:R}=S;const M=T?(0,We.mn)(T.url,f.url(),{selectedServer:y.selectedServer()}):"";let D=v.getIn(["op"]),F=D.get("responses"),B=(0,L.gp)(D,["parameters"]),$=f.operationScheme(E,x),q=["operations",_,O],U=(0,L.nX)(D);const z=u("responses"),V=u("parameters"),W=u("execute"),J=u("clear"),K=u("Collapse"),H=u("Markdown",!0),G=u("schemes"),Z=u("OperationServers"),Y=u("OperationExt"),X=u("OperationSummary"),Q=u("Link"),{showExtensions:ee}=p();if(F&&t&&t.size>0){let e=!F.get(String(t.get("status")))&&!F.get("default");t=t.set("notDocumented",e)}let te=[E,x];const ne=f.validationErrors([E,x]);return A.createElement("div",{className:b?"opblock opblock-deprecated":w?`opblock opblock-${x} is-open`:`opblock opblock-${x}`,id:(0,L.J6)(q.join("-"))},A.createElement(X,{operationProps:v,isShown:w,toggleShown:o,getComponent:u,authActions:d,authSelectors:m,specPath:e}),A.createElement(K,{isOpened:w},A.createElement("div",{className:"opblock-body"},D&&D.size||null===D?null:A.createElement("img",{height:"32px",width:"32px",src:n(2517),className:"opblock-loading-animation"}),b&&A.createElement("h4",{className:"opblock-title_normal"}," Warning: Deprecated"),I&&A.createElement("div",{className:"opblock-description-wrapper"},A.createElement("div",{className:"opblock-description"},A.createElement(H,{source:I}))),M?A.createElement("div",{className:"opblock-external-docs-wrapper"},A.createElement("h4",{className:"opblock-title_normal"},"Find more 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errors-wrapper"},"Please correct the following validation errors and try again.",A.createElement("ul",null,j()(ne).call(ne,((e,t)=>A.createElement("li",{key:t}," ",e," "))))),A.createElement("div",{className:P&&t&&k?"btn-group":"execute-wrapper"},P&&k?A.createElement(W,{operation:D,specActions:h,specSelectors:f,oas3Selectors:y,oas3Actions:g,path:E,method:x,onExecute:l,disabled:N}):null,P&&t&&k?A.createElement(J,{specActions:h,path:E,method:x}):null),N?A.createElement("div",{className:"loading-container"},A.createElement("div",{className:"loading"})):null,F?A.createElement(z,{responses:F,request:r,tryItOutResponse:t,getComponent:u,getConfigs:p,specSelectors:f,oas3Actions:g,oas3Selectors:y,specActions:h,produces:f.producesOptionsFor([E,x]),producesValue:f.currentProducesFor([E,x]),specPath:e.push("responses"),path:E,method:x,displayRequestDuration:C,fn:c}):null,ee&&U.size?A.createElement(Y,{extensions:U,getComponent:u}):null)))}}ce()(Ke,"defaultProps",{operation:null,response:null,request:null,specPath:(0,P.List)(),summary:""});var He=n(79833),Ge=n.n(He);class Ze extends A.PureComponent{render(){let{isShown:e,toggleShown:t,getComponent:n,authActions:r,authSelectors:o,operationProps:s,specPath:i}=this.props,{summary:a,isAuthorized:l,method:c,op:u,showSummary:p,path:h,operationId:f,originalOperationId:d,displayOperationId:m}=s.toJS(),{summary:g}=u,y=s.get("security");const v=n("authorizeOperationBtn",!0),b=n("OperationSummaryMethod"),w=n("OperationSummaryPath"),E=n("JumpToPath",!0),x=n("CopyToClipboardBtn",!0),S=n("ArrowUpIcon"),_=n("ArrowDownIcon"),j=y&&!!y.count(),O=j&&1===y.size&&y.first().isEmpty(),k=!j||O;return A.createElement("div",{className:`opblock-summary opblock-summary-${c}`},A.createElement("button",{"aria-label":`${c} ${h.replace(/\//g,"​/")}`,"aria-expanded":e,className:"opblock-summary-control",onClick:t},A.createElement(b,{method:c}),A.createElement(w,{getComponent:n,operationProps:s,specPath:i}),p?A.createElement("div",{className:"opblock-summary-description"},Ge()(g||a)):null,m&&(d||f)?A.createElement("span",{className:"opblock-summary-operation-id"},d||f):null,e?A.createElement(S,{className:"arrow"}):A.createElement(_,{className:"arrow"})),k?null:A.createElement(v,{isAuthorized:l,onClick:()=>{const e=o.definitionsForRequirements(y);r.showDefinitions(e)}}),A.createElement(x,{textToCopy:`${i.get(1)}`}),A.createElement(E,{path:i}))}}ce()(Ze,"defaultProps",{operationProps:null,specPath:(0,P.List)(),summary:""});class Ye extends A.PureComponent{render(){let{method:e}=this.props;return A.createElement("span",{className:"opblock-summary-method"},e.toUpperCase())}}ce()(Ye,"defaultProps",{operationProps:null});var Xe=n(39940),Qe=n.n(Xe);class et extends A.PureComponent{render(){let{getComponent:e,operationProps:t}=this.props,{deprecated:n,isShown:r,path:o,tag:s,operationId:i,isDeepLinkingEnabled:a}=t.toJS();const l=o.split(/(?=\/)/g);for(let e=1;e<l.length;e+=2)Qe()(l).call(l,e,0,A.createElement("wbr",{key:e}));const c=e("DeepLink");return A.createElement("span",{className:n?"opblock-summary-path__deprecated":"opblock-summary-path","data-path":o},A.createElement(c,{enabled:a,isShown:r,path:(0,L.oJ)(`${s}/${i}`),text:l}))}}const tt=e=>{var t;let{extensions:n,getComponent:r}=e,o=r("OperationExtRow");return A.createElement("div",{className:"opblock-section"},A.createElement("div",{className:"opblock-section-header"},A.createElement("h4",null,"Extensions")),A.createElement("div",{className:"table-container"},A.createElement("table",null,A.createElement("thead",null,A.createElement("tr",null,A.createElement("td",{className:"col_header"},"Field"),A.createElement("td",{className:"col_header"},"Value"))),A.createElement("tbody",null,j()(t=n.entrySeq()).call(t,(e=>{let[t,n]=e;return A.createElement(o,{key:`${t}-${n}`,xKey:t,xVal:n})}))))))},nt=e=>{let{xKey:t,xVal:n}=e;const r=n?n.toJS?n.toJS():n:null;return A.createElement("tr",null,A.createElement("td",null,t),A.createElement("td",null,p()(r)))};var rt=n(86),ot=n.n(rt),st=n(94184),it=n.n(st),at=n(33424),lt=n(27361),ct=n.n(lt),ut=n(23560),pt=n.n(ut),ht=n(35823),ft=n.n(ht),dt=n(74855);const mt=e=>{let{value:t,fileName:n,className:r,downloadable:o,getConfigs:s,canCopy:i,language:l}=e;const c=pt()(s)?s():null,u=!1!==ct()(c,"syntaxHighlight")&&ct()(c,"syntaxHighlight.activated",!0),p=(0,A.useRef)(null);(0,A.useEffect)((()=>{var e;const t=a()(e=Ne()(p.current.childNodes)).call(e,(e=>!!e.nodeType&&e.classList.contains("microlight")));return ot()(t).call(t,(e=>e.addEventListener("mousewheel",h,{passive:!1}))),()=>{ot()(t).call(t,(e=>e.removeEventListener("mousewheel",h)))}}),[t,r,l]);const h=e=>{const{target:t,deltaY:n}=e,{scrollHeight:r,offsetHeight:o,scrollTop:s}=t;r>o&&(0===s&&n<0||o+s>=r&&n>0)&&e.preventDefault()};return A.createElement("div",{className:"highlight-code",ref:p},o?A.createElement("div",{className:"download-contents",onClick:()=>{ft()(t,n)}},"Download"):null,i&&A.createElement("div",{className:"copy-to-clipboard"},A.createElement(dt.CopyToClipboard,{text:t},A.createElement("button",null))),u?A.createElement(at.d3,{language:l,className:it()(r,"microlight"),style:(0,at.C2)(ct()(c,"syntaxHighlight.theme","agate"))},t):A.createElement("pre",{className:it()(r,"microlight")},t))};mt.defaultProps={fileName:"response.txt"};const gt=mt;class yt extends A.Component{constructor(){super(...arguments),ce()(this,"onChangeProducesWrapper",(e=>this.props.specActions.changeProducesValue([this.props.path,this.props.method],e))),ce()(this,"onResponseContentTypeChange",(e=>{let{controlsAcceptHeader:t,value:n}=e;const{oas3Actions:r,path:o,method:s}=this.props;t&&r.setResponseContentType({value:n,path:o,method:s})}))}render(){var e;let{responses:t,tryItOutResponse:n,getComponent:r,getConfigs:o,specSelectors:s,fn:i,producesValue:a,displayRequestDuration:l,specPath:c,path:u,method:p,oas3Selectors:h,oas3Actions:f}=this.props,d=(0,L.iQ)(t);const m=r("contentType"),g=r("liveResponse"),y=r("response");let v=this.props.produces&&this.props.produces.size?this.props.produces:yt.defaultProps.produces;const b=s.isOAS3()?(0,L.QG)(t):null,w=function(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"_";return e.replace(/[^\w-]/g,t)}(`${p}${u}_responses`),E=`${w}_select`;return A.createElement("div",{className:"responses-wrapper"},A.createElement("div",{className:"opblock-section-header"},A.createElement("h4",null,"Responses"),s.isOAS3()?null:A.createElement("label",{htmlFor:E},A.createElement("span",null,"Response content type"),A.createElement(m,{value:a,ariaControls:w,ariaLabel:"Response content 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n||o})),this.state={responseContentType:""}}render(){var e,t;let{path:n,method:r,code:o,response:s,className:i,specPath:a,fn:l,getComponent:c,getConfigs:u,specSelectors:p,contentType:h,controlsAcceptHeader:f,oas3Actions:d}=this.props,{inferSchema:m,getSampleSchema:g}=l,y=p.isOAS3();const{showExtensions:v}=u();let b=v?(0,L.nX)(s):null,w=s.get("headers"),E=s.get("links");const x=c("ResponseExtension"),S=c("headers"),_=c("highlightCode"),O=c("modelExample"),k=c("Markdown",!0),C=c("operationLink"),N=c("contentType"),I=c("ExamplesSelect"),T=c("Example");var R,M;const D=this.state.responseContentType||h,F=s.getIn(["content",D],(0,P.Map)({})),B=F.get("examples",null);if(y){const e=F.get("schema");R=e?m(e.toJS()):null,M=e?(0,P.List)(["content",this.state.responseContentType,"schema"]):a}else R=s.get("schema"),M=s.has("schema")?a.push("schema"):a;let $,q,U=!1,z={includeReadOnly:!0};if(y){var V;if(q=null===(V=F.get("schema"))||void 0===V?void 0:V.toJS(),B){const 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A.createElement(x,{key:`${t}-${n}`,xKey:t,xVal:n})})):null,y&&s.get("content")?A.createElement("section",{className:"response-controls"},A.createElement("div",{className:it()("response-control-media-type",{"response-control-media-type--accept-controller":f})},A.createElement("small",{className:"response-control-media-type__title"},"Media type"),A.createElement(N,{value:this.state.responseContentType,contentTypes:s.get("content")?s.get("content").keySeq():(0,P.Seq)(),onChange:this._onContentTypeChange,ariaLabel:"Media Type"}),f?A.createElement("small",{className:"response-control-media-type__accept-message"},"Controls ",A.createElement("code",null,"Accept")," 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xt=e=>{let{xKey:t,xVal:n}=e;return A.createElement("div",{className:"response__extension"},t,": ",String(n))};var St=n(63460),_t=n.n(St),jt=n(3131),Ot=n.n(jt),kt=n(7334),At=n.n(kt);class Ct extends A.PureComponent{constructor(){super(...arguments),ce()(this,"state",{parsedContent:null}),ce()(this,"updateParsedContent",(e=>{const{content:t}=this.props;if(e!==t)if(t&&t instanceof Blob){var n=new FileReader;n.onload=()=>{this.setState({parsedContent:n.result})},n.readAsText(t)}else this.setState({parsedContent:t.toString()})}))}componentDidMount(){this.updateParsedContent(null)}componentDidUpdate(e){this.updateParsedContent(e.content)}render(){let{content:e,contentType:t,url:n,headers:r={},getConfigs:o,getComponent:i}=this.props;const{parsedContent:a}=this.state,l=i("highlightCode"),c="response_"+(new Date).getTime();let u,h;if(n=n||"",(/^application\/octet-stream/i.test(t)||r["Content-Disposition"]&&/attachment/i.test(r["Content-Disposition"])||r["content-disposition"]&&/attachment/i.test(r["content-disposition"])||r["Content-Description"]&&/File Transfer/i.test(r["Content-Description"])||r["content-description"]&&/File Transfer/i.test(r["content-description"]))&&e.size>0)if("Blob"in window){let o=t||"text/html",i=e instanceof Blob?e:new Blob([e],{type:o}),a=_t().createObjectURL(i),l=[o,n.substr(s()(n).call(n,"/")+1),a].join(":"),c=r["content-disposition"]||r["Content-Disposition"];if(void 0!==c){let e=(0,L.DR)(c);null!==e&&(l=e)}h=F.Z.navigator&&F.Z.navigator.msSaveOrOpenBlob?A.createElement("div",null,A.createElement("a",{href:a,onClick:()=>F.Z.navigator.msSaveOrOpenBlob(i,l)},"Download file")):A.createElement("div",null,A.createElement("a",{href:a,download:l},"Download file"))}else h=A.createElement("pre",{className:"microlight"},"Download headers detected but your browser does not support 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e?A.createElement(l,{downloadable:!0,fileName:`${c}.txt`,value:e,getConfigs:o,canCopy:!0}):e.size>0?a?A.createElement("div",null,A.createElement("p",{className:"i"},"Unrecognized response type; displaying content as text."),A.createElement(l,{downloadable:!0,fileName:`${c}.txt`,value:a,getConfigs:o,canCopy:!0})):A.createElement("p",{className:"i"},"Unrecognized response type; unable to display."):null;return h?A.createElement("div",null,A.createElement("h5",null,"Response body"),h):null}}var Pt=n(3665),Nt=n.n(Pt);class It extends A.Component{constructor(e){super(e),ce()(this,"onChange",((e,t,n)=>{let{specActions:{changeParamByIdentity:r},onChangeKey:o}=this.props;r(o,e,t,n)})),ce()(this,"onChangeConsumesWrapper",(e=>{let{specActions:{changeConsumesValue:t},onChangeKey:n}=this.props;t(n,e)})),ce()(this,"toggleTab",(e=>"parameters"===e?this.setState({parametersVisible:!0,callbackVisible:!1}):"callbacks"===e?this.setState({callbackVisible:!0,parametersVisible:!1}):void 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e[n]??(e[n]=[]),e[n].push(t),e}),{}))).call(e,((e,t)=>k()(e).call(e,t)),[]);return A.createElement("div",{className:"opblock-section"},A.createElement("div",{className:"opblock-section-header"},_?A.createElement("div",{className:"tab-header"},A.createElement("div",{onClick:()=>this.toggleTab("parameters"),className:`tab-item ${this.state.parametersVisible&&"active"}`},A.createElement("h4",{className:"opblock-title"},A.createElement("span",null,"Parameters"))),m.get("callbacks")?A.createElement("div",{onClick:()=>this.toggleTab("callbacks"),className:`tab-item 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f.setRequestBodyValue({pathMethod:h,value:r.setIn(t,e)})}f.setRequestBodyValue({value:e,pathMethod:h})},onChangeIncludeEmpty:(e,t)=>{f.setRequestBodyInclusion({pathMethod:h,value:t,name:e})},contentType:d.requestContentType(...h)}))))}}ce()(It,"defaultProps",{onTryoutClick:Function.prototype,onCancelClick:Function.prototype,tryItOutEnabled:!1,allowTryItOut:!0,onChangeKey:[],specPath:[]});const Tt=e=>{let{xKey:t,xVal:n}=e;return A.createElement("div",{className:"parameter__extension"},t,": ",String(n))},Rt={onChange:()=>{},isIncludedOptions:{}};class Mt extends A.Component{constructor(){super(...arguments),ce()(this,"onCheckboxChange",(e=>{const{onChange:t}=this.props;t(e.target.checked)}))}componentDidMount(){const{isIncludedOptions:e,onChange:t}=this.props,{shouldDispatchInit:n,defaultValue:r}=e;n&&t(r)}render(){let{isIncluded:e,isDisabled:t}=this.props;return A.createElement("div",null,A.createElement("label",{className:it()("parameter__empty_value_toggle",{disabled:t})},A.createElement("input",{type:"checkbox",disabled:t,checked:!t&&e,onChange:this.onCheckboxChange}),"Send empty value"))}}ce()(Mt,"defaultProps",Rt);var Dt=n(19069);class Ft extends A.Component{constructor(e,t){var n;super(e,t),n=this,ce()(this,"onChangeWrapper",(function(e){let t,r=arguments.length>1&&void 0!==arguments[1]&&arguments[1],{onChange:o,rawParam:s}=n.props;return t=""===e||e&&0===e.size?null:e,o(s,t,r)})),ce()(this,"_onExampleSelect",(e=>{this.props.oas3Actions.setActiveExamplesMember({name:e,pathMethod:this.props.pathMethod,contextType:"parameters",contextName:this.getParamKey()})})),ce()(this,"onChangeIncludeEmpty",(e=>{let{specActions:t,param:n,pathMethod:r}=this.props;const o=n.get("name"),s=n.get("in");return t.updateEmptyParamInclusion(r,o,s,e)})),ce()(this,"setDefaultValue",(()=>{let{specSelectors:e,pathMethod:t,rawParam:n,oas3Selectors:r,fn:o}=this.props;const s=e.parameterWithMetaByIdentity(t,n)||(0,P.Map)(),{schema:i}=(0,Dt.Z)(s,{isOAS3:e.isOAS3()}),a=s.get("content",(0,P.Map)()).keySeq().first(),l=i?o.getSampleSchema(i.toJS(),a,{includeWriteOnly:!0}):null;if(s&&void 0===s.get("value")&&"body"!==s.get("in")){let n;if(e.isSwagger2())n=void 0!==s.get("x-example")?s.get("x-example"):void 0!==s.getIn(["schema","example"])?s.getIn(["schema","example"]):i&&i.getIn(["default"]);else if(e.isOAS3()){const e=r.activeExamplesMember(...t,"parameters",this.getParamKey());n=void 0!==s.getIn(["examples",e,"value"])?s.getIn(["examples",e,"value"]):void 0!==s.getIn(["content",a,"example"])?s.getIn(["content",a,"example"]):void 0!==s.get("example")?s.get("example"):void 0!==(i&&i.get("example"))?i&&i.get("example"):void 0!==(i&&i.get("default"))?i&&i.get("default"):s.get("default")}void 0===n||P.List.isList(n)||(n=(0,L.Pz)(n)),void 0!==n?this.onChangeWrapper(n):i&&"object"===i.get("type")&&l&&!s.get("examples")&&this.onChangeWrapper(P.List.isList(l)?l:(0,L.Pz)(l))}})),this.setDefaultValue()}UNSAFE_componentWillReceiveProps(e){let t,{specSelectors:n,pathMethod:r,rawParam:o}=e,s=n.isOAS3(),i=n.parameterWithMetaByIdentity(r,o)||new P.Map;if(i=i.isEmpty()?o:i,s){let{schema:e}=(0,Dt.Z)(i,{isOAS3:s});t=e?e.get("enum"):void 0}else t=i?i.get("enum"):void 0;let a,l=i?i.get("value"):void 0;void 0!==l?a=l:o.get("required")&&t&&t.size&&(a=t.first()),void 0!==a&&a!==l&&this.onChangeWrapper((0,L.D$)(a)),this.setDefaultValue()}getParamKey(){const{param:e}=this.props;return e?`${e.get("name")}-${e.get("in")}`:null}render(){var e,t;let{param:n,rawParam:r,getComponent:o,getConfigs:s,isExecute:i,fn:a,onChangeConsumes:l,specSelectors:c,pathMethod:u,specPath:p,oas3Selectors:h}=this.props,f=c.isOAS3();const{showExtensions:d,showCommonExtensions:m}=s();if(n||(n=r),!r)return null;const g=o("JsonSchemaForm"),y=o("ParamBody");let v=n.get("in"),b="body"!==v?null:A.createElement(y,{getComponent:o,getConfigs:s,fn:a,param:n,consumes:c.consumesOptionsFor(u),consumesValue:c.contentTypeValues(u).get("requestContentType"),onChange:this.onChangeWrapper,onChangeConsumes:l,isExecute:i,specSelectors:c,pathMethod:u});const w=o("modelExample"),E=o("Markdown",!0),x=o("ParameterExt"),S=o("ParameterIncludeEmpty"),_=o("ExamplesSelectValueRetainer"),O=o("Example");let k,C,N,I,{schema:T}=(0,Dt.Z)(n,{isOAS3:f}),R=c.parameterWithMetaByIdentity(u,r)||(0,P.Map)(),M=T?T.get("format"):null,D=T?T.get("type"):null,B=T?T.getIn(["items","type"]):null,$="formData"===v,q="FormData"in F.Z,U=n.get("required"),z=R?R.get("value"):"",V=m?(0,L.po)(T):null,W=d?(0,L.nX)(n):null,J=!1;return void 0!==n&&T&&(k=T.get("items")),void 0!==k?(C=k.get("enum"),N=k.get("default")):T&&(C=T.get("enum")),C&&C.size&&C.size>0&&(J=!0),void 0!==n&&(T&&(N=T.get("default")),void 0===N&&(N=n.get("default")),I=n.get("example"),void 0===I&&(I=n.get("x-example"))),A.createElement("tr",{"data-param-name":n.get("name"),"data-param-in":n.get("in")},A.createElement("td",{className:"parameters-col_name"},A.createElement("div",{className:U?"parameter__name required":"parameter__name"},n.get("name"),U?A.createElement("span",null," *"):null),A.createElement("div",{className:"parameter__type"},D,B&&`[${B}]`,M&&A.createElement("span",{className:"prop-format"},"($",M,")")),A.createElement("div",{className:"parameter__deprecated"},f&&n.get("deprecated")?"deprecated":null),A.createElement("div",{className:"parameter__in"},"(",n.get("in"),")"),m&&V.size?j()(e=V.entrySeq()).call(e,(e=>{let[t,n]=e;return A.createElement(x,{key:`${t}-${n}`,xKey:t,xVal:n})})):null,d&&W.size?j()(t=W.entrySeq()).call(t,(e=>{let[t,n]=e;return A.createElement(x,{key:`${t}-${n}`,xKey:t,xVal:n})})):null),A.createElement("td",{className:"parameters-col_description"},n.get("description")?A.createElement(E,{source:n.get("description")}):null,!b&&i||!J?null:A.createElement(E,{className:"parameter__enum",source:"<i>Available values</i> : "+j()(C).call(C,(function(e){return e})).toArray().join(", ")}),!b&&i||void 0===N?null:A.createElement(E,{className:"parameter__default",source:"<i>Default value</i> : "+N}),!b&&i||void 0===I?null:A.createElement(E,{source:"<i>Example</i> : "+I}),$&&!q&&A.createElement("div",null,"Error: your browser does not support 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Lt=n(87198),Bt=n.n(Lt);class $t extends A.Component{constructor(){super(...arguments),ce()(this,"handleValidateParameters",(()=>{let{specSelectors:e,specActions:t,path:n,method:r}=this.props;return t.validateParams([n,r]),e.validateBeforeExecute([n,r])})),ce()(this,"handleValidateRequestBody",(()=>{let{path:e,method:t,specSelectors:n,oas3Selectors:r,oas3Actions:o}=this.props,s={missingBodyValue:!1,missingRequiredKeys:[]};o.clearRequestBodyValidateError({path:e,method:t});let i=n.getOAS3RequiredRequestBodyContentType([e,t]),a=r.requestBodyValue(e,t),l=r.validateBeforeExecute([e,t]),c=r.requestContentType(e,t);if(!l)return s.missingBodyValue=!0,o.setRequestBodyValidateError({path:e,method:t,validationErrors:s}),!1;if(!i)return!0;let u=r.validateShallowRequired({oas3RequiredRequestBodyContentType:i,oas3RequestContentType:c,oas3RequestBodyValue:a});return!u||u.length<1||(ot()(u).call(u,(e=>{s.missingRequiredKeys.push(e)})),o.setRequestBodyValidateError({path:e,method:t,validationErrors:s}),!1)})),ce()(this,"handleValidationResultPass",(()=>{let{specActions:e,operation:t,path:n,method:r}=this.props;this.props.onExecute&&this.props.onExecute(),e.execute({operation:t,path:n,method:r})})),ce()(this,"handleValidationResultFail",(()=>{let{specActions:e,path:t,method:n}=this.props;e.clearValidateParams([t,n]),Bt()((()=>{e.validateParams([t,n])}),40)})),ce()(this,"handleValidationResult",(e=>{e?this.handleValidationResultPass():this.handleValidationResultFail()})),ce()(this,"onClick",(()=>{let e=this.handleValidateParameters(),t=this.handleValidateRequestBody(),n=e&&t;this.handleValidationResult(n)})),ce()(this,"onChangeProducesWrapper",(e=>this.props.specActions.changeProducesValue([this.props.path,this.props.method],e)))}render(){const{disabled:e}=this.props;return A.createElement("button",{className:"btn execute opblock-control__btn",onClick:this.onClick,disabled:e},"Execute")}}class qt extends A.Component{render(){var e;let{headers:t,getComponent:n}=this.props;const r=n("Property"),o=n("Markdown",!0);return t&&t.size?A.createElement("div",{className:"headers-wrapper"},A.createElement("h4",{className:"headers__title"},"Headers:"),A.createElement("table",{className:"headers"},A.createElement("thead",null,A.createElement("tr",{className:"header-row"},A.createElement("th",{className:"header-col"},"Name"),A.createElement("th",{className:"header-col"},"Description"),A.createElement("th",{className:"header-col"},"Type"))),A.createElement("tbody",null,j()(e=t.entrySeq()).call(e,(e=>{let[t,n]=e;if(!N().Map.isMap(n))return null;const s=n.get("description"),i=n.getIn(["schema"])?n.getIn(["schema","type"]):n.getIn(["type"]),a=n.getIn(["schema","example"]);return A.createElement("tr",{key:t},A.createElement("td",{className:"header-col"},t),A.createElement("td",{className:"header-col"},s?A.createElement(o,{source:s}):null),A.createElement("td",{className:"header-col"},i," ",a?A.createElement(r,{propKey:"Example",propVal:a,propClass:"header-example"}):null))})).toArray()))):null}}class Ut extends A.Component{render(){let{editorActions:e,errSelectors:t,layoutSelectors:n,layoutActions:r,getComponent:o}=this.props;const s=o("Collapse");if(e&&e.jumpToLine)var i=e.jumpToLine;let l=t.allErrors(),c=a()(l).call(l,(e=>"thrown"===e.get("type")||"error"===e.get("level")));if(!c||c.count()<1)return null;let u=n.isShown(["errorPane"],!0),p=c.sortBy((e=>e.get("line")));return A.createElement("pre",{className:"errors-wrapper"},A.createElement("hgroup",{className:"error"},A.createElement("h4",{className:"errors__title"},"Errors"),A.createElement("button",{className:"btn errors__clear-btn",onClick:()=>r.show(["errorPane"],!u)},u?"Hide":"Show")),A.createElement(s,{isOpened:u,animated:!0},A.createElement("div",{className:"errors"},j()(p).call(p,((e,t)=>{let n=e.get("type");return"thrown"===n||"auth"===n?A.createElement(zt,{key:t,error:e.get("error")||e,jumpToLine:i}):"spec"===n?A.createElement(Vt,{key:t,error:e,jumpToLine:i}):void 0})))))}}const zt=e=>{let{error:t,jumpToLine:n}=e;if(!t)return null;let r=t.get("line");return A.createElement("div",{className:"error-wrapper"},t?A.createElement("div",null,A.createElement("h4",null,t.get("source")&&t.get("level")?Wt(t.get("source"))+" "+t.get("level"):"",t.get("path")?A.createElement("small",null," at ",t.get("path")):null),A.createElement("span",{className:"message thrown"},t.get("message")),A.createElement("div",{className:"error-line"},r&&n?A.createElement("a",{onClick:m()(n).call(n,null,r)},"Jump to line ",r):null)):null)},Vt=e=>{let{error:t,jumpToLine:n}=e,r=null;return t.get("path")?r=P.List.isList(t.get("path"))?A.createElement("small",null,"at ",t.get("path").join(".")):A.createElement("small",null,"at ",t.get("path")):t.get("line")&&!n&&(r=A.createElement("small",null,"on line ",t.get("line"))),A.createElement("div",{className:"error-wrapper"},t?A.createElement("div",null,A.createElement("h4",null,Wt(t.get("source"))+" "+t.get("level")," ",r),A.createElement("span",{className:"message"},t.get("message")),A.createElement("div",{className:"error-line"},n?A.createElement("a",{onClick:m()(n).call(n,null,t.get("line"))},"Jump to line ",t.get("line")):null)):null)};function Wt(e){var t;return j()(t=(e||"").split(" ")).call(t,(e=>e[0].toUpperCase()+b()(e).call(e,1))).join(" ")}zt.defaultProps={jumpToLine:null};class Jt extends A.Component{constructor(){super(...arguments),ce()(this,"onChangeWrapper",(e=>this.props.onChange(e.target.value)))}componentDidMount(){this.props.contentTypes&&this.props.onChange(this.props.contentTypes.first())}UNSAFE_componentWillReceiveProps(e){var t;e.contentTypes&&e.contentTypes.size&&(Me()(t=e.contentTypes).call(t,e.value)||e.onChange(e.contentTypes.first()))}render(){let{ariaControls:e,ariaLabel:t,className:n,contentTypes:r,controlId:o,value:s}=this.props;return r&&r.size?A.createElement("div",{className:"content-type-wrapper "+(n||"")},A.createElement("select",{"aria-controls":e,"aria-label":t,className:"content-type",id:o,onChange:this.onChangeWrapper,value:s||""},j()(r).call(r,(e=>A.createElement("option",{key:e,value:e},e))).toArray())):null}}ce()(Jt,"defaultProps",{onChange:()=>{},value:null,contentTypes:(0,P.fromJS)(["application/json"])});var Kt=n(23101),Ht=n.n(Kt),Gt=n(81607),Zt=n.n(Gt);function Yt(){for(var e,t=arguments.length,n=new Array(t),r=0;r<t;r++)n[r]=arguments[r];return Zt()(e=a()(n).call(n,(e=>!!e)).join(" ")).call(e)}class Xt extends A.Component{render(){let{fullscreen:e,full:t,...n}=this.props;if(e)return A.createElement("section",n);let r="swagger-container"+(t?"-full":"");return A.createElement("section",Ht()({},n,{className:Yt(n.className,r)}))}}const Qt={mobile:"",tablet:"-tablet",desktop:"-desktop",large:"-hd"};class en extends A.Component{render(){const{hide:e,keepContents:t,mobile:n,tablet:r,desktop:o,large:s,...i}=this.props;if(e&&!t)return A.createElement("span",null);let a=[];for(let e in Qt){if(!Object.prototype.hasOwnProperty.call(Qt,e))continue;let t=Qt[e];if(e in this.props){let n=this.props[e];if(n<1){a.push("none"+t);continue}a.push("block"+t),a.push("col-"+n+t)}}e&&a.push("hidden");let l=Yt(i.className,...a);return A.createElement("section",Ht()({},i,{className:l}))}}class tn extends A.Component{render(){return A.createElement("div",Ht()({},this.props,{className:Yt(this.props.className,"wrapper")}))}}class nn extends A.Component{render(){return A.createElement("button",Ht()({},this.props,{className:Yt(this.props.className,"button")}))}}ce()(nn,"defaultProps",{className:""});const rn=e=>A.createElement("textarea",e),on=e=>A.createElement("input",e);class sn extends A.Component{constructor(e,t){let n;super(e,t),ce()(this,"onChange",(e=>{let t,{onChange:n,multiple:r}=this.props,o=b()([]).call(e.target.options);var s;r?t=j()(s=a()(o).call(o,(function(e){return e.selected}))).call(s,(function(e){return e.value})):t=e.target.value;this.setState({value:t}),n&&n(t)})),n=e.value?e.value:e.multiple?[""]:"",this.state={value:n}}UNSAFE_componentWillReceiveProps(e){e.value!==this.props.value&&this.setState({value:e.value})}render(){var e,t;let{allowedValues:n,multiple:r,allowEmptyValue:o,disabled:s}=this.props,i=(null===(e=this.state.value)||void 0===e||null===(t=e.toJS)||void 0===t?void 0:t.call(e))||this.state.value;return A.createElement("select",{className:this.props.className,multiple:r,value:i,onChange:this.onChange,disabled:s},o?A.createElement("option",{value:""},"--"):null,j()(n).call(n,(function(e,t){return A.createElement("option",{key:t,value:String(e)},String(e))})))}}ce()(sn,"defaultProps",{multiple:!1,allowEmptyValue:!0});class an extends A.Component{render(){return A.createElement("a",Ht()({},this.props,{rel:"noopener noreferrer",className:Yt(this.props.className,"link")}))}}const ln=e=>{let{children:t}=e;return A.createElement("div",{className:"no-margin"}," ",t," ")};class cn extends A.Component{renderNotAnimated(){return this.props.isOpened?A.createElement(ln,null,this.props.children):A.createElement("noscript",null)}render(){let{animated:e,isOpened:t,children:n}=this.props;return e?(n=t?n:null,A.createElement(ln,null,n)):this.renderNotAnimated()}}ce()(cn,"defaultProps",{isOpened:!1,animated:!1});class un extends A.Component{constructor(){var e;super(...arguments),this.setTagShown=m()(e=this._setTagShown).call(e,this)}_setTagShown(e,t){this.props.layoutActions.show(e,t)}showOp(e,t){let{layoutActions:n}=this.props;n.show(e,t)}render(){let{specSelectors:e,layoutSelectors:t,layoutActions:n,getComponent:r}=this.props,o=e.taggedOperations();const s=r("Collapse");return A.createElement("div",null,A.createElement("h4",{className:"overview-title"},"Overview"),j()(o).call(o,((e,r)=>{let o=e.get("operations"),i=["overview-tags",r],a=t.isShown(i,!0);return A.createElement("div",{key:"overview-"+r},A.createElement("h4",{onClick:()=>n.show(i,!a),className:"link overview-tag"}," ",a?"-":"+",r),A.createElement(s,{isOpened:a,animated:!0},j()(o).call(o,(e=>{let{path:r,method:o,id:s}=e.toObject(),i="operations",a=s,l=t.isShown([i,a]);return A.createElement(pn,{key:s,path:r,method:o,id:r+"-"+o,shown:l,showOpId:a,showOpIdPrefix:i,href:`#operation-${a}`,onClick:n.show})})).toArray()))})).toArray(),o.size<1&&A.createElement("h3",null," No operations defined in spec! "))}}class pn extends A.Component{constructor(e){var t;super(e),this.onClick=m()(t=this._onClick).call(t,this)}_onClick(){let{showOpId:e,showOpIdPrefix:t,onClick:n,shown:r}=this.props;n([t,e],!r)}render(){let{id:e,method:t,shown:n,href:r}=this.props;return A.createElement(an,{href:r,onClick:this.onClick,className:"block opblock-link "+(n?"shown":"")},A.createElement("div",null,A.createElement("small",{className:`bold-label-${t}`},t.toUpperCase()),A.createElement("span",{className:"bold-label"},e)))}}class hn extends A.Component{componentDidMount(){this.props.initialValue&&(this.inputRef.value=this.props.initialValue)}render(){const{value:e,defaultValue:t,initialValue:n,...r}=this.props;return A.createElement("input",Ht()({},r,{ref:e=>this.inputRef=e}))}}class fn extends A.Component{render(){const{host:e,basePath:t}=this.props;return A.createElement("pre",{className:"base-url"},"[ Base URL: ",e,t," ]")}}class dn extends A.PureComponent{render(){const{url:e,getComponent:t}=this.props,n=t("Link");return A.createElement(n,{target:"_blank",href:(0,L.Nm)(e)},A.createElement("span",{className:"url"}," ",e))}}class mn extends A.Component{render(){const{info:e,url:t,host:n,basePath:r,getComponent:o,externalDocs:s,selectedServer:i,url:a}=this.props,l=e.get("version"),c=e.get("description"),u=e.get("title"),p=(0,We.mn)(e.get("termsOfService"),a,{selectedServer:i}),h=e.get("contact"),f=e.get("license"),d=s&&s.get("url"),m=(0,We.mn)(d,a,{selectedServer:i}),g=s&&s.get("description"),y=o("Markdown",!0),v=o("Link"),b=o("VersionStamp"),w=o("InfoUrl"),E=o("InfoBasePath"),x=o("License"),S=o("Contact");return A.createElement("div",{className:"info"},A.createElement("hgroup",{className:"main"},A.createElement("h2",{className:"title"},u,l&&A.createElement(b,{version:l})),n||r?A.createElement(E,{host:n,basePath:r}):null,t&&A.createElement(w,{getComponent:o,url:t})),A.createElement("div",{className:"description"},A.createElement(y,{source:c})),p&&A.createElement("div",{className:"info__tos"},A.createElement(v,{target:"_blank",href:(0,L.Nm)(p)},"Terms of service")),(null==h?void 0:h.size)>0&&A.createElement(S,{getComponent:o,data:h,selectedServer:i,url:t}),(null==f?void 0:f.size)>0&&A.createElement(x,{getComponent:o,license:f,selectedServer:i,url:t}),m?A.createElement(v,{className:"info__extdocs",target:"_blank",href:(0,L.Nm)(m)},g||m):null)}}const gn=mn;class yn extends A.Component{render(){const{specSelectors:e,getComponent:t,oas3Selectors:n}=this.props,r=e.info(),o=e.url(),s=e.basePath(),i=e.host(),a=e.externalDocs(),l=n.selectedServer(),c=t("info");return A.createElement("div",null,r&&r.count()?A.createElement(c,{info:r,url:o,host:i,basePath:s,externalDocs:a,getComponent:t,selectedServer:l}):null)}}class vn extends A.Component{render(){const{data:e,getComponent:t,selectedServer:n,url:r}=this.props,o=e.get("name","the developer"),s=(0,We.mn)(e.get("url"),r,{selectedServer:n}),i=e.get("email"),a=t("Link");return A.createElement("div",{className:"info__contact"},s&&A.createElement("div",null,A.createElement(a,{href:(0,L.Nm)(s),target:"_blank"},o," - Website")),i&&A.createElement(a,{href:(0,L.Nm)(`mailto:${i}`)},s?`Send email to ${o}`:`Contact ${o}`))}}const bn=vn;class wn extends A.Component{render(){const{license:e,getComponent:t,selectedServer:n,url:r}=this.props,o=e.get("name","License"),s=(0,We.mn)(e.get("url"),r,{selectedServer:n}),i=t("Link");return A.createElement("div",{className:"info__license"},s?A.createElement("div",{className:"info__license__url"},A.createElement(i,{target:"_blank",href:(0,L.Nm)(s)},o)):A.createElement("span",null,o))}}const En=wn;class xn extends A.Component{render(){return null}}class Sn extends A.Component{render(){let{getComponent:e}=this.props;const t=e("CopyIcon");return A.createElement("div",{className:"view-line-link copy-to-clipboard",title:"Copy to clipboard"},A.createElement(dt.CopyToClipboard,{text:this.props.textToCopy},A.createElement(t,null)))}}class _n extends A.Component{render(){return A.createElement("div",{className:"footer"})}}class jn extends A.Component{constructor(){super(...arguments),ce()(this,"onFilterChange",(e=>{const{target:{value:t}}=e;this.props.layoutActions.updateFilter(t)}))}render(){const{specSelectors:e,layoutSelectors:t,getComponent:n}=this.props,r=n("Col"),o="loading"===e.loadingStatus(),s="failed"===e.loadingStatus(),i=t.currentFilter(),a=["operation-filter-input"];return s&&a.push("failed"),o&&a.push("loading"),A.createElement("div",null,null===i||!1===i||"false"===i?null:A.createElement("div",{className:"filter-container"},A.createElement(r,{className:"filter wrapper",mobile:12},A.createElement("input",{className:a.join(" "),placeholder:"Filter by tag",type:"text",onChange:this.onFilterChange,value:!0===i||"true"===i?"":i,disabled:o}))))}}const On=Function.prototype;class kn extends A.PureComponent{constructor(e,t){super(e,t),ce()(this,"updateValues",(e=>{let{param:t,isExecute:n,consumesValue:r=""}=e,o=/xml/i.test(r),s=/json/i.test(r),i=o?t.get("value_xml"):t.get("value");if(void 0!==i){let e=!i&&s?"{}":i;this.setState({value:e}),this.onChange(e,{isXml:o,isEditBox:n})}else o?this.onChange(this.sample("xml"),{isXml:o,isEditBox:n}):this.onChange(this.sample(),{isEditBox:n})})),ce()(this,"sample",(e=>{let{param:t,fn:n}=this.props,r=n.inferSchema(t.toJS());return n.getSampleSchema(r,e,{includeWriteOnly:!0})})),ce()(this,"onChange",((e,t)=>{let{isEditBox:n,isXml:r}=t;this.setState({value:e,isEditBox:n}),this._onChange(e,r)})),ce()(this,"_onChange",((e,t)=>{(this.props.onChange||On)(e,t)})),ce()(this,"handleOnChange",(e=>{const{consumesValue:t}=this.props,n=/xml/i.test(t),r=e.target.value;this.onChange(r,{isXml:n,isEditBox:this.state.isEditBox})})),ce()(this,"toggleIsEditBox",(()=>this.setState((e=>({isEditBox:!e.isEditBox}))))),this.state={isEditBox:!1,value:""}}componentDidMount(){this.updateValues.call(this,this.props)}UNSAFE_componentWillReceiveProps(e){this.updateValues.call(this,e)}render(){let{onChangeConsumes:e,param:t,isExecute:n,specSelectors:r,pathMethod:o,getConfigs:s,getComponent:i}=this.props;const a=i("Button"),l=i("TextArea"),c=i("highlightCode"),u=i("contentType");let p=(r?r.parameterWithMetaByIdentity(o,t):t).get("errors",(0,P.List)()),h=r.contentTypeValues(o).get("requestContentType"),f=this.props.consumes&&this.props.consumes.size?this.props.consumes:kn.defaultProp.consumes,{value:d,isEditBox:m}=this.state,g=null;return(0,wt.O)(d)&&(g="json"),A.createElement("div",{className:"body-param","data-param-name":t.get("name"),"data-param-in":t.get("in")},m&&n?A.createElement(l,{className:"body-param__text"+(p.count()?" invalid":""),value:d,onChange:this.handleOnChange}):d&&A.createElement(c,{className:"body-param__example",language:g,getConfigs:s,value:d}),A.createElement("div",{className:"body-param-options"},n?A.createElement("div",{className:"body-param-edit"},A.createElement(a,{className:m?"btn cancel body-param__example-edit":"btn edit body-param__example-edit",onClick:this.toggleIsEditBox},m?"Cancel":"Edit")):null,A.createElement("label",{htmlFor:""},A.createElement("span",null,"Parameter content type"),A.createElement(u,{value:h,contentTypes:f,onChange:e,className:"body-param-content-type",ariaLabel:"Parameter content type"}))))}}ce()(kn,"defaultProp",{consumes:(0,P.fromJS)(["application/json"]),param:(0,P.fromJS)({}),onChange:On,onChangeConsumes:On});var An=n(92135);class Cn extends A.Component{render(){let{request:e,getConfigs:t}=this.props,n=(0,An.requestSnippetGenerator_curl_bash)(e);const r=t(),o=ct()(r,"syntaxHighlight.activated")?A.createElement(at.d3,{language:"bash",className:"curl microlight",style:(0,at.C2)(ct()(r,"syntaxHighlight.theme"))},n):A.createElement("textarea",{readOnly:!0,className:"curl",value:n});return A.createElement("div",{className:"curl-command"},A.createElement("h4",null,"Curl"),A.createElement("div",{className:"copy-to-clipboard"},A.createElement(dt.CopyToClipboard,{text:n},A.createElement("button",null))),A.createElement("div",null,o))}}class Pn extends A.Component{constructor(){super(...arguments),ce()(this,"onChange",(e=>{this.setScheme(e.target.value)})),ce()(this,"setScheme",(e=>{let{path:t,method:n,specActions:r}=this.props;r.setScheme(e,t,n)}))}UNSAFE_componentWillMount(){let{schemes:e}=this.props;this.setScheme(e.first())}UNSAFE_componentWillReceiveProps(e){var t;this.props.currentScheme&&Me()(t=e.schemes).call(t,this.props.currentScheme)||this.setScheme(e.schemes.first())}render(){var e;let{schemes:t,currentScheme:n}=this.props;return A.createElement("label",{htmlFor:"schemes"},A.createElement("span",{className:"schemes-title"},"Schemes"),A.createElement("select",{onChange:this.onChange,value:n},j()(e=t.valueSeq()).call(e,(e=>A.createElement("option",{value:e,key:e},e))).toArray()))}}class Nn extends A.Component{render(){const{specActions:e,specSelectors:t,getComponent:n}=this.props,r=t.operationScheme(),o=t.schemes(),s=n("schemes");return o&&o.size?A.createElement(s,{currentScheme:r,schemes:o,specActions:e}):null}}class In extends A.Component{constructor(e,t){super(e,t),ce()(this,"toggleCollapsed",(()=>{this.props.onToggle&&this.props.onToggle(this.props.modelName,!this.state.expanded),this.setState({expanded:!this.state.expanded})})),ce()(this,"onLoad",(e=>{if(e&&this.props.layoutSelectors){const t=this.props.layoutSelectors.getScrollToKey();N().is(t,this.props.specPath)&&this.toggleCollapsed(),this.props.layoutActions.readyToScroll(this.props.specPath,e.parentElement)}}));let{expanded:n,collapsedContent:r}=this.props;this.state={expanded:n,collapsedContent:r||In.defaultProps.collapsedContent}}componentDidMount(){const{hideSelfOnExpand:e,expanded:t,modelName:n}=this.props;e&&t&&this.props.onToggle(n,t)}UNSAFE_componentWillReceiveProps(e){this.props.expanded!==e.expanded&&this.setState({expanded:e.expanded})}render(){const{title:e,classes:t}=this.props;return this.state.expanded&&this.props.hideSelfOnExpand?A.createElement("span",{className:t||""},this.props.children):A.createElement("span",{className:t||"",ref:this.onLoad},A.createElement("button",{"aria-expanded":this.state.expanded,className:"model-box-control",onClick:this.toggleCollapsed},e&&A.createElement("span",{className:"pointer"},e),A.createElement("span",{className:"model-toggle"+(this.state.expanded?"":" collapsed")}),!this.state.expanded&&A.createElement("span",null,this.state.collapsedContent)),this.state.expanded&&this.props.children)}}ce()(In,"defaultProps",{collapsedContent:"{...}",expanded:!1,title:null,onToggle:()=>{},hideSelfOnExpand:!1,specPath:N().List([])});var Tn=n(92282),Rn=n.n(Tn);class Mn extends A.Component{constructor(e,t){super(e,t),ce()(this,"activeTab",(e=>{let{target:{dataset:{name:t}}}=e;this.setState({activeTab:t})}));let{getConfigs:n,isExecute:r}=this.props,{defaultModelRendering:o}=n(),s=o;"example"!==o&&"model"!==o&&(s="example"),r&&(s="example"),this.state={activeTab:s}}UNSAFE_componentWillReceiveProps(e){e.isExecute&&!this.props.isExecute&&this.props.example&&this.setState({activeTab:"example"})}render(){let{getComponent:e,specSelectors:t,schema:n,example:r,isExecute:o,getConfigs:s,specPath:i,includeReadOnly:a,includeWriteOnly:l}=this.props,{defaultModelExpandDepth:c}=s();const u=e("ModelWrapper"),p=e("highlightCode"),h=Rn()(5).toString("base64"),f=Rn()(5).toString("base64"),d=Rn()(5).toString("base64"),m=Rn()(5).toString("base64");let g=t.isOAS3();return A.createElement("div",{className:"model-example"},A.createElement("ul",{className:"tab",role:"tablist"},A.createElement("li",{className:it()("tabitem",{active:"example"===this.state.activeTab}),role:"presentation"},A.createElement("button",{"aria-controls":f,"aria-selected":"example"===this.state.activeTab,className:"tablinks","data-name":"example",id:h,onClick:this.activeTab,role:"tab"},o?"Edit Value":"Example Value")),n&&A.createElement("li",{className:it()("tabitem",{active:"model"===this.state.activeTab}),role:"presentation"},A.createElement("button",{"aria-controls":m,"aria-selected":"model"===this.state.activeTab,className:it()("tablinks",{inactive:o}),"data-name":"model",id:d,onClick:this.activeTab,role:"tab"},g?"Schema":"Model"))),"example"===this.state.activeTab&&A.createElement("div",{"aria-hidden":"example"!==this.state.activeTab,"aria-labelledby":h,"data-name":"examplePanel",id:f,role:"tabpanel",tabIndex:"0"},r||A.createElement(p,{value:"(no example available)",getConfigs:s})),"model"===this.state.activeTab&&A.createElement("div",{"aria-hidden":"example"===this.state.activeTab,"aria-labelledby":d,"data-name":"modelPanel",id:m,role:"tabpanel",tabIndex:"0"},A.createElement(u,{schema:n,getComponent:e,getConfigs:s,specSelectors:t,expandDepth:c,specPath:i,includeReadOnly:a,includeWriteOnly:l})))}}class Dn extends A.Component{constructor(){super(...arguments),ce()(this,"onToggle",((e,t)=>{this.props.layoutActions&&this.props.layoutActions.show(this.props.fullPath,t)}))}render(){let{getComponent:e,getConfigs:t}=this.props;const n=e("Model");let r;return this.props.layoutSelectors&&(r=this.props.layoutSelectors.isShown(this.props.fullPath)),A.createElement("div",{className:"model-box"},A.createElement(n,Ht()({},this.props,{getConfigs:t,expanded:r,depth:1,onToggle:this.onToggle,expandDepth:this.props.expandDepth||0})))}}var Fn=n(53795);class Ln extends A.Component{constructor(){super(...arguments),ce()(this,"getSchemaBasePath",(()=>this.props.specSelectors.isOAS3()?["components","schemas"]:["definitions"])),ce()(this,"getCollapsedContent",(()=>" ")),ce()(this,"handleToggle",((e,t)=>{const{layoutActions:n}=this.props;n.show([...this.getSchemaBasePath(),e],t),t&&this.props.specActions.requestResolvedSubtree([...this.getSchemaBasePath(),e])})),ce()(this,"onLoadModels",(e=>{e&&this.props.layoutActions.readyToScroll(this.getSchemaBasePath(),e)})),ce()(this,"onLoadModel",(e=>{if(e){const t=e.getAttribute("data-name");this.props.layoutActions.readyToScroll([...this.getSchemaBasePath(),t],e)}}))}render(){var e;let{specSelectors:t,getComponent:n,layoutSelectors:r,layoutActions:o,getConfigs:s}=this.props,i=t.definitions(),{docExpansion:a,defaultModelsExpandDepth:l}=s();if(!i.size||l<0)return null;const c=this.getSchemaBasePath();let u=r.isShown(c,l>0&&"none"!==a);const p=t.isOAS3(),h=n("ModelWrapper"),f=n("Collapse"),d=n("ModelCollapse"),m=n("JumpToPath",!0),g=n("ArrowUpIcon"),y=n("ArrowDownIcon");return A.createElement("section",{className:u?"models is-open":"models",ref:this.onLoadModels},A.createElement("h4",null,A.createElement("button",{"aria-expanded":u,className:"models-control",onClick:()=>o.show(c,!u)},A.createElement("span",null,p?"Schemas":"Models"),u?A.createElement(g,null):A.createElement(y,null))),A.createElement(f,{isOpened:u},j()(e=i.entrySeq()).call(e,(e=>{let[i]=e;const a=[...c,i],u=N().List(a),p=t.specResolvedSubtree(a),f=t.specJson().getIn(a),g=P.Map.isMap(p)?p:N().Map(),y=P.Map.isMap(f)?f:N().Map(),v=g.get("title")||y.get("title")||i,b=r.isShown(a,!1);b&&0===g.size&&y.size>0&&this.props.specActions.requestResolvedSubtree(a);const w=A.createElement(h,{name:i,expandDepth:l,schema:g||N().Map(),displayName:v,fullPath:a,specPath:u,getComponent:n,specSelectors:t,getConfigs:s,layoutSelectors:r,layoutActions:o,includeReadOnly:!0,includeWriteOnly:!0}),E=A.createElement("span",{className:"model-box"},A.createElement("span",{className:"model model-title"},v));return A.createElement("div",{id:`model-${i}`,className:"model-container",key:`models-section-${i}`,"data-name":i,ref:this.onLoadModel},A.createElement("span",{className:"models-jump-to-path"},A.createElement(m,{specPath:u})),A.createElement(d,{classes:"model-box",collapsedContent:this.getCollapsedContent(i),onToggle:this.handleToggle,title:E,displayName:v,modelName:i,specPath:u,layoutSelectors:r,layoutActions:o,hideSelfOnExpand:!0,expanded:l>0&&b},w))})).toArray()))}}const Bn=e=>{let{value:t,getComponent:n}=e,r=n("ModelCollapse"),o=A.createElement("span",null,"Array [ ",t.count()," ]");return A.createElement("span",{className:"prop-enum"},"Enum:",A.createElement("br",null),A.createElement(r,{collapsedContent:o},"[ ",t.join(", ")," ]"))};class $n extends A.Component{render(){var e,t,n,r;let{schema:o,name:s,displayName:i,isRef:l,getComponent:c,getConfigs:u,depth:h,onToggle:f,expanded:d,specPath:m,...g}=this.props,{specSelectors:y,expandDepth:v,includeReadOnly:w,includeWriteOnly:E}=g;const{isOAS3:x}=y;if(!o)return null;const{showExtensions:S}=u();let _=o.get("description"),O=o.get("properties"),k=o.get("additionalProperties"),C=o.get("title")||i||s,N=o.get("required"),I=a()(o).call(o,((e,t)=>{var n;return-1!==pe()(n=["maxProperties","minProperties","nullable","example"]).call(n,t)})),T=o.get("deprecated"),R=o.getIn(["externalDocs","url"]),M=o.getIn(["externalDocs","description"]);const 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A.createElement(_,{key:`${t}-${n}`,propKey:t,propVal:n,propClass:Un})})):null,y?A.createElement(x,{source:y}):null,w&&A.createElement("div",{className:"external-docs"},A.createElement(k,{target:"_blank",href:(0,L.Nm)(w)},E||w)),d&&d.size?A.createElement("span",null,A.createElement("br",null),A.createElement("span",{className:Un},"xml:"),j()(n=d.entrySeq()).call(n,(e=>{let[t,n]=e;return A.createElement("span",{key:`${t}-${n}`,className:Un},A.createElement("br",null),"   ",t,": ",String(n))})).toArray()):null,m&&A.createElement(S,{value:m,getComponent:o}))))}}const Vn=e=>{let{propKey:t,propVal:n,propClass:r}=e;return A.createElement("span",{className:r},A.createElement("br",null),t,": ",String(n))};class Wn extends A.Component{render(){const{onTryoutClick:e,onCancelClick:t,onResetClick:n,enabled:r,hasUserEditedBody:o,isOAS3:s}=this.props,i=s&&o;return A.createElement("div",{className:i?"try-out btn-group":"try-out"},r?A.createElement("button",{className:"btn try-out__btn 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t=cr(this.state.value);this.setState((()=>({value:t.push(e.getSampleSchema(this.state.schema.get("items"),!1,{includeWriteOnly:!0}))})),this.onChange)})),ce()(this,"onEnumChange",(e=>{this.setState((()=>({value:e})),this.onChange)})),this.state={value:cr(e.value),schema:e.schema}}UNSAFE_componentWillReceiveProps(e){const t=cr(e.value);t!==this.state.value&&this.setState({value:t}),e.schema!==this.state.schema&&this.setState({schema:e.schema})}render(){var e;let{getComponent:t,required:n,schema:r,errors:o,fn:s,disabled:i}=this.props;o=o.toJS?o.toJS():E()(o)?o:[];const l=a()(o).call(o,(e=>"string"==typeof e)),c=j()(e=a()(o).call(o,(e=>void 0!==e.needRemove))).call(e,(e=>e.error)),u=this.state.value,p=!!(u&&u.count&&u.count()>0),h=r.getIn(["items","enum"]),f=r.getIn(["items","type"]),d=r.getIn(["items","format"]),m=r.get("items");let g,y=!1,v="file"===f||"string"===f&&"binary"===d;if(f&&d?g=t(`JsonSchema_${f}_${d}`):"boolean"!==f&&"array"!==f&&"object"!==f||(g=t(`JsonSchema_${f}`)),g||v||(y=!0),h){const e=t("Select");return A.createElement(e,{className:o.length?"invalid":"",title:o.length?o:"",multiple:!0,value:u,disabled:i,allowedValues:h,allowEmptyValue:!n,onChange:this.onEnumChange})}const b=t("Button");return A.createElement("div",{className:"json-schema-array"},p?j()(u).call(u,((e,n)=>{var r;const l=(0,P.fromJS)([...j()(r=a()(o).call(o,(e=>e.index===n))).call(r,(e=>e.error))]);return A.createElement("div",{key:n,className:"json-schema-form-item"},v?A.createElement(sr,{value:e,onChange:e=>this.onItemChange(e,n),disabled:i,errors:l,getComponent:t}):y?A.createElement(or,{value:e,onChange:e=>this.onItemChange(e,n),disabled:i,errors:l}):A.createElement(g,Ht()({},this.props,{value:e,onChange:e=>this.onItemChange(e,n),disabled:i,errors:l,schema:m,getComponent:t,fn:s})),i?null:A.createElement(b,{className:`btn btn-sm 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t=e.target.files[0];this.props.onChange(t,this.props.keyName)}))}render(){let{getComponent:e,errors:t,disabled:n}=this.props;const r=e("Input"),o=n||!("FormData"in window);return A.createElement(r,{type:"file",className:t.length?"invalid":"",title:t.length?t:"",onChange:this.onFileChange,disabled:o})}}ce()(sr,"defaultProps",er);class ir extends A.Component{constructor(){super(...arguments),ce()(this,"onEnumChange",(e=>this.props.onChange(e)))}render(){let{getComponent:e,value:t,errors:n,schema:r,required:o,disabled:s}=this.props;n=n.toJS?n.toJS():[];let i=r&&r.get?r.get("enum"):null,a=!i||!o,l=!i&&["true","false"];const c=e("Select");return A.createElement(c,{className:n.length?"invalid":"",title:n.length?n:"",value:String(t),disabled:s,allowedValues:i?[...i]:l,allowEmptyValue:a,onChange:this.onEnumChange})}}ce()(ir,"defaultProps",er);const ar=e=>j()(e).call(e,(e=>{const t=void 0!==e.propKey?e.propKey:e.index;let n="string"==typeof e?e:"string"==typeof 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n={components:{App:de,authorizationPopup:me,authorizeBtn:ge,AuthorizeBtnContainer:ye,authorizeOperationBtn:ve,auths:be,AuthItem:we,authError:Ee,oauth2:Le,apiKeyAuth:xe,basicAuth:Se,clear:Be,liveResponse:Ue,InitializedInput:hn,info:gn,InfoContainer:yn,InfoUrl:dn,InfoBasePath:fn,Contact:bn,License:En,JumpToPath:xn,CopyToClipboardBtn:Sn,onlineValidatorBadge:ze.Z,operations:Ve,operation:Ke,OperationSummary:Ze,OperationSummaryMethod:Ye,OperationSummaryPath:et,highlightCode:gt,responses:yt,response:Et,ResponseExtension:xt,responseBody:Ct,parameters:It,parameterRow:Ft,execute:$t,headers:qt,errors:Ut,contentType:Jt,overview:un,footer:_n,FilterContainer:jn,ParamBody:kn,curl:Cn,schemes:Pn,SchemesContainer:Nn,modelExample:Mn,ModelWrapper:Dn,ModelCollapse:In,Model:Fn.Z,Models:Ln,EnumModel:Bn,ObjectModel:$n,ArrayModel:qn,PrimitiveModel:zn,Property:Vn,TryItOutButton:Wn,Markdown:Zn.Z,BaseLayout:Yn,VersionPragmaFilter:Jn,VersionStamp:Kn,OperationExt:tt,OperationExtRow:nt,ParameterExt:Tt,ParameterIncludeEmpty:Mt,OperationTag:Je,OperationContainer:fe,DeepLink:Hn,SvgAssets:Gn,Example:_e,ExamplesSelect:ke,ExamplesSelectValueRetainer:Ce}},r={components:e},o={components:t};return[ne.default,ee.default,Y.default,H.default,K.default,W.default,J.default,G.default,n,r,X.default,o,Q.default,te.default,re.default,oe.default,se.default,Z.default,ae.default,(0,ie.default)()]}ce()(lr,"defaultProps",er);var pr=n(97451),hr=n(29806),fr=n(97139);function dr(){return[ur,pr.default,fr.default,hr.default]}var mr=n(45308);const{GIT_DIRTY:gr,GIT_COMMIT:yr,PACKAGE_VERSION:vr,BUILD_TIME:br}={PACKAGE_VERSION:"5.3.1",GIT_COMMIT:"g733e51a",GIT_DIRTY:!0,BUILD_TIME:"Wed, 02 Aug 2023 13:12:32 GMT"};function wr(e){var t;F.Z.versions=F.Z.versions||{},F.Z.versions.swaggerUi={version:vr,gitRevision:yr,gitDirty:gr,buildTimestamp:br};const n={dom_id:null,domNode:null,spec:{},url:"",urls:null,layout:"BaseLayout",docExpansion:"list",maxDisplayedTags:null,filter:null,validatorUrl:"https://validator.swagger.io/validator",oauth2RedirectUrl:`${window.location.protocol}//${window.location.host}${window.location.pathname.substring(0,s()(t=window.location.pathname).call(t,"/"))}/oauth2-redirect.html`,persistAuthorization:!1,configs:{},custom:{},displayOperationId:!1,displayRequestDuration:!1,deepLinking:!1,tryItOutEnabled:!1,requestInterceptor:e=>e,responseInterceptor:e=>e,showMutatedRequest:!0,defaultModelRendering:"example",defaultModelExpandDepth:1,defaultModelsExpandDepth:1,showExtensions:!1,showCommonExtensions:!1,withCredentials:void 0,requestSnippetsEnabled:!1,requestSnippets:{generators:{curl_bash:{title:"cURL (bash)",syntax:"bash"},curl_powershell:{title:"cURL (PowerShell)",syntax:"powershell"},curl_cmd:{title:"cURL (CMD)",syntax:"bash"}},defaultExpanded:!0,languages:null},supportedSubmitMethods:["get","put","post","delete","options","head","patch","trace"],queryConfigEnabled:!1,presets:[dr],plugins:[],pluginsOptions:{pluginLoadType:"legacy"},initialState:{},fn:{},components:{},syntaxHighlight:{activated:!0,theme:"agate"}};let r=e.queryConfigEnabled?(0,L.UG)():{};const o=e.domNode;delete e.domNode;const i=f()({},n,e,r),l={system:{configs:i.configs},plugins:i.presets,pluginsOptions:i.pluginsOptions,state:f()({layout:{layout:i.layout,filter:a()(i)},spec:{spec:"",url:i.url},requestSnippets:i.requestSnippets},i.initialState)};if(i.initialState)for(var u in i.initialState)Object.prototype.hasOwnProperty.call(i.initialState,u)&&void 0===i.initialState[u]&&delete l.state[u];var h=new $(l);h.register([i.plugins,()=>({fn:i.fn,components:i.components,state:i.state})]);var d=h.getSystem();const m=e=>{let t=d.specSelectors.getLocalConfig?d.specSelectors.getLocalConfig():{},n=f()({},t,i,e||{},r);if(o&&(n.domNode=o),h.setConfigs(n),d.configsActions.loaded(),null!==e&&(!r.url&&"object"==typeof n.spec&&c()(n.spec).length?(d.specActions.updateUrl(""),d.specActions.updateLoadingStatus("success"),d.specActions.updateSpec(p()(n.spec))):d.specActions.download&&n.url&&!n.urls&&(d.specActions.updateUrl(n.url),d.specActions.download(n.url))),n.domNode)d.render(n.domNode,"App");else if(n.dom_id){let e=document.querySelector(n.dom_id);d.render(e,"App")}else null===n.dom_id||null===n.domNode||console.error("Skipped rendering: no `dom_id` or `domNode` was specified");return d},g=r.config||i.configUrl;return g&&d.specActions&&d.specActions.getConfigByUrl?(d.specActions.getConfigByUrl({url:g,loadRemoteConfig:!0,requestInterceptor:i.requestInterceptor,responseInterceptor:i.responseInterceptor},m),d):m()}wr.presets={apis:dr},wr.plugins=mr.default;const Er=wr})(),r=r.default})()));
+//# sourceMappingURL=swagger-ui-bundle.js.map
\ No newline at end of file
diff --git a/speakers/server/static/swagger-ui.css b/speakers/server/static/swagger-ui.css
new file mode 100644
index 0000000000000000000000000000000000000000..c247abc339caf9e351491d80e32fe318849f443b
--- /dev/null
+++ b/speakers/server/static/swagger-ui.css
@@ -0,0 +1,9 @@
+/**
+ * Skipped minification because the original files appears to be already minified.
+ * Original file: /npm/swagger-ui-dist@5.3.1/swagger-ui.css
+ *
+ * Do NOT use SRI with dynamically generated files! More information: https://www.jsdelivr.com/using-sri-with-dynamic-files
+ */
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+
+ /*# sourceMappingURL=swagger-ui.css.map*/
\ No newline at end of file
diff --git a/speakers/server/utils.py b/speakers/server/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d263a657917c43b1053a494bbca4ccc29709334
--- /dev/null
+++ b/speakers/server/utils.py
@@ -0,0 +1,82 @@
+from speakers.common.registry import registry
+from fastapi import FastAPI
+from fastapi.staticfiles import StaticFiles
+from starlette.responses import HTMLResponse
+from typing import Any, Optional
+from pathlib import Path
+
+
+def MakeFastAPIOffline(
+        app: FastAPI,
+        static_dir="/static",
+        static_url="/static-offline-docs",
+        docs_url: Optional[str] = "/docs",
+        redoc_url: Optional[str] = "/redoc",
+) -> None:
+    """patch the FastAPI obj that doesn't rely on CDN for the documentation page"""
+    from fastapi import Request
+    from fastapi.openapi.docs import (
+        get_redoc_html,
+        get_swagger_ui_html,
+        get_swagger_ui_oauth2_redirect_html,
+    )
+
+    openapi_url = app.openapi_url
+    swagger_ui_oauth2_redirect_url = app.swagger_ui_oauth2_redirect_url
+
+    def remove_route(url: str) -> None:
+        '''
+        remove original route from app
+        '''
+        index = None
+        for i, r in enumerate(app.routes):
+            if r.path.lower() == url.lower():
+                index = i
+                break
+        if isinstance(index, int):
+            app.routes.pop(i)
+
+    # Set up static file mount
+    app.mount(
+        static_url,
+        StaticFiles(directory=Path(f"{registry.get_path('server_library_root')}{static_dir}").as_posix()),
+        name="static-offline-docs",
+    )
+
+    if docs_url is not None:
+        remove_route(docs_url)
+        remove_route(swagger_ui_oauth2_redirect_url)
+
+        # Define the doc and redoc pages, pointing at the right files
+        @app.get(docs_url, include_in_schema=False)
+        async def custom_swagger_ui_html(request: Request) -> HTMLResponse:
+            root = request.scope.get("root_path")
+            favicon = f"{root}{static_url}/favicon.png"
+            return get_swagger_ui_html(
+                openapi_url=f"{root}{openapi_url}",
+                title=app.title + " - Swagger UI",
+                oauth2_redirect_url=swagger_ui_oauth2_redirect_url,
+                swagger_js_url=f"{root}{static_url}/swagger-ui-bundle.js",
+                swagger_css_url=f"{root}{static_url}/swagger-ui.css",
+                swagger_favicon_url=favicon,
+            )
+
+        @app.get(swagger_ui_oauth2_redirect_url, include_in_schema=False)
+        async def swagger_ui_redirect() -> HTMLResponse:
+            return get_swagger_ui_oauth2_redirect_html()
+
+    if redoc_url is not None:
+        remove_route(redoc_url)
+
+        @app.get(redoc_url, include_in_schema=False)
+        async def redoc_html(request: Request) -> HTMLResponse:
+            root = request.scope.get("root_path")
+            favicon = f"{root}{static_url}/favicon.png"
+
+            return get_redoc_html(
+                openapi_url=f"{root}{openapi_url}",
+                title=app.title + " - ReDoc",
+                redoc_js_url=f"{root}{static_url}/redoc.standalone.js",
+                with_google_fonts=False,
+                redoc_favicon_url=favicon,
+            )
diff --git a/speakers/speakers.py b/speakers/speakers.py
new file mode 100644
index 0000000000000000000000000000000000000000..73eef736f945cdbd3c85872d4d008b7e2a406b85
--- /dev/null
+++ b/speakers/speakers.py
@@ -0,0 +1,199 @@
+import asyncio
+import logging
+import os
+import traceback
+
+import requests
+from omegaconf import OmegaConf
+from scipy.io.wavfile import write as write_wav
+
+from speakers.common.log import add_file_logger, remove_file_logger
+from speakers.common.registry import registry
+from speakers.common.utils import get_abs_path, get_tmp_path
+from speakers.processors import (load_preprocess
+                                 )
+from speakers.server.model.flow_data import PayLoad
+from speakers.tasks import get_task, load_task, tasks_cache
+
+logger = logging.getLogger('speaker_runner')
+
+
+def set_main_logger(l):
+    global logger
+    logger = l
+
+
+class Speaker:
+    """
+        在任务系统中，"runner" 通常指的是负责执行任务的组件或模块。Runner 的生命周期是指它从创建到销毁的整个过程，
+        其中包含了一些关键的阶段和操作。虽然不同的任务系统可能会有不同的实现细节，但通常可以概括为以下几个主要的生命周期阶段：
+
+        创建（Creation）：在任务被提交或调度之前，runner 需要被创建。这个阶段可能涉及资源分配、
+        初始化操作、配置设置等。Runner 的创建过程通常包括为任务分配所需的资源，准备运行环境，建立与任务管理器或调度器的连接等。
+
+        准备（Preparation）：在 runner 开始执行任务之前，需要对任务本身进行一些准备工作。这可能包括下载所需的文件、
+        加载依赖项、设置环境变量、配置运行参数等。准备阶段的目标是确保任务在执行过程中所需的一切都已就绪。
+
+        执行（Execution）：执行阶段是 runner 的主要功能，它负责实际运行任务的代码逻辑。
+        这可能涉及到执行计算、处理数据、调用外部服务等，具体取决于任务的性质。在执行阶段，runner 需要监控任务的进展，
+        处理异常情况，并可能与其他系统组件进行交互。
+
+        监控与报告（Monitoring and Reporting）：在任务执行期间，runner 需要持续监控任务的状态和进度。
+        这可能包括收集性能指标、记录日志、报告错误或异常等。监控与报告是确保任务在预期范围内执行的重要手段。
+
+        完成与清理（Completion and Cleanup）：当任务执行结束后，runner 需要处理任务的完成操作。
+        这可能涉及处理执行结果、释放资源、清理临时文件、关闭连接等。完成与清理阶段的目标是确保任务执行后不会留下不必要的残留状态。
+
+        销毁（Destruction）：在 runner 的生命周期结束时，
+        需要进行销毁操作。这可能包括释放占用的资源、关闭连接、清理临时数据等。销毁阶段的目标是确保系统不会因为不再需要的 runner 而产生资源浪费或不稳定性。
+
+    """
+
+    def __init__(self, speakers_config_file: str = 'speakers.yaml',
+                 verbose: bool = False):
+        self.verbose = verbose
+
+        config = OmegaConf.load(get_abs_path(speakers_config_file))
+        load_preprocess(config=config.get('preprocess'))
+        load_task(config.get("tasks"))
+
+    async def preparation_runner(self, task_id: str, payload: PayLoad = None):
+        voice_task = get_task(payload.parameter.task_name)
+        runner = voice_task.prepare(payload=payload)
+
+        out_sr, output = await voice_task.dispatch(runner=runner)
+        if output is not None:
+            # 当前生命周期 saved
+            # save audio to disk
+            write_wav(self._result_path(f"{task_id}.wav"), out_sr, output)
+            del output
+            await voice_task.report_progress(task_id=runner.task_id, runner_stat='voice_task', state='saved',
+                                             finished=True)
+
+    def _result_path(self, path: str) -> str:
+        return get_tmp_path(f'result/{path}')
+
+
+class WebSpeaker(Speaker):
+    def __init__(self, speakers_config_file: str = 'speakers.yaml',
+                 verbose: bool = False,
+                 nonce: str = ''):
+        super().__init__(speakers_config_file=speakers_config_file, verbose=verbose)
+
+        config = OmegaConf.load(get_abs_path(speakers_config_file))
+        remote_infos = {}
+        for bootstraps in config.get("bootstrap"):
+            for key, bootstrap_cfg in bootstraps.items():  # 使用 .items() 方法获取键值对
+
+                remote_infos[bootstrap_cfg.name] = {
+                    'host': bootstrap_cfg.host,
+                    'port': bootstrap_cfg.port
+                }
+
+        self.remote_infos = remote_infos
+        self.nonce = nonce
+        self._task_results = {}
+
+    async def listen(self):
+        """
+        监听server端任务，注册任务监听器接收消息通知
+        """
+        logger.info('Waiting for WebSpeaker tasks')
+
+        async def sync_state(task_id: str, runner_stat: str, state: str, finished: bool):
+            # wait for translation to be saved first (bad solution?)
+            finished = finished and not state == 'finished'
+            while True:
+                try:
+                    data = {
+                        'task_id': task_id,
+                        'runner_stat': runner_stat,
+                        'nonce': self.nonce,
+                        'state': state,
+                        'finished': finished,
+                    }
+                    # 处理每个runner的调度
+                    for _key, _remote_info in self.remote_infos.items():
+
+                        if self._task_results.get(_key) is None or self._task_results.get(_key).get("task_id") is None:
+                            continue
+                        if task_id in self._task_results.get(_key).get("task_id"):
+                            host = '127.0.0.1'
+                            if not '0.0.0.0' in _remote_info.get("host"):
+                                host = _remote_info.get("host")
+
+                            requests.post(
+                                f'http://{host}:{_remote_info.get("port")}/runner/task-update-internal',
+                                json=data, timeout=20)
+                            break
+                    break
+                except Exception:
+                    # if translation is finished server has to know
+                    if finished:
+                        continue
+                    else:
+                        break
+
+        for key, task in tasks_cache.items():
+            task.add_progress_hook(sync_state)
+
+        while True:
+            self._task_results = self._get_task()
+
+            wait_flag = False
+
+            if not self._task_results:
+                wait_flag = True
+
+            if wait_flag:
+                await asyncio.sleep(1)
+                continue
+
+            # 处理每个runner的调度,如果有任何一个返回了任务，则执行调度
+            for key, remote_info in self.remote_infos.items():
+                if self._task_results.get(key) is None or self._task_results.get(key).get("task_id") is None:
+                    wait_flag = True
+                else:
+                    wait_flag = False
+                    break
+
+            if wait_flag:
+                await asyncio.sleep(1)
+                continue
+
+            if self.verbose:
+                # Write log file
+                log_file = self._result_path('log.txt')
+                add_file_logger(log_file)
+
+            # 处理每个runner的调度
+            for key, remote_info in self.remote_infos.items():
+                logger.info(f'Processing task {self._task_results.get(key).get("task_id")}')
+
+                await self.preparation_runner(task_id=self._task_results.get(key).get("task_id"),
+                                              payload=PayLoad.parse_obj(self._task_results.get(key).get("data")))
+
+            if self.verbose:
+                # Write log file
+                log_file = self._result_path('log.txt')
+                remove_file_logger(log_file)
+
+    def _get_task(self):
+        try:
+            task_results = {}
+            for key, _remote_info in self.remote_infos.items():  # 使用 .items() 方法获取键值对
+
+                host = '127.0.0.1'
+                if not '0.0.0.0' in _remote_info.get("host"):
+                    host = _remote_info.get("host")
+
+                response = requests.get(
+                    f'http://{host}:{_remote_info["port"]}/runner/task-internal?nonce={self.nonce}',
+                    timeout=3600)
+                # 检查响应状态码
+                if response.status_code == 200:
+                    task_results[key] = response.json().get("data")
+            return task_results
+        except Exception:
+            traceback.print_exc()
+            return None
diff --git a/speakers/speakers.yaml b/speakers/speakers.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..27d26aa28341da81d1fb4767e59b0e7a5b60ecc5
--- /dev/null
+++ b/speakers/speakers.yaml
@@ -0,0 +1,47 @@
+ # Copyright (c) 2022, salesforce.com, inc.
+ # All rights reserved.
+ # SPDX-License-Identifier: BSD-3-Clause
+ # For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+
+preprocess:
+    - vits_processor:
+        name: "vits_to_voice"
+        vits_model_path: "model/G_953000.pth"
+        voice_config_file: "model/config.json"
+    - rvc_processor:
+        name: "rvc_speakers"
+        hubert_model_path: "model/hubert_base.pt"
+        rvc_config_file: "rvc.yaml"
+    - bark_processor:
+        name: "bark_to_voice"
+        tokenizer_path: "model/bert-base-multilingual-cased"
+        text_model_path: "model/suno/bark_v0/text_2.pt"
+        coarse_model_path: "model/suno/bark_v0/coarse_2.pt"
+        fine_model_path: "model/suno/bark_v0/fine_2.pt"
+
+
+tasks:
+  - vits_voice_task:
+      name: "vits_voice_task"
+      preprocess:
+        - vits:
+            processor: "vits_processor"
+            processor_name: "VITS"
+        - rvc:
+            processor: "rvc_processor"
+            processor_name: "RVC"
+  - bark_voice_task:
+      name: "bark_voice_task"
+      preprocess:
+        - bark:
+            processor: "bark_processor"
+            processor_name: "BARK"
+        - rvc:
+            processor: "rvc_processor"
+            processor_name: "RVC"
+
+bootstrap:
+  - runner_bootstrap_web:
+      name: "runner_bootstrap_web"
+      host: "0.0.0.0"
+      port: 7860
diff --git a/speakers/tasks/__init__.py b/speakers/tasks/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3603e147c1c516fa2713e8fbf7179cf91c02dafc
--- /dev/null
+++ b/speakers/tasks/__init__.py
@@ -0,0 +1,59 @@
+import numpy as np
+from typing import List
+
+from speakers.common.registry import registry
+from speakers.tasks.base_task import BaseTask, Runner, FlowData
+from speakers.tasks.vits_voice_task import VitsVoiceTask, VitsVoiceFlowData
+from speakers.tasks.bark_voice_task import BarkVoiceTask, BarkVoiceFlowData
+
+__all__ = [
+    "BaseTask",
+    "Runner",
+    "FlowData",
+    "load_task",
+    "get_task",
+    "VitsVoiceFlowData",
+    "BarkVoiceFlowData",
+    "tasks_cache"
+]
+
+tasks_cache = {}
+
+
+def load_task(config: dict = None):
+    """
+    Load preprocessor configs and construct preprocessors.
+
+    If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.
+
+    Args:
+        config (dict): preprocessor configs.
+
+    Returns:
+        vits_processors (dict): preprocessors for vits inputs.
+        rvc_processors (dict): preprocessors for rvc inputs.
+
+    """
+
+    if config is None:
+        raise RuntimeError("Load preprocessor configs is None.")
+
+    def _build_task_from_cfg(cfg):
+        return (
+            registry.get_task_class(cfg.name).from_config(cfg)
+            if cfg is not None
+            else BaseTask()
+        )
+    for task in config:
+        for key, task_cfg in task.items():  # 使用 .items() 方法获取键值对
+            processors = _build_task_from_cfg(task_cfg)
+            tasks_cache[key] = processors
+
+
+def get_task(key: str) -> BaseTask:
+
+    if not tasks_cache.get(key):
+        raise ValueError(f'Could not find task for: "{key}". '
+                         f'Choose from the following: %s' % ','.join(tasks_cache))
+
+    return tasks_cache[key]
diff --git a/speakers/tasks/__pycache__/__init__.cpython-310.pyc b/speakers/tasks/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/speakers/tasks/__pycache__/bark_voice_task.cpython-310.pyc b/speakers/tasks/__pycache__/bark_voice_task.cpython-310.pyc
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index 0000000000000000000000000000000000000000..68a3bc64e4446dd2981f56f3e4a9ceaf873d1e09
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diff --git a/speakers/tasks/__pycache__/base_task.cpython-310.pyc b/speakers/tasks/__pycache__/base_task.cpython-310.pyc
new file mode 100644
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diff --git a/speakers/tasks/__pycache__/vits_voice_task.cpython-310.pyc b/speakers/tasks/__pycache__/vits_voice_task.cpython-310.pyc
new file mode 100644
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diff --git a/speakers/tasks/bark_voice_task.py b/speakers/tasks/bark_voice_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..22f46f857b7d8bfd213fa5fe607cb840687c062c
--- /dev/null
+++ b/speakers/tasks/bark_voice_task.py
@@ -0,0 +1,164 @@
+from typing import Dict
+from speakers.processors import ProcessorData, BaseProcessor, get_processors, BarkProcessorData, RvcProcessorData
+from speakers.tasks import BaseTask, Runner, FlowData
+from speakers.common.registry import registry
+from speakers.server.model.flow_data import PayLoad
+import traceback
+import hashlib
+
+
+class BarkVoiceFlowData(FlowData):
+    bark: BarkProcessorData
+    rvc: RvcProcessorData
+
+    @property
+    def type(self) -> str:
+        """Type of the FlowData Message, used for serialization."""
+        return "bark_voice"
+
+
+def calculate_md5(input_string):
+    md5_hash = hashlib.md5()
+    md5_hash.update(input_string.encode('utf-8'))
+    return md5_hash.hexdigest()
+
+
+@registry.register_task("bark_voice_task")
+class BarkVoiceTask(BaseTask):
+    SAMPLE_RATE: int = 22050
+
+    def __init__(self, preprocess_dict: Dict[str, BaseProcessor]):
+        super().__init__(preprocess_dict=preprocess_dict)
+        self._preprocess_dict = preprocess_dict
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        preprocess_dict = {}
+        for preprocess in cfg.get('preprocess'):
+            for key, preprocess_info in preprocess.items():
+                preprocess_object = get_processors(preprocess_info.processor)
+                preprocess_dict[preprocess_info.processor_name] = preprocess_object
+
+        return cls(preprocess_dict=preprocess_dict)
+
+    @property
+    def preprocess_dict(self) -> Dict[str, BaseProcessor]:
+        return self._preprocess_dict
+
+    @classmethod
+    def prepare(cls, payload: PayLoad) -> Runner:
+        """
+        runner任务构建
+        """
+        params = payload.payload
+        # 获取payload中的vits和rvc的值
+        bark_data = params.get("bark", {})
+        rvc_data = params.get("rvc", {})
+
+        speaker_history_prompt = bark_data.get("speaker_history_prompt")
+        text_temp = bark_data.get("text_temp")
+        waveform_temp = bark_data.get("waveform_temp")
+        text = bark_data.get("text")
+
+        # 创建一个 BarkProcessorData 实例
+        bark_processor_data = BarkProcessorData(text=text,
+                                                speaker_history_prompt=speaker_history_prompt,
+                                                text_temp=text_temp,
+                                                waveform_temp=waveform_temp)
+        # 获取rvc中的值
+        model_index = rvc_data.get("model_index")
+
+        # 变调(整数, 半音数量, 升八度12降八度-12)
+        f0_up_key = rvc_data.get("f0_up_key")
+        f0_method = rvc_data.get("f0_method")
+
+        # 检索特征占比
+        index_rate = rvc_data.get("index_rate")
+        # >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音
+        filter_radius = rvc_data.get("filter_radius")
+        # 输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络
+        rms_mix_rate = rvc_data.get("rms_mix_rate")
+        # 后处理重采样至最终采样率，0为不进行重采样
+        resample_rate = rvc_data.get("resample_sr")
+
+        rvc_protect = rvc_data.get("protect")
+        rvc_f0_file = rvc_data.get("f0_file")
+
+        rvc_processor_data = RvcProcessorData(
+            model_index=model_index,
+            f0_up_key=f0_up_key,
+            f0_method=f0_method,
+            index_rate=index_rate,
+            filter_radius=filter_radius,
+            rms_mix_rate=rms_mix_rate,
+            resample_sr=resample_rate,
+            f0_file=rvc_f0_file,
+            protect=rvc_protect
+        )
+
+        # 创建一个 BarkVoiceFlowData 实例，并将 VitsProcessorData 实例作为参数传递
+        voice_flow_data = BarkVoiceFlowData(bark=bark_processor_data,
+                                            rvc=rvc_processor_data)
+
+        # 创建 Runner 实例并传递上面创建的 BarkVoiceFlowData 实例作为参数
+        task_id = f'{calculate_md5(text)}-{speaker_history_prompt}-{text_temp}' \
+                  f'-{waveform_temp}' \
+                  f'-{model_index}-{f0_up_key}'
+        runner = Runner(
+            task_id=task_id,
+            flow_data=voice_flow_data
+        )
+
+        return runner
+
+    async def dispatch(self, runner: Runner):
+
+        try:
+            # 加载task
+            self.logger.info('dispatch')
+
+            # 开启任务1
+            await self.report_progress(task_id=runner.task_id,
+                                       runner_stat='bark_voice_task',
+                                       state='dispatch_bark_voice_task')
+            data = runner.flow_data
+            if 'bark_voice' in data.type:
+                if 'BARK' in data.bark.type:
+                    bark_preprocess_object = self.preprocess_dict.get(data.bark.type)
+                    if not bark_preprocess_object.match(data.bark):
+                        raise RuntimeError('不支持的process')
+                    audio_np = bark_preprocess_object(data.bark)
+                    if audio_np is not None and 'RVC' in data.rvc.type:
+                        # 将 NumPy 数组转换为 Python 列表
+                        audio_samples_list = audio_np.tolist()
+                        data.rvc.sample_rate = self.SAMPLE_RATE
+                        data.rvc.audio_samples = audio_samples_list
+                        rvc_preprocess_object = self.preprocess_dict.get(data.rvc.type)
+                        if not rvc_preprocess_object.match(data.rvc):
+                            raise RuntimeError('不支持的process')
+
+                        out_sr, output_audio = rvc_preprocess_object(data.rvc)
+
+                        # 完成任务，构建响应数据
+                        await self.report_progress(task_id=runner.task_id,
+                                                   runner_stat='bark_voice_task',
+                                                   state='finished',
+                                                   finished=True)
+
+                        del audio_np
+                        del runner
+                        return out_sr, output_audio
+
+        except Exception as e:
+            await self.report_progress(task_id=runner.task_id, runner_stat='bark_voice_task',
+                                       state='error', finished=True)
+
+            self.logger.error(f'{e.__class__.__name__}: {e}',
+                              exc_info=e)
+
+            traceback.print_exc()
+
+        return None, None
+
+    def complete(self, runner: Runner):
+        pass
diff --git a/speakers/tasks/base_task.py b/speakers/tasks/base_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..9df43bd1f44c86f8889adc51e2adfc8323f3a932
--- /dev/null
+++ b/speakers/tasks/base_task.py
@@ -0,0 +1,143 @@
+from abc import abstractmethod
+from typing import List, Dict
+
+from speakers.load.serializable import Serializable
+from speakers.processors import ProcessorData, BaseProcessor
+from speakers.server.model.flow_data import PayLoad
+
+import logging
+
+
+class FlowData(Serializable):
+    """
+    当前runner的任务参数
+    """
+
+    @property
+    @abstractmethod
+    def type(self) -> str:
+        """Type of the Message, used for serialization."""
+
+    @property
+    def lc_serializable(self) -> bool:
+        """Whether this class is Processor serializable."""
+        return True
+
+
+class Runner(Serializable):
+    """ runner的任务id"""
+    task_id: str
+    flow_data: FlowData
+
+    @property
+    def type(self) -> str:
+        """Type of the Runner Message, used for serialization."""
+        return 'runner'
+
+    @property
+    def lc_serializable(self) -> bool:
+        """Whether this class is Processor serializable."""
+        return True
+
+
+# Define a base class for tasks
+class BaseTask:
+    """
+        基础任务处理器由任务管理器创建，用于执行runner flow 的任务，子类实现具体的处理流程
+        此类定义了流程runner task的生命周期
+    """
+
+    def __init__(self, preprocess_dict: Dict[str, BaseProcessor]):
+        self._progress_hooks = []
+        self._add_logger_hook()
+        self._preprocess_dict = preprocess_dict
+        self.logger = logging.getLogger('base_task_runner')
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        return cls(preprocess_dict={})
+
+    def _add_logger_hook(self):
+        """
+        默认的任务日志监听者
+        :return:
+        """
+        LOG_MESSAGES = {
+            'dispatch_voice_task': 'dispatch_voice_task',
+            'saved': 'Saving results',
+        }
+        LOG_MESSAGES_SKIP = {
+            'skip-no-text': 'No text regions with text! - Skipping',
+        }
+        LOG_MESSAGES_ERROR = {
+            'error': 'task error',
+        }
+
+        async def ph(task_id: str, runner_stat: str, state: str, finished: bool = False):
+            if state in LOG_MESSAGES:
+                self.logger.info(LOG_MESSAGES[state])
+            elif state in LOG_MESSAGES_SKIP:
+                self.logger.warn(LOG_MESSAGES_SKIP[state])
+            elif state in LOG_MESSAGES_ERROR:
+                self.logger.error(LOG_MESSAGES_ERROR[state])
+
+        self.add_progress_hook(ph)
+
+    def add_progress_hook(self, ph):
+        """
+        注册监听器
+        :param ph: 监听者
+        :return:
+        """
+        self._progress_hooks.append(ph)
+
+    async def report_progress(self, task_id: str, runner_stat: str, state: str, finished: bool = False):
+        """
+        任务通知监听器
+        :param task_id: 任务id
+        :param runner_stat: 任务执行位置
+        :param state: 状态
+        :param finished: 是否完成
+        :return:
+        """
+        for ph in self._progress_hooks:
+            await ph(task_id, runner_stat, state, finished)
+
+    @classmethod
+    def prepare(cls, payload: PayLoad) -> Runner:
+        """
+        预处理
+
+        Args:
+            payload (PayLoad): runner flow data
+
+        Raises:
+            NotImplementedError: This method should be overridden by subclasses.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    async def dispatch(cls, runner: Runner):
+        """
+        当前runner task具体flow data
+
+        Args:
+            runner (ProcessorData): runner flow data
+
+        Raises:
+            NotImplementedError: This method should be overridden by subclasses.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def complete(cls, runner: Runner):
+        """
+        后置处理
+
+        Args:
+            runner (Runner): runner flow data
+
+        Raises:
+            NotImplementedError: This method should be overridden by subclasses.
+        """
+        raise NotImplementedError
diff --git a/speakers/tasks/vits_voice_task.py b/speakers/tasks/vits_voice_task.py
new file mode 100644
index 0000000000000000000000000000000000000000..10a14936137211a067a274e7b79a95ffc4987562
--- /dev/null
+++ b/speakers/tasks/vits_voice_task.py
@@ -0,0 +1,174 @@
+from typing import Dict
+from speakers.processors import ProcessorData, BaseProcessor, get_processors, VitsProcessorData, RvcProcessorData
+from speakers.tasks import BaseTask, Runner, FlowData
+from speakers.common.registry import registry
+from speakers.server.model.flow_data import PayLoad
+import traceback
+import hashlib
+
+
+class VitsVoiceFlowData(FlowData):
+    vits: VitsProcessorData
+    rvc: RvcProcessorData
+
+    @property
+    def type(self) -> str:
+        """Type of the FlowData Message, used for serialization."""
+        return "vits_voice"
+
+
+def calculate_md5(input_string):
+    md5_hash = hashlib.md5()
+    md5_hash.update(input_string.encode('utf-8'))
+    return md5_hash.hexdigest()
+
+
+@registry.register_task("vits_voice_task")
+class VitsVoiceTask(BaseTask):
+    SAMPLE_RATE: int = 22050
+
+    def __init__(self, preprocess_dict: Dict[str, BaseProcessor]):
+        super().__init__(preprocess_dict=preprocess_dict)
+        self._preprocess_dict = preprocess_dict
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        preprocess_dict = {}
+        for preprocess in cfg.get('preprocess'):
+            for key, preprocess_info in preprocess.items():
+                preprocess_object = get_processors(preprocess_info.processor)
+                preprocess_dict[preprocess_info.processor_name] = preprocess_object
+
+        return cls(preprocess_dict=preprocess_dict)
+
+    @property
+    def preprocess_dict(self) -> Dict[str, BaseProcessor]:
+        return self._preprocess_dict
+
+    @classmethod
+    def prepare(cls, payload: PayLoad) -> Runner:
+        """
+        runner任务构建
+        """
+        params = payload.payload
+        # 获取payload中的vits和rvc的值
+        vits_data = params.get("vits", {})
+        rvc_data = params.get("rvc", {})
+
+        # noise_scale_w: noise_scale_w(控制音素发音长度)
+        noise_scale_w = vits_data.get("noise_scale_w")
+        # noise_scale(控制感情变化程度)
+        noise_scale = vits_data.get("noise_scale")
+        # length_scale(控制整体语速)
+        speed = vits_data.get("speed")
+        # 语言
+        language = vits_data.get("language")
+        # vits 讲话人
+        speaker_id = vits_data.get("speaker_id")
+        text = vits_data.get("text")
+
+        # 创建一个 VitsProcessorData 实例
+        vits_processor_data = VitsProcessorData(
+            text=text,
+            language=language,
+            speaker_id=speaker_id,
+            noise_scale=noise_scale,
+            speed=speed,
+            noise_scale_w=noise_scale_w
+        )
+        # 获取rvc中的值
+
+        model_index = rvc_data.get("model_index")
+
+        # 变调(整数, 半音数量, 升八度12降八度-12)
+        f0_up_key = rvc_data.get("f0_up_key")
+        f0_method = rvc_data.get("f0_method")
+
+        # 检索特征占比
+        index_rate = rvc_data.get("index_rate")
+        # >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音
+        filter_radius = rvc_data.get("filter_radius")
+        # 输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络
+        rms_mix_rate = rvc_data.get("rms_mix_rate")
+        # 后处理重采样至最终采样率，0为不进行重采样
+        resample_rate = rvc_data.get("resample_sr")
+
+        rvc_protect = rvc_data.get("protect")
+        rvc_f0_file = rvc_data.get("f0_file")
+
+        rvc_processor_data = RvcProcessorData(
+            model_index=model_index,
+            f0_up_key=f0_up_key,
+            f0_method=f0_method,
+            index_rate=index_rate,
+            filter_radius=filter_radius,
+            rms_mix_rate=rms_mix_rate,
+            resample_sr=resample_rate,
+            f0_file=rvc_f0_file,
+            protect=rvc_protect
+        )
+
+        # 创建一个 VitsVoiceFlowData 实例，并将 VitsProcessorData 实例作为参数传递
+        voice_flow_data = VitsVoiceFlowData(vits=vits_processor_data,
+                                        rvc=rvc_processor_data)
+
+        # 创建 Runner 实例并传递上面创建的 VitsVoiceFlowData 实例作为参数
+        task_id = f'{calculate_md5(text)}-{speaker_id}-{language}' \
+                  f'-{noise_scale}-{speed}-{noise_scale_w}' \
+                  f'-{model_index}-{f0_up_key}'
+        runner = Runner(
+            task_id=task_id,
+            flow_data=voice_flow_data
+        )
+
+        return runner
+
+    async def dispatch(self, runner: Runner):
+
+        try:
+            # 加载task
+            self.logger.info('dispatch')
+
+            # 开启任务1
+            await self.report_progress(task_id=runner.task_id, runner_stat='vits_voice_task', state='dispatch_vits_voice_task')
+            data = runner.flow_data
+            if 'vits_voice' in data.type:
+                if 'VITS' in data.vits.type:
+                    vits_preprocess_object = self.preprocess_dict.get(data.vits.type)
+                    if not vits_preprocess_object.match(data.vits):
+                        raise RuntimeError('不支持的process')
+                    audio_np = vits_preprocess_object(data.vits)
+                    if audio_np is not None and 'RVC' in data.rvc.type:
+                        # 将 NumPy 数组转换为 Python 列表
+                        audio_samples_list = audio_np.tolist()
+                        data.rvc.sample_rate = self.SAMPLE_RATE
+                        data.rvc.audio_samples = audio_samples_list
+                        rvc_preprocess_object = self.preprocess_dict.get(data.rvc.type)
+                        if not rvc_preprocess_object.match(data.rvc):
+                            raise RuntimeError('不支持的process')
+
+                        out_sr, output_audio = rvc_preprocess_object(data.rvc)
+
+                        # 完成任务，构建响应数据
+                        await self.report_progress(task_id=runner.task_id,
+                                                   runner_stat='vits_voice_task',
+                                                   state='finished',
+                                                   finished=True)
+
+                        del audio_np
+                        del runner
+                        return out_sr, output_audio
+
+        except Exception as e:
+            await self.report_progress(task_id=runner.task_id, runner_stat='vits_voice_task',
+                                       state='error', finished=True)
+
+            self.logger.error(f'{e.__class__.__name__}: {e}',
+                              exc_info=e)
+
+            traceback.print_exc()
+
+        return None, None
+
+    def complete(self, runner: Runner):
+        pass
diff --git a/start.py b/start.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f42ab16e0e4f2962f153cff4a3ce2978c4b8745
--- /dev/null
+++ b/start.py
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a1a6232e9a0cd1298eb588ddc28e50ba93650cfd909b29f2c52c7973b43070e
+size 74
diff --git a/util.py b/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..98a50582a6bcbc712d4d58f4e71917f6237776f9
--- /dev/null
+++ b/util.py
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b628fec56a4c3f113cc5d2720c9edd83cccc963c26d76bc41fa594f3cfcf2229
+size 2714
diff --git a/vits/__init__.py b/vits/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..493bfc1db166d0c65c332cf1f3e29d45941f0377
--- /dev/null
+++ b/vits/__init__.py
@@ -0,0 +1,6 @@
+from speakers.common.registry import registry
+import os
+
+root_dir = os.path.dirname(os.path.abspath(__file__))
+registry.register_path("vits_library_root", root_dir)
+
diff --git a/vits/__pycache__/__init__.cpython-310.pyc b/vits/__pycache__/__init__.cpython-310.pyc
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diff --git a/vits/model/D_0-p.pth b/vits/model/D_0-p.pth
new file mode 100644
index 0000000000000000000000000000000000000000..8358e7cc2b0f48373bec06d6d34be3292c542b15
--- /dev/null
+++ b/vits/model/D_0-p.pth
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+version https://git-lfs.github.com/spec/v1
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+size 187022499
diff --git a/vits/model/D_0.pth b/vits/model/D_0.pth
new file mode 100644
index 0000000000000000000000000000000000000000..9f5683f7296dae4d4014a60eceb4455df8f6386e
--- /dev/null
+++ b/vits/model/D_0.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 561093193
diff --git a/vits/model/G_0-p.pth b/vits/model/G_0-p.pth
new file mode 100644
index 0000000000000000000000000000000000000000..c5e4cb13a0b591a5463b91f308223e29df3c9ef2
--- /dev/null
+++ b/vits/model/G_0-p.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 158856783
diff --git a/vits/model/G_0.pth b/vits/model/G_0.pth
new file mode 100644
index 0000000000000000000000000000000000000000..8b5eaafbb9d93a731919eed0a3e6e3e54b771eaa
--- /dev/null
+++ b/vits/model/G_0.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/vits/model/G_953000.pth b/vits/model/G_953000.pth
new file mode 100644
index 0000000000000000000000000000000000000000..a5898793739fb6c05ae99f4aabcad2204e145d7f
--- /dev/null
+++ b/vits/model/G_953000.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 479276657
diff --git a/vits/model/config.json b/vits/model/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ee7a75dd63144891293b69ddd008f596e75513d6
--- /dev/null
+++ b/vits/model/config.json
@@ -0,0 +1,55 @@
+{
+  "train": {
+    "log_interval": 200,
+    "eval_interval": 1000,
+    "seed": 1234,
+    "epochs": 10000,
+    "learning_rate": 2e-4,
+    "betas": [0.8, 0.99],
+    "eps": 1e-9,
+    "batch_size": 64,
+    "fp16_run": true,
+    "lr_decay": 0.999875,
+    "segment_size": 8192,
+    "init_lr_ratio": 1,
+    "warmup_epochs": 0,
+    "c_mel": 45,
+    "c_kl": 1.0
+  },
+  "data": {
+    "training_files":"filelists/uma_genshin_genshinjp_bh3_train.txt.cleaned",
+    "validation_files":"filelists/uma_genshin_genshinjp_bh3_val.txt.cleaned",
+    "text_cleaners":["zh_ja_mixture_cleaners"],
+    "max_wav_value": 32768.0,
+    "sampling_rate": 22050,
+    "filter_length": 1024,
+    "hop_length": 256,
+    "win_length": 1024,
+    "n_mel_channels": 80,
+    "mel_fmin": 0.0,
+    "mel_fmax": null,
+    "add_blank": true,
+    "n_speakers": 804,
+    "cleaned_text": true
+  },
+  "model": {
+    "inter_channels": 192,
+    "hidden_channels": 192,
+    "filter_channels": 768,
+    "n_heads": 2,
+    "n_layers": 6,
+    "kernel_size": 3,
+    "p_dropout": 0.1,
+    "resblock": "1",
+    "resblock_kernel_sizes": [3,7,11],
+    "resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
+    "upsample_rates": [8,8,2,2],
+    "upsample_initial_channel": 512,
+    "upsample_kernel_sizes": [16,16,4,4],
+    "n_layers_q": 3,
+    "use_spectral_norm": false,
+    "gin_channels": 256
+  },
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"\u65e5\u8bed\u963f\u8d1d\u591a\uff08\u91ce\u5c9b\u5065\u513f\uff09", "\u65e5\u8bed\u57c3\u6d1b\u4f0a\uff08\u9ad8\u57a3\u5f69\u9633\uff09", "\u65e5\u8bed\u5b89\u67cf\uff08\u77f3\u89c1\u821e\u83dc\u9999\uff09", "\u65e5\u8bed\u795e\u91cc\u7eeb\u534e\uff08\u65e9\u89c1\u6c99\u7ec7\uff09", "\u65e5\u8bed\u795e\u91cc\u7eeb\u4eba\uff08\u77f3\u7530\u5f70\uff09", "\u65e5\u8bed\u767d\u672f\uff08\u6e38\u4f50\u6d69\u4e8c\uff09", "\u65e5\u8bed\u82ad\u82ad\u62c9\uff08\u9b3c\u5934\u660e\u91cc\uff09", "\u65e5\u8bed\u5317\u6597\uff08\u5c0f\u6e05\u6c34\u4e9a\u7f8e\uff09", "\u65e5\u8bed\u73ed\u5c3c\u7279\uff08\u9022\u5742\u826f\u592a\uff09", "\u65e5\u8bed\u574e\u8482\u4e1d\uff08\u67da\u6728\u51c9\u9999\uff09", "\u65e5\u8bed\u91cd\u4e91\uff08\u9f50\u85e4\u58ee\u9a6c\uff09", "\u65e5\u8bed\u67ef\u83b1\uff08\u524d\u5ddd\u51c9\u5b50\uff09", "\u65e5\u8bed\u8d5b\u8bfa\uff08\u5165\u91ce\u81ea\u7531\uff09", "\u65e5\u8bed\u6234\u56e0\u65af\u96f7\u5e03\uff08\u6d25\u7530\u5065\u6b21\u90ce\uff09", "\u65e5\u8bed\u8fea\u5362\u514b\uff08\u5c0f\u91ce\u8d24\u7ae0\uff09", "\u65e5\u8bed\u8fea\u5965\u5a1c\uff08\u4e95\u6cfd\u8bd7\u7ec7\uff09", "\u65e5\u8bed\u591a\u8389\uff08\u91d1\u7530\u670b\u5b50\uff09", "\u65e5\u8bed\u4f18\u83c8\uff08\u4f50\u85e4\u5229\u5948\uff09", "\u65e5\u8bed\u83f2\u8c22\u5c14\uff08\u5185\u7530\u771f\u793c\uff09", "\u65e5\u8bed\u7518\u96e8\uff08\u4e0a\u7530\u4e3d\u5948\uff09", "\u65e5\u8bed\uff08\u7560\u4e2d\u7950\uff09", "\u65e5\u8bed\u9e7f\u91ce\u9662\u5e73\u85cf\uff08\u4e95\u53e3\u7950\u4e00\uff09", "\u65e5\u8bed\u7a7a\uff08\u5800\u6c5f\u77ac\uff09", "\u65e5\u8bed\u8367\uff08\u60a0\u6728\u78a7\uff09", "\u65e5\u8bed\u80e1\u6843\uff08\u9ad8\u6865\u674e\u4f9d\uff09", "\u65e5\u8bed\u4e00\u6597\uff08\u897f\u5ddd\u8d35\u6559\uff09", "\u65e5\u8bed\u51ef\u4e9a\uff08\u9e1f\u6d77\u6d69\u8f85\uff09", "\u65e5\u8bed\u4e07\u53f6\uff08\u5c9b\u5d0e\u4fe1\u957f\uff09", "\u65e5\u8bed\u523b\u6674\uff08\u559c\u591a\u6751\u82f1\u68a8\uff09", "\u65e5\u8bed\u53ef\u8389\uff08\u4e45\u91ce\u7f8e\u54b2\uff09", "\u65e5\u8bed\u5fc3\u6d77\uff08\u4e09\u68ee\u94c3\u5b50\uff09", "\u65e5\u8bed\u4e5d\u6761\u88df\u7f57\uff08\u6fd1\u6237\u9ebb\u6c99\u7f8e\uff09", "\u65e5\u8bed\u4e3d\u838e\uff08\u7530\u4e2d\u7406\u60e0\uff09", "\u65e5\u8bed\u83ab\u5a1c\uff08\u5c0f\u539f\u597d\u7f8e\uff09", "\u65e5\u8bed\u7eb3\u897f\u59b2\uff08\u7530\u6751\u7531\u52a0\u8389\uff09", "\u65e5\u8bed\u59ae\u9732\uff08\u91d1\u5143\u5bff\u5b50\uff09", "\u65e5\u8bed\u51dd\u5149\uff08\u5927\u539f\u6c99\u8036\u9999\uff09", "\u65e5\u8bed\u8bfa\u827e\u5c14\uff08\u9ad8\u5c3e\u594f\u97f3\uff09", "\u65e5\u8bed\u5965\u5179\uff08\u589e\u8c37\u5eb7\u7eaa\uff09", "\u65e5\u8bed\u6d3e\u8499\uff08\u53e4\u8d3a\u8475\uff09", "\u65e5\u8bed\u7434\uff08\u658b\u85e4\u5343\u548c\uff09", "\u65e5\u8bed\u4e03\u4e03\uff08\u7530\u6751\u7531\u52a0\u8389\uff09", "\u65e5\u8bed\u96f7\u7535\u5c06\u519b\uff08\u6cfd\u57ce\u7f8e\u96ea\uff09", "\u65e5\u8bed\u96f7\u6cfd\uff08\u5185\u5c71\u6602\u8f89\uff09", "\u65e5\u8bed\u7f57\u838e\u8389\u4e9a\uff08\u52a0\u9688\u4e9a\u8863\uff09", "\u65e5\u8bed\u65e9\u67da\uff08\u6d32\u5d0e\u7eeb\uff09", "\u65e5\u8bed\u6563\u5175\uff08\u67ff\u539f\u5f7b\u4e5f\uff09", "\u65e5\u8bed\u7533\u9e64\uff08\u5ddd\u6f84\u7eeb\u5b50\uff09", "\u65e5\u8bed\u4e45\u5c90\u5fcd\uff08\u6c34\u6865\u9999\u7ec7\uff09", "\u65e5\u8bed\u5973\u58eb\uff08\u5e84\u5b50\u88d5\u8863\uff09", "\u65e5\u8bed\u7802\u7cd6\uff08\u85e4\u7530\u831c\uff09", "\u65e5\u8bed\u8fbe\u8fbe\u5229\u4e9a\uff08\u6728\u6751\u826f\u5e73\uff09", "\u65e5\u8bed\u6258\u9a6c\uff08\u68ee\u7530\u6210\u4e00\uff09", "\u65e5\u8bed\u63d0\u7eb3\u91cc\uff08\u5c0f\u6797\u6c99\u82d7\uff09", "\u65e5\u8bed\u6e29\u8fea\uff08\u6751\u6fd1\u6b65\uff09", "\u65e5\u8bed\u9999\u83f1\uff08\u5c0f\u6cfd\u4e9a\u674e\uff09", "\u65e5\u8bed\u9b48\uff08\u677e\u5188\u796f\u4e1e\uff09", "\u65e5\u8bed\u884c\u79cb\uff08\u7686\u5ddd\u7eaf\u5b50\uff09", "\u65e5\u8bed\u8f9b\u7131\uff08\u9ad8\u6865\u667a\u79cb\uff09", "\u65e5\u8bed\u516b\u91cd\u795e\u5b50\uff08\u4f50\u4ed3\u7eeb\u97f3\uff09", "\u65e5\u8bed\u70df\u7eef\uff08\u82b1\u5b88\u7531\u7f8e\u91cc\uff09", "\u65e5\u8bed\u591c\u5170\uff08\u8fdc\u85e4\u7eeb\uff09", "\u65e5\u8bed\u5bb5\u5bab\uff08\u690d\u7530\u4f73\u5948\uff09", "\u65e5\u8bed\u4e91\u5807\uff08\u5c0f\u5ca9\u4e95\u5c0f\u9e1f\uff09", "\u65e5\u8bed\u949f\u79bb\uff08\u524d\u91ce\u667a\u662d\uff09", "\u6770\u514b", "\u963f\u5409", "\u6c5f\u821f", "\u9274\u79cb", "\u5609\u4e49", "\u7eaa\u82b3", "\u666f\u6f84", "\u7ecf\u7eb6", "\u666f\u660e", "\u664b\u4f18", "\u963f\u9e20", "\u9152\u5ba2", "\u4e54\u5c14", "\u4e54\u745f\u592b", "\u7ea6\u987f", "\u4e54\u4f0a\u65af", "\u5c45\u5b89", "\u541b\u541b", "\u987a\u5409", "\u7eaf\u4e5f", "\u91cd\u4f50", "\u5927\u5c9b\u7eaf\u5e73", "\u84b2\u6cfd", "\u52d8\u89e3\u7531\u5c0f\u8def\u5065\u4e09\u90ce", "\u67ab", "\u67ab\u539f\u4e49\u5e86", "\u836b\u5c71", "\u7532\u6590\u7530\u9f8d\u99ac", "\u6d77\u6597", "\u60df\u795e\u6674\u4e4b\u4ecb", "\u9e7f\u91ce\u5948\u5948", "\u5361\u7435\u8389\u4e9a", "\u51ef\u745f\u7433", "\u52a0\u85e4\u4fe1\u609f", "\u52a0\u85e4\u6d0b\u5e73", "\u80dc\u5bb6", "\u8305\u847a\u4e00\u5e86", "\u548c\u662d", "\u4e00\u6b63", "\u4e00\u9053", "\u6842\u4e00", "\u5e86\u6b21\u90ce", "\u963f\u8d24", "\u5065\u53f8", "\u5065\u6b21\u90ce", "\u5065\u4e09\u90ce", "\u5929\u7406", "\u6740\u624ba", "\u6740\u624bb", "\u6728\u5357\u674f\u5948", "\u6728\u6751", "\u56fd\u738b", "\u6728\u4e0b", "\u5317\u6751", "\u6e05\u60e0", "\u6e05\u4eba", "\u514b\u5217\u95e8\u7279", "\u9a91\u58eb", "\u5c0f\u6797", "\u5c0f\u6625", "\u5eb7\u62c9\u5fb7", "\u5927\u8089\u4e38", "\u7434\u7f8e", "\u5b8f\u4e00", "\u5eb7\u4ecb", "\u5e78\u5fb7", "\u9ad8\u5584", "\u68a2", "\u514b\u7f57\u7d22", "\u4e45\u4fdd", "\u4e5d\u6761\u9570\u6cbb", "\u4e45\u6728\u7530", "\u6606\u94a7", "\u83ca\u5730\u541b", "\u4e45\u5229\u987b", "\u9ed1\u7530", "\u9ed1\u6cfd\u4eac\u4e4b\u4ecb", "\u54cd\u592a", "\u5c9a\u59d0", "\u5170\u6eaa", "\u6f9c\u9633", "\u52b3\u4f26\u65af", "\u4e50\u660e", "\u83b1\u8bfa", "\u83b2", "\u826f\u5b50", "\u674e\u5f53", "\u674e\u4e01", "\u5c0f\u4e50", "\u7075", "\u5c0f\u73b2", "\u7433\u7405a", "\u7433\u7405b", "\u5c0f\u5f6c", "\u5c0f\u5fb7", "\u5c0f\u697d", "\u5c0f\u9f99", "\u5c0f\u5434", "\u5c0f\u5434\u7684\u8bb0\u5fc6", "\u7406\u6b63", "\u963f\u9f99", "\u5362\u5361", "\u6d1b\u6210", "\u7f57\u5de7", "\u5317\u98ce\u72fc", "\u5362\u6b63", "\u840d\u59e5\u59e5", "\u524d\u7530", "\u771f\u663c", "\u9ebb\u7eaa", "\u771f", "\u611a\u4eba\u4f17-\u9a6c\u514b\u897f\u59c6", "\u5973\u6027a", "\u5973\u6027b", "\u5973\u6027a\u7684\u8ddf\u968f\u8005", "\u963f\u5b88", "\u739b\u683c\u4e3d\u7279", "\u771f\u7406", "\u739b\u4e54\u4e3d", "\u739b\u6587", "\u6b63\u80dc", "\u660c\u4fe1", "\u5c06\u53f8", "\u6b63\u4eba", "\u8def\u7237", "\u8001\u7ae0", "\u677e\u7530", "\u677e\u672c", "\u677e\u6d66", "\u677e\u5742", "\u8001\u5b5f", "\u5b5f\u4e39", "\u5546\u4eba\u968f\u4ece", "\u4f20\u4ee4\u5175", "\u7c73\u6b47\u5c14", "\u5fa1\u8206\u6e90\u4e00\u90ce", "\u5fa1\u8206\u6e90\u6b21\u90ce", "\u5343\u5ca9\u519b\u6559\u5934", "\u5343\u5ca9\u519b\u58eb\u5175", "\u660e\u535a", "\u660e\u4fca", "\u7f8e\u94c3", "\u7f8e\u548c", "\u963f\u5e78", "\u524a\u6708\u7b51\u9633\u771f\u541b", "\u94b1\u773c\u513f", "\u68ee\u5f66", "\u5143\u52a9", "\u7406\u6c34\u53e0\u5c71\u771f\u541b", "\u7406\u6c34\u758a\u5c71\u771f\u541b", "\u6731\u8001\u677f", "\u6728\u6728", "\u6751\u4e0a", "\u6751\u7530", "\u6c38\u91ce", "\u957f\u91ce\u539f\u9f99\u4e4b\u4ecb", "\u957f\u6fd1", "\u4e2d\u91ce\u5fd7\u4e43", "\u83dc\u83dc\u5b50", "\u6960\u6960", "\u6210\u6fd1", "\u963f\u5185", "\u5b81\u7984", "\u725b\u5fd7", "\u4fe1\u535a", "\u4f38\u592b", "\u91ce\u65b9", "\u8bfa\u62c9", "\u7eaa\u9999", "\u8bfa\u66fc", "\u4fee\u5973", "\u7eaf\u6c34\u7cbe\u7075", "\u5c0f\u5ddd", "\u5c0f\u4ed3\u6faa", "\u5188\u6797", "\u5188\u5d0e\u7ed8\u91cc\u9999", "\u5188\u5d0e\u9646\u6597", "\u5965\u62c9\u592b", "\u8001\u79d1", "\u9b3c\u5a46\u5a46", "\u5c0f\u91ce\u5bfa", "\u5927\u6cb3\u539f\u4e94\u53f3\u536b\u95e8", "\u5927\u4e45\u4fdd\u5927\u4ecb", "\u5927\u68ee", "\u5927\u52a9", "\u5965\u7279", "\u6d3e\u8499", "\u6d3e\u84992", "\u75c5\u4ebaa", "\u75c5\u4ebab", "\u5df4\u987f", "\u6d3e\u6069", "\u670b\u4e49", "\u56f4\u89c2\u7fa4\u4f17", "\u56f4\u89c2\u7fa4\u4f17a", "\u56f4\u89c2\u7fa4\u4f17b", "\u56f4\u89c2\u7fa4\u4f17c", "\u56f4\u89c2\u7fa4\u4f17d", "\u56f4\u89c2\u7fa4\u4f17e", "\u94dc\u96c0", "\u963f\u80a5", "\u5174\u53d4", "\u8001\u5468\u53d4", "\u516c\u4e3b", "\u5f7c\u5f97", "\u4e7e\u5b50", "\u828a\u828a", "\u4e7e\u73ae", "\u7eee\u547d", "\u675e\u5e73", "\u79cb\u6708", "\u6606\u6069", "\u96f7\u7535\u5f71", "\u5170\u9053\u5c14", "\u96f7\u8499\u5fb7", "\u5192\u5931\u7684\u5e15\u62c9\u5fb7", "\u4f36\u4e00", "\u73b2\u82b1", "\u963f\u4ec1", "\u5bb6\u81e3\u4eec", "\u68a8\u7ed8", "\u8363\u6c5f", "\u620e\u4e16", "\u6d6a\u4eba", "\u7f57\u4f0a\u65af", "\u5982\u610f", "\u51c9\u5b50", "\u5f69\u9999", "\u9152\u4e95", "\u5742\u672c", "\u6714\u6b21\u90ce", "\u6b66\u58eba", "\u6b66\u58ebb", "\u6b66\u58ebc", "\u6b66\u58ebd", "\u73ca\u745a", "\u4e09\u7530", "\u838e\u62c9", "\u7b39\u91ce", "\u806a\u7f8e", "\u806a", "\u5c0f\u767e\u5408", "\u6563\u5175", "\u5bb3\u6015\u7684\u5c0f\u5218", "\u8212\u4f2f\u7279", "\u8212\u8328", "\u6d77\u9f99", "\u4e16\u5b50", "\u8c22\u5c14\u76d6", "\u5bb6\u4e01", "\u5546\u534e", "\u6c99\u5bc5", "\u963f\u5347", "\u67f4\u7530", "\u963f\u8302", "\u5f0f\u5927\u5c06", "\u6e05\u6c34", "\u5fd7\u6751\u52d8\u5175\u536b", "\u65b0\u4e4b\u4e1e", "\u5fd7\u7ec7", "\u77f3\u5934", "\u8bd7\u7fbd", "\u8bd7\u7b60", "\u77f3\u58ee", "\u7fd4\u592a", "\u6b63\u4e8c", "\u5468\u5e73", "\u8212\u6768", "\u9f50\u683c\u8299\u4e3d\u96c5", "\u5973\u58eb", "\u601d\u52e4", "\u516d\u6307\u4e54\u745f", "\u611a\u4eba\u4f17\u5c0f\u5175d", "\u611a\u4eba\u4f17\u5c0f\u5175a", "\u611a\u4eba\u4f17\u5c0f\u5175b", "\u611a\u4eba\u4f17\u5c0f\u5175c", "\u5434\u8001\u4e94", "\u5434\u8001\u4e8c", "\u6ed1\u5934\u9b3c", "\u8a00\u7b11", "\u5434\u8001\u4e03", "\u58eb\u5175h", "\u58eb\u5175i", "\u58eb\u5175a", "\u58eb\u5175b", "\u58eb\u5175c", "\u58eb\u5175d", "\u58eb\u5175e", "\u58eb\u5175f", "\u58eb\u5175g", "\u594f\u592a", "\u65af\u5766\u5229", "\u6387\u661f\u652b\u8fb0\u5929\u541b", "\u5c0f\u5934", "\u5927\u6b66", "\u9676\u4e49\u9686", "\u6749\u672c", "\u82cf\u897f", "\u5acc\u7591\u4ebaa", "\u5acc\u7591\u4ebab", "\u5acc\u7591\u4ebac", "\u5acc\u7591\u4ebad", "\u65af\u4e07", "\u5251\u5ba2a", "\u5251\u5ba2b", "\u963f\u4e8c", "\u5fe0\u80dc", "\u5fe0\u592b", "\u963f\u656c", "\u5b5d\u5229", "\u9e70\u53f8\u8fdb", "\u9ad8\u5c71", "\u4e5d\u6761\u5b5d\u884c", "\u6bc5", "\u7af9\u5185", "\u62d3\u771f", "\u5353\u4e5f", "\u592a\u90ce\u4e38", "\u6cf0\u52d2", "\u624b\u5c9b", "\u54f2\u5e73", "\u54f2\u592b", "\u6258\u514b", "\u5927boss", "\u963f\u5f3a", "\u6258\u5c14\u5fb7\u62c9", "\u65c1\u89c2\u8005", "\u5929\u6210", "\u963f\u5927", "\u8482\u739b\u4e4c\u65af", "\u63d0\u7c73", "\u6237\u7530", "\u963f\u4e09", "\u4e00\u8d77\u7684\u4eba", "\u5fb7\u7530", "\u5fb7\u957f", "\u667a\u6811", "\u5229\u5f66", "\u80d6\u4e4e\u4e4e\u7684\u65c5\u884c\u8005", "\u85cf\u5b9d\u4ebaa", "\u85cf\u5b9d\u4ebab", "\u85cf\u5b9d\u4ebac", "\u85cf\u5b9d\u4ebad", "\u963f\u7947", "\u6052\u96c4", "\u9732\u5b50", "\u8bdd\u5267\u56e2\u56e2\u957f", "\u5185\u6751", "\u4e0a\u91ce", "\u4e0a\u6749", "\u8001\u6234", "\u8001\u9ad8", "\u8001\u8d3e", "\u8001\u58a8", "\u8001\u5b59", "\u5929\u67a2\u661f", "\u8001\u4e91", "\u6709\u4e50\u658b", "\u4e11\u96c4", "\u4e4c\u7ef4", "\u74e6\u4eac", "\u83f2\u5c14\u6208\u9edb\u7279", "\u7ef4\u591a\u5229\u4e9a", "\u8587\u5c14", "\u74e6\u683c\u7eb3", "\u963f\u5916", "\u4f8d\u5973", "\u74e6\u62c9", "\u671b\u96c5", "\u5b9b\u70df", "\u742c\u7389", "\u6218\u58eba", "\u6218\u58ebb", "\u6e21\u8fba", "\u6e21\u90e8", "\u963f\u4f1f", "\u6587\u749f", "\u6587\u6e0a", "\u97e6\u5c14\u7eb3", "\u738b\u6273\u624b", "\u6b66\u6c9b", "\u6653\u98de", "\u8f9b\u7a0b", "\u661f\u706b", "\u661f\u7a00", "\u8f9b\u79c0", "\u79c0\u534e", "\u963f\u65ed", "\u5f90\u5218\u5e08", "\u77e2\u90e8", "\u516b\u6728", "\u5c71\u4e0a", "\u963f\u9633", "\u989c\u7b11", "\u5eb7\u660e", "\u6cf0\u4e45", "\u5b89\u6b66", "\u77e2\u7530\u5e78\u559c", "\u77e2\u7530\u8f9b\u559c", "\u4e49\u575a", "\u83ba\u513f", "\u76c8\u4e30", "\u5b9c\u5e74", "\u94f6\u674f", "\u9038\u8f69", "\u6a2a\u5c71", "\u6c38\u8d35", "\u6c38\u4e1a", "\u5609\u4e45", "\u5409\u5ddd", "\u4e49\u9ad8", "\u7528\u9ad8", "\u9633\u592a", "\u5143\u84c9", "\u73a5\u8f89", "\u6bd3\u534e", "\u6709\u9999", "\u5e78\u4e5f", "\u7531\u771f", "\u7ed3\u83dc", "\u97f5\u5b81", "\u767e\u5408", "\u767e\u5408\u534e", "\u5c24\u82cf\u6ce2\u592b", "\u88d5\u5b50", "\u60a0\u7b56", "\u60a0\u4e5f", "\u4e8e\u5ae3", "\u67da\u5b50", "\u8001\u90d1", "\u6b63\u8302", "\u5fd7\u6210", "\u82b7\u5de7", "\u77e5\u6613", "\u652f\u652f", "\u5468\u826f", "\u73e0\u51fd", "\u795d\u660e", "\u795d\u6d9b"],
+  "symbols": ["_", ",", ".", "!", "?", "-", "~", "\u2026", "A", "E", "I", "N", "O", "Q", "U", "a", "b", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "r", "s", "t", "u", "v", "w", "y", "z", "\u0283", "\u02a7", "\u02a6", "\u026f", "\u0279", "\u0259", "\u0265", "\u207c", "\u02b0", "`", "\u2192", "\u2193", "\u2191", " "]
+}
\ No newline at end of file
diff --git a/vits/models.py b/vits/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..859744ca0d8e72202c61c9876c12c33e79ac1887
--- /dev/null
+++ b/vits/models.py
@@ -0,0 +1,536 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from vits.modules import commons
+from vits.modules import modules
+from vits.modules import attentions
+from vits import monotonic_align
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from vits.modules.commons import init_weights, get_padding
+
+
+class StochasticDurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+    super().__init__()
+    filter_channels = in_channels # it needs to be removed from future version.
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.log_flow = modules.Log()
+    self.flows = nn.ModuleList()
+    self.flows.append(modules.ElementwiseAffine(2))
+    for i in range(n_flows):
+      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.flows.append(modules.Flip())
+
+    self.post_pre = nn.Conv1d(1, filter_channels, 1)
+    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    self.post_flows = nn.ModuleList()
+    self.post_flows.append(modules.ElementwiseAffine(2))
+    for i in range(4):
+      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.post_flows.append(modules.Flip())
+
+    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+    x = torch.detach(x)
+    x = self.pre(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.convs(x, x_mask)
+    x = self.proj(x) * x_mask
+
+    if not reverse:
+      flows = self.flows
+      assert w is not None
+
+      logdet_tot_q = 0
+      h_w = self.post_pre(w)
+      h_w = self.post_convs(h_w, x_mask)
+      h_w = self.post_proj(h_w) * x_mask
+      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+      z_q = e_q
+      for flow in self.post_flows:
+        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+        logdet_tot_q += logdet_q
+      z_u, z1 = torch.split(z_q, [1, 1], 1)
+      u = torch.sigmoid(z_u) * x_mask
+      z0 = (w - u) * x_mask
+      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
+      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
+
+      logdet_tot = 0
+      z0, logdet = self.log_flow(z0, x_mask)
+      logdet_tot += logdet
+      z = torch.cat([z0, z1], 1)
+      for flow in flows:
+        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+        logdet_tot = logdet_tot + logdet
+      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
+      return nll + logq # [b]
+    else:
+      flows = list(reversed(self.flows))
+      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
+      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+      for flow in flows:
+        z = flow(z, x_mask, g=x, reverse=reverse)
+      z0, z1 = torch.split(z, [1, 1], 1)
+      logw = z0
+      return logw
+
+
+class DurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+    super().__init__()
+
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.gin_channels = gin_channels
+
+    self.drop = nn.Dropout(p_dropout)
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_1 = modules.LayerNorm(filter_channels)
+    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_2 = modules.LayerNorm(filter_channels)
+    self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+  def forward(self, x, x_mask, g=None):
+    x = torch.detach(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.conv_1(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_1(x)
+    x = self.drop(x)
+    x = self.conv_2(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_2(x)
+    x = self.drop(x)
+    x = self.proj(x * x_mask)
+    return x * x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      n_vocab,
+      out_channels,
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout):
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+
+    self.emb = nn.Embedding(n_vocab, hidden_channels)
+    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+    self.encoder = attentions.Encoder(
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout)
+    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths):
+    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+    x = self.encoder(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class PosteriorEncoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self,
+    n_vocab,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock,
+    resblock_kernel_sizes,
+    resblock_dilation_sizes,
+    upsample_rates,
+    upsample_initial_channel,
+    upsample_kernel_sizes,
+    n_speakers=0,
+    gin_channels=0,
+    use_sdp=True,
+    **kwargs):
+
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.n_speakers = n_speakers
+    self.gin_channels = gin_channels
+
+    self.use_sdp = use_sdp
+
+    self.enc_p = TextEncoder(n_vocab,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+    if use_sdp:
+      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+    else:
+      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+    if n_speakers > 1:
+      self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+  def forward(self, x, x_lengths, y, y_lengths, sid=None):
+
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+    z_p = self.flow(z, y_mask, g=g)
+
+    with torch.no_grad():
+      # negative cross-entropy
+      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
+      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+    w = attn.sum(2)
+    if self.use_sdp:
+      l_length = self.dp(x, x_mask, w, g=g)
+      l_length = l_length / torch.sum(x_mask)
+    else:
+      logw_ = torch.log(w + 1e-6) * x_mask
+      logw = self.dp(x, x_mask, g=g)
+      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
+
+    # expand prior
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1, max_len=None):
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    if self.use_sdp:
+      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    else:
+      logw = self.dp(x, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = self.flow(z_p, y_mask, g=g, reverse=True)
+    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    """
+    o 三维张量
+    """
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)
+
+  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
+    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
+    g_src = self.emb_g(sid_src).unsqueeze(-1)
+    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
+    z_p = self.flow(z, y_mask, g=g_src)
+    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
+    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
+    return o_hat, y_mask, (z, z_p, z_hat)
+
diff --git a/vits/models_v2.py b/vits/models_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f25f93cebd913fd2d65486d0237bc6eb1e4a96c
--- /dev/null
+++ b/vits/models_v2.py
@@ -0,0 +1,532 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from vits.modules.attentions import attentions
+from vits import monotonic_align
+
+from torch.nn import Conv1d, ConvTranspose1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+from vits.modules.commons import init_weights, get_padding
+from vits.modules import modules
+from vits.modules import commons
+
+
+class StochasticDurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
+    super().__init__()
+    filter_channels = in_channels # it needs to be removed from future version.
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.log_flow = modules.Log()
+    self.flows = nn.ModuleList()
+    self.flows.append(modules.ElementwiseAffine(2))
+    for i in range(n_flows):
+      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.flows.append(modules.Flip())
+
+    self.post_pre = nn.Conv1d(1, filter_channels, 1)
+    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    self.post_flows = nn.ModuleList()
+    self.post_flows.append(modules.ElementwiseAffine(2))
+    for i in range(4):
+      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
+      self.post_flows.append(modules.Flip())
+
+    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
+    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
+    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
+
+  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
+    x = torch.detach(x)
+    x = self.pre(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.convs(x, x_mask)
+    x = self.proj(x) * x_mask
+
+    if not reverse:
+      flows = self.flows
+      assert w is not None
+
+      logdet_tot_q = 0
+      h_w = self.post_pre(w)
+      h_w = self.post_convs(h_w, x_mask)
+      h_w = self.post_proj(h_w) * x_mask
+      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
+      z_q = e_q
+      for flow in self.post_flows:
+        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
+        logdet_tot_q += logdet_q
+      z_u, z1 = torch.split(z_q, [1, 1], 1)
+      u = torch.sigmoid(z_u) * x_mask
+      z0 = (w - u) * x_mask
+      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
+      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
+
+      logdet_tot = 0
+      z0, logdet = self.log_flow(z0, x_mask)
+      logdet_tot += logdet
+      z = torch.cat([z0, z1], 1)
+      for flow in flows:
+        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
+        logdet_tot = logdet_tot + logdet
+      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
+      return nll + logq # [b]
+    else:
+      flows = list(reversed(self.flows))
+      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
+      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
+      for flow in flows:
+        z = flow(z, x_mask, g=x, reverse=reverse)
+      z0, z1 = torch.split(z, [1, 1], 1)
+      logw = z0
+      return logw
+
+
+class DurationPredictor(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
+    super().__init__()
+
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.gin_channels = gin_channels
+
+    self.drop = nn.Dropout(p_dropout)
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_1 = modules.LayerNorm(filter_channels)
+    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+    self.norm_2 = modules.LayerNorm(filter_channels)
+    self.proj = nn.Conv1d(filter_channels, 1, 1)
+
+    if gin_channels != 0:
+      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+  def forward(self, x, x_mask, g=None):
+    x = torch.detach(x)
+    if g is not None:
+      g = torch.detach(g)
+      x = x + self.cond(g)
+    x = self.conv_1(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_1(x)
+    x = self.drop(x)
+    x = self.conv_2(x * x_mask)
+    x = torch.relu(x)
+    x = self.norm_2(x)
+    x = self.drop(x)
+    x = self.proj(x * x_mask)
+    return x * x_mask
+
+
+class TextEncoder(nn.Module):
+  def __init__(self,
+      n_vocab,
+      out_channels,
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout):
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+
+    self.emb = nn.Embedding(n_vocab, hidden_channels)
+    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+    self.encoder = attentions.Encoder(
+      hidden_channels,
+      filter_channels,
+      n_heads,
+      n_layers,
+      kernel_size,
+      p_dropout)
+    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths):
+    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
+    x = torch.transpose(x, 1, -1) # [b, h, t]
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+    x = self.encoder(x * x_mask, x_mask)
+    stats = self.proj(x) * x_mask
+
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    return x, m, logs, x_mask
+
+
+class ResidualCouplingBlock(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      n_flows=4,
+      gin_channels=0):
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.n_flows = n_flows
+    self.gin_channels = gin_channels
+
+    self.flows = nn.ModuleList()
+    for i in range(n_flows):
+      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
+      self.flows.append(modules.Flip())
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    if not reverse:
+      for flow in self.flows:
+        x, _ = flow(x, x_mask, g=g, reverse=reverse)
+    else:
+      for flow in reversed(self.flows):
+        x = flow(x, x_mask, g=g, reverse=reverse)
+    return x
+
+
+class PosteriorEncoder(nn.Module):
+  def __init__(self,
+      in_channels,
+      out_channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      gin_channels=0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+
+    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
+    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
+    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
+
+  def forward(self, x, x_lengths, g=None):
+    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+    x = self.pre(x) * x_mask
+    x = self.enc(x, x_mask, g=g)
+    stats = self.proj(x) * x_mask
+    m, logs = torch.split(stats, self.out_channels, dim=1)
+    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
+    return z, m, logs, x_mask
+
+
+class Generator(torch.nn.Module):
+    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
+        super(Generator, self).__init__()
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_rates)
+        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
+        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
+
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(weight_norm(
+                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
+                                k, u, padding=(k-u)//2)))
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel//(2**(i+1))
+            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+
+        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
+        self.ups.apply(init_weights)
+
+        if gin_channels != 0:
+            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
+
+    def forward(self, x, g=None):
+        x = self.conv_pre(x)
+        if g is not None:
+          x = x + self.cond(g)
+
+        for i in range(self.num_upsamples):
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            x = self.ups[i](x)
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i*self.num_kernels+j](x)
+                else:
+                    xs += self.resblocks[i*self.num_kernels+j](x)
+            x = xs / self.num_kernels
+        x = F.leaky_relu(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_weight_norm(self):
+        print('Removing weight norm...')
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+
+
+class DiscriminatorP(torch.nn.Module):
+    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
+        super(DiscriminatorP, self).__init__()
+        self.period = period
+        self.use_spectral_norm = use_spectral_norm
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
+            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
+        ])
+        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
+
+    def forward(self, x):
+        fmap = []
+
+        # 1d to 2d
+        b, c, t = x.shape
+        if t % self.period != 0: # pad first
+            n_pad = self.period - (t % self.period)
+            x = F.pad(x, (0, n_pad), "reflect")
+            t = t + n_pad
+        x = x.view(b, c, t // self.period, self.period)
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class DiscriminatorS(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(DiscriminatorS, self).__init__()
+        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
+        self.convs = nn.ModuleList([
+            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
+            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
+            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
+            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
+            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
+            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
+        ])
+        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
+
+    def forward(self, x):
+        fmap = []
+
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, modules.LRELU_SLOPE)
+            fmap.append(x)
+        x = self.conv_post(x)
+        fmap.append(x)
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+
+class MultiPeriodDiscriminator(torch.nn.Module):
+    def __init__(self, use_spectral_norm=False):
+        super(MultiPeriodDiscriminator, self).__init__()
+        periods = [2,3,5,7,11]
+
+        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
+        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
+        self.discriminators = nn.ModuleList(discs)
+
+    def forward(self, y, y_hat):
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+        for i, d in enumerate(self.discriminators):
+            y_d_r, fmap_r = d(y)
+            y_d_g, fmap_g = d(y_hat)
+            y_d_rs.append(y_d_r)
+            y_d_gs.append(y_d_g)
+            fmap_rs.append(fmap_r)
+            fmap_gs.append(fmap_g)
+
+        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
+
+
+
+class SynthesizerTrn(nn.Module):
+  """
+  Synthesizer for Training
+  """
+
+  def __init__(self,
+    n_vocab,
+    spec_channels,
+    segment_size,
+    inter_channels,
+    hidden_channels,
+    filter_channels,
+    n_heads,
+    n_layers,
+    kernel_size,
+    p_dropout,
+    resblock,
+    resblock_kernel_sizes,
+    resblock_dilation_sizes,
+    upsample_rates,
+    upsample_initial_channel,
+    upsample_kernel_sizes,
+    n_speakers=0,
+    gin_channels=0,
+    use_sdp=True,
+    **kwargs):
+
+    super().__init__()
+    self.n_vocab = n_vocab
+    self.spec_channels = spec_channels
+    self.inter_channels = inter_channels
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.resblock = resblock
+    self.resblock_kernel_sizes = resblock_kernel_sizes
+    self.resblock_dilation_sizes = resblock_dilation_sizes
+    self.upsample_rates = upsample_rates
+    self.upsample_initial_channel = upsample_initial_channel
+    self.upsample_kernel_sizes = upsample_kernel_sizes
+    self.segment_size = segment_size
+    self.n_speakers = n_speakers
+    self.gin_channels = gin_channels
+
+    self.use_sdp = use_sdp
+
+    self.enc_p = TextEncoder(n_vocab,
+        inter_channels,
+        hidden_channels,
+        filter_channels,
+        n_heads,
+        n_layers,
+        kernel_size,
+        p_dropout)
+    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
+    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
+    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
+
+    if use_sdp:
+      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
+    else:
+      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
+
+    if n_speakers >= 1:
+      self.emb_g = nn.Embedding(n_speakers, gin_channels)
+
+  def forward(self, x, x_lengths, y, y_lengths, sid=None):
+
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
+    z_p = self.flow(z, y_mask, g=g)
+
+    with torch.no_grad():
+      # negative cross-entropy
+      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
+      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
+      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
+      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
+
+      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
+
+    w = attn.sum(2)
+    if self.use_sdp:
+      l_length = self.dp(x, x_mask, w, g=g)
+      l_length = l_length / torch.sum(x_mask)
+    else:
+      logw_ = torch.log(w + 1e-6) * x_mask
+      logw = self.dp(x, x_mask, g=g)
+      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
+
+    # expand prior
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
+
+    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
+    o = self.dec(z_slice, g=g)
+    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
+
+  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
+    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
+    if self.n_speakers > 0:
+      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
+    else:
+      g = None
+
+    if self.use_sdp:
+      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
+    else:
+      logw = self.dp(x, x_mask, g=g)
+    w = torch.exp(logw) * x_mask * length_scale
+    w_ceil = torch.ceil(w)
+    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
+    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
+    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
+    attn = commons.generate_path(w_ceil, attn_mask)
+
+    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
+
+    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
+    z = self.flow(z_p, y_mask, g=g, reverse=True)
+    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
+    return o, attn, y_mask, (z, z_p, m_p, logs_p)
+
+  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
+    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
+    g_src = self.emb_g(sid_src).unsqueeze(-1)
+    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
+    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
+    z_p = self.flow(z, y_mask, g=g_src)
+    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
+    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
+    return o_hat, y_mask, (z, z_p, z_hat)
diff --git a/vits/modules/__init__.py b/vits/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b947bd657e268fac3d733746d6ba9c12c458f8f
--- /dev/null
+++ b/vits/modules/__init__.py
@@ -0,0 +1,4 @@
+from vits.modules.layer import *
+from vits.modules.attentions import *
+from vits.modules.commons import *
+from vits.modules.transforms import *
diff --git a/vits/modules/__pycache__/__init__.cpython-310.pyc b/vits/modules/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..a954d7d4b2ab8de2c9c5e61e18690c184c73d33b
Binary files /dev/null and b/vits/modules/__pycache__/__init__.cpython-310.pyc differ
diff --git a/vits/modules/attentions/__init__.py b/vits/modules/attentions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..651ca58b2bcd864167a5199f06bf70373c15dc2c
--- /dev/null
+++ b/vits/modules/attentions/__init__.py
@@ -0,0 +1 @@
+from vits.modules.attentions.attentions import *
diff --git a/vits/modules/attentions/__pycache__/__init__.cpython-310.pyc b/vits/modules/attentions/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f7e0052f894f8a3eed218910b7ed314a0dfb0e77
Binary files /dev/null and b/vits/modules/attentions/__pycache__/__init__.cpython-310.pyc differ
diff --git a/vits/modules/attentions/__pycache__/attentions.cpython-310.pyc b/vits/modules/attentions/__pycache__/attentions.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..d3a6a956e498afbbbb1e2a6a468efb9cc5f13938
Binary files /dev/null and b/vits/modules/attentions/__pycache__/attentions.cpython-310.pyc differ
diff --git a/vits/modules/attentions/attentions.py b/vits/modules/attentions/attentions.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f87f022cd632def0235358300f124e77b7ba6a5
--- /dev/null
+++ b/vits/modules/attentions/attentions.py
@@ -0,0 +1,300 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from vits.modules import commons
+from vits.modules import LayerNorm
+
+
+class Encoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+
+    self.drop = nn.Dropout(p_dropout)
+    self.attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask):
+    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.attn_layers[i](x, x, attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class Decoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+
+    self.drop = nn.Dropout(p_dropout)
+    self.self_attn_layers = nn.ModuleList()
+    self.norm_layers_0 = nn.ModuleList()
+    self.encdec_attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
+      self.norm_layers_0.append(LayerNorm(hidden_channels))
+      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask, h, h_mask):
+    """
+    x: decoder input
+    h: encoder output
+    """
+    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.self_attn_layers[i](x, x, self_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_0[i](x + y)
+
+      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class MultiHeadAttention(nn.Module):
+  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
+    super().__init__()
+    assert channels % n_heads == 0
+
+    self.channels = channels
+    self.out_channels = out_channels
+    self.n_heads = n_heads
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+    self.heads_share = heads_share
+    self.block_length = block_length
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+    self.attn = None
+
+    self.k_channels = channels // n_heads
+    self.conv_q = nn.Conv1d(channels, channels, 1)
+    self.conv_k = nn.Conv1d(channels, channels, 1)
+    self.conv_v = nn.Conv1d(channels, channels, 1)
+    self.conv_o = nn.Conv1d(channels, out_channels, 1)
+    self.drop = nn.Dropout(p_dropout)
+
+    if window_size is not None:
+      n_heads_rel = 1 if heads_share else n_heads
+      rel_stddev = self.k_channels**-0.5
+      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+    nn.init.xavier_uniform_(self.conv_q.weight)
+    nn.init.xavier_uniform_(self.conv_k.weight)
+    nn.init.xavier_uniform_(self.conv_v.weight)
+    if proximal_init:
+      with torch.no_grad():
+        self.conv_k.weight.copy_(self.conv_q.weight)
+        self.conv_k.bias.copy_(self.conv_q.bias)
+
+  def forward(self, x, c, attn_mask=None):
+    q = self.conv_q(x)
+    k = self.conv_k(c)
+    v = self.conv_v(c)
+
+    x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+    x = self.conv_o(x)
+    return x
+
+  def attention(self, query, key, value, mask=None):
+    # reshape [b, d, t] -> [b, n_h, t, d_k]
+    b, d, t_s, t_t = (*key.size(), query.size(2))
+    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+    if self.window_size is not None:
+      assert t_s == t_t, "Relative attention is only available for self-attention."
+      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
+      scores_local = self._relative_position_to_absolute_position(rel_logits)
+      scores = scores + scores_local
+    if self.proximal_bias:
+      assert t_s == t_t, "Proximal bias is only available for self-attention."
+      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+    if mask is not None:
+      scores = scores.masked_fill(mask == 0, -1e4)
+      if self.block_length is not None:
+        assert t_s == t_t, "Local attention is only available for self-attention."
+        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+        scores = scores.masked_fill(block_mask == 0, -1e4)
+    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+    p_attn = self.drop(p_attn)
+    output = torch.matmul(p_attn, value)
+    if self.window_size is not None:
+      relative_weights = self._absolute_position_to_relative_position(p_attn)
+      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+    return output, p_attn
+
+  def _matmul_with_relative_values(self, x, y):
+    """
+    x: [b, h, l, m]
+    y: [h or 1, m, d]
+    ret: [b, h, l, d]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0))
+    return ret
+
+  def _matmul_with_relative_keys(self, x, y):
+    """
+    x: [b, h, l, d]
+    y: [h or 1, m, d]
+    ret: [b, h, l, m]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+    return ret
+
+  def _get_relative_embeddings(self, relative_embeddings, length):
+    max_relative_position = 2 * self.window_size + 1
+    # Pad first before slice to avoid using cond ops.
+    pad_length = max(length - (self.window_size + 1), 0)
+    slice_start_position = max((self.window_size + 1) - length, 0)
+    slice_end_position = slice_start_position + 2 * length - 1
+    if pad_length > 0:
+      padded_relative_embeddings = F.pad(
+          relative_embeddings,
+          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+    else:
+      padded_relative_embeddings = relative_embeddings
+    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
+    return used_relative_embeddings
+
+  def _relative_position_to_absolute_position(self, x):
+    """
+    x: [b, h, l, 2*l-1]
+    ret: [b, h, l, l]
+    """
+    batch, heads, length, _ = x.size()
+    # Concat columns of pad to shift from relative to absolute indexing.
+    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
+
+    # Concat extra elements so to add up to shape (len+1, 2*len-1).
+    x_flat = x.view([batch, heads, length * 2 * length])
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
+
+    # Reshape and slice out the padded elements.
+    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
+    return x_final
+
+  def _absolute_position_to_relative_position(self, x):
+    """
+    x: [b, h, l, l]
+    ret: [b, h, l, 2*l-1]
+    """
+    batch, heads, length, _ = x.size()
+    # padd along column
+    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
+    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
+    # add 0's in the beginning that will skew the elements after reshape
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
+    return x_final
+
+  def _attention_bias_proximal(self, length):
+    """Bias for self-attention to encourage attention to close positions.
+    Args:
+      length: an integer scalar.
+    Returns:
+      a Tensor with shape [1, 1, length, length]
+    """
+    r = torch.arange(length, dtype=torch.float32)
+    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.activation = activation
+    self.causal = causal
+
+    if causal:
+      self.padding = self._causal_padding
+    else:
+      self.padding = self._same_padding
+
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+    self.drop = nn.Dropout(p_dropout)
+
+  def forward(self, x, x_mask):
+    x = self.conv_1(self.padding(x * x_mask))
+    if self.activation == "gelu":
+      x = x * torch.sigmoid(1.702 * x)
+    else:
+      x = torch.relu(x)
+    x = self.drop(x)
+    x = self.conv_2(self.padding(x * x_mask))
+    return x * x_mask
+
+  def _causal_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = self.kernel_size - 1
+    pad_r = 0
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
+
+  def _same_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = (self.kernel_size - 1) // 2
+    pad_r = self.kernel_size // 2
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
diff --git a/vits/modules/attentions/attentions_v2.py b/vits/modules/attentions/attentions_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..26306f83caf8fbb6fdb1e3d028a26992e23775ed
--- /dev/null
+++ b/vits/modules/attentions/attentions_v2.py
@@ -0,0 +1,300 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from vits.modules.layer import LayerNorm
+from vits.modules import commons
+
+
+class Encoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+
+    self.drop = nn.Dropout(p_dropout)
+    self.attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask):
+    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.attn_layers[i](x, x, attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class Decoder(nn.Module):
+  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
+    super().__init__()
+    self.hidden_channels = hidden_channels
+    self.filter_channels = filter_channels
+    self.n_heads = n_heads
+    self.n_layers = n_layers
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+
+    self.drop = nn.Dropout(p_dropout)
+    self.self_attn_layers = nn.ModuleList()
+    self.norm_layers_0 = nn.ModuleList()
+    self.encdec_attn_layers = nn.ModuleList()
+    self.norm_layers_1 = nn.ModuleList()
+    self.ffn_layers = nn.ModuleList()
+    self.norm_layers_2 = nn.ModuleList()
+    for i in range(self.n_layers):
+      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
+      self.norm_layers_0.append(LayerNorm(hidden_channels))
+      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
+      self.norm_layers_1.append(LayerNorm(hidden_channels))
+      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
+      self.norm_layers_2.append(LayerNorm(hidden_channels))
+
+  def forward(self, x, x_mask, h, h_mask):
+    """
+    x: decoder input
+    h: encoder output
+    """
+    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
+    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+    x = x * x_mask
+    for i in range(self.n_layers):
+      y = self.self_attn_layers[i](x, x, self_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_0[i](x + y)
+
+      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
+      y = self.drop(y)
+      x = self.norm_layers_1[i](x + y)
+
+      y = self.ffn_layers[i](x, x_mask)
+      y = self.drop(y)
+      x = self.norm_layers_2[i](x + y)
+    x = x * x_mask
+    return x
+
+
+class MultiHeadAttention(nn.Module):
+  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
+    super().__init__()
+    assert channels % n_heads == 0
+
+    self.channels = channels
+    self.out_channels = out_channels
+    self.n_heads = n_heads
+    self.p_dropout = p_dropout
+    self.window_size = window_size
+    self.heads_share = heads_share
+    self.block_length = block_length
+    self.proximal_bias = proximal_bias
+    self.proximal_init = proximal_init
+    self.attn = None
+
+    self.k_channels = channels // n_heads
+    self.conv_q = nn.Conv1d(channels, channels, 1)
+    self.conv_k = nn.Conv1d(channels, channels, 1)
+    self.conv_v = nn.Conv1d(channels, channels, 1)
+    self.conv_o = nn.Conv1d(channels, out_channels, 1)
+    self.drop = nn.Dropout(p_dropout)
+
+    if window_size is not None:
+      n_heads_rel = 1 if heads_share else n_heads
+      rel_stddev = self.k_channels**-0.5
+      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+
+    nn.init.xavier_uniform_(self.conv_q.weight)
+    nn.init.xavier_uniform_(self.conv_k.weight)
+    nn.init.xavier_uniform_(self.conv_v.weight)
+    if proximal_init:
+      with torch.no_grad():
+        self.conv_k.weight.copy_(self.conv_q.weight)
+        self.conv_k.bias.copy_(self.conv_q.bias)
+
+  def forward(self, x, c, attn_mask=None):
+    q = self.conv_q(x)
+    k = self.conv_k(c)
+    v = self.conv_v(c)
+
+    x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+    x = self.conv_o(x)
+    return x
+
+  def attention(self, query, key, value, mask=None):
+    # reshape [b, d, t] -> [b, n_h, t, d_k]
+    b, d, t_s, t_t = (*key.size(), query.size(2))
+    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
+    if self.window_size is not None:
+      assert t_s == t_t, "Relative attention is only available for self-attention."
+      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
+      scores_local = self._relative_position_to_absolute_position(rel_logits)
+      scores = scores + scores_local
+    if self.proximal_bias:
+      assert t_s == t_t, "Proximal bias is only available for self-attention."
+      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+    if mask is not None:
+      scores = scores.masked_fill(mask == 0, -1e4)
+      if self.block_length is not None:
+        assert t_s == t_t, "Local attention is only available for self-attention."
+        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+        scores = scores.masked_fill(block_mask == 0, -1e4)
+    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
+    p_attn = self.drop(p_attn)
+    output = torch.matmul(p_attn, value)
+    if self.window_size is not None:
+      relative_weights = self._absolute_position_to_relative_position(p_attn)
+      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
+    return output, p_attn
+
+  def _matmul_with_relative_values(self, x, y):
+    """
+    x: [b, h, l, m]
+    y: [h or 1, m, d]
+    ret: [b, h, l, d]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0))
+    return ret
+
+  def _matmul_with_relative_keys(self, x, y):
+    """
+    x: [b, h, l, d]
+    y: [h or 1, m, d]
+    ret: [b, h, l, m]
+    """
+    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+    return ret
+
+  def _get_relative_embeddings(self, relative_embeddings, length):
+    max_relative_position = 2 * self.window_size + 1
+    # Pad first before slice to avoid using cond ops.
+    pad_length = max(length - (self.window_size + 1), 0)
+    slice_start_position = max((self.window_size + 1) - length, 0)
+    slice_end_position = slice_start_position + 2 * length - 1
+    if pad_length > 0:
+      padded_relative_embeddings = F.pad(
+          relative_embeddings,
+          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+    else:
+      padded_relative_embeddings = relative_embeddings
+    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
+    return used_relative_embeddings
+
+  def _relative_position_to_absolute_position(self, x):
+    """
+    x: [b, h, l, 2*l-1]
+    ret: [b, h, l, l]
+    """
+    batch, heads, length, _ = x.size()
+    # Concat columns of pad to shift from relative to absolute indexing.
+    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
+
+    # Concat extra elements so to add up to shape (len+1, 2*len-1).
+    x_flat = x.view([batch, heads, length * 2 * length])
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
+
+    # Reshape and slice out the padded elements.
+    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
+    return x_final
+
+  def _absolute_position_to_relative_position(self, x):
+    """
+    x: [b, h, l, l]
+    ret: [b, h, l, 2*l-1]
+    """
+    batch, heads, length, _ = x.size()
+    # padd along column
+    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
+    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
+    # add 0's in the beginning that will skew the elements after reshape
+    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
+    return x_final
+
+  def _attention_bias_proximal(self, length):
+    """Bias for self-attention to encourage attention to close positions.
+    Args:
+      length: an integer scalar.
+    Returns:
+      a Tensor with shape [1, 1, length, length]
+    """
+    r = torch.arange(length, dtype=torch.float32)
+    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
+    super().__init__()
+    self.in_channels = in_channels
+    self.out_channels = out_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.p_dropout = p_dropout
+    self.activation = activation
+    self.causal = causal
+
+    if causal:
+      self.padding = self._causal_padding
+    else:
+      self.padding = self._same_padding
+
+    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
+    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
+    self.drop = nn.Dropout(p_dropout)
+
+  def forward(self, x, x_mask):
+    x = self.conv_1(self.padding(x * x_mask))
+    if self.activation == "gelu":
+      x = x * torch.sigmoid(1.702 * x)
+    else:
+      x = torch.relu(x)
+    x = self.drop(x)
+    x = self.conv_2(self.padding(x * x_mask))
+    return x * x_mask
+
+  def _causal_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = self.kernel_size - 1
+    pad_r = 0
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
+
+  def _same_padding(self, x):
+    if self.kernel_size == 1:
+      return x
+    pad_l = (self.kernel_size - 1) // 2
+    pad_r = self.kernel_size // 2
+    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+    x = F.pad(x, commons.convert_pad_shape(padding))
+    return x
diff --git a/vits/modules/commons/__init__.py b/vits/modules/commons/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a17b0e4cb6c20e86f84cc5d0be75eb41306d1fce
--- /dev/null
+++ b/vits/modules/commons/__init__.py
@@ -0,0 +1 @@
+from vits.modules.commons.commons import *
diff --git a/vits/modules/commons/__pycache__/__init__.cpython-310.pyc b/vits/modules/commons/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bb7d55e2a9cafee3e8a3ccf66d1c25798295dc45
Binary files /dev/null and b/vits/modules/commons/__pycache__/__init__.cpython-310.pyc differ
diff --git a/vits/modules/commons/__pycache__/commons.cpython-310.pyc b/vits/modules/commons/__pycache__/commons.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..edf90ce4fef800658b40870d2f619965ab8e05a0
Binary files /dev/null and b/vits/modules/commons/__pycache__/commons.cpython-310.pyc differ
diff --git a/vits/modules/commons/commons.py b/vits/modules/commons/commons.py
new file mode 100644
index 0000000000000000000000000000000000000000..40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9
--- /dev/null
+++ b/vits/modules/commons/commons.py
@@ -0,0 +1,172 @@
+import math
+import torch
+from torch.nn import functional as F
+import torch.jit
+
+
+def script_method(fn, _rcb=None):
+  return fn
+
+
+def script(obj, optimize=True, _frames_up=0, _rcb=None):
+  return obj
+
+
+torch.jit.script_method = script_method
+torch.jit.script = script
+
+
+def init_weights(m, mean=0.0, std=0.01):
+  classname = m.__class__.__name__
+  if classname.find("Conv") != -1:
+    m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+  return int((kernel_size*dilation - dilation)/2)
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def intersperse(lst, item):
+  result = [item] * (len(lst) * 2 + 1)
+  result[1::2] = lst
+  return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+  """KL(P||Q)"""
+  kl = (logs_q - logs_p) - 0.5
+  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
+  return kl
+
+
+def rand_gumbel(shape):
+  """Sample from the Gumbel distribution, protect from overflows."""
+  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+  return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+  return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    ret[i] = x[i, :, idx_str:idx_end]
+  return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def get_timing_signal_1d(
+    length, channels, min_timescale=1.0, max_timescale=1.0e4):
+  position = torch.arange(length, dtype=torch.float)
+  num_timescales = channels // 2
+  log_timescale_increment = (
+      math.log(float(max_timescale) / float(min_timescale)) /
+      (num_timescales - 1))
+  inv_timescales = min_timescale * torch.exp(
+      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+  signal = F.pad(signal, [0, 0, 0, channels % 2])
+  signal = signal.view(1, channels, length)
+  return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+  return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+  n_channels_int = n_channels[0]
+  in_act = input_a + input_b
+  t_act = torch.tanh(in_act[:, :n_channels_int, :])
+  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+  acts = t_act * s_act
+  return acts
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def shift_1d(x):
+  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+  return x
+
+
+def sequence_mask(length, max_length=None):
+  if max_length is None:
+    max_length = length.max()
+  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+  return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+  """
+  duration: [b, 1, t_x]
+  mask: [b, 1, t_y, t_x]
+  """
+  device = duration.device
+  
+  b, _, t_y, t_x = mask.shape
+  cum_duration = torch.cumsum(duration, -1)
+  
+  cum_duration_flat = cum_duration.view(b * t_x)
+  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+  path = path.view(b, t_x, t_y)
+  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+  path = path.unsqueeze(1).transpose(2,3) * mask
+  return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm
diff --git a/vits/modules/commons/commons_v2.py b/vits/modules/commons/commons_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..db17cf0914ba6e445fe613e3ec3411b3a74b28aa
--- /dev/null
+++ b/vits/modules/commons/commons_v2.py
@@ -0,0 +1,164 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+
+def init_weights(m, mean=0.0, std=0.01):
+  classname = m.__class__.__name__
+  if classname.find("Conv") != -1:
+    m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+  return int((kernel_size*dilation - dilation)/2)
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def intersperse(lst, item):
+  result = [item] * (len(lst) * 2 + 1)
+  result[1::2] = lst
+  return result
+
+
+def kl_divergence(m_p, logs_p, m_q, logs_q):
+  """KL(P||Q)"""
+  kl = (logs_q - logs_p) - 0.5
+  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
+  return kl
+
+
+def rand_gumbel(shape):
+  """Sample from the Gumbel distribution, protect from overflows."""
+  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
+  return -torch.log(-torch.log(uniform_samples))
+
+
+def rand_gumbel_like(x):
+  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
+  return g
+
+
+def slice_segments(x, ids_str, segment_size=4):
+  ret = torch.zeros_like(x[:, :, :segment_size])
+  for i in range(x.size(0)):
+    idx_str = ids_str[i]
+    idx_end = idx_str + segment_size
+    try:
+      ret[i] = x[i, :, idx_str:idx_end]
+    except RuntimeError:
+      print("?")
+  return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+  b, d, t = x.size()
+  if x_lengths is None:
+    x_lengths = t
+  ids_str_max = x_lengths - segment_size + 1
+  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
+  ret = slice_segments(x, ids_str, segment_size)
+  return ret, ids_str
+
+
+def get_timing_signal_1d(
+    length, channels, min_timescale=1.0, max_timescale=1.0e4):
+  position = torch.arange(length, dtype=torch.float)
+  num_timescales = channels // 2
+  log_timescale_increment = (
+      math.log(float(max_timescale) / float(min_timescale)) /
+      (num_timescales - 1))
+  inv_timescales = min_timescale * torch.exp(
+      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
+  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
+  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
+  signal = F.pad(signal, [0, 0, 0, channels % 2])
+  signal = signal.view(1, channels, length)
+  return signal
+
+
+def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return x + signal.to(dtype=x.dtype, device=x.device)
+
+
+def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
+  b, channels, length = x.size()
+  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
+  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
+
+
+def subsequent_mask(length):
+  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
+  return mask
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+  n_channels_int = n_channels[0]
+  in_act = input_a + input_b
+  t_act = torch.tanh(in_act[:, :n_channels_int, :])
+  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+  acts = t_act * s_act
+  return acts
+
+
+def convert_pad_shape(pad_shape):
+  l = pad_shape[::-1]
+  pad_shape = [item for sublist in l for item in sublist]
+  return pad_shape
+
+
+def shift_1d(x):
+  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+  return x
+
+
+def sequence_mask(length, max_length=None):
+  if max_length is None:
+    max_length = length.max()
+  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+  return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def generate_path(duration, mask):
+  """
+  duration: [b, 1, t_x]
+  mask: [b, 1, t_y, t_x]
+  """
+  device = duration.device
+  
+  b, _, t_y, t_x = mask.shape
+  cum_duration = torch.cumsum(duration, -1)
+  
+  cum_duration_flat = cum_duration.view(b * t_x)
+  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
+  path = path.view(b, t_x, t_y)
+  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
+  path = path.unsqueeze(1).transpose(2,3) * mask
+  return path
+
+
+def clip_grad_value_(parameters, clip_value, norm_type=2):
+  if isinstance(parameters, torch.Tensor):
+    parameters = [parameters]
+  parameters = list(filter(lambda p: p.grad is not None, parameters))
+  norm_type = float(norm_type)
+  if clip_value is not None:
+    clip_value = float(clip_value)
+
+  total_norm = 0
+  for p in parameters:
+    param_norm = p.grad.data.norm(norm_type)
+    total_norm += param_norm.item() ** norm_type
+    if clip_value is not None:
+      p.grad.data.clamp_(min=-clip_value, max=clip_value)
+  total_norm = total_norm ** (1. / norm_type)
+  return total_norm
diff --git a/vits/modules/layer/__init__.py b/vits/modules/layer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fd834d984753844d67100a951f3f5a5a0834d6b
--- /dev/null
+++ b/vits/modules/layer/__init__.py
@@ -0,0 +1 @@
+from vits.modules.layer.modules import *
diff --git a/vits/modules/layer/__pycache__/__init__.cpython-310.pyc b/vits/modules/layer/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..344c1deff6f14d61a65132674856ae3bb52c9244
Binary files /dev/null and b/vits/modules/layer/__pycache__/__init__.cpython-310.pyc differ
diff --git a/vits/modules/layer/__pycache__/modules.cpython-310.pyc b/vits/modules/layer/__pycache__/modules.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..6281ade50175aa81cf8a8243b1d60a6a2d7ca29e
Binary files /dev/null and b/vits/modules/layer/__pycache__/modules.cpython-310.pyc differ
diff --git a/vits/modules/layer/modules.py b/vits/modules/layer/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f31ca61a2ec664a428f354ff2b1b3b28453c564
--- /dev/null
+++ b/vits/modules/layer/modules.py
@@ -0,0 +1,388 @@
+import math
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+from vits.modules import commons
+from vits.modules.commons import init_weights, get_padding
+from vits.modules.transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    super().__init__()
+    self.channels = channels
+    self.eps = eps
+
+    self.gamma = nn.Parameter(torch.ones(channels))
+    self.beta = nn.Parameter(torch.zeros(channels))
+
+  def forward(self, x):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+    super().__init__()
+    self.in_channels = in_channels
+    self.hidden_channels = hidden_channels
+    self.out_channels = out_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+    assert n_layers > 1, "Number of layers should be larger than 0."
+
+    self.conv_layers = nn.ModuleList()
+    self.norm_layers = nn.ModuleList()
+    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+    self.norm_layers.append(LayerNorm(hidden_channels))
+    self.relu_drop = nn.Sequential(
+        nn.ReLU(),
+        nn.Dropout(p_dropout))
+    for _ in range(n_layers-1):
+      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+      self.norm_layers.append(LayerNorm(hidden_channels))
+    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask):
+    x_org = x
+    for i in range(self.n_layers):
+      x = self.conv_layers[i](x * x_mask)
+      x = self.norm_layers[i](x)
+      x = self.relu_drop(x)
+    x = x_org + self.proj(x)
+    return x * x_mask
+
+
+class DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    self.hidden_channels =hidden_channels
+    self.kernel_size = kernel_size,
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.p_dropout = p_dropout
+
+    self.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
+      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+    for i in range(n_layers):
+      dilation = dilation_rate ** i
+      padding = int((kernel_size * dilation - dilation) / 2)
+      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
+                                 dilation=dilation, padding=padding)
+      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+      self.in_layers.append(in_layer)
+
+      # last one is not necessary
+      if i < n_layers - 1:
+        res_skip_channels = 2 * hidden_channels
+      else:
+        res_skip_channels = hidden_channels
+
+      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+      self.res_skip_layers.append(res_skip_layer)
+
+  def forward(self, x, x_mask, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      if g is not None:
+        cond_offset = i * 2 * self.hidden_channels
+        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
+      else:
+        g_l = torch.zeros_like(x_in)
+
+      acts = commons.fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * x_mask
+        output = output + res_skip_acts[:,self.hidden_channels:,:]
+      else:
+        output = output + res_skip_acts
+    return output * x_mask
+
+  def remove_weight_norm(self):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x
+
+
+class ConvFlow(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.num_bins = num_bins
+    self.tail_bound = tail_bound
+    self.half_channels = in_channels // 2
+
+    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
+    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0)
+    h = self.convs(h, x_mask, g=g)
+    h = self.proj(h) * x_mask
+
+    b, c, t = x0.shape
+    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
+
+    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_derivatives = h[..., 2 * self.num_bins:]
+
+    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+        unnormalized_widths,
+        unnormalized_heights,
+        unnormalized_derivatives,
+        inverse=reverse,
+        tails='linear',
+        tail_bound=self.tail_bound
+    )
+
+    x = torch.cat([x0, x1], 1) * x_mask
+    logdet = torch.sum(logabsdet * x_mask, [1,2])
+    if not reverse:
+        return x, logdet
+    else:
+        return x
diff --git a/vits/modules/layer/modules_v2.py b/vits/modules/layer/modules_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d08953fb810225f6c8d2cc3beed89aa7ad0d938e
--- /dev/null
+++ b/vits/modules/layer/modules_v2.py
@@ -0,0 +1,387 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from torch.nn import Conv1d
+from torch.nn.utils import weight_norm, remove_weight_norm
+
+import vits.modules.commons
+from vits.modules.commons import init_weights, get_padding
+from vits.modules.transforms import piecewise_rational_quadratic_transform
+
+
+LRELU_SLOPE = 0.1
+
+
+class LayerNorm(nn.Module):
+  def __init__(self, channels, eps=1e-5):
+    super().__init__()
+    self.channels = channels
+    self.eps = eps
+
+    self.gamma = nn.Parameter(torch.ones(channels))
+    self.beta = nn.Parameter(torch.zeros(channels))
+
+  def forward(self, x):
+    x = x.transpose(1, -1)
+    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
+    return x.transpose(1, -1)
+
+
+class ConvReluNorm(nn.Module):
+  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+    super().__init__()
+    self.in_channels = in_channels
+    self.hidden_channels = hidden_channels
+    self.out_channels = out_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+    assert n_layers > 1, "Number of layers should be larger than 0."
+
+    self.conv_layers = nn.ModuleList()
+    self.norm_layers = nn.ModuleList()
+    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+    self.norm_layers.append(LayerNorm(hidden_channels))
+    self.relu_drop = nn.Sequential(
+        nn.ReLU(),
+        nn.Dropout(p_dropout))
+    for _ in range(n_layers-1):
+      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
+      self.norm_layers.append(LayerNorm(hidden_channels))
+    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask):
+    x_org = x
+    for i in range(self.n_layers):
+      x = self.conv_layers[i](x * x_mask)
+      x = self.norm_layers[i](x)
+      x = self.relu_drop(x)
+    x = x_org + self.proj(x)
+    return x * x_mask
+
+
+class DDSConv(nn.Module):
+  """
+  Dialted and Depth-Separable Convolution
+  """
+  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
+    super().__init__()
+    self.channels = channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.p_dropout = p_dropout
+
+    self.drop = nn.Dropout(p_dropout)
+    self.convs_sep = nn.ModuleList()
+    self.convs_1x1 = nn.ModuleList()
+    self.norms_1 = nn.ModuleList()
+    self.norms_2 = nn.ModuleList()
+    for i in range(n_layers):
+      dilation = kernel_size ** i
+      padding = (kernel_size * dilation - dilation) // 2
+      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
+          groups=channels, dilation=dilation, padding=padding
+      ))
+      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
+      self.norms_1.append(LayerNorm(channels))
+      self.norms_2.append(LayerNorm(channels))
+
+  def forward(self, x, x_mask, g=None):
+    if g is not None:
+      x = x + g
+    for i in range(self.n_layers):
+      y = self.convs_sep[i](x * x_mask)
+      y = self.norms_1[i](y)
+      y = F.gelu(y)
+      y = self.convs_1x1[i](y)
+      y = self.norms_2[i](y)
+      y = F.gelu(y)
+      y = self.drop(y)
+      x = x + y
+    return x * x_mask
+
+
+class WN(torch.nn.Module):
+  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
+    super(WN, self).__init__()
+    assert(kernel_size % 2 == 1)
+    self.hidden_channels =hidden_channels
+    self.kernel_size = kernel_size,
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.gin_channels = gin_channels
+    self.p_dropout = p_dropout
+
+    self.in_layers = torch.nn.ModuleList()
+    self.res_skip_layers = torch.nn.ModuleList()
+    self.drop = nn.Dropout(p_dropout)
+
+    if gin_channels != 0:
+      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
+      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+    for i in range(n_layers):
+      dilation = dilation_rate ** i
+      padding = int((kernel_size * dilation - dilation) / 2)
+      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
+                                 dilation=dilation, padding=padding)
+      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+      self.in_layers.append(in_layer)
+
+      # last one is not necessary
+      if i < n_layers - 1:
+        res_skip_channels = 2 * hidden_channels
+      else:
+        res_skip_channels = hidden_channels
+
+      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
+      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+      self.res_skip_layers.append(res_skip_layer)
+
+  def forward(self, x, x_mask, g=None, **kwargs):
+    output = torch.zeros_like(x)
+    n_channels_tensor = torch.IntTensor([self.hidden_channels])
+
+    if g is not None:
+      g = self.cond_layer(g)
+
+    for i in range(self.n_layers):
+      x_in = self.in_layers[i](x)
+      if g is not None:
+        cond_offset = i * 2 * self.hidden_channels
+        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
+      else:
+        g_l = torch.zeros_like(x_in)
+
+      acts = commons.fused_add_tanh_sigmoid_multiply(
+          x_in,
+          g_l,
+          n_channels_tensor)
+      acts = self.drop(acts)
+
+      res_skip_acts = self.res_skip_layers[i](acts)
+      if i < self.n_layers - 1:
+        res_acts = res_skip_acts[:,:self.hidden_channels,:]
+        x = (x + res_acts) * x_mask
+        output = output + res_skip_acts[:,self.hidden_channels:,:]
+      else:
+        output = output + res_skip_acts
+    return output * x_mask
+
+  def remove_weight_norm(self):
+    if self.gin_channels != 0:
+      torch.nn.utils.remove_weight_norm(self.cond_layer)
+    for l in self.in_layers:
+      torch.nn.utils.remove_weight_norm(l)
+    for l in self.res_skip_layers:
+     torch.nn.utils.remove_weight_norm(l)
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super(ResBlock1, self).__init__()
+        self.convs1 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
+                               padding=get_padding(kernel_size, dilation[2])))
+        ])
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
+                               padding=get_padding(kernel_size, 1)))
+        ])
+        self.convs2.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c2(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super(ResBlock2, self).__init__()
+        self.convs = nn.ModuleList([
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
+                               padding=get_padding(kernel_size, dilation[0]))),
+            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
+                               padding=get_padding(kernel_size, dilation[1])))
+        ])
+        self.convs.apply(init_weights)
+
+    def forward(self, x, x_mask=None):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            if x_mask is not None:
+                xt = xt * x_mask
+            xt = c(xt)
+            x = xt + x
+        if x_mask is not None:
+            x = x * x_mask
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class Log(nn.Module):
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
+      logdet = torch.sum(-y, [1, 2])
+      return y, logdet
+    else:
+      x = torch.exp(x) * x_mask
+      return x
+
+
+class Flip(nn.Module):
+  def forward(self, x, *args, reverse=False, **kwargs):
+    x = torch.flip(x, [1])
+    if not reverse:
+      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
+      return x, logdet
+    else:
+      return x
+
+
+class ElementwiseAffine(nn.Module):
+  def __init__(self, channels):
+    super().__init__()
+    self.channels = channels
+    self.m = nn.Parameter(torch.zeros(channels,1))
+    self.logs = nn.Parameter(torch.zeros(channels,1))
+
+  def forward(self, x, x_mask, reverse=False, **kwargs):
+    if not reverse:
+      y = self.m + torch.exp(self.logs) * x
+      y = y * x_mask
+      logdet = torch.sum(self.logs * x_mask, [1,2])
+      return y, logdet
+    else:
+      x = (x - self.m) * torch.exp(-self.logs) * x_mask
+      return x
+
+
+class ResidualCouplingLayer(nn.Module):
+  def __init__(self,
+      channels,
+      hidden_channels,
+      kernel_size,
+      dilation_rate,
+      n_layers,
+      p_dropout=0,
+      gin_channels=0,
+      mean_only=False):
+    assert channels % 2 == 0, "channels should be divisible by 2"
+    super().__init__()
+    self.channels = channels
+    self.hidden_channels = hidden_channels
+    self.kernel_size = kernel_size
+    self.dilation_rate = dilation_rate
+    self.n_layers = n_layers
+    self.half_channels = channels // 2
+    self.mean_only = mean_only
+
+    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
+    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
+    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
+    self.post.weight.data.zero_()
+    self.post.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0) * x_mask
+    h = self.enc(h, x_mask, g=g)
+    stats = self.post(h) * x_mask
+    if not self.mean_only:
+      m, logs = torch.split(stats, [self.half_channels]*2, 1)
+    else:
+      m = stats
+      logs = torch.zeros_like(m)
+
+    if not reverse:
+      x1 = m + x1 * torch.exp(logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      logdet = torch.sum(logs, [1,2])
+      return x, logdet
+    else:
+      x1 = (x1 - m) * torch.exp(-logs) * x_mask
+      x = torch.cat([x0, x1], 1)
+      return x
+
+
+class ConvFlow(nn.Module):
+  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
+    super().__init__()
+    self.in_channels = in_channels
+    self.filter_channels = filter_channels
+    self.kernel_size = kernel_size
+    self.n_layers = n_layers
+    self.num_bins = num_bins
+    self.tail_bound = tail_bound
+    self.half_channels = in_channels // 2
+
+    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
+    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
+    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
+    self.proj.weight.data.zero_()
+    self.proj.bias.data.zero_()
+
+  def forward(self, x, x_mask, g=None, reverse=False):
+    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
+    h = self.pre(x0)
+    h = self.convs(h, x_mask, g=g)
+    h = self.proj(h) * x_mask
+
+    b, c, t = x0.shape
+    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
+
+    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
+    unnormalized_derivatives = h[..., 2 * self.num_bins:]
+
+    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
+        unnormalized_widths,
+        unnormalized_heights,
+        unnormalized_derivatives,
+        inverse=reverse,
+        tails='linear',
+        tail_bound=self.tail_bound
+    )
+
+    x = torch.cat([x0, x1], 1) * x_mask
+    logdet = torch.sum(logabsdet * x_mask, [1,2])
+    if not reverse:
+        return x, logdet
+    else:
+        return x
diff --git a/vits/modules/transforms/__init__.py b/vits/modules/transforms/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40f1b24e08f5f7789fd0e6306c60948ee9951b08
--- /dev/null
+++ b/vits/modules/transforms/__init__.py
@@ -0,0 +1 @@
+from vits.modules.transforms.transforms import *
diff --git a/vits/modules/transforms/__pycache__/__init__.cpython-310.pyc b/vits/modules/transforms/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..1bd764c0878cec0997a8aba5162d0f0740f2c4f0
Binary files /dev/null and b/vits/modules/transforms/__pycache__/__init__.cpython-310.pyc differ
diff --git a/vits/modules/transforms/__pycache__/transforms.cpython-310.pyc b/vits/modules/transforms/__pycache__/transforms.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..93a23d3e503592a277e43ab95c75e53c1357e414
Binary files /dev/null and b/vits/modules/transforms/__pycache__/transforms.cpython-310.pyc differ
diff --git a/vits/modules/transforms/transforms.py b/vits/modules/transforms/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..4793d67ca5a5630e0ffe0f9fb29445c949e64dae
--- /dev/null
+++ b/vits/modules/transforms/transforms.py
@@ -0,0 +1,193 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(inputs, 
+                                           unnormalized_widths,
+                                           unnormalized_heights,
+                                           unnormalized_derivatives,
+                                           inverse=False,
+                                           tails=None, 
+                                           tail_bound=1.,
+                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
+
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {
+            'tails': tails,
+            'tail_bound': tail_bound
+        }
+
+    outputs, logabsdet = spline_fn(
+            inputs=inputs,
+            unnormalized_widths=unnormalized_widths,
+            unnormalized_heights=unnormalized_heights,
+            unnormalized_derivatives=unnormalized_derivatives,
+            inverse=inverse,
+            min_bin_width=min_bin_width,
+            min_bin_height=min_bin_height,
+            min_derivative=min_derivative,
+            **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(
+        inputs[..., None] >= bin_locations,
+        dim=-1
+    ) - 1
+
+
+def unconstrained_rational_quadratic_spline(inputs,
+                                            unnormalized_widths,
+                                            unnormalized_heights,
+                                            unnormalized_derivatives,
+                                            inverse=False,
+                                            tails='linear',
+                                            tail_bound=1.,
+                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == 'linear':
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError('{} tails are not implemented.'.format(tails))
+
+    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative
+    )
+
+    return outputs, logabsdet
+
+def rational_quadratic_spline(inputs,
+                              unnormalized_widths,
+                              unnormalized_heights,
+                              unnormalized_derivatives,
+                              inverse=False,
+                              left=0., right=1., bottom=0., top=1.,
+                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                              min_derivative=DEFAULT_MIN_DERIVATIVE):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError('Input to a transform is not within its domain')
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError('Minimal bin width too large for the number of bins')
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError('Minimal bin height too large for the number of bins')
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (((inputs - input_cumheights) * (input_derivatives
+                                             + input_derivatives_plus_one
+                                             - 2 * input_delta)
+              + input_heights * (input_delta - input_derivatives)))
+        b = (input_heights * input_derivatives
+             - (inputs - input_cumheights) * (input_derivatives
+                                              + input_derivatives_plus_one
+                                              - 2 * input_delta))
+        c = - input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - root).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (input_delta * theta.pow(2)
+                                     + input_derivatives * theta_one_minus_theta)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - theta).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet
diff --git a/vits/modules/transforms/transforms_v2.py b/vits/modules/transforms/transforms_v2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4793d67ca5a5630e0ffe0f9fb29445c949e64dae
--- /dev/null
+++ b/vits/modules/transforms/transforms_v2.py
@@ -0,0 +1,193 @@
+import torch
+from torch.nn import functional as F
+
+import numpy as np
+
+
+DEFAULT_MIN_BIN_WIDTH = 1e-3
+DEFAULT_MIN_BIN_HEIGHT = 1e-3
+DEFAULT_MIN_DERIVATIVE = 1e-3
+
+
+def piecewise_rational_quadratic_transform(inputs, 
+                                           unnormalized_widths,
+                                           unnormalized_heights,
+                                           unnormalized_derivatives,
+                                           inverse=False,
+                                           tails=None, 
+                                           tail_bound=1.,
+                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
+
+    if tails is None:
+        spline_fn = rational_quadratic_spline
+        spline_kwargs = {}
+    else:
+        spline_fn = unconstrained_rational_quadratic_spline
+        spline_kwargs = {
+            'tails': tails,
+            'tail_bound': tail_bound
+        }
+
+    outputs, logabsdet = spline_fn(
+            inputs=inputs,
+            unnormalized_widths=unnormalized_widths,
+            unnormalized_heights=unnormalized_heights,
+            unnormalized_derivatives=unnormalized_derivatives,
+            inverse=inverse,
+            min_bin_width=min_bin_width,
+            min_bin_height=min_bin_height,
+            min_derivative=min_derivative,
+            **spline_kwargs
+    )
+    return outputs, logabsdet
+
+
+def searchsorted(bin_locations, inputs, eps=1e-6):
+    bin_locations[..., -1] += eps
+    return torch.sum(
+        inputs[..., None] >= bin_locations,
+        dim=-1
+    ) - 1
+
+
+def unconstrained_rational_quadratic_spline(inputs,
+                                            unnormalized_widths,
+                                            unnormalized_heights,
+                                            unnormalized_derivatives,
+                                            inverse=False,
+                                            tails='linear',
+                                            tail_bound=1.,
+                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
+    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
+    outside_interval_mask = ~inside_interval_mask
+
+    outputs = torch.zeros_like(inputs)
+    logabsdet = torch.zeros_like(inputs)
+
+    if tails == 'linear':
+        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
+        constant = np.log(np.exp(1 - min_derivative) - 1)
+        unnormalized_derivatives[..., 0] = constant
+        unnormalized_derivatives[..., -1] = constant
+
+        outputs[outside_interval_mask] = inputs[outside_interval_mask]
+        logabsdet[outside_interval_mask] = 0
+    else:
+        raise RuntimeError('{} tails are not implemented.'.format(tails))
+
+    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
+        inputs=inputs[inside_interval_mask],
+        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
+        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
+        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
+        inverse=inverse,
+        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
+        min_bin_width=min_bin_width,
+        min_bin_height=min_bin_height,
+        min_derivative=min_derivative
+    )
+
+    return outputs, logabsdet
+
+def rational_quadratic_spline(inputs,
+                              unnormalized_widths,
+                              unnormalized_heights,
+                              unnormalized_derivatives,
+                              inverse=False,
+                              left=0., right=1., bottom=0., top=1.,
+                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
+                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
+                              min_derivative=DEFAULT_MIN_DERIVATIVE):
+    if torch.min(inputs) < left or torch.max(inputs) > right:
+        raise ValueError('Input to a transform is not within its domain')
+
+    num_bins = unnormalized_widths.shape[-1]
+
+    if min_bin_width * num_bins > 1.0:
+        raise ValueError('Minimal bin width too large for the number of bins')
+    if min_bin_height * num_bins > 1.0:
+        raise ValueError('Minimal bin height too large for the number of bins')
+
+    widths = F.softmax(unnormalized_widths, dim=-1)
+    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
+    cumwidths = torch.cumsum(widths, dim=-1)
+    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
+    cumwidths = (right - left) * cumwidths + left
+    cumwidths[..., 0] = left
+    cumwidths[..., -1] = right
+    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
+
+    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
+
+    heights = F.softmax(unnormalized_heights, dim=-1)
+    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
+    cumheights = torch.cumsum(heights, dim=-1)
+    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
+    cumheights = (top - bottom) * cumheights + bottom
+    cumheights[..., 0] = bottom
+    cumheights[..., -1] = top
+    heights = cumheights[..., 1:] - cumheights[..., :-1]
+
+    if inverse:
+        bin_idx = searchsorted(cumheights, inputs)[..., None]
+    else:
+        bin_idx = searchsorted(cumwidths, inputs)[..., None]
+
+    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
+    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
+
+    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
+    delta = heights / widths
+    input_delta = delta.gather(-1, bin_idx)[..., 0]
+
+    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
+    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
+
+    input_heights = heights.gather(-1, bin_idx)[..., 0]
+
+    if inverse:
+        a = (((inputs - input_cumheights) * (input_derivatives
+                                             + input_derivatives_plus_one
+                                             - 2 * input_delta)
+              + input_heights * (input_delta - input_derivatives)))
+        b = (input_heights * input_derivatives
+             - (inputs - input_cumheights) * (input_derivatives
+                                              + input_derivatives_plus_one
+                                              - 2 * input_delta))
+        c = - input_delta * (inputs - input_cumheights)
+
+        discriminant = b.pow(2) - 4 * a * c
+        assert (discriminant >= 0).all()
+
+        root = (2 * c) / (-b - torch.sqrt(discriminant))
+        outputs = root * input_bin_widths + input_cumwidths
+
+        theta_one_minus_theta = root * (1 - root)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - root).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, -logabsdet
+    else:
+        theta = (inputs - input_cumwidths) / input_bin_widths
+        theta_one_minus_theta = theta * (1 - theta)
+
+        numerator = input_heights * (input_delta * theta.pow(2)
+                                     + input_derivatives * theta_one_minus_theta)
+        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
+                                     * theta_one_minus_theta)
+        outputs = input_cumheights + numerator / denominator
+
+        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
+                                                     + 2 * input_delta * theta_one_minus_theta
+                                                     + input_derivatives * (1 - theta).pow(2))
+        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
+
+        return outputs, logabsdet
diff --git a/vits/monotonic_align/__init__.py b/vits/monotonic_align/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffffd2426e11721d9b4b334a485552a395ccfdba
--- /dev/null
+++ b/vits/monotonic_align/__init__.py
@@ -0,0 +1,19 @@
+import numpy as np
+import torch
+from vits.monotonic_align.core import maximum_path_c
+
+
+def maximum_path(neg_cent, mask):
+  """ Cython optimized version.
+  neg_cent: [b, t_t, t_s]
+  mask: [b, t_t, t_s]
+  """
+  device = neg_cent.device
+  dtype = neg_cent.dtype
+  neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
+  path = np.zeros(neg_cent.shape, dtype=np.int32)
+
+  t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
+  t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
+  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
+  return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/vits/monotonic_align/__pycache__/__init__.cpython-310.pyc b/vits/monotonic_align/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..666562af085d93faf38c2a1dde13f1b70b71c64f
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diff --git a/vits/monotonic_align/build/lib.linux-x86_64-cpython-310/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so b/vits/monotonic_align/build/lib.linux-x86_64-cpython-310/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so
new file mode 100755
index 0000000000000000000000000000000000000000..867feaaff6cf61b9bdf17f495a55e6c6fe8b74a5
Binary files /dev/null and b/vits/monotonic_align/build/lib.linux-x86_64-cpython-310/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so differ
diff --git a/vits/monotonic_align/build/temp.linux-x86_64-cpython-310/core.o b/vits/monotonic_align/build/temp.linux-x86_64-cpython-310/core.o
new file mode 100644
index 0000000000000000000000000000000000000000..880c89eee281ce8ac0887104d1ad3dd4f1f04798
Binary files /dev/null and b/vits/monotonic_align/build/temp.linux-x86_64-cpython-310/core.o differ
diff --git a/vits/monotonic_align/core.c b/vits/monotonic_align/core.c
new file mode 100644
index 0000000000000000000000000000000000000000..18675a50479c5b21b0e02af12cca72a58f6d8461
--- /dev/null
+++ b/vits/monotonic_align/core.c
@@ -0,0 +1,21299 @@
+/* Generated by Cython 0.29.21 */
+
+/* BEGIN: Cython Metadata
+{
+    "distutils": {
+        "name": "vits.monotonic_align.core",
+        "sources": [
+            "core.pyx"
+        ]
+    },
+    "module_name": "vits.monotonic_align.core"
+}
+END: Cython Metadata */
+
+#define PY_SSIZE_T_CLEAN
+#include "Python.h"
+#ifndef Py_PYTHON_H
+    #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
+    #error Cython requires Python 2.6+ or Python 3.3+.
+#else
+#define CYTHON_ABI "0_29_21"
+#define CYTHON_HEX_VERSION 0x001D15F0
+#define CYTHON_FUTURE_DIVISION 0
+#include <stddef.h>
+#ifndef offsetof
+  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+  #ifndef __stdcall
+    #define __stdcall
+  #endif
+  #ifndef __cdecl
+    #define __cdecl
+  #endif
+  #ifndef __fastcall
+    #define __fastcall
+  #endif
+#endif
+#ifndef DL_IMPORT
+  #define DL_IMPORT(t) t
+#endif
+#ifndef DL_EXPORT
+  #define DL_EXPORT(t) t
+#endif
+#define __PYX_COMMA ,
+#ifndef HAVE_LONG_LONG
+  #if PY_VERSION_HEX >= 0x02070000
+    #define HAVE_LONG_LONG
+  #endif
+#endif
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#ifndef Py_HUGE_VAL
+  #define Py_HUGE_VAL HUGE_VAL
+#endif
+#ifdef PYPY_VERSION
+  #define CYTHON_COMPILING_IN_PYPY 1
+  #define CYTHON_COMPILING_IN_PYSTON 0
+  #define CYTHON_COMPILING_IN_CPYTHON 0
+  #undef CYTHON_USE_TYPE_SLOTS
+  #define CYTHON_USE_TYPE_SLOTS 0
+  #undef CYTHON_USE_PYTYPE_LOOKUP
+  #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #if PY_VERSION_HEX < 0x03050000
+    #undef CYTHON_USE_ASYNC_SLOTS
+    #define CYTHON_USE_ASYNC_SLOTS 0
+  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+    #define CYTHON_USE_ASYNC_SLOTS 1
+  #endif
+  #undef CYTHON_USE_PYLIST_INTERNALS
+  #define CYTHON_USE_PYLIST_INTERNALS 0
+  #undef CYTHON_USE_UNICODE_INTERNALS
+  #define CYTHON_USE_UNICODE_INTERNALS 0
+  #undef CYTHON_USE_UNICODE_WRITER
+  #define CYTHON_USE_UNICODE_WRITER 0
+  #undef CYTHON_USE_PYLONG_INTERNALS
+  #define CYTHON_USE_PYLONG_INTERNALS 0
+  #undef CYTHON_AVOID_BORROWED_REFS
+  #define CYTHON_AVOID_BORROWED_REFS 1
+  #undef CYTHON_ASSUME_SAFE_MACROS
+  #define CYTHON_ASSUME_SAFE_MACROS 0
+  #undef CYTHON_UNPACK_METHODS
+  #define CYTHON_UNPACK_METHODS 0
+  #undef CYTHON_FAST_THREAD_STATE
+  #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_PYCALL
+  #define CYTHON_FAST_PYCALL 0
+  #undef CYTHON_PEP489_MULTI_PHASE_INIT
+  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+  #undef CYTHON_USE_TP_FINALIZE
+  #define CYTHON_USE_TP_FINALIZE 0
+  #undef CYTHON_USE_DICT_VERSIONS
+  #define CYTHON_USE_DICT_VERSIONS 0
+  #undef CYTHON_USE_EXC_INFO_STACK
+  #define CYTHON_USE_EXC_INFO_STACK 0
+#elif defined(PYSTON_VERSION)
+  #define CYTHON_COMPILING_IN_PYPY 0
+  #define CYTHON_COMPILING_IN_PYSTON 1
+  #define CYTHON_COMPILING_IN_CPYTHON 0
+  #ifndef CYTHON_USE_TYPE_SLOTS
+    #define CYTHON_USE_TYPE_SLOTS 1
+  #endif
+  #undef CYTHON_USE_PYTYPE_LOOKUP
+  #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #undef CYTHON_USE_ASYNC_SLOTS
+  #define CYTHON_USE_ASYNC_SLOTS 0
+  #undef CYTHON_USE_PYLIST_INTERNALS
+  #define CYTHON_USE_PYLIST_INTERNALS 0
+  #ifndef CYTHON_USE_UNICODE_INTERNALS
+    #define CYTHON_USE_UNICODE_INTERNALS 1
+  #endif
+  #undef CYTHON_USE_UNICODE_WRITER
+  #define CYTHON_USE_UNICODE_WRITER 0
+  #undef CYTHON_USE_PYLONG_INTERNALS
+  #define CYTHON_USE_PYLONG_INTERNALS 0
+  #ifndef CYTHON_AVOID_BORROWED_REFS
+    #define CYTHON_AVOID_BORROWED_REFS 0
+  #endif
+  #ifndef CYTHON_ASSUME_SAFE_MACROS
+    #define CYTHON_ASSUME_SAFE_MACROS 1
+  #endif
+  #ifndef CYTHON_UNPACK_METHODS
+    #define CYTHON_UNPACK_METHODS 1
+  #endif
+  #undef CYTHON_FAST_THREAD_STATE
+  #define CYTHON_FAST_THREAD_STATE 0
+  #undef CYTHON_FAST_PYCALL
+  #define CYTHON_FAST_PYCALL 0
+  #undef CYTHON_PEP489_MULTI_PHASE_INIT
+  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
+  #undef CYTHON_USE_TP_FINALIZE
+  #define CYTHON_USE_TP_FINALIZE 0
+  #undef CYTHON_USE_DICT_VERSIONS
+  #define CYTHON_USE_DICT_VERSIONS 0
+  #undef CYTHON_USE_EXC_INFO_STACK
+  #define CYTHON_USE_EXC_INFO_STACK 0
+#else
+  #define CYTHON_COMPILING_IN_PYPY 0
+  #define CYTHON_COMPILING_IN_PYSTON 0
+  #define CYTHON_COMPILING_IN_CPYTHON 1
+  #ifndef CYTHON_USE_TYPE_SLOTS
+    #define CYTHON_USE_TYPE_SLOTS 1
+  #endif
+  #if PY_VERSION_HEX < 0x02070000
+    #undef CYTHON_USE_PYTYPE_LOOKUP
+    #define CYTHON_USE_PYTYPE_LOOKUP 0
+  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
+    #define CYTHON_USE_PYTYPE_LOOKUP 1
+  #endif
+  #if PY_MAJOR_VERSION < 3
+    #undef CYTHON_USE_ASYNC_SLOTS
+    #define CYTHON_USE_ASYNC_SLOTS 0
+  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
+    #define CYTHON_USE_ASYNC_SLOTS 1
+  #endif
+  #if PY_VERSION_HEX < 0x02070000
+    #undef CYTHON_USE_PYLONG_INTERNALS
+    #define CYTHON_USE_PYLONG_INTERNALS 0
+  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
+    #define CYTHON_USE_PYLONG_INTERNALS 1
+  #endif
+  #ifndef CYTHON_USE_PYLIST_INTERNALS
+    #define CYTHON_USE_PYLIST_INTERNALS 1
+  #endif
+  #ifndef CYTHON_USE_UNICODE_INTERNALS
+    #define CYTHON_USE_UNICODE_INTERNALS 1
+  #endif
+  #if PY_VERSION_HEX < 0x030300F0
+    #undef CYTHON_USE_UNICODE_WRITER
+    #define CYTHON_USE_UNICODE_WRITER 0
+  #elif !defined(CYTHON_USE_UNICODE_WRITER)
+    #define CYTHON_USE_UNICODE_WRITER 1
+  #endif
+  #ifndef CYTHON_AVOID_BORROWED_REFS
+    #define CYTHON_AVOID_BORROWED_REFS 0
+  #endif
+  #ifndef CYTHON_ASSUME_SAFE_MACROS
+    #define CYTHON_ASSUME_SAFE_MACROS 1
+  #endif
+  #ifndef CYTHON_UNPACK_METHODS
+    #define CYTHON_UNPACK_METHODS 1
+  #endif
+  #ifndef CYTHON_FAST_THREAD_STATE
+    #define CYTHON_FAST_THREAD_STATE 1
+  #endif
+  #ifndef CYTHON_FAST_PYCALL
+    #define CYTHON_FAST_PYCALL 1
+  #endif
+  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
+    #define CYTHON_PEP489_MULTI_PHASE_INIT (PY_VERSION_HEX >= 0x03050000)
+  #endif
+  #ifndef CYTHON_USE_TP_FINALIZE
+    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
+  #endif
+  #ifndef CYTHON_USE_DICT_VERSIONS
+    #define CYTHON_USE_DICT_VERSIONS (PY_VERSION_HEX >= 0x030600B1)
+  #endif
+  #ifndef CYTHON_USE_EXC_INFO_STACK
+    #define CYTHON_USE_EXC_INFO_STACK (PY_VERSION_HEX >= 0x030700A3)
+  #endif
+#endif
+#if !defined(CYTHON_FAST_PYCCALL)
+#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
+#endif
+#if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+  #undef SHIFT
+  #undef BASE
+  #undef MASK
+  #ifdef SIZEOF_VOID_P
+    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
+  #endif
+#endif
+#ifndef __has_attribute
+  #define __has_attribute(x) 0
+#endif
+#ifndef __has_cpp_attribute
+  #define __has_cpp_attribute(x) 0
+#endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+#     define CYTHON_UNUSED __attribute__ ((__unused__))
+#   else
+#     define CYTHON_UNUSED
+#   endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+#ifndef CYTHON_MAYBE_UNUSED_VAR
+#  if defined(__cplusplus)
+     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
+#  else
+#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
+#  endif
+#endif
+#ifndef CYTHON_NCP_UNUSED
+# if CYTHON_COMPILING_IN_CPYTHON
+#  define CYTHON_NCP_UNUSED
+# else
+#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
+# endif
+#endif
+#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
+#ifdef _MSC_VER
+    #ifndef _MSC_STDINT_H_
+        #if _MSC_VER < 1300
+           typedef unsigned char     uint8_t;
+           typedef unsigned int      uint32_t;
+        #else
+           typedef unsigned __int8   uint8_t;
+           typedef unsigned __int32  uint32_t;
+        #endif
+    #endif
+#else
+   #include <stdint.h>
+#endif
+#ifndef CYTHON_FALLTHROUGH
+  #if defined(__cplusplus) && __cplusplus >= 201103L
+    #if __has_cpp_attribute(fallthrough)
+      #define CYTHON_FALLTHROUGH [[fallthrough]]
+    #elif __has_cpp_attribute(clang::fallthrough)
+      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
+    #elif __has_cpp_attribute(gnu::fallthrough)
+      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
+    #endif
+  #endif
+  #ifndef CYTHON_FALLTHROUGH
+    #if __has_attribute(fallthrough)
+      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
+    #else
+      #define CYTHON_FALLTHROUGH
+    #endif
+  #endif
+  #if defined(__clang__ ) && defined(__apple_build_version__)
+    #if __apple_build_version__ < 7000000
+      #undef  CYTHON_FALLTHROUGH
+      #define CYTHON_FALLTHROUGH
+    #endif
+  #endif
+#endif
+
+#ifndef CYTHON_INLINE
+  #if defined(__clang__)
+    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
+  #elif defined(__GNUC__)
+    #define CYTHON_INLINE __inline__
+  #elif defined(_MSC_VER)
+    #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
+  #else
+    #define CYTHON_INLINE
+  #endif
+#endif
+
+#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
+  #define Py_OptimizeFlag 0
+#endif
+#define __PYX_BUILD_PY_SSIZE_T "n"
+#define CYTHON_FORMAT_SSIZE_T "z"
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyClass_Type
+#else
+  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+#if PY_VERSION_HEX >= 0x030800A4 && PY_VERSION_HEX < 0x030800B2
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a, 0, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+#else
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
+          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+#endif
+  #define __Pyx_DefaultClassType PyType_Type
+#endif
+#ifndef Py_TPFLAGS_CHECKTYPES
+  #define Py_TPFLAGS_CHECKTYPES 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_INDEX
+  #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
+  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+#endif
+#ifndef Py_TPFLAGS_HAVE_FINALIZE
+  #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
+#ifndef METH_STACKLESS
+  #define METH_STACKLESS 0
+#endif
+#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
+  #ifndef METH_FASTCALL
+     #define METH_FASTCALL 0x80
+  #endif
+  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
+  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
+                                                          Py_ssize_t nargs, PyObject *kwnames);
+#else
+  #define __Pyx_PyCFunctionFast _PyCFunctionFast
+  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
+#endif
+#if CYTHON_FAST_PYCCALL
+#define __Pyx_PyFastCFunction_Check(func)\
+    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)))))
+#else
+#define __Pyx_PyFastCFunction_Check(func) 0
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
+  #define PyObject_Malloc(s)   PyMem_Malloc(s)
+  #define PyObject_Free(p)     PyMem_Free(p)
+  #define PyObject_Realloc(p)  PyMem_Realloc(p)
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030400A1
+  #define PyMem_RawMalloc(n)           PyMem_Malloc(n)
+  #define PyMem_RawRealloc(p, n)       PyMem_Realloc(p, n)
+  #define PyMem_RawFree(p)             PyMem_Free(p)
+#endif
+#if CYTHON_COMPILING_IN_PYSTON
+  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
+#else
+  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
+  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
+#endif
+#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
+  #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#elif PY_VERSION_HEX >= 0x03060000
+  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
+#elif PY_VERSION_HEX >= 0x03000000
+  #define __Pyx_PyThreadState_Current PyThreadState_GET()
+#else
+  #define __Pyx_PyThreadState_Current _PyThreadState_Current
+#endif
+#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)
+#include "pythread.h"
+#define Py_tss_NEEDS_INIT 0
+typedef int Py_tss_t;
+static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
+  *key = PyThread_create_key();
+  return 0;
+}
+static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
+  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
+  *key = Py_tss_NEEDS_INIT;
+  return key;
+}
+static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
+  PyObject_Free(key);
+}
+static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
+  return *key != Py_tss_NEEDS_INIT;
+}
+static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
+  PyThread_delete_key(*key);
+  *key = Py_tss_NEEDS_INIT;
+}
+static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
+  return PyThread_set_key_value(*key, value);
+}
+static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
+  return PyThread_get_key_value(*key);
+}
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
+#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
+#else
+#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
+#endif
+#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
+#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
+#else
+#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
+#endif
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+  #define CYTHON_PEP393_ENABLED 1
+  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
+                                              0 : _PyUnicode_Ready((PyObject *)(op)))
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
+  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
+  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
+  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
+  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
+  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
+  #else
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
+  #endif
+#else
+  #define CYTHON_PEP393_ENABLED 0
+  #define PyUnicode_1BYTE_KIND  1
+  #define PyUnicode_2BYTE_KIND  2
+  #define PyUnicode_4BYTE_KIND  4
+  #define __Pyx_PyUnicode_READY(op)       (0)
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
+  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
+  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
+  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
+  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
+#else
+  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
+      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
+  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
+  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
+#endif
+#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
+  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
+#endif
+#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
+#else
+  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
+#endif
+#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
+  #define PyObject_ASCII(o)            PyObject_Repr(o)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBaseString_Type            PyUnicode_Type
+  #define PyStringObject               PyUnicodeObject
+  #define PyString_Type                PyUnicode_Type
+  #define PyString_Check               PyUnicode_Check
+  #define PyString_CheckExact          PyUnicode_CheckExact
+#ifndef PyObject_Unicode
+  #define PyObject_Unicode             PyObject_Str
+#endif
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+#else
+  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
+  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+#endif
+#ifndef PySet_CheckExact
+  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
+#endif
+#if PY_VERSION_HEX >= 0x030900A4
+  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
+  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
+#else
+  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
+  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
+#endif
+#if CYTHON_ASSUME_SAFE_MACROS
+  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
+#else
+  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyIntObject                  PyLongObject
+  #define PyInt_Type                   PyLong_Type
+  #define PyInt_Check(op)              PyLong_Check(op)
+  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
+  #define PyInt_FromString             PyLong_FromString
+  #define PyInt_FromUnicode            PyLong_FromUnicode
+  #define PyInt_FromLong               PyLong_FromLong
+  #define PyInt_FromSize_t             PyLong_FromSize_t
+  #define PyInt_FromSsize_t            PyLong_FromSsize_t
+  #define PyInt_AsLong                 PyLong_AsLong
+  #define PyInt_AS_LONG                PyLong_AS_LONG
+  #define PyInt_AsSsize_t              PyLong_AsSsize_t
+  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
+  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define PyNumber_Int                 PyNumber_Long
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBoolObject                 PyLongObject
+#endif
+#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
+  #ifndef PyUnicode_InternFromString
+    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
+  #endif
+#endif
+#if PY_VERSION_HEX < 0x030200A4
+  typedef long Py_hash_t;
+  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
+#else
+  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
+#else
+  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
+#endif
+#if CYTHON_USE_ASYNC_SLOTS
+  #if PY_VERSION_HEX >= 0x030500B1
+    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
+    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
+  #else
+    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
+  #endif
+#else
+  #define __Pyx_PyType_AsAsync(obj) NULL
+#endif
+#ifndef __Pyx_PyAsyncMethodsStruct
+    typedef struct {
+        unaryfunc am_await;
+        unaryfunc am_aiter;
+        unaryfunc am_anext;
+    } __Pyx_PyAsyncMethodsStruct;
+#endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+  #define _USE_MATH_DEFINES
+#endif
+#include <math.h>
+#ifdef NAN
+#define __PYX_NAN() ((float) NAN)
+#else
+static CYTHON_INLINE float __PYX_NAN() {
+  float value;
+  memset(&value, 0xFF, sizeof(value));
+  return value;
+}
+#endif
+#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
+#define __Pyx_truncl trunc
+#else
+#define __Pyx_truncl truncl
+#endif
+
+#define __PYX_MARK_ERR_POS(f_index, lineno) \
+    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
+#define __PYX_ERR(f_index, lineno, Ln_error) \
+    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
+
+#ifndef __PYX_EXTERN_C
+  #ifdef __cplusplus
+    #define __PYX_EXTERN_C extern "C"
+  #else
+    #define __PYX_EXTERN_C extern
+  #endif
+#endif
+
+#define __PYX_HAVE__vits__monotonic_align__core
+#define __PYX_HAVE_API__vits__monotonic_align__core
+/* Early includes */
+#include "pythread.h"
+#include <string.h>
+#include <stdlib.h>
+#include <stdio.h>
+#include "pystate.h"
+#ifdef _OPENMP
+#include <omp.h>
+#endif /* _OPENMP */
+
+#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
+                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
+
+#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
+#define __PYX_DEFAULT_STRING_ENCODING ""
+#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_uchar_cast(c) ((unsigned char)c)
+#define __Pyx_long_cast(x) ((long)x)
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
+    (sizeof(type) < sizeof(Py_ssize_t))  ||\
+    (sizeof(type) > sizeof(Py_ssize_t) &&\
+          likely(v < (type)PY_SSIZE_T_MAX ||\
+                 v == (type)PY_SSIZE_T_MAX)  &&\
+          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
+                                v == (type)PY_SSIZE_T_MIN)))  ||\
+    (sizeof(type) == sizeof(Py_ssize_t) &&\
+          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
+                               v == (type)PY_SSIZE_T_MAX)))  )
+static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
+    return (size_t) i < (size_t) limit;
+}
+#if defined (__cplusplus) && __cplusplus >= 201103L
+    #include <cstdlib>
+    #define __Pyx_sst_abs(value) std::abs(value)
+#elif SIZEOF_INT >= SIZEOF_SIZE_T
+    #define __Pyx_sst_abs(value) abs(value)
+#elif SIZEOF_LONG >= SIZEOF_SIZE_T
+    #define __Pyx_sst_abs(value) labs(value)
+#elif defined (_MSC_VER)
+    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
+#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define __Pyx_sst_abs(value) llabs(value)
+#elif defined (__GNUC__)
+    #define __Pyx_sst_abs(value) __builtin_llabs(value)
+#else
+    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
+#endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+#define __Pyx_PyBytes_FromString        PyBytes_FromString
+#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
+#if PY_MAJOR_VERSION < 3
+    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#else
+    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+#endif
+#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
+#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
+#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
+#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
+#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
+#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
+    const Py_UNICODE *u_end = u;
+    while (*u_end++) ;
+    return (size_t)(u_end - u - 1);
+}
+#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
+#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
+#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
+static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
+#define __Pyx_PySequence_Tuple(obj)\
+    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+#if CYTHON_ASSUME_SAFE_MACROS
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+#else
+#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+#endif
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
+#else
+#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
+#endif
+#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+static int __Pyx_sys_getdefaultencoding_not_ascii;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    PyObject* ascii_chars_u = NULL;
+    PyObject* ascii_chars_b = NULL;
+    const char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    if (strcmp(default_encoding_c, "ascii") == 0) {
+        __Pyx_sys_getdefaultencoding_not_ascii = 0;
+    } else {
+        char ascii_chars[128];
+        int c;
+        for (c = 0; c < 128; c++) {
+            ascii_chars[c] = c;
+        }
+        __Pyx_sys_getdefaultencoding_not_ascii = 1;
+        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+        if (!ascii_chars_u) goto bad;
+        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+            PyErr_Format(
+                PyExc_ValueError,
+                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+                default_encoding_c);
+            goto bad;
+        }
+        Py_DECREF(ascii_chars_u);
+        Py_DECREF(ascii_chars_b);
+    }
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    Py_XDECREF(ascii_chars_u);
+    Py_XDECREF(ascii_chars_b);
+    return -1;
+}
+#endif
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
+#else
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+static char* __PYX_DEFAULT_STRING_ENCODING;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
+    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
+    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    return -1;
+}
+#endif
+#endif
+
+
+/* Test for GCC > 2.95 */
+#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
+  #define likely(x)   __builtin_expect(!!(x), 1)
+  #define unlikely(x) __builtin_expect(!!(x), 0)
+#else /* !__GNUC__ or GCC < 2.95 */
+  #define likely(x)   (x)
+  #define unlikely(x) (x)
+#endif /* __GNUC__ */
+static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
+
+static PyObject *__pyx_m = NULL;
+static PyObject *__pyx_d;
+static PyObject *__pyx_b;
+static PyObject *__pyx_cython_runtime = NULL;
+static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
+static PyObject *__pyx_empty_unicode;
+static int __pyx_lineno;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+
+
+static const char *__pyx_f[] = {
+  "core.pyx",
+  "stringsource",
+};
+/* NoFastGil.proto */
+#define __Pyx_PyGILState_Ensure PyGILState_Ensure
+#define __Pyx_PyGILState_Release PyGILState_Release
+#define __Pyx_FastGIL_Remember()
+#define __Pyx_FastGIL_Forget()
+#define __Pyx_FastGilFuncInit()
+
+/* MemviewSliceStruct.proto */
+struct __pyx_memoryview_obj;
+typedef struct {
+  struct __pyx_memoryview_obj *memview;
+  char *data;
+  Py_ssize_t shape[8];
+  Py_ssize_t strides[8];
+  Py_ssize_t suboffsets[8];
+} __Pyx_memviewslice;
+#define __Pyx_MemoryView_Len(m)  (m.shape[0])
+
+/* Atomics.proto */
+#include <pythread.h>
+#ifndef CYTHON_ATOMICS
+    #define CYTHON_ATOMICS 1
+#endif
+#define __pyx_atomic_int_type int
+#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||\
+                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) &&\
+                    !defined(__i386__)
+    #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1)
+    #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1)
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Using GNU atomics"
+    #endif
+#elif CYTHON_ATOMICS && defined(_MSC_VER) && 0
+    #include <Windows.h>
+    #undef __pyx_atomic_int_type
+    #define __pyx_atomic_int_type LONG
+    #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value)
+    #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value)
+    #ifdef __PYX_DEBUG_ATOMICS
+        #pragma message ("Using MSVC atomics")
+    #endif
+#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0
+    #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value)
+    #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value)
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Using Intel atomics"
+    #endif
+#else
+    #undef CYTHON_ATOMICS
+    #define CYTHON_ATOMICS 0
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Not using atomics"
+    #endif
+#endif
+typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
+#if CYTHON_ATOMICS
+    #define __pyx_add_acquisition_count(memview)\
+             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
+    #define __pyx_sub_acquisition_count(memview)\
+            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
+#else
+    #define __pyx_add_acquisition_count(memview)\
+            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
+    #define __pyx_sub_acquisition_count(memview)\
+            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
+#endif
+
+/* ForceInitThreads.proto */
+#ifndef __PYX_FORCE_INIT_THREADS
+  #define __PYX_FORCE_INIT_THREADS 0
+#endif
+
+/* BufferFormatStructs.proto */
+#define IS_UNSIGNED(type) (((type) -1) > 0)
+struct __Pyx_StructField_;
+#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
+typedef struct {
+  const char* name;
+  struct __Pyx_StructField_* fields;
+  size_t size;
+  size_t arraysize[8];
+  int ndim;
+  char typegroup;
+  char is_unsigned;
+  int flags;
+} __Pyx_TypeInfo;
+typedef struct __Pyx_StructField_ {
+  __Pyx_TypeInfo* type;
+  const char* name;
+  size_t offset;
+} __Pyx_StructField;
+typedef struct {
+  __Pyx_StructField* field;
+  size_t parent_offset;
+} __Pyx_BufFmt_StackElem;
+typedef struct {
+  __Pyx_StructField root;
+  __Pyx_BufFmt_StackElem* head;
+  size_t fmt_offset;
+  size_t new_count, enc_count;
+  size_t struct_alignment;
+  int is_complex;
+  char enc_type;
+  char new_packmode;
+  char enc_packmode;
+  char is_valid_array;
+} __Pyx_BufFmt_Context;
+
+
+/*--- Type declarations ---*/
+struct __pyx_array_obj;
+struct __pyx_MemviewEnum_obj;
+struct __pyx_memoryview_obj;
+struct __pyx_memoryviewslice_obj;
+struct __pyx_opt_args_4vits_15monotonic_align_4core_maximum_path_each;
+
+/* "vits/monotonic_align/core.pyx":7
+ * @cython.boundscheck(False)
+ * @cython.wraparound(False)
+ * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
+ *   cdef int x
+ *   cdef int y
+ */
+struct __pyx_opt_args_4vits_15monotonic_align_4core_maximum_path_each {
+  int __pyx_n;
+  float max_neg_val;
+};
+
+/* "View.MemoryView":105
+ * 
+ * @cname("__pyx_array")
+ * cdef class array:             # <<<<<<<<<<<<<<
+ * 
+ *     cdef:
+ */
+struct __pyx_array_obj {
+  PyObject_HEAD
+  struct __pyx_vtabstruct_array *__pyx_vtab;
+  char *data;
+  Py_ssize_t len;
+  char *format;
+  int ndim;
+  Py_ssize_t *_shape;
+  Py_ssize_t *_strides;
+  Py_ssize_t itemsize;
+  PyObject *mode;
+  PyObject *_format;
+  void (*callback_free_data)(void *);
+  int free_data;
+  int dtype_is_object;
+};
+
+
+/* "View.MemoryView":279
+ * 
+ * @cname('__pyx_MemviewEnum')
+ * cdef class Enum(object):             # <<<<<<<<<<<<<<
+ *     cdef object name
+ *     def __init__(self, name):
+ */
+struct __pyx_MemviewEnum_obj {
+  PyObject_HEAD
+  PyObject *name;
+};
+
+
+/* "View.MemoryView":330
+ * 
+ * @cname('__pyx_memoryview')
+ * cdef class memoryview(object):             # <<<<<<<<<<<<<<
+ * 
+ *     cdef object obj
+ */
+struct __pyx_memoryview_obj {
+  PyObject_HEAD
+  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
+  PyObject *obj;
+  PyObject *_size;
+  PyObject *_array_interface;
+  PyThread_type_lock lock;
+  __pyx_atomic_int acquisition_count[2];
+  __pyx_atomic_int *acquisition_count_aligned_p;
+  Py_buffer view;
+  int flags;
+  int dtype_is_object;
+  __Pyx_TypeInfo *typeinfo;
+};
+
+
+/* "View.MemoryView":965
+ * 
+ * @cname('__pyx_memoryviewslice')
+ * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
+ *     "Internal class for passing memoryview slices to Python"
+ * 
+ */
+struct __pyx_memoryviewslice_obj {
+  struct __pyx_memoryview_obj __pyx_base;
+  __Pyx_memviewslice from_slice;
+  PyObject *from_object;
+  PyObject *(*to_object_func)(char *);
+  int (*to_dtype_func)(char *, PyObject *);
+};
+
+
+
+/* "View.MemoryView":105
+ * 
+ * @cname("__pyx_array")
+ * cdef class array:             # <<<<<<<<<<<<<<
+ * 
+ *     cdef:
+ */
+
+struct __pyx_vtabstruct_array {
+  PyObject *(*get_memview)(struct __pyx_array_obj *);
+};
+static struct __pyx_vtabstruct_array *__pyx_vtabptr_array;
+
+
+/* "View.MemoryView":330
+ * 
+ * @cname('__pyx_memoryview')
+ * cdef class memoryview(object):             # <<<<<<<<<<<<<<
+ * 
+ *     cdef object obj
+ */
+
+struct __pyx_vtabstruct_memoryview {
+  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);
+  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
+  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
+  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
+  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
+  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
+  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
+};
+static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;
+
+
+/* "View.MemoryView":965
+ * 
+ * @cname('__pyx_memoryviewslice')
+ * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
+ *     "Internal class for passing memoryview slices to Python"
+ * 
+ */
+
+struct __pyx_vtabstruct__memoryviewslice {
+  struct __pyx_vtabstruct_memoryview __pyx_base;
+};
+static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice;
+
+/* --- Runtime support code (head) --- */
+/* Refnanny.proto */
+#ifndef CYTHON_REFNANNY
+  #define CYTHON_REFNANNY 0
+#endif
+#if CYTHON_REFNANNY
+  typedef struct {
+    void (*INCREF)(void*, PyObject*, int);
+    void (*DECREF)(void*, PyObject*, int);
+    void (*GOTREF)(void*, PyObject*, int);
+    void (*GIVEREF)(void*, PyObject*, int);
+    void* (*SetupContext)(const char*, int, const char*);
+    void (*FinishContext)(void**);
+  } __Pyx_RefNannyAPIStruct;
+  static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
+  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname);
+  #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
+#ifdef WITH_THREAD
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)\
+          if (acquire_gil) {\
+              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\
+              PyGILState_Release(__pyx_gilstate_save);\
+          } else {\
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\
+          }
+#else
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)\
+          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
+#endif
+  #define __Pyx_RefNannyFinishContext()\
+          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
+  #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_XINCREF(r)  do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0)
+  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
+  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
+  #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
+#else
+  #define __Pyx_RefNannyDeclarations
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)
+  #define __Pyx_RefNannyFinishContext()
+  #define __Pyx_INCREF(r) Py_INCREF(r)
+  #define __Pyx_DECREF(r) Py_DECREF(r)
+  #define __Pyx_GOTREF(r)
+  #define __Pyx_GIVEREF(r)
+  #define __Pyx_XINCREF(r) Py_XINCREF(r)
+  #define __Pyx_XDECREF(r) Py_XDECREF(r)
+  #define __Pyx_XGOTREF(r)
+  #define __Pyx_XGIVEREF(r)
+#endif
+#define __Pyx_XDECREF_SET(r, v) do {\
+        PyObject *tmp = (PyObject *) r;\
+        r = v; __Pyx_XDECREF(tmp);\
+    } while (0)
+#define __Pyx_DECREF_SET(r, v) do {\
+        PyObject *tmp = (PyObject *) r;\
+        r = v; __Pyx_DECREF(tmp);\
+    } while (0)
+#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
+
+/* PyObjectGetAttrStr.proto */
+#if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name);
+#else
+#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
+#endif
+
+/* GetBuiltinName.proto */
+static PyObject *__Pyx_GetBuiltinName(PyObject *name);
+
+/* MemviewSliceInit.proto */
+#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
+#define __Pyx_MEMVIEW_DIRECT   1
+#define __Pyx_MEMVIEW_PTR      2
+#define __Pyx_MEMVIEW_FULL     4
+#define __Pyx_MEMVIEW_CONTIG   8
+#define __Pyx_MEMVIEW_STRIDED  16
+#define __Pyx_MEMVIEW_FOLLOW   32
+#define __Pyx_IS_C_CONTIG 1
+#define __Pyx_IS_F_CONTIG 2
+static int __Pyx_init_memviewslice(
+                struct __pyx_memoryview_obj *memview,
+                int ndim,
+                __Pyx_memviewslice *memviewslice,
+                int memview_is_new_reference);
+static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
+    __pyx_atomic_int *acquisition_count, PyThread_type_lock lock);
+static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
+    __pyx_atomic_int *acquisition_count, PyThread_type_lock lock);
+#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p)
+#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview))
+#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
+#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__)
+static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
+static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int);
+
+/* RaiseArgTupleInvalid.proto */
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
+
+/* RaiseDoubleKeywords.proto */
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
+
+/* ParseKeywords.proto */
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\
+    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\
+    const char* function_name);
+
+/* None.proto */
+static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
+
+/* ArgTypeTest.proto */
+#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\
+    ((likely((Py_TYPE(obj) == type) | (none_allowed && (obj == Py_None)))) ? 1 :\
+        __Pyx__ArgTypeTest(obj, type, name, exact))
+static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact);
+
+/* PyObjectCall.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
+#else
+#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
+#endif
+
+/* PyThreadStateGet.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyThreadState_declare  PyThreadState *__pyx_tstate;
+#define __Pyx_PyThreadState_assign  __pyx_tstate = __Pyx_PyThreadState_Current;
+#define __Pyx_PyErr_Occurred()  __pyx_tstate->curexc_type
+#else
+#define __Pyx_PyThreadState_declare
+#define __Pyx_PyThreadState_assign
+#define __Pyx_PyErr_Occurred()  PyErr_Occurred()
+#endif
+
+/* PyErrFetchRestore.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
+#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
+#else
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#endif
+#else
+#define __Pyx_PyErr_Clear() PyErr_Clear()
+#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
+#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestoreInState(tstate, type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetchInState(tstate, type, value, tb)  PyErr_Fetch(type, value, tb)
+#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
+#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
+#endif
+
+/* RaiseException.proto */
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+
+/* PyCFunctionFastCall.proto */
+#if CYTHON_FAST_PYCCALL
+static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs);
+#else
+#define __Pyx_PyCFunction_FastCall(func, args, nargs)  (assert(0), NULL)
+#endif
+
+/* PyFunctionFastCall.proto */
+#if CYTHON_FAST_PYCALL
+#define __Pyx_PyFunction_FastCall(func, args, nargs)\
+    __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
+#if 1 || PY_VERSION_HEX < 0x030600B1
+static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs);
+#else
+#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs)
+#endif
+#define __Pyx_BUILD_ASSERT_EXPR(cond)\
+    (sizeof(char [1 - 2*!(cond)]) - 1)
+#ifndef Py_MEMBER_SIZE
+#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member)
+#endif
+  static size_t __pyx_pyframe_localsplus_offset = 0;
+  #include "frameobject.h"
+  #define __Pxy_PyFrame_Initialize_Offsets()\
+    ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\
+     (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus)))
+  #define __Pyx_PyFrame_GetLocalsplus(frame)\
+    (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset))
+#endif
+
+/* PyObjectCall2Args.proto */
+static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2);
+
+/* PyObjectCallMethO.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
+#endif
+
+/* PyObjectCallOneArg.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
+
+/* IncludeStringH.proto */
+#include <string.h>
+
+/* BytesEquals.proto */
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
+
+/* UnicodeEquals.proto */
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
+
+/* StrEquals.proto */
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
+#else
+#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
+#endif
+
+/* None.proto */
+static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t);
+
+/* UnaryNegOverflows.proto */
+#define UNARY_NEG_WOULD_OVERFLOW(x)\
+        (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))
+
+static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/
+/* GetAttr.proto */
+static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
+
+/* GetItemInt.proto */
+#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
+    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
+               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
+#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
+    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
+    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
+    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+                                                     int is_list, int wraparound, int boundscheck);
+
+/* ObjectGetItem.proto */
+#if CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key);
+#else
+#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
+#endif
+
+/* decode_c_string_utf16.proto */
+static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {
+    int byteorder = 0;
+    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
+}
+static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {
+    int byteorder = -1;
+    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
+}
+static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {
+    int byteorder = 1;
+    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
+}
+
+/* decode_c_string.proto */
+static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
+         const char* encoding, const char* errors,
+         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
+
+/* PyErrExceptionMatches.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
+#else
+#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
+#endif
+
+/* GetAttr3.proto */
+static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
+
+/* PyDictVersioning.proto */
+#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
+#define __PYX_DICT_VERSION_INIT  ((PY_UINT64_T) -1)
+#define __PYX_GET_DICT_VERSION(dict)  (((PyDictObject*)(dict))->ma_version_tag)
+#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\
+    (version_var) = __PYX_GET_DICT_VERSION(dict);\
+    (cache_var) = (value);
+#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\
+    static PY_UINT64_T __pyx_dict_version = 0;\
+    static PyObject *__pyx_dict_cached_value = NULL;\
+    if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
+        (VAR) = __pyx_dict_cached_value;\
+    } else {\
+        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
+        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
+    }\
+}
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
+static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
+#else
+#define __PYX_GET_DICT_VERSION(dict)  (0)
+#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
+#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
+#endif
+
+/* GetModuleGlobalName.proto */
+#if CYTHON_USE_DICT_VERSIONS
+#define __Pyx_GetModuleGlobalName(var, name)  {\
+    static PY_UINT64_T __pyx_dict_version = 0;\
+    static PyObject *__pyx_dict_cached_value = NULL;\
+    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
+        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
+        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
+}
+#define __Pyx_GetModuleGlobalNameUncached(var, name)  {\
+    PY_UINT64_T __pyx_dict_version;\
+    PyObject *__pyx_dict_cached_value;\
+    (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
+}
+static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
+#else
+#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
+#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
+static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
+#endif
+
+/* RaiseTooManyValuesToUnpack.proto */
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
+
+/* RaiseNeedMoreValuesToUnpack.proto */
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
+
+/* RaiseNoneIterError.proto */
+static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
+
+/* ExtTypeTest.proto */
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
+
+/* GetTopmostException.proto */
+#if CYTHON_USE_EXC_INFO_STACK
+static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
+#endif
+
+/* SaveResetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
+#else
+#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
+#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
+#endif
+
+/* GetException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
+#endif
+
+/* SwapException.proto */
+#if CYTHON_FAST_THREAD_STATE
+#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
+static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
+#else
+static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
+#endif
+
+/* Import.proto */
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
+
+/* FastTypeChecks.proto */
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
+#else
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
+#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
+#endif
+#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+
+static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+/* ListCompAppend.proto */
+#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len)) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        __Pyx_SET_SIZE(list, len + 1);
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
+#endif
+
+/* PyIntBinop.proto */
+#if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
+#else
+#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
+    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
+#endif
+
+/* ListExtend.proto */
+static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject* none = _PyList_Extend((PyListObject*)L, v);
+    if (unlikely(!none))
+        return -1;
+    Py_DECREF(none);
+    return 0;
+#else
+    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
+#endif
+}
+
+/* ListAppend.proto */
+#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        __Pyx_SET_SIZE(list, len + 1);
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
+#endif
+
+/* None.proto */
+static CYTHON_INLINE long __Pyx_div_long(long, long);
+
+/* ImportFrom.proto */
+static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
+
+/* HasAttr.proto */
+static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
+
+/* PyObject_GenericGetAttrNoDict.proto */
+#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
+#else
+#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
+#endif
+
+/* PyObject_GenericGetAttr.proto */
+#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
+#else
+#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
+#endif
+
+/* SetVTable.proto */
+static int __Pyx_SetVtable(PyObject *dict, void *vtable);
+
+/* PyObjectGetAttrStrNoError.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);
+
+/* SetupReduce.proto */
+static int __Pyx_setup_reduce(PyObject* type_obj);
+
+/* CLineInTraceback.proto */
+#ifdef CYTHON_CLINE_IN_TRACEBACK
+#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
+#else
+static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
+#endif
+
+/* CodeObjectCache.proto */
+typedef struct {
+    PyCodeObject* code_object;
+    int code_line;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+    int count;
+    int max_count;
+    __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+/* AddTraceback.proto */
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename);
+
+#if PY_MAJOR_VERSION < 3
+    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
+    static void __Pyx_ReleaseBuffer(Py_buffer *view);
+#else
+    #define __Pyx_GetBuffer PyObject_GetBuffer
+    #define __Pyx_ReleaseBuffer PyBuffer_Release
+#endif
+
+
+/* BufferStructDeclare.proto */
+typedef struct {
+  Py_ssize_t shape, strides, suboffsets;
+} __Pyx_Buf_DimInfo;
+typedef struct {
+  size_t refcount;
+  Py_buffer pybuffer;
+} __Pyx_Buffer;
+typedef struct {
+  __Pyx_Buffer *rcbuffer;
+  char *data;
+  __Pyx_Buf_DimInfo diminfo[8];
+} __Pyx_LocalBuf_ND;
+
+/* MemviewSliceIsContig.proto */
+static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
+
+/* OverlappingSlices.proto */
+static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
+                                __Pyx_memviewslice *slice2,
+                                int ndim, size_t itemsize);
+
+/* Capsule.proto */
+static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
+
+/* IsLittleEndian.proto */
+static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
+
+/* BufferFormatCheck.proto */
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type);
+
+/* TypeInfoCompare.proto */
+static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
+
+/* MemviewSliceValidateAndInit.proto */
+static int __Pyx_ValidateAndInit_memviewslice(
+                int *axes_specs,
+                int c_or_f_flag,
+                int buf_flags,
+                int ndim,
+                __Pyx_TypeInfo *dtype,
+                __Pyx_BufFmt_StackElem stack[],
+                __Pyx_memviewslice *memviewslice,
+                PyObject *original_obj);
+
+/* ObjectToMemviewSlice.proto */
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
+
+/* ObjectToMemviewSlice.proto */
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
+
+/* ObjectToMemviewSlice.proto */
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
+
+/* CIntToPy.proto */
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+/* MemviewSliceCopyTemplate.proto */
+static __Pyx_memviewslice
+__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
+                                 const char *mode, int ndim,
+                                 size_t sizeof_dtype, int contig_flag,
+                                 int dtype_is_object);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+/* CIntFromPy.proto */
+static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
+
+/* CheckBinaryVersion.proto */
+static int __Pyx_check_binary_version(void);
+
+/* InitStrings.proto */
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
+
+static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
+static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
+static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
+static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
+static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
+static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
+static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
+static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
+static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
+static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
+
+/* Module declarations from 'cython.view' */
+
+/* Module declarations from 'cython' */
+
+/* Module declarations from 'vits.monotonic_align.core' */
+static PyTypeObject *__pyx_array_type = 0;
+static PyTypeObject *__pyx_MemviewEnum_type = 0;
+static PyTypeObject *__pyx_memoryview_type = 0;
+static PyTypeObject *__pyx_memoryviewslice_type = 0;
+static PyObject *generic = 0;
+static PyObject *strided = 0;
+static PyObject *indirect = 0;
+static PyObject *contiguous = 0;
+static PyObject *indirect_contiguous = 0;
+static int __pyx_memoryview_thread_locks_used;
+static PyThread_type_lock __pyx_memoryview_thread_locks[8];
+static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_4vits_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/
+static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/
+static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
+static void *__pyx_align_pointer(void *, size_t); /*proto*/
+static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
+static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
+static PyObject *_unellipsify(PyObject *, int); /*proto*/
+static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
+static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
+static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
+static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
+static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
+static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
+static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
+static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
+static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
+static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
+static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
+static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
+static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
+static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
+static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
+static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
+static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
+static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
+static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
+static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
+static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
+static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
+static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
+static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
+static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
+static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
+static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
+static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
+static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 };
+static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
+#define __Pyx_MODULE_NAME "vits.monotonic_align.core"
+extern int __pyx_module_is_main_vits__monotonic_align__core;
+int __pyx_module_is_main_vits__monotonic_align__core = 0;
+
+/* Implementation of 'vits.monotonic_align.core' */
+static PyObject *__pyx_builtin_range;
+static PyObject *__pyx_builtin_ValueError;
+static PyObject *__pyx_builtin_MemoryError;
+static PyObject *__pyx_builtin_enumerate;
+static PyObject *__pyx_builtin_TypeError;
+static PyObject *__pyx_builtin_Ellipsis;
+static PyObject *__pyx_builtin_id;
+static PyObject *__pyx_builtin_IndexError;
+static const char __pyx_k_O[] = "O";
+static const char __pyx_k_c[] = "c";
+static const char __pyx_k_id[] = "id";
+static const char __pyx_k_new[] = "__new__";
+static const char __pyx_k_obj[] = "obj";
+static const char __pyx_k_base[] = "base";
+static const char __pyx_k_dict[] = "__dict__";
+static const char __pyx_k_main[] = "__main__";
+static const char __pyx_k_mode[] = "mode";
+static const char __pyx_k_name[] = "name";
+static const char __pyx_k_ndim[] = "ndim";
+static const char __pyx_k_pack[] = "pack";
+static const char __pyx_k_size[] = "size";
+static const char __pyx_k_step[] = "step";
+static const char __pyx_k_stop[] = "stop";
+static const char __pyx_k_t_xs[] = "t_xs";
+static const char __pyx_k_t_ys[] = "t_ys";
+static const char __pyx_k_test[] = "__test__";
+static const char __pyx_k_ASCII[] = "ASCII";
+static const char __pyx_k_class[] = "__class__";
+static const char __pyx_k_error[] = "error";
+static const char __pyx_k_flags[] = "flags";
+static const char __pyx_k_paths[] = "paths";
+static const char __pyx_k_range[] = "range";
+static const char __pyx_k_shape[] = "shape";
+static const char __pyx_k_start[] = "start";
+static const char __pyx_k_encode[] = "encode";
+static const char __pyx_k_format[] = "format";
+static const char __pyx_k_import[] = "__import__";
+static const char __pyx_k_name_2[] = "__name__";
+static const char __pyx_k_pickle[] = "pickle";
+static const char __pyx_k_reduce[] = "__reduce__";
+static const char __pyx_k_struct[] = "struct";
+static const char __pyx_k_unpack[] = "unpack";
+static const char __pyx_k_update[] = "update";
+static const char __pyx_k_values[] = "values";
+static const char __pyx_k_fortran[] = "fortran";
+static const char __pyx_k_memview[] = "memview";
+static const char __pyx_k_Ellipsis[] = "Ellipsis";
+static const char __pyx_k_getstate[] = "__getstate__";
+static const char __pyx_k_itemsize[] = "itemsize";
+static const char __pyx_k_pyx_type[] = "__pyx_type";
+static const char __pyx_k_setstate[] = "__setstate__";
+static const char __pyx_k_TypeError[] = "TypeError";
+static const char __pyx_k_enumerate[] = "enumerate";
+static const char __pyx_k_pyx_state[] = "__pyx_state";
+static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
+static const char __pyx_k_IndexError[] = "IndexError";
+static const char __pyx_k_ValueError[] = "ValueError";
+static const char __pyx_k_pyx_result[] = "__pyx_result";
+static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
+static const char __pyx_k_MemoryError[] = "MemoryError";
+static const char __pyx_k_PickleError[] = "PickleError";
+static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
+static const char __pyx_k_stringsource[] = "stringsource";
+static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
+static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
+static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
+static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
+static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
+static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
+static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
+static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
+static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
+static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
+static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
+static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
+static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
+static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
+static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
+static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
+static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
+static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
+static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
+static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
+static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
+static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
+static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
+static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
+static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
+static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))";
+static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
+static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
+static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
+static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
+static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
+static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
+static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
+static PyObject *__pyx_n_s_ASCII;
+static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
+static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
+static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
+static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
+static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
+static PyObject *__pyx_n_s_Ellipsis;
+static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
+static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0;
+static PyObject *__pyx_n_s_IndexError;
+static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
+static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
+static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
+static PyObject *__pyx_n_s_MemoryError;
+static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
+static PyObject *__pyx_kp_s_MemoryView_of_r_object;
+static PyObject *__pyx_n_b_O;
+static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
+static PyObject *__pyx_n_s_PickleError;
+static PyObject *__pyx_n_s_TypeError;
+static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
+static PyObject *__pyx_n_s_ValueError;
+static PyObject *__pyx_n_s_View_MemoryView;
+static PyObject *__pyx_n_s_allocate_buffer;
+static PyObject *__pyx_n_s_base;
+static PyObject *__pyx_n_s_c;
+static PyObject *__pyx_n_u_c;
+static PyObject *__pyx_n_s_class;
+static PyObject *__pyx_n_s_cline_in_traceback;
+static PyObject *__pyx_kp_s_contiguous_and_direct;
+static PyObject *__pyx_kp_s_contiguous_and_indirect;
+static PyObject *__pyx_n_s_dict;
+static PyObject *__pyx_n_s_dtype_is_object;
+static PyObject *__pyx_n_s_encode;
+static PyObject *__pyx_n_s_enumerate;
+static PyObject *__pyx_n_s_error;
+static PyObject *__pyx_n_s_flags;
+static PyObject *__pyx_n_s_format;
+static PyObject *__pyx_n_s_fortran;
+static PyObject *__pyx_n_u_fortran;
+static PyObject *__pyx_n_s_getstate;
+static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
+static PyObject *__pyx_n_s_id;
+static PyObject *__pyx_n_s_import;
+static PyObject *__pyx_n_s_itemsize;
+static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_memview;
+static PyObject *__pyx_n_s_mode;
+static PyObject *__pyx_n_s_name;
+static PyObject *__pyx_n_s_name_2;
+static PyObject *__pyx_n_s_ndim;
+static PyObject *__pyx_n_s_new;
+static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
+static PyObject *__pyx_n_s_obj;
+static PyObject *__pyx_n_s_pack;
+static PyObject *__pyx_n_s_paths;
+static PyObject *__pyx_n_s_pickle;
+static PyObject *__pyx_n_s_pyx_PickleError;
+static PyObject *__pyx_n_s_pyx_checksum;
+static PyObject *__pyx_n_s_pyx_getbuffer;
+static PyObject *__pyx_n_s_pyx_result;
+static PyObject *__pyx_n_s_pyx_state;
+static PyObject *__pyx_n_s_pyx_type;
+static PyObject *__pyx_n_s_pyx_unpickle_Enum;
+static PyObject *__pyx_n_s_pyx_vtable;
+static PyObject *__pyx_n_s_range;
+static PyObject *__pyx_n_s_reduce;
+static PyObject *__pyx_n_s_reduce_cython;
+static PyObject *__pyx_n_s_reduce_ex;
+static PyObject *__pyx_n_s_setstate;
+static PyObject *__pyx_n_s_setstate_cython;
+static PyObject *__pyx_n_s_shape;
+static PyObject *__pyx_n_s_size;
+static PyObject *__pyx_n_s_start;
+static PyObject *__pyx_n_s_step;
+static PyObject *__pyx_n_s_stop;
+static PyObject *__pyx_kp_s_strided_and_direct;
+static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
+static PyObject *__pyx_kp_s_strided_and_indirect;
+static PyObject *__pyx_kp_s_stringsource;
+static PyObject *__pyx_n_s_struct;
+static PyObject *__pyx_n_s_t_xs;
+static PyObject *__pyx_n_s_t_ys;
+static PyObject *__pyx_n_s_test;
+static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
+static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
+static PyObject *__pyx_n_s_unpack;
+static PyObject *__pyx_n_s_update;
+static PyObject *__pyx_n_s_values;
+static PyObject *__pyx_pf_4vits_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs); /* proto */
+static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
+static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
+static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
+static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
+static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
+static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
+static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
+static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
+static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
+static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
+static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
+static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
+static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
+static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
+static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_int_0;
+static PyObject *__pyx_int_1;
+static PyObject *__pyx_int_184977713;
+static PyObject *__pyx_int_neg_1;
+static float __pyx_k_;
+static PyObject *__pyx_tuple__2;
+static PyObject *__pyx_tuple__3;
+static PyObject *__pyx_tuple__4;
+static PyObject *__pyx_tuple__5;
+static PyObject *__pyx_tuple__6;
+static PyObject *__pyx_tuple__7;
+static PyObject *__pyx_tuple__8;
+static PyObject *__pyx_tuple__9;
+static PyObject *__pyx_slice__16;
+static PyObject *__pyx_tuple__10;
+static PyObject *__pyx_tuple__11;
+static PyObject *__pyx_tuple__12;
+static PyObject *__pyx_tuple__13;
+static PyObject *__pyx_tuple__14;
+static PyObject *__pyx_tuple__15;
+static PyObject *__pyx_tuple__17;
+static PyObject *__pyx_tuple__18;
+static PyObject *__pyx_tuple__19;
+static PyObject *__pyx_tuple__20;
+static PyObject *__pyx_tuple__21;
+static PyObject *__pyx_tuple__22;
+static PyObject *__pyx_tuple__23;
+static PyObject *__pyx_tuple__24;
+static PyObject *__pyx_tuple__25;
+static PyObject *__pyx_codeobj__26;
+/* Late includes */
+
+/* "vits/monotonic_align/core.pyx":7
+ * @cython.boundscheck(False)
+ * @cython.wraparound(False)
+ * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
+ *   cdef int x
+ *   cdef int y
+ */
+
+static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice __pyx_v_path, __Pyx_memviewslice __pyx_v_value, int __pyx_v_t_y, int __pyx_v_t_x, struct __pyx_opt_args_4vits_15monotonic_align_4core_maximum_path_each *__pyx_optional_args) {
+  float __pyx_v_max_neg_val = __pyx_k_;
+  int __pyx_v_x;
+  int __pyx_v_y;
+  float __pyx_v_v_prev;
+  float __pyx_v_v_cur;
+  int __pyx_v_index;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  long __pyx_t_4;
+  int __pyx_t_5;
+  long __pyx_t_6;
+  long __pyx_t_7;
+  int __pyx_t_8;
+  Py_ssize_t __pyx_t_9;
+  Py_ssize_t __pyx_t_10;
+  float __pyx_t_11;
+  float __pyx_t_12;
+  float __pyx_t_13;
+  int __pyx_t_14;
+  Py_ssize_t __pyx_t_15;
+  Py_ssize_t __pyx_t_16;
+  if (__pyx_optional_args) {
+    if (__pyx_optional_args->__pyx_n > 0) {
+      __pyx_v_max_neg_val = __pyx_optional_args->max_neg_val;
+    }
+  }
+
+  /* "vits/monotonic_align/core.pyx":13
+ *   cdef float v_cur
+ *   cdef float tmp
+ *   cdef int index = t_x - 1             # <<<<<<<<<<<<<<
+ * 
+ *   for y in range(t_y):
+ */
+  __pyx_v_index = (__pyx_v_t_x - 1);
+
+  /* "vits/monotonic_align/core.pyx":15
+ *   cdef int index = t_x - 1
+ * 
+ *   for y in range(t_y):             # <<<<<<<<<<<<<<
+ *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+ *       if x == y:
+ */
+  __pyx_t_1 = __pyx_v_t_y;
+  __pyx_t_2 = __pyx_t_1;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_y = __pyx_t_3;
+
+    /* "vits/monotonic_align/core.pyx":16
+ * 
+ *   for y in range(t_y):
+ *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):             # <<<<<<<<<<<<<<
+ *       if x == y:
+ *         v_cur = max_neg_val
+ */
+    __pyx_t_4 = (__pyx_v_y + 1);
+    __pyx_t_5 = __pyx_v_t_x;
+    if (((__pyx_t_4 < __pyx_t_5) != 0)) {
+      __pyx_t_6 = __pyx_t_4;
+    } else {
+      __pyx_t_6 = __pyx_t_5;
+    }
+    __pyx_t_4 = __pyx_t_6;
+    __pyx_t_5 = ((__pyx_v_t_x + __pyx_v_y) - __pyx_v_t_y);
+    __pyx_t_6 = 0;
+    if (((__pyx_t_5 > __pyx_t_6) != 0)) {
+      __pyx_t_7 = __pyx_t_5;
+    } else {
+      __pyx_t_7 = __pyx_t_6;
+    }
+    __pyx_t_6 = __pyx_t_4;
+    for (__pyx_t_5 = __pyx_t_7; __pyx_t_5 < __pyx_t_6; __pyx_t_5+=1) {
+      __pyx_v_x = __pyx_t_5;
+
+      /* "vits/monotonic_align/core.pyx":17
+ *   for y in range(t_y):
+ *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+ *       if x == y:             # <<<<<<<<<<<<<<
+ *         v_cur = max_neg_val
+ *       else:
+ */
+      __pyx_t_8 = ((__pyx_v_x == __pyx_v_y) != 0);
+      if (__pyx_t_8) {
+
+        /* "vits/monotonic_align/core.pyx":18
+ *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+ *       if x == y:
+ *         v_cur = max_neg_val             # <<<<<<<<<<<<<<
+ *       else:
+ *         v_cur = value[y-1, x]
+ */
+        __pyx_v_v_cur = __pyx_v_max_neg_val;
+
+        /* "vits/monotonic_align/core.pyx":17
+ *   for y in range(t_y):
+ *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+ *       if x == y:             # <<<<<<<<<<<<<<
+ *         v_cur = max_neg_val
+ *       else:
+ */
+        goto __pyx_L7;
+      }
+
+      /* "vits/monotonic_align/core.pyx":20
+ *         v_cur = max_neg_val
+ *       else:
+ *         v_cur = value[y-1, x]             # <<<<<<<<<<<<<<
+ *       if x == 0:
+ *         if y == 0:
+ */
+      /*else*/ {
+        __pyx_t_9 = (__pyx_v_y - 1);
+        __pyx_t_10 = __pyx_v_x;
+        __pyx_v_v_cur = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )));
+      }
+      __pyx_L7:;
+
+      /* "vits/monotonic_align/core.pyx":21
+ *       else:
+ *         v_cur = value[y-1, x]
+ *       if x == 0:             # <<<<<<<<<<<<<<
+ *         if y == 0:
+ *           v_prev = 0.
+ */
+      __pyx_t_8 = ((__pyx_v_x == 0) != 0);
+      if (__pyx_t_8) {
+
+        /* "vits/monotonic_align/core.pyx":22
+ *         v_cur = value[y-1, x]
+ *       if x == 0:
+ *         if y == 0:             # <<<<<<<<<<<<<<
+ *           v_prev = 0.
+ *         else:
+ */
+        __pyx_t_8 = ((__pyx_v_y == 0) != 0);
+        if (__pyx_t_8) {
+
+          /* "vits/monotonic_align/core.pyx":23
+ *       if x == 0:
+ *         if y == 0:
+ *           v_prev = 0.             # <<<<<<<<<<<<<<
+ *         else:
+ *           v_prev = max_neg_val
+ */
+          __pyx_v_v_prev = 0.;
+
+          /* "vits/monotonic_align/core.pyx":22
+ *         v_cur = value[y-1, x]
+ *       if x == 0:
+ *         if y == 0:             # <<<<<<<<<<<<<<
+ *           v_prev = 0.
+ *         else:
+ */
+          goto __pyx_L9;
+        }
+
+        /* "vits/monotonic_align/core.pyx":25
+ *           v_prev = 0.
+ *         else:
+ *           v_prev = max_neg_val             # <<<<<<<<<<<<<<
+ *       else:
+ *         v_prev = value[y-1, x-1]
+ */
+        /*else*/ {
+          __pyx_v_v_prev = __pyx_v_max_neg_val;
+        }
+        __pyx_L9:;
+
+        /* "vits/monotonic_align/core.pyx":21
+ *       else:
+ *         v_cur = value[y-1, x]
+ *       if x == 0:             # <<<<<<<<<<<<<<
+ *         if y == 0:
+ *           v_prev = 0.
+ */
+        goto __pyx_L8;
+      }
+
+      /* "vits/monotonic_align/core.pyx":27
+ *           v_prev = max_neg_val
+ *       else:
+ *         v_prev = value[y-1, x-1]             # <<<<<<<<<<<<<<
+ *       value[y, x] += max(v_prev, v_cur)
+ * 
+ */
+      /*else*/ {
+        __pyx_t_10 = (__pyx_v_y - 1);
+        __pyx_t_9 = (__pyx_v_x - 1);
+        __pyx_v_v_prev = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_10 * __pyx_v_value.strides[0]) )) + __pyx_t_9)) )));
+      }
+      __pyx_L8:;
+
+      /* "vits/monotonic_align/core.pyx":28
+ *       else:
+ *         v_prev = value[y-1, x-1]
+ *       value[y, x] += max(v_prev, v_cur)             # <<<<<<<<<<<<<<
+ * 
+ *   for y in range(t_y - 1, -1, -1):
+ */
+      __pyx_t_11 = __pyx_v_v_cur;
+      __pyx_t_12 = __pyx_v_v_prev;
+      if (((__pyx_t_11 > __pyx_t_12) != 0)) {
+        __pyx_t_13 = __pyx_t_11;
+      } else {
+        __pyx_t_13 = __pyx_t_12;
+      }
+      __pyx_t_9 = __pyx_v_y;
+      __pyx_t_10 = __pyx_v_x;
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+}
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+static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
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+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):
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+ *         if type(self) is memoryview or obj is not None:
+ */
+  __Pyx_INCREF(__pyx_v_obj);
+  __Pyx_GIVEREF(__pyx_v_obj);
+  __Pyx_GOTREF(__pyx_v_self->obj);
+  __Pyx_DECREF(__pyx_v_self->obj);
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+  /* "View.MemoryView":347
+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):
+ *         self.obj = obj
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+ *             __Pyx_GetBuffer(obj, &self.view, flags)
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+  __pyx_v_self->flags = __pyx_v_flags;
+
+  /* "View.MemoryView":348
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+ *             if <PyObject *> self.view.obj == NULL:
+ */
+  __pyx_t_2 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)__pyx_memoryview_type));
+  __pyx_t_3 = (__pyx_t_2 != 0);
+  if (!__pyx_t_3) {
+  } else {
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+    goto __pyx_L4_bool_binop_done;
+  }
+  __pyx_t_3 = (__pyx_v_obj != Py_None);
+  __pyx_t_2 = (__pyx_t_3 != 0);
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+    /* "View.MemoryView":349
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+    /* "View.MemoryView":350
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+ *             if <PyObject *> self.view.obj == NULL:             # <<<<<<<<<<<<<<
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+ *                 (<__pyx_buffer *> &self.view).obj = Py_None             # <<<<<<<<<<<<<<
+ *                 Py_INCREF(Py_None)
+ * 
+ */
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+
+      /* "View.MemoryView":352
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+ *                 Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
+ * 
+ *         global __pyx_memoryview_thread_locks_used
+ */
+      Py_INCREF(Py_None);
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+      /* "View.MemoryView":350
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+ *             __Pyx_GetBuffer(obj, &self.view, flags)
+ *             if <PyObject *> self.view.obj == NULL:             # <<<<<<<<<<<<<<
+ *                 (<__pyx_buffer *> &self.view).obj = Py_None
+ *                 Py_INCREF(Py_None)
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+    }
+
+    /* "View.MemoryView":348
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+ *         self.flags = flags
+ *         if type(self) is memoryview or obj is not None:             # <<<<<<<<<<<<<<
+ *             __Pyx_GetBuffer(obj, &self.view, flags)
+ *             if <PyObject *> self.view.obj == NULL:
+ */
+  }
+
+  /* "View.MemoryView":355
+ * 
+ *         global __pyx_memoryview_thread_locks_used
+ *         if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:             # <<<<<<<<<<<<<<
+ *             self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]
+ *             __pyx_memoryview_thread_locks_used += 1
+ */
+  __pyx_t_1 = ((__pyx_memoryview_thread_locks_used < 8) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":356
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+ *         if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:
+ *             self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]             # <<<<<<<<<<<<<<
+ *             __pyx_memoryview_thread_locks_used += 1
+ *         if self.lock is NULL:
+ */
+    __pyx_v_self->lock = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);
+
+    /* "View.MemoryView":357
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+ *             self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]
+ *             __pyx_memoryview_thread_locks_used += 1             # <<<<<<<<<<<<<<
+ *         if self.lock is NULL:
+ *             self.lock = PyThread_allocate_lock()
+ */
+    __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used + 1);
+
+    /* "View.MemoryView":355
+ * 
+ *         global __pyx_memoryview_thread_locks_used
+ *         if __pyx_memoryview_thread_locks_used < THREAD_LOCKS_PREALLOCATED:             # <<<<<<<<<<<<<<
+ *             self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]
+ *             __pyx_memoryview_thread_locks_used += 1
+ */
+  }
+
+  /* "View.MemoryView":358
+ *             self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]
+ *             __pyx_memoryview_thread_locks_used += 1
+ *         if self.lock is NULL:             # <<<<<<<<<<<<<<
+ *             self.lock = PyThread_allocate_lock()
+ *             if self.lock is NULL:
+ */
+  __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":359
+ *             __pyx_memoryview_thread_locks_used += 1
+ *         if self.lock is NULL:
+ *             self.lock = PyThread_allocate_lock()             # <<<<<<<<<<<<<<
+ *             if self.lock is NULL:
+ *                 raise MemoryError
+ */
+    __pyx_v_self->lock = PyThread_allocate_lock();
+
+    /* "View.MemoryView":360
+ *         if self.lock is NULL:
+ *             self.lock = PyThread_allocate_lock()
+ *             if self.lock is NULL:             # <<<<<<<<<<<<<<
+ *                 raise MemoryError
+ * 
+ */
+    __pyx_t_1 = ((__pyx_v_self->lock == NULL) != 0);
+    if (unlikely(__pyx_t_1)) {
+
+      /* "View.MemoryView":361
+ *             self.lock = PyThread_allocate_lock()
+ *             if self.lock is NULL:
+ *                 raise MemoryError             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_FORMAT:
+ */
+      PyErr_NoMemory(); __PYX_ERR(1, 361, __pyx_L1_error)
+
+      /* "View.MemoryView":360
+ *         if self.lock is NULL:
+ *             self.lock = PyThread_allocate_lock()
+ *             if self.lock is NULL:             # <<<<<<<<<<<<<<
+ *                 raise MemoryError
+ * 
+ */
+    }
+
+    /* "View.MemoryView":358
+ *             self.lock = __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]
+ *             __pyx_memoryview_thread_locks_used += 1
+ *         if self.lock is NULL:             # <<<<<<<<<<<<<<
+ *             self.lock = PyThread_allocate_lock()
+ *             if self.lock is NULL:
+ */
+  }
+
+  /* "View.MemoryView":363
+ *                 raise MemoryError
+ * 
+ *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
+ *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0')
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":364
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+ *         if flags & PyBUF_FORMAT:
+ *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0')             # <<<<<<<<<<<<<<
+ *         else:
+ *             self.dtype_is_object = dtype_is_object
+ */
+    __pyx_t_2 = (((__pyx_v_self->view.format[0]) == 'O') != 0);
+    if (__pyx_t_2) {
+    } else {
+      __pyx_t_1 = __pyx_t_2;
+      goto __pyx_L11_bool_binop_done;
+    }
+    __pyx_t_2 = (((__pyx_v_self->view.format[1]) == '\x00') != 0);
+    __pyx_t_1 = __pyx_t_2;
+    __pyx_L11_bool_binop_done:;
+    __pyx_v_self->dtype_is_object = __pyx_t_1;
+
+    /* "View.MemoryView":363
+ *                 raise MemoryError
+ * 
+ *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
+ *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0')
+ *         else:
+ */
+    goto __pyx_L10;
+  }
+
+  /* "View.MemoryView":366
+ *             self.dtype_is_object = (self.view.format[0] == b'O' and self.view.format[1] == b'\0')
+ *         else:
+ *             self.dtype_is_object = dtype_is_object             # <<<<<<<<<<<<<<
+ * 
+ *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(
+ */
+  /*else*/ {
+    __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object;
+  }
+  __pyx_L10:;
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+  /* "View.MemoryView":368
+ *             self.dtype_is_object = dtype_is_object
+ * 
+ *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(             # <<<<<<<<<<<<<<
+ *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
+ *         self.typeinfo = NULL
+ */
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+
+  /* "View.MemoryView":370
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+ *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
+ *         self.typeinfo = NULL             # <<<<<<<<<<<<<<
+ * 
+ *     def __dealloc__(memoryview self):
+ */
+  __pyx_v_self->typeinfo = NULL;
+
+  /* "View.MemoryView":345
+ *     cdef __Pyx_TypeInfo *typeinfo
+ * 
+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<
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+ *         self.flags = flags
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
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+}
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+/* "View.MemoryView":372
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+ * 
+ *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
+ *         if self.obj is not None:
+ *             __Pyx_ReleaseBuffer(&self.view)
+ */
+
+/* Python wrapper */
+static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/
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+  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
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+  /* function exit code */
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+}
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+static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  int __pyx_v_i;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  int __pyx_t_5;
+  PyThread_type_lock __pyx_t_6;
+  PyThread_type_lock __pyx_t_7;
+  __Pyx_RefNannySetupContext("__dealloc__", 0);
+
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+ *     def __dealloc__(memoryview self):
+ *         if self.obj is not None:             # <<<<<<<<<<<<<<
+ *             __Pyx_ReleaseBuffer(&self.view)
+ *         elif (<__pyx_buffer *> &self.view).obj == Py_None:
+ */
+  __pyx_t_1 = (__pyx_v_self->obj != Py_None);
+  __pyx_t_2 = (__pyx_t_1 != 0);
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+
+    /* "View.MemoryView":374
+ *     def __dealloc__(memoryview self):
+ *         if self.obj is not None:
+ *             __Pyx_ReleaseBuffer(&self.view)             # <<<<<<<<<<<<<<
+ *         elif (<__pyx_buffer *> &self.view).obj == Py_None:
+ * 
+ */
+    __Pyx_ReleaseBuffer((&__pyx_v_self->view));
+
+    /* "View.MemoryView":373
+ * 
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+ *         if self.obj is not None:             # <<<<<<<<<<<<<<
+ *             __Pyx_ReleaseBuffer(&self.view)
+ *         elif (<__pyx_buffer *> &self.view).obj == Py_None:
+ */
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":375
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+ *         elif (<__pyx_buffer *> &self.view).obj == Py_None:             # <<<<<<<<<<<<<<
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+ *             (<__pyx_buffer *> &self.view).obj = NULL
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+  __pyx_t_2 = ((((Py_buffer *)(&__pyx_v_self->view))->obj == Py_None) != 0);
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+    /* "View.MemoryView":377
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+ *             (<__pyx_buffer *> &self.view).obj = NULL             # <<<<<<<<<<<<<<
+ *             Py_DECREF(Py_None)
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+ */
+    ((Py_buffer *)(&__pyx_v_self->view))->obj = NULL;
+
+    /* "View.MemoryView":378
+ * 
+ *             (<__pyx_buffer *> &self.view).obj = NULL
+ *             Py_DECREF(Py_None)             # <<<<<<<<<<<<<<
+ * 
+ *         cdef int i
+ */
+    Py_DECREF(Py_None);
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+    /* "View.MemoryView":375
+ *         if self.obj is not None:
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+ *         elif (<__pyx_buffer *> &self.view).obj == Py_None:             # <<<<<<<<<<<<<<
+ * 
+ *             (<__pyx_buffer *> &self.view).obj = NULL
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+  __pyx_L3:;
+
+  /* "View.MemoryView":382
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+ *         if self.lock != NULL:             # <<<<<<<<<<<<<<
+ *             for i in range(__pyx_memoryview_thread_locks_used):
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:
+ */
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+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":383
+ *         global __pyx_memoryview_thread_locks_used
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+ *             for i in range(__pyx_memoryview_thread_locks_used):             # <<<<<<<<<<<<<<
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:
+ *                     __pyx_memoryview_thread_locks_used -= 1
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+    __pyx_t_4 = __pyx_t_3;
+    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
+      __pyx_v_i = __pyx_t_5;
+
+      /* "View.MemoryView":384
+ *         if self.lock != NULL:
+ *             for i in range(__pyx_memoryview_thread_locks_used):
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<
+ *                     __pyx_memoryview_thread_locks_used -= 1
+ *                     if i != __pyx_memoryview_thread_locks_used:
+ */
+      __pyx_t_2 = (((__pyx_memoryview_thread_locks[__pyx_v_i]) == __pyx_v_self->lock) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":385
+ *             for i in range(__pyx_memoryview_thread_locks_used):
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:
+ *                     __pyx_memoryview_thread_locks_used -= 1             # <<<<<<<<<<<<<<
+ *                     if i != __pyx_memoryview_thread_locks_used:
+ *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
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+        __pyx_memoryview_thread_locks_used = (__pyx_memoryview_thread_locks_used - 1);
+
+        /* "View.MemoryView":386
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:
+ *                     __pyx_memoryview_thread_locks_used -= 1
+ *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<
+ *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
+ *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])
+ */
+        __pyx_t_2 = ((__pyx_v_i != __pyx_memoryview_thread_locks_used) != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":388
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+ *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
+ *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])             # <<<<<<<<<<<<<<
+ *                     break
+ *             else:
+ */
+          __pyx_t_6 = (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]);
+          __pyx_t_7 = (__pyx_memoryview_thread_locks[__pyx_v_i]);
+
+          /* "View.MemoryView":387
+ *                     __pyx_memoryview_thread_locks_used -= 1
+ *                     if i != __pyx_memoryview_thread_locks_used:
+ *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (             # <<<<<<<<<<<<<<
+ *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])
+ *                     break
+ */
+          (__pyx_memoryview_thread_locks[__pyx_v_i]) = __pyx_t_6;
+          (__pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used]) = __pyx_t_7;
+
+          /* "View.MemoryView":386
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:
+ *                     __pyx_memoryview_thread_locks_used -= 1
+ *                     if i != __pyx_memoryview_thread_locks_used:             # <<<<<<<<<<<<<<
+ *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
+ *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])
+ */
+        }
+
+        /* "View.MemoryView":389
+ *                         __pyx_memoryview_thread_locks[i], __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used] = (
+ *                             __pyx_memoryview_thread_locks[__pyx_memoryview_thread_locks_used], __pyx_memoryview_thread_locks[i])
+ *                     break             # <<<<<<<<<<<<<<
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+ *                 PyThread_free_lock(self.lock)
+ */
+        goto __pyx_L6_break;
+
+        /* "View.MemoryView":384
+ *         if self.lock != NULL:
+ *             for i in range(__pyx_memoryview_thread_locks_used):
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:             # <<<<<<<<<<<<<<
+ *                     __pyx_memoryview_thread_locks_used -= 1
+ *                     if i != __pyx_memoryview_thread_locks_used:
+ */
+      }
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":391
+ *                     break
+ *             else:
+ *                 PyThread_free_lock(self.lock)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef char *get_item_pointer(memoryview self, object index) except NULL:
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+      PyThread_free_lock(__pyx_v_self->lock);
+    }
+    __pyx_L6_break:;
+
+    /* "View.MemoryView":382
+ *         cdef int i
+ *         global __pyx_memoryview_thread_locks_used
+ *         if self.lock != NULL:             # <<<<<<<<<<<<<<
+ *             for i in range(__pyx_memoryview_thread_locks_used):
+ *                 if __pyx_memoryview_thread_locks[i] is self.lock:
+ */
+  }
+
+  /* "View.MemoryView":372
+ *         self.typeinfo = NULL
+ * 
+ *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
+ *         if self.obj is not None:
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+  /* function exit code */
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+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
+ *         memviewsliceobj = memview
+ *         p_src = &memviewsliceobj.from_slice
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); 
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":725
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+ *     if isinstance(memview, _memoryviewslice):
+ *         memviewsliceobj = memview             # <<<<<<<<<<<<<<
+ *         p_src = &memviewsliceobj.from_slice
+ *     else:
+ */
+    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) __PYX_ERR(1, 725, __pyx_L1_error)
+    __pyx_t_3 = ((PyObject *)__pyx_v_memview);
+    __Pyx_INCREF(__pyx_t_3);
+    __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+
+    /* "View.MemoryView":726
+ *     if isinstance(memview, _memoryviewslice):
+ *         memviewsliceobj = memview
+ *         p_src = &memviewsliceobj.from_slice             # <<<<<<<<<<<<<<
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+ *         slice_copy(memview, &src)
+ */
+    __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice);
+
+    /* "View.MemoryView":724
+ *     assert memview.view.ndim > 0
+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
+ *         memviewsliceobj = memview
+ *         p_src = &memviewsliceobj.from_slice
+ */
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":728
+ *         p_src = &memviewsliceobj.from_slice
+ *     else:
+ *         slice_copy(memview, &src)             # <<<<<<<<<<<<<<
+ *         p_src = &src
+ * 
+ */
+  /*else*/ {
+    __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src));
+
+    /* "View.MemoryView":729
+ *     else:
+ *         slice_copy(memview, &src)
+ *         p_src = &src             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_v_p_src = (&__pyx_v_src);
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":735
+ * 
+ * 
+ *     dst.memview = p_src.memview             # <<<<<<<<<<<<<<
+ *     dst.data = p_src.data
+ * 
+ */
+  __pyx_t_4 = __pyx_v_p_src->memview;
+  __pyx_v_dst.memview = __pyx_t_4;
+
+  /* "View.MemoryView":736
+ * 
+ *     dst.memview = p_src.memview
+ *     dst.data = p_src.data             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_5 = __pyx_v_p_src->data;
+  __pyx_v_dst.data = __pyx_t_5;
+
+  /* "View.MemoryView":741
+ * 
+ * 
+ *     cdef __Pyx_memviewslice *p_dst = &dst             # <<<<<<<<<<<<<<
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+ *     cdef Py_ssize_t start, stop, step
+ */
+  __pyx_v_p_dst = (&__pyx_v_dst);
+
+  /* "View.MemoryView":742
+ * 
+ *     cdef __Pyx_memviewslice *p_dst = &dst
+ *     cdef int *p_suboffset_dim = &suboffset_dim             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t start, stop, step
+ *     cdef bint have_start, have_stop, have_step
+ */
+  __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim);
+
+  /* "View.MemoryView":746
+ *     cdef bint have_start, have_stop, have_step
+ * 
+ *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
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+ */
+  __pyx_t_6 = 0;
+  if (likely(PyList_CheckExact(__pyx_v_indices)) || PyTuple_CheckExact(__pyx_v_indices)) {
+    __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0;
+    __pyx_t_8 = NULL;
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+    __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 746, __pyx_L1_error)
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext; if (unlikely(!__pyx_t_8)) __PYX_ERR(1, 746, __pyx_L1_error)
+  }
+  for (;;) {
+    if (likely(!__pyx_t_8)) {
+      if (likely(PyList_CheckExact(__pyx_t_3))) {
+        if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break;
+        #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+        __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) __PYX_ERR(1, 746, __pyx_L1_error)
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+        __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 746, __pyx_L1_error)
+        __Pyx_GOTREF(__pyx_t_9);
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+        #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
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+        #else
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+      __pyx_t_9 = __pyx_t_8(__pyx_t_3);
+      if (unlikely(!__pyx_t_9)) {
+        PyObject* exc_type = PyErr_Occurred();
+        if (exc_type) {
+          if (likely(__Pyx_PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else __PYX_ERR(1, 746, __pyx_L1_error)
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+        break;
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+      __Pyx_GOTREF(__pyx_t_9);
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+    __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9);
+    __pyx_t_9 = 0;
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+
+    /* "View.MemoryView":747
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+ *     for dim, index in enumerate(indices):
+ *         if PyIndex_Check(index):             # <<<<<<<<<<<<<<
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+ *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
+ */
+    __pyx_t_2 = (PyIndex_Check(__pyx_v_index) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":751
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+ */
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+      /* "View.MemoryView":748
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+      /* "View.MemoryView":747
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+ *     for dim, index in enumerate(indices):
+ *         if PyIndex_Check(index):             # <<<<<<<<<<<<<<
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+ */
+      goto __pyx_L6;
+    }
+
+    /* "View.MemoryView":754
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+ *                 False)
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+    __pyx_t_1 = (__pyx_t_2 != 0);
+    if (__pyx_t_1) {
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+      /* "View.MemoryView":755
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+ */
+      (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1;
+
+      /* "View.MemoryView":756
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+ *             p_dst.shape[new_ndim] = 1
+ *             p_dst.strides[new_ndim] = 0             # <<<<<<<<<<<<<<
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+ */
+      (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0;
+
+      /* "View.MemoryView":757
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+ *             p_dst.strides[new_ndim] = 0
+ *             p_dst.suboffsets[new_ndim] = -1             # <<<<<<<<<<<<<<
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+ */
+      (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1L;
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+      /* "View.MemoryView":758
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+ *             p_dst.suboffsets[new_ndim] = -1
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+      /* "View.MemoryView":754
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+ *         elif index is None:             # <<<<<<<<<<<<<<
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+ *             p_dst.strides[new_ndim] = 0
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+      goto __pyx_L6;
+    }
+
+    /* "View.MemoryView":760
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+ *         else:
+ *             start = index.start or 0             # <<<<<<<<<<<<<<
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+ *             step = index.step or 0
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+      __Pyx_GOTREF(__pyx_t_9);
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+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
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+        __pyx_t_10 = __pyx_t_12;
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+        goto __pyx_L7_bool_binop_done;
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+      __pyx_t_10 = 0;
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+      /* "View.MemoryView":761
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+ *             start = index.start or 0
+ *             stop = index.stop or 0             # <<<<<<<<<<<<<<
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+ * 
+ */
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 761, __pyx_L1_error)
+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) __PYX_ERR(1, 761, __pyx_L1_error)
+      if (!__pyx_t_1) {
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      } else {
+        __pyx_t_12 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_12 == (Py_ssize_t)-1) && PyErr_Occurred())) __PYX_ERR(1, 761, __pyx_L1_error)
+        __pyx_t_10 = __pyx_t_12;
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+        goto __pyx_L9_bool_binop_done;
+      }
+      __pyx_t_10 = 0;
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+ *             have_start = index.start is not None
+ */
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 762, __pyx_L1_error)
+      __Pyx_GOTREF(__pyx_t_9);
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+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
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+        __pyx_t_10 = __pyx_t_12;
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+        goto __pyx_L11_bool_binop_done;
+      }
+      __pyx_t_10 = 0;
+      __pyx_L11_bool_binop_done:;
+      __pyx_v_step = __pyx_t_10;
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+      /* "View.MemoryView":764
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+ *             have_start = index.start is not None             # <<<<<<<<<<<<<<
+ *             have_stop = index.stop is not None
+ *             have_step = index.step is not None
+ */
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+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_1 = (__pyx_t_9 != Py_None);
+      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      __pyx_v_have_start = __pyx_t_1;
+
+      /* "View.MemoryView":765
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+ *             have_start = index.start is not None
+ *             have_stop = index.stop is not None             # <<<<<<<<<<<<<<
+ *             have_step = index.step is not None
+ * 
+ */
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 765, __pyx_L1_error)
+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_1 = (__pyx_t_9 != Py_None);
+      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      __pyx_v_have_stop = __pyx_t_1;
+
+      /* "View.MemoryView":766
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+ *             have_stop = index.stop is not None
+ *             have_step = index.step is not None             # <<<<<<<<<<<<<<
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+ *             slice_memviewslice(
+ */
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) __PYX_ERR(1, 766, __pyx_L1_error)
+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_1 = (__pyx_t_9 != Py_None);
+      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      __pyx_v_have_step = __pyx_t_1;
+
+      /* "View.MemoryView":768
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+ *                 dim, new_ndim, p_suboffset_dim,
+ */
+      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == ((int)-1))) __PYX_ERR(1, 768, __pyx_L1_error)
+
+      /* "View.MemoryView":774
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+ *                 True)
+ *             new_ndim += 1             # <<<<<<<<<<<<<<
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ */
+      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
+    }
+    __pyx_L6:;
+
+    /* "View.MemoryView":746
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+ * 
+ *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
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+ *             slice_memviewslice(
+ */
+  }
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+
+  /* "View.MemoryView":776
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+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
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+ *                                     memviewsliceobj.to_object_func,
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); 
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":777
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+ *     if isinstance(memview, _memoryviewslice):
+ *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
+ *                                     memviewsliceobj.to_object_func,
+ *                                     memviewsliceobj.to_dtype_func,
+ */
+    __Pyx_XDECREF(((PyObject *)__pyx_r));
+
+    /* "View.MemoryView":778
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+ *         return memoryview_fromslice(dst, new_ndim,
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+ *                                     memviewsliceobj.to_dtype_func,
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+
+    /* "View.MemoryView":779
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+ *                                     memviewsliceobj.to_object_func,
+ *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
+ *                                     memview.dtype_is_object)
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+ */
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+
+    /* "View.MemoryView":777
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+ *     if isinstance(memview, _memoryviewslice):
+ *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
+ *                                     memviewsliceobj.to_object_func,
+ *                                     memviewsliceobj.to_dtype_func,
+ */
+    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 777, __pyx_L1_error)
+    __Pyx_GOTREF(__pyx_t_3);
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 777, __pyx_L1_error)
+    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+    goto __pyx_L0;
+
+    /* "View.MemoryView":776
+ *             new_ndim += 1
+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
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+  }
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+  /* "View.MemoryView":782
+ *                                     memview.dtype_is_object)
+ *     else:
+ *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
+ *                                     memview.dtype_is_object)
+ * 
+ */
+  /*else*/ {
+    __Pyx_XDECREF(((PyObject *)__pyx_r));
+
+    /* "View.MemoryView":783
+ *     else:
+ *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,
+ *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 782, __pyx_L1_error)
+    __Pyx_GOTREF(__pyx_t_3);
+
+    /* "View.MemoryView":782
+ *                                     memview.dtype_is_object)
+ *     else:
+ *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
+ *                                     memview.dtype_is_object)
+ * 
+ */
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) __PYX_ERR(1, 782, __pyx_L1_error)
+    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":710
+ * 
+ * @cname('__pyx_memview_slice')
+ * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
+ *     cdef int new_ndim = 0, suboffset_dim = -1, dim
+ *     cdef bint negative_step
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_9);
+  __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);
+  __Pyx_XDECREF(__pyx_v_index);
+  __Pyx_XGIVEREF((PyObject *)__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":807
+ * 
+ * @cname('__pyx_memoryview_slice_memviewslice')
+ * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
+ *         __Pyx_memviewslice *dst,
+ *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
+ */
+
+static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
+  Py_ssize_t __pyx_v_new_shape;
+  int __pyx_v_negative_step;
+  int __pyx_r;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+
+  /* "View.MemoryView":827
+ *     cdef bint negative_step
+ * 
+ *     if not is_slice:             # <<<<<<<<<<<<<<
+ * 
+ *         if start < 0:
+ */
+  __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":829
+ *     if not is_slice:
+ * 
+ *         if start < 0:             # <<<<<<<<<<<<<<
+ *             start += shape
+ *         if not 0 <= start < shape:
+ */
+    __pyx_t_1 = ((__pyx_v_start < 0) != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":830
+ * 
+ *         if start < 0:
+ *             start += shape             # <<<<<<<<<<<<<<
+ *         if not 0 <= start < shape:
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
+ */
+      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
+
+      /* "View.MemoryView":829
+ *     if not is_slice:
+ * 
+ *         if start < 0:             # <<<<<<<<<<<<<<
+ *             start += shape
+ *         if not 0 <= start < shape:
+ */
+    }
+
+    /* "View.MemoryView":831
+ *         if start < 0:
+ *             start += shape
+ *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
+ *     else:
+ */
+    __pyx_t_1 = (0 <= __pyx_v_start);
+    if (__pyx_t_1) {
+      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
+    }
+    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":832
+ *             start += shape
+ *         if not 0 <= start < shape:
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
+ *     else:
+ * 
+ */
+      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, ((char *)"Index out of bounds (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 832, __pyx_L1_error)
+
+      /* "View.MemoryView":831
+ *         if start < 0:
+ *             start += shape
+ *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
+ *     else:
+ */
+    }
+
+    /* "View.MemoryView":827
+ *     cdef bint negative_step
+ * 
+ *     if not is_slice:             # <<<<<<<<<<<<<<
+ * 
+ *         if start < 0:
+ */
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":835
+ *     else:
+ * 
+ *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
+ * 
+ *         if have_step and step == 0:
+ */
+  /*else*/ {
+    __pyx_t_1 = ((__pyx_v_have_step != 0) != 0);
+    if (__pyx_t_1) {
+    } else {
+      __pyx_t_2 = __pyx_t_1;
+      goto __pyx_L6_bool_binop_done;
+    }
+    __pyx_t_1 = ((__pyx_v_step < 0) != 0);
+    __pyx_t_2 = __pyx_t_1;
+    __pyx_L6_bool_binop_done:;
+    __pyx_v_negative_step = __pyx_t_2;
+
+    /* "View.MemoryView":837
+ *         negative_step = have_step != 0 and step < 0
+ * 
+ *         if have_step and step == 0:             # <<<<<<<<<<<<<<
+ *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
+ * 
+ */
+    __pyx_t_1 = (__pyx_v_have_step != 0);
+    if (__pyx_t_1) {
+    } else {
+      __pyx_t_2 = __pyx_t_1;
+      goto __pyx_L9_bool_binop_done;
+    }
+    __pyx_t_1 = ((__pyx_v_step == 0) != 0);
+    __pyx_t_2 = __pyx_t_1;
+    __pyx_L9_bool_binop_done:;
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":838
+ * 
+ *         if have_step and step == 0:
+ *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Step may not be zero (axis %d)"), __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 838, __pyx_L1_error)
+
+      /* "View.MemoryView":837
+ *         negative_step = have_step != 0 and step < 0
+ * 
+ *         if have_step and step == 0:             # <<<<<<<<<<<<<<
+ *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
+ * 
+ */
+    }
+
+    /* "View.MemoryView":841
+ * 
+ * 
+ *         if have_start:             # <<<<<<<<<<<<<<
+ *             if start < 0:
+ *                 start += shape
+ */
+    __pyx_t_2 = (__pyx_v_have_start != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":842
+ * 
+ *         if have_start:
+ *             if start < 0:             # <<<<<<<<<<<<<<
+ *                 start += shape
+ *                 if start < 0:
+ */
+      __pyx_t_2 = ((__pyx_v_start < 0) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":843
+ *         if have_start:
+ *             if start < 0:
+ *                 start += shape             # <<<<<<<<<<<<<<
+ *                 if start < 0:
+ *                     start = 0
+ */
+        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
+
+        /* "View.MemoryView":844
+ *             if start < 0:
+ *                 start += shape
+ *                 if start < 0:             # <<<<<<<<<<<<<<
+ *                     start = 0
+ *             elif start >= shape:
+ */
+        __pyx_t_2 = ((__pyx_v_start < 0) != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":845
+ *                 start += shape
+ *                 if start < 0:
+ *                     start = 0             # <<<<<<<<<<<<<<
+ *             elif start >= shape:
+ *                 if negative_step:
+ */
+          __pyx_v_start = 0;
+
+          /* "View.MemoryView":844
+ *             if start < 0:
+ *                 start += shape
+ *                 if start < 0:             # <<<<<<<<<<<<<<
+ *                     start = 0
+ *             elif start >= shape:
+ */
+        }
+
+        /* "View.MemoryView":842
+ * 
+ *         if have_start:
+ *             if start < 0:             # <<<<<<<<<<<<<<
+ *                 start += shape
+ *                 if start < 0:
+ */
+        goto __pyx_L12;
+      }
+
+      /* "View.MemoryView":846
+ *                 if start < 0:
+ *                     start = 0
+ *             elif start >= shape:             # <<<<<<<<<<<<<<
+ *                 if negative_step:
+ *                     start = shape - 1
+ */
+      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":847
+ *                     start = 0
+ *             elif start >= shape:
+ *                 if negative_step:             # <<<<<<<<<<<<<<
+ *                     start = shape - 1
+ *                 else:
+ */
+        __pyx_t_2 = (__pyx_v_negative_step != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":848
+ *             elif start >= shape:
+ *                 if negative_step:
+ *                     start = shape - 1             # <<<<<<<<<<<<<<
+ *                 else:
+ *                     start = shape
+ */
+          __pyx_v_start = (__pyx_v_shape - 1);
+
+          /* "View.MemoryView":847
+ *                     start = 0
+ *             elif start >= shape:
+ *                 if negative_step:             # <<<<<<<<<<<<<<
+ *                     start = shape - 1
+ *                 else:
+ */
+          goto __pyx_L14;
+        }
+
+        /* "View.MemoryView":850
+ *                     start = shape - 1
+ *                 else:
+ *                     start = shape             # <<<<<<<<<<<<<<
+ *         else:
+ *             if negative_step:
+ */
+        /*else*/ {
+          __pyx_v_start = __pyx_v_shape;
+        }
+        __pyx_L14:;
+
+        /* "View.MemoryView":846
+ *                 if start < 0:
+ *                     start = 0
+ *             elif start >= shape:             # <<<<<<<<<<<<<<
+ *                 if negative_step:
+ *                     start = shape - 1
+ */
+      }
+      __pyx_L12:;
+
+      /* "View.MemoryView":841
+ * 
+ * 
+ *         if have_start:             # <<<<<<<<<<<<<<
+ *             if start < 0:
+ *                 start += shape
+ */
+      goto __pyx_L11;
+    }
+
+    /* "View.MemoryView":852
+ *                     start = shape
+ *         else:
+ *             if negative_step:             # <<<<<<<<<<<<<<
+ *                 start = shape - 1
+ *             else:
+ */
+    /*else*/ {
+      __pyx_t_2 = (__pyx_v_negative_step != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":853
+ *         else:
+ *             if negative_step:
+ *                 start = shape - 1             # <<<<<<<<<<<<<<
+ *             else:
+ *                 start = 0
+ */
+        __pyx_v_start = (__pyx_v_shape - 1);
+
+        /* "View.MemoryView":852
+ *                     start = shape
+ *         else:
+ *             if negative_step:             # <<<<<<<<<<<<<<
+ *                 start = shape - 1
+ *             else:
+ */
+        goto __pyx_L15;
+      }
+
+      /* "View.MemoryView":855
+ *                 start = shape - 1
+ *             else:
+ *                 start = 0             # <<<<<<<<<<<<<<
+ * 
+ *         if have_stop:
+ */
+      /*else*/ {
+        __pyx_v_start = 0;
+      }
+      __pyx_L15:;
+    }
+    __pyx_L11:;
+
+    /* "View.MemoryView":857
+ *                 start = 0
+ * 
+ *         if have_stop:             # <<<<<<<<<<<<<<
+ *             if stop < 0:
+ *                 stop += shape
+ */
+    __pyx_t_2 = (__pyx_v_have_stop != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":858
+ * 
+ *         if have_stop:
+ *             if stop < 0:             # <<<<<<<<<<<<<<
+ *                 stop += shape
+ *                 if stop < 0:
+ */
+      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":859
+ *         if have_stop:
+ *             if stop < 0:
+ *                 stop += shape             # <<<<<<<<<<<<<<
+ *                 if stop < 0:
+ *                     stop = 0
+ */
+        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
+
+        /* "View.MemoryView":860
+ *             if stop < 0:
+ *                 stop += shape
+ *                 if stop < 0:             # <<<<<<<<<<<<<<
+ *                     stop = 0
+ *             elif stop > shape:
+ */
+        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":861
+ *                 stop += shape
+ *                 if stop < 0:
+ *                     stop = 0             # <<<<<<<<<<<<<<
+ *             elif stop > shape:
+ *                 stop = shape
+ */
+          __pyx_v_stop = 0;
+
+          /* "View.MemoryView":860
+ *             if stop < 0:
+ *                 stop += shape
+ *                 if stop < 0:             # <<<<<<<<<<<<<<
+ *                     stop = 0
+ *             elif stop > shape:
+ */
+        }
+
+        /* "View.MemoryView":858
+ * 
+ *         if have_stop:
+ *             if stop < 0:             # <<<<<<<<<<<<<<
+ *                 stop += shape
+ *                 if stop < 0:
+ */
+        goto __pyx_L17;
+      }
+
+      /* "View.MemoryView":862
+ *                 if stop < 0:
+ *                     stop = 0
+ *             elif stop > shape:             # <<<<<<<<<<<<<<
+ *                 stop = shape
+ *         else:
+ */
+      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":863
+ *                     stop = 0
+ *             elif stop > shape:
+ *                 stop = shape             # <<<<<<<<<<<<<<
+ *         else:
+ *             if negative_step:
+ */
+        __pyx_v_stop = __pyx_v_shape;
+
+        /* "View.MemoryView":862
+ *                 if stop < 0:
+ *                     stop = 0
+ *             elif stop > shape:             # <<<<<<<<<<<<<<
+ *                 stop = shape
+ *         else:
+ */
+      }
+      __pyx_L17:;
+
+      /* "View.MemoryView":857
+ *                 start = 0
+ * 
+ *         if have_stop:             # <<<<<<<<<<<<<<
+ *             if stop < 0:
+ *                 stop += shape
+ */
+      goto __pyx_L16;
+    }
+
+    /* "View.MemoryView":865
+ *                 stop = shape
+ *         else:
+ *             if negative_step:             # <<<<<<<<<<<<<<
+ *                 stop = -1
+ *             else:
+ */
+    /*else*/ {
+      __pyx_t_2 = (__pyx_v_negative_step != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":866
+ *         else:
+ *             if negative_step:
+ *                 stop = -1             # <<<<<<<<<<<<<<
+ *             else:
+ *                 stop = shape
+ */
+        __pyx_v_stop = -1L;
+
+        /* "View.MemoryView":865
+ *                 stop = shape
+ *         else:
+ *             if negative_step:             # <<<<<<<<<<<<<<
+ *                 stop = -1
+ *             else:
+ */
+        goto __pyx_L19;
+      }
+
+      /* "View.MemoryView":868
+ *                 stop = -1
+ *             else:
+ *                 stop = shape             # <<<<<<<<<<<<<<
+ * 
+ *         if not have_step:
+ */
+      /*else*/ {
+        __pyx_v_stop = __pyx_v_shape;
+      }
+      __pyx_L19:;
+    }
+    __pyx_L16:;
+
+    /* "View.MemoryView":870
+ *                 stop = shape
+ * 
+ *         if not have_step:             # <<<<<<<<<<<<<<
+ *             step = 1
+ * 
+ */
+    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":871
+ * 
+ *         if not have_step:
+ *             step = 1             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_v_step = 1;
+
+      /* "View.MemoryView":870
+ *                 stop = shape
+ * 
+ *         if not have_step:             # <<<<<<<<<<<<<<
+ *             step = 1
+ * 
+ */
+    }
+
+    /* "View.MemoryView":875
+ * 
+ *         with cython.cdivision(True):
+ *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
+ * 
+ *             if (stop - start) - step * new_shape:
+ */
+    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
+
+    /* "View.MemoryView":877
+ *             new_shape = (stop - start) // step
+ * 
+ *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
+ *                 new_shape += 1
+ * 
+ */
+    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":878
+ * 
+ *             if (stop - start) - step * new_shape:
+ *                 new_shape += 1             # <<<<<<<<<<<<<<
+ * 
+ *         if new_shape < 0:
+ */
+      __pyx_v_new_shape = (__pyx_v_new_shape + 1);
+
+      /* "View.MemoryView":877
+ *             new_shape = (stop - start) // step
+ * 
+ *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
+ *                 new_shape += 1
+ * 
+ */
+    }
+
+    /* "View.MemoryView":880
+ *                 new_shape += 1
+ * 
+ *         if new_shape < 0:             # <<<<<<<<<<<<<<
+ *             new_shape = 0
+ * 
+ */
+    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":881
+ * 
+ *         if new_shape < 0:
+ *             new_shape = 0             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_v_new_shape = 0;
+
+      /* "View.MemoryView":880
+ *                 new_shape += 1
+ * 
+ *         if new_shape < 0:             # <<<<<<<<<<<<<<
+ *             new_shape = 0
+ * 
+ */
+    }
+
+    /* "View.MemoryView":884
+ * 
+ * 
+ *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
+ *         dst.shape[new_ndim] = new_shape
+ *         dst.suboffsets[new_ndim] = suboffset
+ */
+    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);
+
+    /* "View.MemoryView":885
+ * 
+ *         dst.strides[new_ndim] = stride * step
+ *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
+ *         dst.suboffsets[new_ndim] = suboffset
+ * 
+ */
+    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;
+
+    /* "View.MemoryView":886
+ *         dst.strides[new_ndim] = stride * step
+ *         dst.shape[new_ndim] = new_shape
+ *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":889
+ * 
+ * 
+ *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
+ *         dst.data += start * stride
+ *     else:
+ */
+  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":890
+ * 
+ *     if suboffset_dim[0] < 0:
+ *         dst.data += start * stride             # <<<<<<<<<<<<<<
+ *     else:
+ *         dst.suboffsets[suboffset_dim[0]] += start * stride
+ */
+    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));
+
+    /* "View.MemoryView":889
+ * 
+ * 
+ *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
+ *         dst.data += start * stride
+ *     else:
+ */
+    goto __pyx_L23;
+  }
+
+  /* "View.MemoryView":892
+ *         dst.data += start * stride
+ *     else:
+ *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
+ * 
+ *     if suboffset >= 0:
+ */
+  /*else*/ {
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+ */
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+  /* "View.MemoryView":1080
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+  /* "View.MemoryView":1087
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+  /* function exit code */
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+  /* "View.MemoryView":1110
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+  Py_ssize_t __pyx_t_6;
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+ *     cdef Py_ssize_t dst_extent = dst_shape[0]
+ *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
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+    } else {
+      __pyx_t_1 = __pyx_t_2;
+      goto __pyx_L5_bool_binop_done;
+    }
+    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
+    if (__pyx_t_2) {
+    } else {
+      __pyx_t_1 = __pyx_t_2;
+      goto __pyx_L5_bool_binop_done;
+    }
+
+    /* "View.MemoryView":1154
+ *     if ndim == 1:
+ *        if (src_stride > 0 and dst_stride > 0 and
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ *        else:
+ */
+    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
+    if (__pyx_t_2) {
+      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
+    }
+    __pyx_t_3 = (__pyx_t_2 != 0);
+    __pyx_t_1 = __pyx_t_3;
+    __pyx_L5_bool_binop_done:;
+
+    /* "View.MemoryView":1153
+ * 
+ *     if ndim == 1:
+ *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ */
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":1155
+ *        if (src_stride > 0 and dst_stride > 0 and
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
+ *        else:
+ *            for i in range(dst_extent):
+ */
+      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
+
+      /* "View.MemoryView":1153
+ * 
+ *     if ndim == 1:
+ *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ */
+      goto __pyx_L4;
+    }
+
+    /* "View.MemoryView":1157
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ *        else:
+ *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
+ *                memcpy(dst_data, src_data, itemsize)
+ *                src_data += src_stride
+ */
+    /*else*/ {
+      __pyx_t_4 = __pyx_v_dst_extent;
+      __pyx_t_5 = __pyx_t_4;
+      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+        __pyx_v_i = __pyx_t_6;
+
+        /* "View.MemoryView":1158
+ *        else:
+ *            for i in range(dst_extent):
+ *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
+ *                src_data += src_stride
+ *                dst_data += dst_stride
+ */
+        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
+
+        /* "View.MemoryView":1159
+ *            for i in range(dst_extent):
+ *                memcpy(dst_data, src_data, itemsize)
+ *                src_data += src_stride             # <<<<<<<<<<<<<<
+ *                dst_data += dst_stride
+ *     else:
+ */
+        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
+
+        /* "View.MemoryView":1160
+ *                memcpy(dst_data, src_data, itemsize)
+ *                src_data += src_stride
+ *                dst_data += dst_stride             # <<<<<<<<<<<<<<
+ *     else:
+ *         for i in range(dst_extent):
+ */
+        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
+      }
+    }
+    __pyx_L4:;
+
+    /* "View.MemoryView":1152
+ *     cdef Py_ssize_t dst_stride = dst_strides[0]
+ * 
+ *     if ndim == 1:             # <<<<<<<<<<<<<<
+ *        if (src_stride > 0 and dst_stride > 0 and
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ */
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":1162
+ *                dst_data += dst_stride
+ *     else:
+ *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
+ *             _copy_strided_to_strided(src_data, src_strides + 1,
+ *                                      dst_data, dst_strides + 1,
+ */
+  /*else*/ {
+    __pyx_t_4 = __pyx_v_dst_extent;
+    __pyx_t_5 = __pyx_t_4;
+    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+      __pyx_v_i = __pyx_t_6;
+
+      /* "View.MemoryView":1163
+ *     else:
+ *         for i in range(dst_extent):
+ *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
+ *                                      dst_data, dst_strides + 1,
+ *                                      src_shape + 1, dst_shape + 1,
+ */
+      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
+
+      /* "View.MemoryView":1167
+ *                                      src_shape + 1, dst_shape + 1,
+ *                                      ndim - 1, itemsize)
+ *             src_data += src_stride             # <<<<<<<<<<<<<<
+ *             dst_data += dst_stride
+ * 
+ */
+      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
+
+      /* "View.MemoryView":1168
+ *                                      ndim - 1, itemsize)
+ *             src_data += src_stride
+ *             dst_data += dst_stride             # <<<<<<<<<<<<<<
+ * 
+ * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
+ */
+      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
+    }
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1140
+ * 
+ * @cython.cdivision(True)
+ * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
+ *                                    char *dst_data, Py_ssize_t *dst_strides,
+ *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1170
+ *             dst_data += dst_stride
+ * 
+ * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice *dst,
+ *                                   int ndim, size_t itemsize) nogil:
+ */
+
+static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
+
+  /* "View.MemoryView":1173
+ *                                   __Pyx_memviewslice *dst,
+ *                                   int ndim, size_t itemsize) nogil:
+ *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
+ *                              src.shape, dst.shape, ndim, itemsize)
+ * 
+ */
+  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
+
+  /* "View.MemoryView":1170
+ *             dst_data += dst_stride
+ * 
+ * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice *dst,
+ *                                   int ndim, size_t itemsize) nogil:
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1177
+ * 
+ * @cname('__pyx_memoryview_slice_get_size')
+ * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
+ *     "Return the size of the memory occupied by the slice in number of bytes"
+ *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
+ */
+
+static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
+  Py_ssize_t __pyx_v_shape;
+  Py_ssize_t __pyx_v_size;
+  Py_ssize_t __pyx_r;
+  Py_ssize_t __pyx_t_1;
+  Py_ssize_t *__pyx_t_2;
+  Py_ssize_t *__pyx_t_3;
+  Py_ssize_t *__pyx_t_4;
+
+  /* "View.MemoryView":1179
+ * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:
+ *     "Return the size of the memory occupied by the slice in number of bytes"
+ *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
+ * 
+ *     for shape in src.shape[:ndim]:
+ */
+  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
+  __pyx_v_size = __pyx_t_1;
+
+  /* "View.MemoryView":1181
+ *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
+ * 
+ *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
+ *         size *= shape
+ * 
+ */
+  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
+  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
+    __pyx_t_2 = __pyx_t_4;
+    __pyx_v_shape = (__pyx_t_2[0]);
+
+    /* "View.MemoryView":1182
+ * 
+ *     for shape in src.shape[:ndim]:
+ *         size *= shape             # <<<<<<<<<<<<<<
+ * 
+ *     return size
+ */
+    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
+  }
+
+  /* "View.MemoryView":1184
+ *         size *= shape
+ * 
+ *     return size             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_fill_contig_strides_array')
+ */
+  __pyx_r = __pyx_v_size;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1177
+ * 
+ * @cname('__pyx_memoryview_slice_get_size')
+ * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
+ *     "Return the size of the memory occupied by the slice in number of bytes"
+ *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1187
+ * 
+ * @cname('__pyx_fill_contig_strides_array')
+ * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
+ *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
+ *                 int ndim, char order) nogil:
+ */
+
+static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
+  int __pyx_v_idx;
+  Py_ssize_t __pyx_r;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+
+  /* "View.MemoryView":1196
+ *     cdef int idx
+ * 
+ *     if order == 'F':             # <<<<<<<<<<<<<<
+ *         for idx in range(ndim):
+ *             strides[idx] = stride
+ */
+  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1197
+ * 
+ *     if order == 'F':
+ *         for idx in range(ndim):             # <<<<<<<<<<<<<<
+ *             strides[idx] = stride
+ *             stride *= shape[idx]
+ */
+    __pyx_t_2 = __pyx_v_ndim;
+    __pyx_t_3 = __pyx_t_2;
+    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+      __pyx_v_idx = __pyx_t_4;
+
+      /* "View.MemoryView":1198
+ *     if order == 'F':
+ *         for idx in range(ndim):
+ *             strides[idx] = stride             # <<<<<<<<<<<<<<
+ *             stride *= shape[idx]
+ *     else:
+ */
+      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
+
+      /* "View.MemoryView":1199
+ *         for idx in range(ndim):
+ *             strides[idx] = stride
+ *             stride *= shape[idx]             # <<<<<<<<<<<<<<
+ *     else:
+ *         for idx in range(ndim - 1, -1, -1):
+ */
+      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
+    }
+
+    /* "View.MemoryView":1196
+ *     cdef int idx
+ * 
+ *     if order == 'F':             # <<<<<<<<<<<<<<
+ *         for idx in range(ndim):
+ *             strides[idx] = stride
+ */
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":1201
+ *             stride *= shape[idx]
+ *     else:
+ *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
+ *             strides[idx] = stride
+ *             stride *= shape[idx]
+ */
+  /*else*/ {
+    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
+      __pyx_v_idx = __pyx_t_2;
+
+      /* "View.MemoryView":1202
+ *     else:
+ *         for idx in range(ndim - 1, -1, -1):
+ *             strides[idx] = stride             # <<<<<<<<<<<<<<
+ *             stride *= shape[idx]
+ * 
+ */
+      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
+
+      /* "View.MemoryView":1203
+ *         for idx in range(ndim - 1, -1, -1):
+ *             strides[idx] = stride
+ *             stride *= shape[idx]             # <<<<<<<<<<<<<<
+ * 
+ *     return stride
+ */
+      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
+    }
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1205
+ *             stride *= shape[idx]
+ * 
+ *     return stride             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_copy_data_to_temp')
+ */
+  __pyx_r = __pyx_v_stride;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1187
+ * 
+ * @cname('__pyx_fill_contig_strides_array')
+ * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
+ *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
+ *                 int ndim, char order) nogil:
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1208
+ * 
+ * @cname('__pyx_memoryview_copy_data_to_temp')
+ * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                              __Pyx_memviewslice *tmpslice,
+ *                              char order,
+ */
+
+static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
+  int __pyx_v_i;
+  void *__pyx_v_result;
+  size_t __pyx_v_itemsize;
+  size_t __pyx_v_size;
+  void *__pyx_r;
+  Py_ssize_t __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  struct __pyx_memoryview_obj *__pyx_t_4;
+  int __pyx_t_5;
+  int __pyx_t_6;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+
+  /* "View.MemoryView":1219
+ *     cdef void *result
+ * 
+ *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
+ *     cdef size_t size = slice_get_size(src, ndim)
+ * 
+ */
+  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
+  __pyx_v_itemsize = __pyx_t_1;
+
+  /* "View.MemoryView":1220
+ * 
+ *     cdef size_t itemsize = src.memview.view.itemsize
+ *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     result = malloc(size)
+ */
+  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
+
+  /* "View.MemoryView":1222
+ *     cdef size_t size = slice_get_size(src, ndim)
+ * 
+ *     result = malloc(size)             # <<<<<<<<<<<<<<
+ *     if not result:
+ *         _err(MemoryError, NULL)
+ */
+  __pyx_v_result = malloc(__pyx_v_size);
+
+  /* "View.MemoryView":1223
+ * 
+ *     result = malloc(size)
+ *     if not result:             # <<<<<<<<<<<<<<
+ *         _err(MemoryError, NULL)
+ * 
+ */
+  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1224
+ *     result = malloc(size)
+ *     if not result:
+ *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1224, __pyx_L1_error)
+
+    /* "View.MemoryView":1223
+ * 
+ *     result = malloc(size)
+ *     if not result:             # <<<<<<<<<<<<<<
+ *         _err(MemoryError, NULL)
+ * 
+ */
+  }
+
+  /* "View.MemoryView":1227
+ * 
+ * 
+ *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
+ *     tmpslice.memview = src.memview
+ *     for i in range(ndim):
+ */
+  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
+
+  /* "View.MemoryView":1228
+ * 
+ *     tmpslice.data = <char *> result
+ *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
+ *     for i in range(ndim):
+ *         tmpslice.shape[i] = src.shape[i]
+ */
+  __pyx_t_4 = __pyx_v_src->memview;
+  __pyx_v_tmpslice->memview = __pyx_t_4;
+
+  /* "View.MemoryView":1229
+ *     tmpslice.data = <char *> result
+ *     tmpslice.memview = src.memview
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         tmpslice.shape[i] = src.shape[i]
+ *         tmpslice.suboffsets[i] = -1
+ */
+  __pyx_t_3 = __pyx_v_ndim;
+  __pyx_t_5 = __pyx_t_3;
+  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+    __pyx_v_i = __pyx_t_6;
+
+    /* "View.MemoryView":1230
+ *     tmpslice.memview = src.memview
+ *     for i in range(ndim):
+ *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
+ *         tmpslice.suboffsets[i] = -1
+ * 
+ */
+    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);
+
+    /* "View.MemoryView":1231
+ *     for i in range(ndim):
+ *         tmpslice.shape[i] = src.shape[i]
+ *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
+ * 
+ *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,
+ */
+    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L;
+  }
+
+  /* "View.MemoryView":1233
+ *         tmpslice.suboffsets[i] = -1
+ * 
+ *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<
+ *                               ndim, order)
+ * 
+ */
+  (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order));
+
+  /* "View.MemoryView":1237
+ * 
+ * 
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         if tmpslice.shape[i] == 1:
+ *             tmpslice.strides[i] = 0
+ */
+  __pyx_t_3 = __pyx_v_ndim;
+  __pyx_t_5 = __pyx_t_3;
+  for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+    __pyx_v_i = __pyx_t_6;
+
+    /* "View.MemoryView":1238
+ * 
+ *     for i in range(ndim):
+ *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
+ *             tmpslice.strides[i] = 0
+ * 
+ */
+    __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1239
+ *     for i in range(ndim):
+ *         if tmpslice.shape[i] == 1:
+ *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
+ * 
+ *     if slice_is_contig(src[0], order, ndim):
+ */
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+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1287
+ *         broadcast_leading(&src, src_ndim, dst_ndim)
+ *     elif dst_ndim < src_ndim:
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef int ndim = max(src_ndim, dst_ndim)
+ */
+    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
+
+    /* "View.MemoryView":1286
+ *     if src_ndim < dst_ndim:
+ *         broadcast_leading(&src, src_ndim, dst_ndim)
+ *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)
+ * 
+ */
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1289
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)
+ * 
+ *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim):
+ */
+  __pyx_t_3 = __pyx_v_dst_ndim;
+  __pyx_t_4 = __pyx_v_src_ndim;
+  if (((__pyx_t_3 > __pyx_t_4) != 0)) {
+    __pyx_t_5 = __pyx_t_3;
+  } else {
+    __pyx_t_5 = __pyx_t_4;
+  }
+  __pyx_v_ndim = __pyx_t_5;
+
+  /* "View.MemoryView":1291
+ *     cdef int ndim = max(src_ndim, dst_ndim)
+ * 
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:
+ */
+  __pyx_t_5 = __pyx_v_ndim;
+  __pyx_t_3 = __pyx_t_5;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_i = __pyx_t_4;
+
+    /* "View.MemoryView":1292
+ * 
+ *     for i in range(ndim):
+ *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True
+ */
+    __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1293
+ *     for i in range(ndim):
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
+ *                 broadcasting = True
+ *                 src.strides[i] = 0
+ */
+      __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":1294
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True             # <<<<<<<<<<<<<<
+ *                 src.strides[i] = 0
+ *             else:
+ */
+        __pyx_v_broadcasting = 1;
+
+        /* "View.MemoryView":1295
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True
+ *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
+ *             else:
+ *                 _err_extents(i, dst.shape[i], src.shape[i])
+ */
+        (__pyx_v_src.strides[__pyx_v_i]) = 0;
+
+        /* "View.MemoryView":1293
+ *     for i in range(ndim):
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
+ *                 broadcasting = True
+ *                 src.strides[i] = 0
+ */
+        goto __pyx_L7;
+      }
+
+      /* "View.MemoryView":1297
+ *                 src.strides[i] = 0
+ *             else:
+ *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
+ * 
+ *         if src.suboffsets[i] >= 0:
+ */
+      /*else*/ {
+        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error)
+      }
+      __pyx_L7:;
+
+      /* "View.MemoryView":1292
+ * 
+ *     for i in range(ndim):
+ *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True
+ */
+    }
+
+    /* "View.MemoryView":1299
+ *                 _err_extents(i, dst.shape[i], src.shape[i])
+ * 
+ *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)
+ * 
+ */
+    __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1300
+ * 
+ *         if src.suboffsets[i] >= 0:
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
+ * 
+ *     if slices_overlap(&src, &dst, ndim, itemsize):
+ */
+      __pyx_t_6 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, ((char *)"Dimension %d is not direct"), __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1300, __pyx_L1_error)
+
+      /* "View.MemoryView":1299
+ *                 _err_extents(i, dst.shape[i], src.shape[i])
+ * 
+ *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)
+ * 
+ */
+    }
+  }
+
+  /* "View.MemoryView":1302
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)
+ * 
+ *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
+ * 
+ *         if not slice_is_contig(src, order, ndim):
+ */
+  __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1304
+ *     if slices_overlap(&src, &dst, ndim, itemsize):
+ * 
+ *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
+ *             order = get_best_order(&dst, ndim)
+ * 
+ */
+    __pyx_t_2 = ((!(__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1305
+ * 
+ *         if not slice_is_contig(src, order, ndim):
+ *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
+ * 
+ *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
+ */
+      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
+
+      /* "View.MemoryView":1304
+ *     if slices_overlap(&src, &dst, ndim, itemsize):
+ * 
+ *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
+ *             order = get_best_order(&dst, ndim)
+ * 
+ */
+    }
+
+    /* "View.MemoryView":1307
+ *             order = get_best_order(&dst, ndim)
+ * 
+ *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
+ *         src = tmp
+ * 
+ */
+    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1307, __pyx_L1_error)
+    __pyx_v_tmpdata = __pyx_t_7;
+
+    /* "View.MemoryView":1308
+ * 
+ *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
+ *         src = tmp             # <<<<<<<<<<<<<<
+ * 
+ *     if not broadcasting:
+ */
+    __pyx_v_src = __pyx_v_tmp;
+
+    /* "View.MemoryView":1302
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)
+ * 
+ *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
+ * 
+ *         if not slice_is_contig(src, order, ndim):
+ */
+  }
+
+  /* "View.MemoryView":1310
+ *         src = tmp
+ * 
+ *     if not broadcasting:             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1313
+ * 
+ * 
+ *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
+ *             direct_copy = slice_is_contig(dst, 'C', ndim)
+ *         elif slice_is_contig(src, 'F', ndim):
+ */
+    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1314
+ * 
+ *         if slice_is_contig(src, 'C', ndim):
+ *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
+ *         elif slice_is_contig(src, 'F', ndim):
+ *             direct_copy = slice_is_contig(dst, 'F', ndim)
+ */
+      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
+
+      /* "View.MemoryView":1313
+ * 
+ * 
+ *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
+ *             direct_copy = slice_is_contig(dst, 'C', ndim)
+ *         elif slice_is_contig(src, 'F', ndim):
+ */
+      goto __pyx_L12;
+    }
+
+    /* "View.MemoryView":1315
+ *         if slice_is_contig(src, 'C', ndim):
+ *             direct_copy = slice_is_contig(dst, 'C', ndim)
+ *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
+ *             direct_copy = slice_is_contig(dst, 'F', ndim)
+ * 
+ */
+    __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1316
+ *             direct_copy = slice_is_contig(dst, 'C', ndim)
+ *         elif slice_is_contig(src, 'F', ndim):
+ *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
+ * 
+ *         if direct_copy:
+ */
+      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
+
+      /* "View.MemoryView":1315
+ *         if slice_is_contig(src, 'C', ndim):
+ *             direct_copy = slice_is_contig(dst, 'C', ndim)
+ *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
+ *             direct_copy = slice_is_contig(dst, 'F', ndim)
+ * 
+ */
+    }
+    __pyx_L12:;
+
+    /* "View.MemoryView":1318
+ *             direct_copy = slice_is_contig(dst, 'F', ndim)
+ * 
+ *         if direct_copy:             # <<<<<<<<<<<<<<
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ */
+    __pyx_t_2 = (__pyx_v_direct_copy != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1320
+ *         if direct_copy:
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ */
+      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
+
+      /* "View.MemoryView":1321
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ *             free(tmpdata)
+ */
+      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
+
+      /* "View.MemoryView":1322
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
+ *             free(tmpdata)
+ *             return 0
+ */
+      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
+
+      /* "View.MemoryView":1323
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ *             free(tmpdata)             # <<<<<<<<<<<<<<
+ *             return 0
+ * 
+ */
+      free(__pyx_v_tmpdata);
+
+      /* "View.MemoryView":1324
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ *             free(tmpdata)
+ *             return 0             # <<<<<<<<<<<<<<
+ * 
+ *     if order == 'F' == get_best_order(&dst, ndim):
+ */
+      __pyx_r = 0;
+      goto __pyx_L0;
+
+      /* "View.MemoryView":1318
+ *             direct_copy = slice_is_contig(dst, 'F', ndim)
+ * 
+ *         if direct_copy:             # <<<<<<<<<<<<<<
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ */
+    }
+
+    /* "View.MemoryView":1310
+ *         src = tmp
+ * 
+ *     if not broadcasting:             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  }
+
+  /* "View.MemoryView":1326
+ *             return 0
+ * 
+ *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_2 = (__pyx_v_order == 'F');
+  if (__pyx_t_2) {
+    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
+  }
+  __pyx_t_8 = (__pyx_t_2 != 0);
+  if (__pyx_t_8) {
+
+    /* "View.MemoryView":1329
+ * 
+ * 
+ *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
+ *         transpose_memslice(&dst)
+ * 
+ */
+    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1329, __pyx_L1_error)
+
+    /* "View.MemoryView":1330
+ * 
+ *         transpose_memslice(&src)
+ *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
+ * 
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)
+ */
+    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)0))) __PYX_ERR(1, 1330, __pyx_L1_error)
+
+    /* "View.MemoryView":1326
+ *             return 0
+ * 
+ *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  }
+
+  /* "View.MemoryView":1332
+ *         transpose_memslice(&dst)
+ * 
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
+ *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)
+ */
+  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
+
+  /* "View.MemoryView":1333
+ * 
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)
+ *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)
+ * 
+ */
+  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
+
+  /* "View.MemoryView":1334
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)
+ *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
+ * 
+ *     free(tmpdata)
+ */
+  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
+
+  /* "View.MemoryView":1336
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)
+ * 
+ *     free(tmpdata)             # <<<<<<<<<<<<<<
+ *     return 0
+ * 
+ */
+  free(__pyx_v_tmpdata);
+
+  /* "View.MemoryView":1337
+ * 
+ *     free(tmpdata)
+ *     return 0             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_broadcast_leading')
+ */
+  __pyx_r = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1268
+ * 
+ * @cname('__pyx_memoryview_copy_contents')
+ * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice dst,
+ *                                   int src_ndim, int dst_ndim,
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  {
+    #ifdef WITH_THREAD
+    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
+    #endif
+    __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    #ifdef WITH_THREAD
+    __Pyx_PyGILState_Release(__pyx_gilstate_save);
+    #endif
+  }
+  __pyx_r = -1;
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1340
+ * 
+ * @cname('__pyx_memoryview_broadcast_leading')
+ * cdef void broadcast_leading(__Pyx_memviewslice *mslice,             # <<<<<<<<<<<<<<
+ *                             int ndim,
+ *                             int ndim_other) nogil:
+ */
+
+static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
+  int __pyx_v_i;
+  int __pyx_v_offset;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+
+  /* "View.MemoryView":1344
+ *                             int ndim_other) nogil:
+ *     cdef int i
+ *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim - 1, -1, -1):
+ */
+  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
+
+  /* "View.MemoryView":1346
+ *     cdef int offset = ndim_other - ndim
+ * 
+ *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
+ *         mslice.shape[i + offset] = mslice.shape[i]
+ *         mslice.strides[i + offset] = mslice.strides[i]
+ */
+  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
+    __pyx_v_i = __pyx_t_1;
+
+    /* "View.MemoryView":1347
+ * 
+ *     for i in range(ndim - 1, -1, -1):
+ *         mslice.shape[i + offset] = mslice.shape[i]             # <<<<<<<<<<<<<<
+ *         mslice.strides[i + offset] = mslice.strides[i]
+ *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
+ */
+    (__pyx_v_mslice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->shape[__pyx_v_i]);
+
+    /* "View.MemoryView":1348
+ *     for i in range(ndim - 1, -1, -1):
+ *         mslice.shape[i + offset] = mslice.shape[i]
+ *         mslice.strides[i + offset] = mslice.strides[i]             # <<<<<<<<<<<<<<
+ *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
+ * 
+ */
+    (__pyx_v_mslice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->strides[__pyx_v_i]);
+
+    /* "View.MemoryView":1349
+ *         mslice.shape[i + offset] = mslice.shape[i]
+ *         mslice.strides[i + offset] = mslice.strides[i]
+ *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(offset):
+ */
+    (__pyx_v_mslice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_mslice->suboffsets[__pyx_v_i]);
+  }
+
+  /* "View.MemoryView":1351
+ *         mslice.suboffsets[i + offset] = mslice.suboffsets[i]
+ * 
+ *     for i in range(offset):             # <<<<<<<<<<<<<<
+ *         mslice.shape[i] = 1
+ *         mslice.strides[i] = mslice.strides[0]
+ */
+  __pyx_t_1 = __pyx_v_offset;
+  __pyx_t_2 = __pyx_t_1;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
+
+    /* "View.MemoryView":1352
+ * 
+ *     for i in range(offset):
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+    /* "View.MemoryView":1353
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+ */
+    (__pyx_v_mslice->strides[__pyx_v_i]) = (__pyx_v_mslice->strides[0]);
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+ * 
+ */
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+ * 
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+  /* function exit code */
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+ */
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+ */
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+static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) {
+
+  /* "View.MemoryView":1400
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+ *     refcount_copying(dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
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+  /* "View.MemoryView":1401
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+ * 
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+
+  /* "View.MemoryView":1397
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+ *                               size_t itemsize, void *item,
+ *                               bint dtype_is_object) nogil:
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1407
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+ * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                               Py_ssize_t *strides, int ndim,
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+  Py_ssize_t __pyx_t_2;
+  Py_ssize_t __pyx_t_3;
+  Py_ssize_t __pyx_t_4;
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+  /* "View.MemoryView":1411
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+ *     cdef Py_ssize_t i
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+ *     cdef Py_ssize_t extent = shape[0]
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+ */
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+  /* function exit code */
+}
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+
+static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_5array_7memview_1__get__(o);
+}
+
+static PyMethodDef __pyx_methods_array[] = {
+  {"__getattr__", (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, 0},
+  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_array_1__reduce_cython__, METH_NOARGS, 0},
+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_array_3__setstate_cython__, METH_O, 0},
+  {0, 0, 0, 0}
+};
+
+static struct PyGetSetDef __pyx_getsets_array[] = {
+  {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, (char *)0, 0},
+  {0, 0, 0, 0, 0}
+};
+
+static PySequenceMethods __pyx_tp_as_sequence_array = {
+  __pyx_array___len__, /*sq_length*/
+  0, /*sq_concat*/
+  0, /*sq_repeat*/
+  __pyx_sq_item_array, /*sq_item*/
+  0, /*sq_slice*/
+  0, /*sq_ass_item*/
+  0, /*sq_ass_slice*/
+  0, /*sq_contains*/
+  0, /*sq_inplace_concat*/
+  0, /*sq_inplace_repeat*/
+};
+
+static PyMappingMethods __pyx_tp_as_mapping_array = {
+  __pyx_array___len__, /*mp_length*/
+  __pyx_array___getitem__, /*mp_subscript*/
+  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/
+};
+
+static PyBufferProcs __pyx_tp_as_buffer_array = {
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getreadbuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getwritebuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getsegcount*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getcharbuffer*/
+  #endif
+  __pyx_array_getbuffer, /*bf_getbuffer*/
+  0, /*bf_releasebuffer*/
+};
+
+static PyTypeObject __pyx_type___pyx_array = {
+  PyVarObject_HEAD_INIT(0, 0)
+  "vits.monotonic_align.core.array", /*tp_name*/
+  sizeof(struct __pyx_array_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_array, /*tp_dealloc*/
+  #if PY_VERSION_HEX < 0x030800b4
+  0, /*tp_print*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4
+  0, /*tp_vectorcall_offset*/
+  #endif
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #endif
+  #if PY_MAJOR_VERSION >= 3
+  0, /*tp_as_async*/
+  #endif
+  0, /*tp_repr*/
+  0, /*tp_as_number*/
+  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
+  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  0, /*tp_str*/
+  __pyx_tp_getattro_array, /*tp_getattro*/
+  0, /*tp_setattro*/
+  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
+  0, /*tp_doc*/
+  0, /*tp_traverse*/
+  0, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_array, /*tp_methods*/
+  0, /*tp_members*/
+  __pyx_getsets_array, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  0, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_array, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  0, /*tp_version_tag*/
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b1
+  0, /*tp_vectorcall*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
+  0, /*tp_print*/
+  #endif
+};
+
+static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+  struct __pyx_MemviewEnum_obj *p;
+  PyObject *o;
+  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
+    o = (*t->tp_alloc)(t, 0);
+  } else {
+    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
+  }
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_MemviewEnum_obj *)o);
+  p->name = Py_None; Py_INCREF(Py_None);
+  return o;
+}
+
+static void __pyx_tp_dealloc_Enum(PyObject *o) {
+  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
+  #if CYTHON_USE_TP_FINALIZE
+  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  PyObject_GC_UnTrack(o);
+  Py_CLEAR(p->name);
+  (*Py_TYPE(o)->tp_free)(o);
+}
+
+static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
+  int e;
+  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
+  if (p->name) {
+    e = (*v)(p->name, a); if (e) return e;
+  }
+  return 0;
+}
+
+static int __pyx_tp_clear_Enum(PyObject *o) {
+  PyObject* tmp;
+  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
+  tmp = ((PyObject*)p->name);
+  p->name = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  return 0;
+}
+
+static PyMethodDef __pyx_methods_Enum[] = {
+  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, METH_NOARGS, 0},
+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, METH_O, 0},
+  {0, 0, 0, 0}
+};
+
+static PyTypeObject __pyx_type___pyx_MemviewEnum = {
+  PyVarObject_HEAD_INIT(0, 0)
+  "vits.monotonic_align.core.Enum", /*tp_name*/
+  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
+  #if PY_VERSION_HEX < 0x030800b4
+  0, /*tp_print*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4
+  0, /*tp_vectorcall_offset*/
+  #endif
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #endif
+  #if PY_MAJOR_VERSION >= 3
+  0, /*tp_as_async*/
+  #endif
+  __pyx_MemviewEnum___repr__, /*tp_repr*/
+  0, /*tp_as_number*/
+  0, /*tp_as_sequence*/
+  0, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  0, /*tp_str*/
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  0, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+  0, /*tp_doc*/
+  __pyx_tp_traverse_Enum, /*tp_traverse*/
+  __pyx_tp_clear_Enum, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_Enum, /*tp_methods*/
+  0, /*tp_members*/
+  0, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  __pyx_MemviewEnum___init__, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_Enum, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  0, /*tp_version_tag*/
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b1
+  0, /*tp_vectorcall*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
+  0, /*tp_print*/
+  #endif
+};
+static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
+
+static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
+  struct __pyx_memoryview_obj *p;
+  PyObject *o;
+  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
+    o = (*t->tp_alloc)(t, 0);
+  } else {
+    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
+  }
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_memoryview_obj *)o);
+  p->__pyx_vtab = __pyx_vtabptr_memoryview;
+  p->obj = Py_None; Py_INCREF(Py_None);
+  p->_size = Py_None; Py_INCREF(Py_None);
+  p->_array_interface = Py_None; Py_INCREF(Py_None);
+  p->view.obj = NULL;
+  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
+  return o;
+  bad:
+  Py_DECREF(o); o = 0;
+  return NULL;
+}
+
+static void __pyx_tp_dealloc_memoryview(PyObject *o) {
+  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
+  #if CYTHON_USE_TP_FINALIZE
+  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  PyObject_GC_UnTrack(o);
+  {
+    PyObject *etype, *eval, *etb;
+    PyErr_Fetch(&etype, &eval, &etb);
+    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
+    __pyx_memoryview___dealloc__(o);
+    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
+    PyErr_Restore(etype, eval, etb);
+  }
+  Py_CLEAR(p->obj);
+  Py_CLEAR(p->_size);
+  Py_CLEAR(p->_array_interface);
+  (*Py_TYPE(o)->tp_free)(o);
+}
+
+static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
+  int e;
+  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
+  if (p->obj) {
+    e = (*v)(p->obj, a); if (e) return e;
+  }
+  if (p->_size) {
+    e = (*v)(p->_size, a); if (e) return e;
+  }
+  if (p->_array_interface) {
+    e = (*v)(p->_array_interface, a); if (e) return e;
+  }
+  if (p->view.obj) {
+    e = (*v)(p->view.obj, a); if (e) return e;
+  }
+  return 0;
+}
+
+static int __pyx_tp_clear_memoryview(PyObject *o) {
+  PyObject* tmp;
+  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
+  tmp = ((PyObject*)p->obj);
+  p->obj = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  tmp = ((PyObject*)p->_size);
+  p->_size = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  tmp = ((PyObject*)p->_array_interface);
+  p->_array_interface = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  Py_CLEAR(p->view.obj);
+  return 0;
+}
+static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
+  PyObject *r;
+  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
+  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
+  Py_DECREF(x);
+  return r;
+}
+
+static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
+  if (v) {
+    return __pyx_memoryview___setitem__(o, i, v);
+  }
+  else {
+    PyErr_Format(PyExc_NotImplementedError,
+      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
+    return -1;
+  }
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
+}
+
+static PyMethodDef __pyx_methods_memoryview[] = {
+  {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},
+  {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},
+  {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
+  {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
+  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
+  {0, 0, 0, 0}
+};
+
+static struct PyGetSetDef __pyx_getsets_memoryview[] = {
+  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
+  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
+  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
+  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
+  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
+  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
+  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
+  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
+  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
+  {0, 0, 0, 0, 0}
+};
+
+static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
+  __pyx_memoryview___len__, /*sq_length*/
+  0, /*sq_concat*/
+  0, /*sq_repeat*/
+  __pyx_sq_item_memoryview, /*sq_item*/
+  0, /*sq_slice*/
+  0, /*sq_ass_item*/
+  0, /*sq_ass_slice*/
+  0, /*sq_contains*/
+  0, /*sq_inplace_concat*/
+  0, /*sq_inplace_repeat*/
+};
+
+static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
+  __pyx_memoryview___len__, /*mp_length*/
+  __pyx_memoryview___getitem__, /*mp_subscript*/
+  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
+};
+
+static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getreadbuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getwritebuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getsegcount*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getcharbuffer*/
+  #endif
+  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
+  0, /*bf_releasebuffer*/
+};
+
+static PyTypeObject __pyx_type___pyx_memoryview = {
+  PyVarObject_HEAD_INIT(0, 0)
+  "vits.monotonic_align.core.memoryview", /*tp_name*/
+  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
+  #if PY_VERSION_HEX < 0x030800b4
+  0, /*tp_print*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4
+  0, /*tp_vectorcall_offset*/
+  #endif
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #endif
+  #if PY_MAJOR_VERSION >= 3
+  0, /*tp_as_async*/
+  #endif
+  __pyx_memoryview___repr__, /*tp_repr*/
+  0, /*tp_as_number*/
+  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
+  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  __pyx_memoryview___str__, /*tp_str*/
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+  0, /*tp_doc*/
+  __pyx_tp_traverse_memoryview, /*tp_traverse*/
+  __pyx_tp_clear_memoryview, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_memoryview, /*tp_methods*/
+  0, /*tp_members*/
+  __pyx_getsets_memoryview, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  0, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_memoryview, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  0, /*tp_version_tag*/
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b1
+  0, /*tp_vectorcall*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
+  0, /*tp_print*/
+  #endif
+};
+static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
+
+static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
+  struct __pyx_memoryviewslice_obj *p;
+  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_memoryviewslice_obj *)o);
+  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
+  p->from_object = Py_None; Py_INCREF(Py_None);
+  p->from_slice.memview = NULL;
+  return o;
+}
+
+static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
+  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
+  #if CYTHON_USE_TP_FINALIZE
+  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  PyObject_GC_UnTrack(o);
+  {
+    PyObject *etype, *eval, *etb;
+    PyErr_Fetch(&etype, &eval, &etb);
+    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
+    __pyx_memoryviewslice___dealloc__(o);
+    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
+    PyErr_Restore(etype, eval, etb);
+  }
+  Py_CLEAR(p->from_object);
+  PyObject_GC_Track(o);
+  __pyx_tp_dealloc_memoryview(o);
+}
+
+static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
+  int e;
+  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
+  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
+  if (p->from_object) {
+    e = (*v)(p->from_object, a); if (e) return e;
+  }
+  return 0;
+}
+
+static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
+  PyObject* tmp;
+  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
+  __pyx_tp_clear_memoryview(o);
+  tmp = ((PyObject*)p->from_object);
+  p->from_object = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
+  return 0;
+}
+
+static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);
+}
+
+static PyMethodDef __pyx_methods__memoryviewslice[] = {
+  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
+  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
+  {0, 0, 0, 0}
+};
+
+static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
+  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},
+  {0, 0, 0, 0, 0}
+};
+
+static PyTypeObject __pyx_type___pyx_memoryviewslice = {
+  PyVarObject_HEAD_INIT(0, 0)
+  "vits.monotonic_align.core._memoryviewslice", /*tp_name*/
+  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
+  #if PY_VERSION_HEX < 0x030800b4
+  0, /*tp_print*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4
+  0, /*tp_vectorcall_offset*/
+  #endif
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #endif
+  #if PY_MAJOR_VERSION >= 3
+  0, /*tp_as_async*/
+  #endif
+  #if CYTHON_COMPILING_IN_PYPY
+  __pyx_memoryview___repr__, /*tp_repr*/
+  #else
+  0, /*tp_repr*/
+  #endif
+  0, /*tp_as_number*/
+  0, /*tp_as_sequence*/
+  0, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  #if CYTHON_COMPILING_IN_PYPY
+  __pyx_memoryview___str__, /*tp_str*/
+  #else
+  0, /*tp_str*/
+  #endif
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  0, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+  "Internal class for passing memoryview slices to Python", /*tp_doc*/
+  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
+  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods__memoryviewslice, /*tp_methods*/
+  0, /*tp_members*/
+  __pyx_getsets__memoryviewslice, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  0, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new__memoryviewslice, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  0, /*tp_version_tag*/
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b1
+  0, /*tp_vectorcall*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
+  0, /*tp_print*/
+  #endif
+};
+
+static PyMethodDef __pyx_methods[] = {
+  {"maximum_path_c", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_4vits_15monotonic_align_4core_1maximum_path_c, METH_VARARGS|METH_KEYWORDS, 0},
+  {0, 0, 0, 0}
+};
+
+#if PY_MAJOR_VERSION >= 3
+#if CYTHON_PEP489_MULTI_PHASE_INIT
+static PyObject* __pyx_pymod_create(PyObject *spec, PyModuleDef *def); /*proto*/
+static int __pyx_pymod_exec_core(PyObject* module); /*proto*/
+static PyModuleDef_Slot __pyx_moduledef_slots[] = {
+  {Py_mod_create, (void*)__pyx_pymod_create},
+  {Py_mod_exec, (void*)__pyx_pymod_exec_core},
+  {0, NULL}
+};
+#endif
+
+static struct PyModuleDef __pyx_moduledef = {
+    PyModuleDef_HEAD_INIT,
+    "core",
+    0, /* m_doc */
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+    0, /* m_size */
+  #else
+    -1, /* m_size */
+  #endif
+    __pyx_methods /* m_methods */,
+  #if CYTHON_PEP489_MULTI_PHASE_INIT
+    __pyx_moduledef_slots, /* m_slots */
+  #else
+    NULL, /* m_reload */
+  #endif
+    NULL, /* m_traverse */
+    NULL, /* m_clear */
+    NULL /* m_free */
+};
+#endif
+#ifndef CYTHON_SMALL_CODE
+#if defined(__clang__)
+    #define CYTHON_SMALL_CODE
+#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3))
+    #define CYTHON_SMALL_CODE __attribute__((cold))
+#else
+    #define CYTHON_SMALL_CODE
+#endif
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+  {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1},
+  {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0},
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+  {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1},
+  {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0},
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+  {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0},
+  {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1},
+  {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0},
+  {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0},
+  {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1},
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+  {&__pyx_n_s_PickleError, __pyx_k_PickleError, sizeof(__pyx_k_PickleError), 0, 0, 1, 1},
+  {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
+  {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0},
+  {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
+  {&__pyx_n_s_View_MemoryView, __pyx_k_View_MemoryView, sizeof(__pyx_k_View_MemoryView), 0, 0, 1, 1},
+  {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1},
+  {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1},
+  {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1},
+  {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1},
+  {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1},
+  {&__pyx_n_s_cline_in_traceback, __pyx_k_cline_in_traceback, sizeof(__pyx_k_cline_in_traceback), 0, 0, 1, 1},
+  {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0},
+  {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0},
+  {&__pyx_n_s_dict, __pyx_k_dict, sizeof(__pyx_k_dict), 0, 0, 1, 1},
+  {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1},
+  {&__pyx_n_s_encode, __pyx_k_encode, sizeof(__pyx_k_encode), 0, 0, 1, 1},
+  {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1},
+  {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1},
+  {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1},
+  {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1},
+  {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1},
+  {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1},
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+  {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0},
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+  {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1},
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+  {&__pyx_n_s_paths, __pyx_k_paths, sizeof(__pyx_k_paths), 0, 0, 1, 1},
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+  {&__pyx_n_s_reduce_cython, __pyx_k_reduce_cython, sizeof(__pyx_k_reduce_cython), 0, 0, 1, 1},
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+  {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0},
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+  {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1},
+  {&__pyx_n_s_t_xs, __pyx_k_t_xs, sizeof(__pyx_k_t_xs), 0, 0, 1, 1},
+  {&__pyx_n_s_t_ys, __pyx_k_t_ys, sizeof(__pyx_k_t_ys), 0, 0, 1, 1},
+  {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
+  {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0},
+  {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0},
+  {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1},
+  {&__pyx_n_s_update, __pyx_k_update, sizeof(__pyx_k_update), 0, 0, 1, 1},
+  {&__pyx_n_s_values, __pyx_k_values, sizeof(__pyx_k_values), 0, 0, 1, 1},
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+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_getattro))
+        return tp->tp_getattro(obj, attr_name);
+#if PY_MAJOR_VERSION < 3
+    if (likely(tp->tp_getattr))
+        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
+#endif
+    return PyObject_GetAttr(obj, attr_name);
+}
+#endif
+
+/* GetBuiltinName */
+static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
+    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
+    if (unlikely(!result)) {
+        PyErr_Format(PyExc_NameError,
+#if PY_MAJOR_VERSION >= 3
+            "name '%U' is not defined", name);
+#else
+            "name '%.200s' is not defined", PyString_AS_STRING(name));
+#endif
+    }
+    return result;
+}
+
+/* MemviewSliceInit */
+static int
+__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
+                        int ndim,
+                        __Pyx_memviewslice *memviewslice,
+                        int memview_is_new_reference)
+{
+    __Pyx_RefNannyDeclarations
+    int i, retval=-1;
+    Py_buffer *buf = &memview->view;
+    __Pyx_RefNannySetupContext("init_memviewslice", 0);
+    if (unlikely(memviewslice->memview || memviewslice->data)) {
+        PyErr_SetString(PyExc_ValueError,
+            "memviewslice is already initialized!");
+        goto fail;
+    }
+    if (buf->strides) {
+        for (i = 0; i < ndim; i++) {
+            memviewslice->strides[i] = buf->strides[i];
+        }
+    } else {
+        Py_ssize_t stride = buf->itemsize;
+        for (i = ndim - 1; i >= 0; i--) {
+            memviewslice->strides[i] = stride;
+            stride *= buf->shape[i];
+        }
+    }
+    for (i = 0; i < ndim; i++) {
+        memviewslice->shape[i]   = buf->shape[i];
+        if (buf->suboffsets) {
+            memviewslice->suboffsets[i] = buf->suboffsets[i];
+        } else {
+            memviewslice->suboffsets[i] = -1;
+        }
+    }
+    memviewslice->memview = memview;
+    memviewslice->data = (char *)buf->buf;
+    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
+        Py_INCREF(memview);
+    }
+    retval = 0;
+    goto no_fail;
+fail:
+    memviewslice->memview = 0;
+    memviewslice->data = 0;
+    retval = -1;
+no_fail:
+    __Pyx_RefNannyFinishContext();
+    return retval;
+}
+#ifndef Py_NO_RETURN
+#define Py_NO_RETURN
+#endif
+static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
+    va_list vargs;
+    char msg[200];
+#ifdef HAVE_STDARG_PROTOTYPES
+    va_start(vargs, fmt);
+#else
+    va_start(vargs);
+#endif
+    vsnprintf(msg, 200, fmt, vargs);
+    va_end(vargs);
+    Py_FatalError(msg);
+}
+static CYTHON_INLINE int
+__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
+                                   PyThread_type_lock lock)
+{
+    int result;
+    PyThread_acquire_lock(lock, 1);
+    result = (*acquisition_count)++;
+    PyThread_release_lock(lock);
+    return result;
+}
+static CYTHON_INLINE int
+__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
+                                   PyThread_type_lock lock)
+{
+    int result;
+    PyThread_acquire_lock(lock, 1);
+    result = (*acquisition_count)--;
+    PyThread_release_lock(lock);
+    return result;
+}
+static CYTHON_INLINE void
+__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
+{
+    int first_time;
+    struct __pyx_memoryview_obj *memview = memslice->memview;
+    if (unlikely(!memview || (PyObject *) memview == Py_None))
+        return;
+    if (unlikely(__pyx_get_slice_count(memview) < 0))
+        __pyx_fatalerror("Acquisition count is %d (line %d)",
+                         __pyx_get_slice_count(memview), lineno);
+    first_time = __pyx_add_acquisition_count(memview) == 0;
+    if (unlikely(first_time)) {
+        if (have_gil) {
+            Py_INCREF((PyObject *) memview);
+        } else {
+            PyGILState_STATE _gilstate = PyGILState_Ensure();
+            Py_INCREF((PyObject *) memview);
+            PyGILState_Release(_gilstate);
+        }
+    }
+}
+static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
+                                             int have_gil, int lineno) {
+    int last_time;
+    struct __pyx_memoryview_obj *memview = memslice->memview;
+    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
+        memslice->memview = NULL;
+        return;
+    }
+    if (unlikely(__pyx_get_slice_count(memview) <= 0))
+        __pyx_fatalerror("Acquisition count is %d (line %d)",
+                         __pyx_get_slice_count(memview), lineno);
+    last_time = __pyx_sub_acquisition_count(memview) == 1;
+    memslice->data = NULL;
+    if (unlikely(last_time)) {
+        if (have_gil) {
+            Py_CLEAR(memslice->memview);
+        } else {
+            PyGILState_STATE _gilstate = PyGILState_Ensure();
+            Py_CLEAR(memslice->memview);
+            PyGILState_Release(_gilstate);
+        }
+    } else {
+        memslice->memview = NULL;
+    }
+}
+
+/* RaiseArgTupleInvalid */
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *more_or_less;
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    PyErr_Format(PyExc_TypeError,
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 func_name, more_or_less, num_expected,
+                 (num_expected == 1) ? "" : "s", num_found);
+}
+
+/* RaiseDoubleKeywords */
+static void __Pyx_RaiseDoubleKeywordsError(
+    const char* func_name,
+    PyObject* kw_name)
+{
+    PyErr_Format(PyExc_TypeError,
+        #if PY_MAJOR_VERSION >= 3
+        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
+        #else
+        "%s() got multiple values for keyword argument '%s'", func_name,
+        PyString_AsString(kw_name));
+        #endif
+}
+
+/* ParseKeywords */
+static int __Pyx_ParseOptionalKeywords(
+    PyObject *kwds,
+    PyObject **argnames[],
+    PyObject *kwds2,
+    PyObject *values[],
+    Py_ssize_t num_pos_args,
+    const char* function_name)
+{
+    PyObject *key = 0, *value = 0;
+    Py_ssize_t pos = 0;
+    PyObject*** name;
+    PyObject*** first_kw_arg = argnames + num_pos_args;
+    while (PyDict_Next(kwds, &pos, &key, &value)) {
+        name = first_kw_arg;
+        while (*name && (**name != key)) name++;
+        if (*name) {
+            values[name-argnames] = value;
+            continue;
+        }
+        name = first_kw_arg;
+        #if PY_MAJOR_VERSION < 3
+        if (likely(PyString_Check(key))) {
+            while (*name) {
+                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
+                        && _PyString_Eq(**name, key)) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    if ((**argname == key) || (
+                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
+                             && _PyString_Eq(**argname, key))) {
+                        goto arg_passed_twice;
+                    }
+                    argname++;
+                }
+            }
+        } else
+        #endif
+        if (likely(PyUnicode_Check(key))) {
+            while (*name) {
+                int cmp = (**name == key) ? 0 :
+                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
+                #endif
+                    PyUnicode_Compare(**name, key);
+                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                if (cmp == 0) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    int cmp = (**argname == key) ? 0 :
+                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
+                    #endif
+                        PyUnicode_Compare(**argname, key);
+                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                    if (cmp == 0) goto arg_passed_twice;
+                    argname++;
+                }
+            }
+        } else
+            goto invalid_keyword_type;
+        if (kwds2) {
+            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
+        } else {
+            goto invalid_keyword;
+        }
+    }
+    return 0;
+arg_passed_twice:
+    __Pyx_RaiseDoubleKeywordsError(function_name, key);
+    goto bad;
+invalid_keyword_type:
+    PyErr_Format(PyExc_TypeError,
+        "%.200s() keywords must be strings", function_name);
+    goto bad;
+invalid_keyword:
+    PyErr_Format(PyExc_TypeError,
+    #if PY_MAJOR_VERSION < 3
+        "%.200s() got an unexpected keyword argument '%.200s'",
+        function_name, PyString_AsString(key));
+    #else
+        "%s() got an unexpected keyword argument '%U'",
+        function_name, key);
+    #endif
+bad:
+    return -1;
+}
+
+/* None */
+static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
+    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
+}
+
+/* ArgTypeTest */
+static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
+{
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    else if (exact) {
+        #if PY_MAJOR_VERSION == 2
+        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
+        #endif
+    }
+    else {
+        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
+    }
+    PyErr_Format(PyExc_TypeError,
+        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
+        name, type->tp_name, Py_TYPE(obj)->tp_name);
+    return 0;
+}
+
+/* PyObjectCall */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+    PyObject *result;
+    ternaryfunc call = func->ob_type->tp_call;
+    if (unlikely(!call))
+        return PyObject_Call(func, arg, kw);
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+    result = (*call)(func, arg, kw);
+    Py_LeaveRecursiveCall();
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+/* PyErrFetchRestore */
+#if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    tmp_type = tstate->curexc_type;
+    tmp_value = tstate->curexc_value;
+    tmp_tb = tstate->curexc_traceback;
+    tstate->curexc_type = type;
+    tstate->curexc_value = value;
+    tstate->curexc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    *type = tstate->curexc_type;
+    *value = tstate->curexc_value;
+    *tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+}
+#endif
+
+/* RaiseException */
+#if PY_MAJOR_VERSION < 3
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
+                        CYTHON_UNUSED PyObject *cause) {
+    __Pyx_PyThreadState_declare
+    Py_XINCREF(type);
+    if (!value || value == Py_None)
+        value = NULL;
+    else
+        Py_INCREF(value);
+    if (!tb || tb == Py_None)
+        tb = NULL;
+    else {
+        Py_INCREF(tb);
+        if (!PyTraceBack_Check(tb)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: arg 3 must be a traceback or None");
+            goto raise_error;
+        }
+    }
+    if (PyType_Check(type)) {
+#if CYTHON_COMPILING_IN_PYPY
+        if (!value) {
+            Py_INCREF(Py_None);
+            value = Py_None;
+        }
+#endif
+        PyErr_NormalizeException(&type, &value, &tb);
+    } else {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto raise_error;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(type);
+        Py_INCREF(type);
+        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: exception class must be a subclass of BaseException");
+            goto raise_error;
+        }
+    }
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrRestore(type, value, tb);
+    return;
+raise_error:
+    Py_XDECREF(value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
+    return;
+}
+#else
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
+    PyObject* owned_instance = NULL;
+    if (tb == Py_None) {
+        tb = 0;
+    } else if (tb && !PyTraceBack_Check(tb)) {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: arg 3 must be a traceback or None");
+        goto bad;
+    }
+    if (value == Py_None)
+        value = 0;
+    if (PyExceptionInstance_Check(type)) {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto bad;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(value);
+    } else if (PyExceptionClass_Check(type)) {
+        PyObject *instance_class = NULL;
+        if (value && PyExceptionInstance_Check(value)) {
+            instance_class = (PyObject*) Py_TYPE(value);
+            if (instance_class != type) {
+                int is_subclass = PyObject_IsSubclass(instance_class, type);
+                if (!is_subclass) {
+                    instance_class = NULL;
+                } else if (unlikely(is_subclass == -1)) {
+                    goto bad;
+                } else {
+                    type = instance_class;
+                }
+            }
+        }
+        if (!instance_class) {
+            PyObject *args;
+            if (!value)
+                args = PyTuple_New(0);
+            else if (PyTuple_Check(value)) {
+                Py_INCREF(value);
+                args = value;
+            } else
+                args = PyTuple_Pack(1, value);
+            if (!args)
+                goto bad;
+            owned_instance = PyObject_Call(type, args, NULL);
+            Py_DECREF(args);
+            if (!owned_instance)
+                goto bad;
+            value = owned_instance;
+            if (!PyExceptionInstance_Check(value)) {
+                PyErr_Format(PyExc_TypeError,
+                             "calling %R should have returned an instance of "
+                             "BaseException, not %R",
+                             type, Py_TYPE(value));
+                goto bad;
+            }
+        }
+    } else {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: exception class must be a subclass of BaseException");
+        goto bad;
+    }
+    if (cause) {
+        PyObject *fixed_cause;
+        if (cause == Py_None) {
+            fixed_cause = NULL;
+        } else if (PyExceptionClass_Check(cause)) {
+            fixed_cause = PyObject_CallObject(cause, NULL);
+            if (fixed_cause == NULL)
+                goto bad;
+        } else if (PyExceptionInstance_Check(cause)) {
+            fixed_cause = cause;
+            Py_INCREF(fixed_cause);
+        } else {
+            PyErr_SetString(PyExc_TypeError,
+                            "exception causes must derive from "
+                            "BaseException");
+            goto bad;
+        }
+        PyException_SetCause(value, fixed_cause);
+    }
+    PyErr_SetObject(type, value);
+    if (tb) {
+#if CYTHON_COMPILING_IN_PYPY
+        PyObject *tmp_type, *tmp_value, *tmp_tb;
+        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
+        Py_INCREF(tb);
+        PyErr_Restore(tmp_type, tmp_value, tb);
+        Py_XDECREF(tmp_tb);
+#else
+        PyThreadState *tstate = __Pyx_PyThreadState_Current;
+        PyObject* tmp_tb = tstate->curexc_traceback;
+        if (tb != tmp_tb) {
+            Py_INCREF(tb);
+            tstate->curexc_traceback = tb;
+            Py_XDECREF(tmp_tb);
+        }
+#endif
+    }
+bad:
+    Py_XDECREF(owned_instance);
+    return;
+}
+#endif
+
+/* PyCFunctionFastCall */
+#if CYTHON_FAST_PYCCALL
+static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
+    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
+    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
+    PyObject *self = PyCFunction_GET_SELF(func);
+    int flags = PyCFunction_GET_FLAGS(func);
+    assert(PyCFunction_Check(func));
+    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
+    assert(nargs >= 0);
+    assert(nargs == 0 || args != NULL);
+    /* _PyCFunction_FastCallDict() must not be called with an exception set,
+       because it may clear it (directly or indirectly) and so the
+       caller loses its exception */
+    assert(!PyErr_Occurred());
+    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
+        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
+    } else {
+        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
+    }
+}
+#endif
+
+/* PyFunctionFastCall */
+#if CYTHON_FAST_PYCALL
+static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
+                                               PyObject *globals) {
+    PyFrameObject *f;
+    PyThreadState *tstate = __Pyx_PyThreadState_Current;
+    PyObject **fastlocals;
+    Py_ssize_t i;
+    PyObject *result;
+    assert(globals != NULL);
+    /* XXX Perhaps we should create a specialized
+       PyFrame_New() that doesn't take locals, but does
+       take builtins without sanity checking them.
+       */
+    assert(tstate != NULL);
+    f = PyFrame_New(tstate, co, globals, NULL);
+    if (f == NULL) {
+        return NULL;
+    }
+    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
+    for (i = 0; i < na; i++) {
+        Py_INCREF(*args);
+        fastlocals[i] = *args++;
+    }
+    result = PyEval_EvalFrameEx(f,0);
+    ++tstate->recursion_depth;
+    Py_DECREF(f);
+    --tstate->recursion_depth;
+    return result;
+}
+#if 1 || PY_VERSION_HEX < 0x030600B1
+static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
+    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
+    PyObject *globals = PyFunction_GET_GLOBALS(func);
+    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
+    PyObject *closure;
+#if PY_MAJOR_VERSION >= 3
+    PyObject *kwdefs;
+#endif
+    PyObject *kwtuple, **k;
+    PyObject **d;
+    Py_ssize_t nd;
+    Py_ssize_t nk;
+    PyObject *result;
+    assert(kwargs == NULL || PyDict_Check(kwargs));
+    nk = kwargs ? PyDict_Size(kwargs) : 0;
+    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
+        return NULL;
+    }
+    if (
+#if PY_MAJOR_VERSION >= 3
+            co->co_kwonlyargcount == 0 &&
+#endif
+            likely(kwargs == NULL || nk == 0) &&
+            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
+        if (argdefs == NULL && co->co_argcount == nargs) {
+            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
+            goto done;
+        }
+        else if (nargs == 0 && argdefs != NULL
+                 && co->co_argcount == Py_SIZE(argdefs)) {
+            /* function called with no arguments, but all parameters have
+               a default value: use default values as arguments .*/
+            args = &PyTuple_GET_ITEM(argdefs, 0);
+            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
+            goto done;
+        }
+    }
+    if (kwargs != NULL) {
+        Py_ssize_t pos, i;
+        kwtuple = PyTuple_New(2 * nk);
+        if (kwtuple == NULL) {
+            result = NULL;
+            goto done;
+        }
+        k = &PyTuple_GET_ITEM(kwtuple, 0);
+        pos = i = 0;
+        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
+            Py_INCREF(k[i]);
+            Py_INCREF(k[i+1]);
+            i += 2;
+        }
+        nk = i / 2;
+    }
+    else {
+        kwtuple = NULL;
+        k = NULL;
+    }
+    closure = PyFunction_GET_CLOSURE(func);
+#if PY_MAJOR_VERSION >= 3
+    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
+#endif
+    if (argdefs != NULL) {
+        d = &PyTuple_GET_ITEM(argdefs, 0);
+        nd = Py_SIZE(argdefs);
+    }
+    else {
+        d = NULL;
+        nd = 0;
+    }
+#if PY_MAJOR_VERSION >= 3
+    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
+                               args, (int)nargs,
+                               k, (int)nk,
+                               d, (int)nd, kwdefs, closure);
+#else
+    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
+                               args, (int)nargs,
+                               k, (int)nk,
+                               d, (int)nd, closure);
+#endif
+    Py_XDECREF(kwtuple);
+done:
+    Py_LeaveRecursiveCall();
+    return result;
+}
+#endif
+#endif
+
+/* PyObjectCall2Args */
+static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
+    PyObject *args, *result = NULL;
+    #if CYTHON_FAST_PYCALL
+    if (PyFunction_Check(function)) {
+        PyObject *args[2] = {arg1, arg2};
+        return __Pyx_PyFunction_FastCall(function, args, 2);
+    }
+    #endif
+    #if CYTHON_FAST_PYCCALL
+    if (__Pyx_PyFastCFunction_Check(function)) {
+        PyObject *args[2] = {arg1, arg2};
+        return __Pyx_PyCFunction_FastCall(function, args, 2);
+    }
+    #endif
+    args = PyTuple_New(2);
+    if (unlikely(!args)) goto done;
+    Py_INCREF(arg1);
+    PyTuple_SET_ITEM(args, 0, arg1);
+    Py_INCREF(arg2);
+    PyTuple_SET_ITEM(args, 1, arg2);
+    Py_INCREF(function);
+    result = __Pyx_PyObject_Call(function, args, NULL);
+    Py_DECREF(args);
+    Py_DECREF(function);
+done:
+    return result;
+}
+
+/* PyObjectCallMethO */
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
+    PyObject *self, *result;
+    PyCFunction cfunc;
+    cfunc = PyCFunction_GET_FUNCTION(func);
+    self = PyCFunction_GET_SELF(func);
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+    result = cfunc(self, arg);
+    Py_LeaveRecursiveCall();
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+/* PyObjectCallOneArg */
+#if CYTHON_COMPILING_IN_CPYTHON
+static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+    PyObject *result;
+    PyObject *args = PyTuple_New(1);
+    if (unlikely(!args)) return NULL;
+    Py_INCREF(arg);
+    PyTuple_SET_ITEM(args, 0, arg);
+    result = __Pyx_PyObject_Call(func, args, NULL);
+    Py_DECREF(args);
+    return result;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+#if CYTHON_FAST_PYCALL
+    if (PyFunction_Check(func)) {
+        return __Pyx_PyFunction_FastCall(func, &arg, 1);
+    }
+#endif
+    if (likely(PyCFunction_Check(func))) {
+        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
+            return __Pyx_PyObject_CallMethO(func, arg);
+#if CYTHON_FAST_PYCCALL
+        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
+            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
+#endif
+        }
+    }
+    return __Pyx__PyObject_CallOneArg(func, arg);
+}
+#else
+static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
+    PyObject *result;
+    PyObject *args = PyTuple_Pack(1, arg);
+    if (unlikely(!args)) return NULL;
+    result = __Pyx_PyObject_Call(func, args, NULL);
+    Py_DECREF(args);
+    return result;
+}
+#endif
+
+/* BytesEquals */
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+    if (s1 == s2) {
+        return (equals == Py_EQ);
+    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
+        const char *ps1, *ps2;
+        Py_ssize_t length = PyBytes_GET_SIZE(s1);
+        if (length != PyBytes_GET_SIZE(s2))
+            return (equals == Py_NE);
+        ps1 = PyBytes_AS_STRING(s1);
+        ps2 = PyBytes_AS_STRING(s2);
+        if (ps1[0] != ps2[0]) {
+            return (equals == Py_NE);
+        } else if (length == 1) {
+            return (equals == Py_EQ);
+        } else {
+            int result;
+#if CYTHON_USE_UNICODE_INTERNALS
+            Py_hash_t hash1, hash2;
+            hash1 = ((PyBytesObject*)s1)->ob_shash;
+            hash2 = ((PyBytesObject*)s2)->ob_shash;
+            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
+                return (equals == Py_NE);
+            }
+#endif
+            result = memcmp(ps1, ps2, (size_t)length);
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
+        return (equals == Py_NE);
+    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
+        return (equals == Py_NE);
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+#endif
+}
+
+/* UnicodeEquals */
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+#if PY_MAJOR_VERSION < 3
+    PyObject* owned_ref = NULL;
+#endif
+    int s1_is_unicode, s2_is_unicode;
+    if (s1 == s2) {
+        goto return_eq;
+    }
+    s1_is_unicode = PyUnicode_CheckExact(s1);
+    s2_is_unicode = PyUnicode_CheckExact(s2);
+#if PY_MAJOR_VERSION < 3
+    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
+        owned_ref = PyUnicode_FromObject(s2);
+        if (unlikely(!owned_ref))
+            return -1;
+        s2 = owned_ref;
+        s2_is_unicode = 1;
+    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
+        owned_ref = PyUnicode_FromObject(s1);
+        if (unlikely(!owned_ref))
+            return -1;
+        s1 = owned_ref;
+        s1_is_unicode = 1;
+    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
+        return __Pyx_PyBytes_Equals(s1, s2, equals);
+    }
+#endif
+    if (s1_is_unicode & s2_is_unicode) {
+        Py_ssize_t length;
+        int kind;
+        void *data1, *data2;
+        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
+            return -1;
+        length = __Pyx_PyUnicode_GET_LENGTH(s1);
+        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
+            goto return_ne;
+        }
+#if CYTHON_USE_UNICODE_INTERNALS
+        {
+            Py_hash_t hash1, hash2;
+        #if CYTHON_PEP393_ENABLED
+            hash1 = ((PyASCIIObject*)s1)->hash;
+            hash2 = ((PyASCIIObject*)s2)->hash;
+        #else
+            hash1 = ((PyUnicodeObject*)s1)->hash;
+            hash2 = ((PyUnicodeObject*)s2)->hash;
+        #endif
+            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
+                goto return_ne;
+            }
+        }
+#endif
+        kind = __Pyx_PyUnicode_KIND(s1);
+        if (kind != __Pyx_PyUnicode_KIND(s2)) {
+            goto return_ne;
+        }
+        data1 = __Pyx_PyUnicode_DATA(s1);
+        data2 = __Pyx_PyUnicode_DATA(s2);
+        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
+            goto return_ne;
+        } else if (length == 1) {
+            goto return_eq;
+        } else {
+            int result = memcmp(data1, data2, (size_t)(length * kind));
+            #if PY_MAJOR_VERSION < 3
+            Py_XDECREF(owned_ref);
+            #endif
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & s2_is_unicode) {
+        goto return_ne;
+    } else if ((s2 == Py_None) & s1_is_unicode) {
+        goto return_ne;
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        #if PY_MAJOR_VERSION < 3
+        Py_XDECREF(owned_ref);
+        #endif
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+return_eq:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_EQ);
+return_ne:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_NE);
+#endif
+}
+
+/* None */
+static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
+    Py_ssize_t q = a / b;
+    Py_ssize_t r = a - q*b;
+    q -= ((r != 0) & ((r ^ b) < 0));
+    return q;
+}
+
+/* GetAttr */
+static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
+#if CYTHON_USE_TYPE_SLOTS
+#if PY_MAJOR_VERSION >= 3
+    if (likely(PyUnicode_Check(n)))
+#else
+    if (likely(PyString_Check(n)))
+#endif
+        return __Pyx_PyObject_GetAttrStr(o, n);
+#endif
+    return PyObject_GetAttr(o, n);
+}
+
+/* GetItemInt */
+static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+    PyObject *r;
+    if (!j) return NULL;
+    r = PyObject_GetItem(o, j);
+    Py_DECREF(j);
+    return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              CYTHON_NCP_UNUSED int wraparound,
+                                                              CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    Py_ssize_t wrapped_i = i;
+    if (wraparound & unlikely(i < 0)) {
+        wrapped_i += PyList_GET_SIZE(o);
+    }
+    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
+        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              CYTHON_NCP_UNUSED int wraparound,
+                                                              CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
+    Py_ssize_t wrapped_i = i;
+    if (wraparound & unlikely(i < 0)) {
+        wrapped_i += PyTuple_GET_SIZE(o);
+    }
+    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
+        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
+                                                     CYTHON_NCP_UNUSED int wraparound,
+                                                     CYTHON_NCP_UNUSED int boundscheck) {
+#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
+    if (is_list || PyList_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
+            PyObject *r = PyList_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    }
+    else if (PyTuple_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
+            PyObject *r = PyTuple_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    } else {
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_item)) {
+            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+                Py_ssize_t l = m->sq_length(o);
+                if (likely(l >= 0)) {
+                    i += l;
+                } else {
+                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
+                        return NULL;
+                    PyErr_Clear();
+                }
+            }
+            return m->sq_item(o, i);
+        }
+    }
+#else
+    if (is_list || PySequence_Check(o)) {
+        return PySequence_GetItem(o, i);
+    }
+#endif
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+}
+
+/* ObjectGetItem */
+#if CYTHON_USE_TYPE_SLOTS
+static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
+    PyObject *runerr;
+    Py_ssize_t key_value;
+    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
+    if (unlikely(!(m && m->sq_item))) {
+        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
+        return NULL;
+    }
+    key_value = __Pyx_PyIndex_AsSsize_t(index);
+    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
+        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
+    }
+    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
+        PyErr_Clear();
+        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
+    }
+    return NULL;
+}
+static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
+    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
+    if (likely(m && m->mp_subscript)) {
+        return m->mp_subscript(obj, key);
+    }
+    return __Pyx_PyObject_GetIndex(obj, key);
+}
+#endif
+
+/* decode_c_string */
+static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
+         const char* encoding, const char* errors,
+         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
+    Py_ssize_t length;
+    if (unlikely((start < 0) | (stop < 0))) {
+        size_t slen = strlen(cstring);
+        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {
+            PyErr_SetString(PyExc_OverflowError,
+                            "c-string too long to convert to Python");
+            return NULL;
+        }
+        length = (Py_ssize_t) slen;
+        if (start < 0) {
+            start += length;
+            if (start < 0)
+                start = 0;
+        }
+        if (stop < 0)
+            stop += length;
+    }
+    if (unlikely(stop <= start))
+        return __Pyx_NewRef(__pyx_empty_unicode);
+    length = stop - start;
+    cstring += start;
+    if (decode_func) {
+        return decode_func(cstring, length, errors);
+    } else {
+        return PyUnicode_Decode(cstring, length, encoding, errors);
+    }
+}
+
+/* PyErrExceptionMatches */
+#if CYTHON_FAST_THREAD_STATE
+static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+    Py_ssize_t i, n;
+    n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+    for (i=0; i<n; i++) {
+        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
+    }
+#endif
+    for (i=0; i<n; i++) {
+        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
+    }
+    return 0;
+}
+static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
+    PyObject *exc_type = tstate->curexc_type;
+    if (exc_type == err) return 1;
+    if (unlikely(!exc_type)) return 0;
+    if (unlikely(PyTuple_Check(err)))
+        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
+    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
+}
+#endif
+
+/* GetAttr3 */
+static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
+    __Pyx_PyThreadState_declare
+    __Pyx_PyThreadState_assign
+    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
+        return NULL;
+    __Pyx_PyErr_Clear();
+    Py_INCREF(d);
+    return d;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
+    PyObject *r = __Pyx_GetAttr(o, n);
+    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
+}
+
+/* PyDictVersioning */
+#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
+    PyObject *dict = Py_TYPE(obj)->tp_dict;
+    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
+}
+static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
+    PyObject **dictptr = NULL;
+    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
+    if (offset) {
+#if CYTHON_COMPILING_IN_CPYTHON
+        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
+#else
+        dictptr = _PyObject_GetDictPtr(obj);
+#endif
+    }
+    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
+}
+static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
+    PyObject *dict = Py_TYPE(obj)->tp_dict;
+    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
+        return 0;
+    return obj_dict_version == __Pyx_get_object_dict_version(obj);
+}
+#endif
+
+/* GetModuleGlobalName */
+#if CYTHON_USE_DICT_VERSIONS
+static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
+#else
+static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
+#endif
+{
+    PyObject *result;
+#if !CYTHON_AVOID_BORROWED_REFS
+#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
+    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
+    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
+    if (likely(result)) {
+        return __Pyx_NewRef(result);
+    } else if (unlikely(PyErr_Occurred())) {
+        return NULL;
+    }
+#else
+    result = PyDict_GetItem(__pyx_d, name);
+    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
+    if (likely(result)) {
+        return __Pyx_NewRef(result);
+    }
+#endif
+#else
+    result = PyObject_GetItem(__pyx_d, name);
+    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
+    if (likely(result)) {
+        return __Pyx_NewRef(result);
+    }
+    PyErr_Clear();
+#endif
+    return __Pyx_GetBuiltinName(name);
+}
+
+/* RaiseTooManyValuesToUnpack */
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+    PyErr_Format(PyExc_ValueError,
+                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
+}
+
+/* RaiseNeedMoreValuesToUnpack */
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+    PyErr_Format(PyExc_ValueError,
+                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
+                 index, (index == 1) ? "" : "s");
+}
+
+/* RaiseNoneIterError */
+static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
+    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+}
+
+/* ExtTypeTest */
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (likely(__Pyx_TypeCheck(obj, type)))
+        return 1;
+    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
+                 Py_TYPE(obj)->tp_name, type->tp_name);
+    return 0;
+}
+
+/* GetTopmostException */
+#if CYTHON_USE_EXC_INFO_STACK
+static _PyErr_StackItem *
+__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
+{
+    _PyErr_StackItem *exc_info = tstate->exc_info;
+    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
+           exc_info->previous_item != NULL)
+    {
+        exc_info = exc_info->previous_item;
+    }
+    return exc_info;
+}
+#endif
+
+/* SaveResetException */
+#if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    #if CYTHON_USE_EXC_INFO_STACK
+    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
+    *type = exc_info->exc_type;
+    *value = exc_info->exc_value;
+    *tb = exc_info->exc_traceback;
+    #else
+    *type = tstate->exc_type;
+    *value = tstate->exc_value;
+    *tb = tstate->exc_traceback;
+    #endif
+    Py_XINCREF(*type);
+    Py_XINCREF(*value);
+    Py_XINCREF(*tb);
+}
+static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    #if CYTHON_USE_EXC_INFO_STACK
+    _PyErr_StackItem *exc_info = tstate->exc_info;
+    tmp_type = exc_info->exc_type;
+    tmp_value = exc_info->exc_value;
+    tmp_tb = exc_info->exc_traceback;
+    exc_info->exc_type = type;
+    exc_info->exc_value = value;
+    exc_info->exc_traceback = tb;
+    #else
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = type;
+    tstate->exc_value = value;
+    tstate->exc_traceback = tb;
+    #endif
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+}
+#endif
+
+/* GetException */
+#if CYTHON_FAST_THREAD_STATE
+static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
+#else
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
+#endif
+{
+    PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_FAST_THREAD_STATE
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    local_type = tstate->curexc_type;
+    local_value = tstate->curexc_value;
+    local_tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_FAST_THREAD_STATE
+    if (unlikely(tstate->curexc_type))
+#else
+    if (unlikely(PyErr_Occurred()))
+#endif
+        goto bad;
+    #if PY_MAJOR_VERSION >= 3
+    if (local_tb) {
+        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+            goto bad;
+    }
+    #endif
+    Py_XINCREF(local_tb);
+    Py_XINCREF(local_type);
+    Py_XINCREF(local_value);
+    *type = local_type;
+    *value = local_value;
+    *tb = local_tb;
+#if CYTHON_FAST_THREAD_STATE
+    #if CYTHON_USE_EXC_INFO_STACK
+    {
+        _PyErr_StackItem *exc_info = tstate->exc_info;
+        tmp_type = exc_info->exc_type;
+        tmp_value = exc_info->exc_value;
+        tmp_tb = exc_info->exc_traceback;
+        exc_info->exc_type = local_type;
+        exc_info->exc_value = local_value;
+        exc_info->exc_traceback = local_tb;
+    }
+    #else
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = local_type;
+    tstate->exc_value = local_value;
+    tstate->exc_traceback = local_tb;
+    #endif
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+    return 0;
+bad:
+    *type = 0;
+    *value = 0;
+    *tb = 0;
+    Py_XDECREF(local_type);
+    Py_XDECREF(local_value);
+    Py_XDECREF(local_tb);
+    return -1;
+}
+
+/* SwapException */
+#if CYTHON_FAST_THREAD_STATE
+static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    #if CYTHON_USE_EXC_INFO_STACK
+    _PyErr_StackItem *exc_info = tstate->exc_info;
+    tmp_type = exc_info->exc_type;
+    tmp_value = exc_info->exc_value;
+    tmp_tb = exc_info->exc_traceback;
+    exc_info->exc_type = *type;
+    exc_info->exc_value = *value;
+    exc_info->exc_traceback = *tb;
+    #else
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = *type;
+    tstate->exc_value = *value;
+    tstate->exc_traceback = *tb;
+    #endif
+    *type = tmp_type;
+    *value = tmp_value;
+    *tb = tmp_tb;
+}
+#else
+static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
+    PyErr_SetExcInfo(*type, *value, *tb);
+    *type = tmp_type;
+    *value = tmp_value;
+    *tb = tmp_tb;
+}
+#endif
+
+/* Import */
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
+    PyObject *empty_list = 0;
+    PyObject *module = 0;
+    PyObject *global_dict = 0;
+    PyObject *empty_dict = 0;
+    PyObject *list;
+    #if PY_MAJOR_VERSION < 3
+    PyObject *py_import;
+    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
+    if (!py_import)
+        goto bad;
+    #endif
+    if (from_list)
+        list = from_list;
+    else {
+        empty_list = PyList_New(0);
+        if (!empty_list)
+            goto bad;
+        list = empty_list;
+    }
+    global_dict = PyModule_GetDict(__pyx_m);
+    if (!global_dict)
+        goto bad;
+    empty_dict = PyDict_New();
+    if (!empty_dict)
+        goto bad;
+    {
+        #if PY_MAJOR_VERSION >= 3
+        if (level == -1) {
+            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
+                module = PyImport_ImportModuleLevelObject(
+                    name, global_dict, empty_dict, list, 1);
+                if (!module) {
+                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
+                        goto bad;
+                    PyErr_Clear();
+                }
+            }
+            level = 0;
+        }
+        #endif
+        if (!module) {
+            #if PY_MAJOR_VERSION < 3
+            PyObject *py_level = PyInt_FromLong(level);
+            if (!py_level)
+                goto bad;
+            module = PyObject_CallFunctionObjArgs(py_import,
+                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
+            Py_DECREF(py_level);
+            #else
+            module = PyImport_ImportModuleLevelObject(
+                name, global_dict, empty_dict, list, level);
+            #endif
+        }
+    }
+bad:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(py_import);
+    #endif
+    Py_XDECREF(empty_list);
+    Py_XDECREF(empty_dict);
+    return module;
+}
+
+/* FastTypeChecks */
+#if CYTHON_COMPILING_IN_CPYTHON
+static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
+    while (a) {
+        a = a->tp_base;
+        if (a == b)
+            return 1;
+    }
+    return b == &PyBaseObject_Type;
+}
+static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
+    PyObject *mro;
+    if (a == b) return 1;
+    mro = a->tp_mro;
+    if (likely(mro)) {
+        Py_ssize_t i, n;
+        n = PyTuple_GET_SIZE(mro);
+        for (i = 0; i < n; i++) {
+            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
+                return 1;
+        }
+        return 0;
+    }
+    return __Pyx_InBases(a, b);
+}
+#if PY_MAJOR_VERSION == 2
+static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
+    PyObject *exception, *value, *tb;
+    int res;
+    __Pyx_PyThreadState_declare
+    __Pyx_PyThreadState_assign
+    __Pyx_ErrFetch(&exception, &value, &tb);
+    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
+    if (unlikely(res == -1)) {
+        PyErr_WriteUnraisable(err);
+        res = 0;
+    }
+    if (!res) {
+        res = PyObject_IsSubclass(err, exc_type2);
+        if (unlikely(res == -1)) {
+            PyErr_WriteUnraisable(err);
+            res = 0;
+        }
+    }
+    __Pyx_ErrRestore(exception, value, tb);
+    return res;
+}
+#else
+static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
+    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
+    if (!res) {
+        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
+    }
+    return res;
+}
+#endif
+static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
+    Py_ssize_t i, n;
+    assert(PyExceptionClass_Check(exc_type));
+    n = PyTuple_GET_SIZE(tuple);
+#if PY_MAJOR_VERSION >= 3
+    for (i=0; i<n; i++) {
+        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
+    }
+#endif
+    for (i=0; i<n; i++) {
+        PyObject *t = PyTuple_GET_ITEM(tuple, i);
+        #if PY_MAJOR_VERSION < 3
+        if (likely(exc_type == t)) return 1;
+        #endif
+        if (likely(PyExceptionClass_Check(t))) {
+            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
+        } else {
+        }
+    }
+    return 0;
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
+    if (likely(err == exc_type)) return 1;
+    if (likely(PyExceptionClass_Check(err))) {
+        if (likely(PyExceptionClass_Check(exc_type))) {
+            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
+        } else if (likely(PyTuple_Check(exc_type))) {
+            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
+        } else {
+        }
+    }
+    return PyErr_GivenExceptionMatches(err, exc_type);
+}
+static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
+    assert(PyExceptionClass_Check(exc_type1));
+    assert(PyExceptionClass_Check(exc_type2));
+    if (likely(err == exc_type1 || err == exc_type2)) return 1;
+    if (likely(PyExceptionClass_Check(err))) {
+        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
+    }
+    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
+}
+#endif
+
+/* PyIntBinop */
+#if !CYTHON_COMPILING_IN_PYPY
+static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
+    (void)inplace;
+    (void)zerodivision_check;
+    #if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_CheckExact(op1))) {
+        const long b = intval;
+        long x;
+        long a = PyInt_AS_LONG(op1);
+            x = (long)((unsigned long)a + b);
+            if (likely((x^a) >= 0 || (x^b) >= 0))
+                return PyInt_FromLong(x);
+            return PyLong_Type.tp_as_number->nb_add(op1, op2);
+    }
+    #endif
+    #if CYTHON_USE_PYLONG_INTERNALS
+    if (likely(PyLong_CheckExact(op1))) {
+        const long b = intval;
+        long a, x;
+#ifdef HAVE_LONG_LONG
+        const PY_LONG_LONG llb = intval;
+        PY_LONG_LONG lla, llx;
+#endif
+        const digit* digits = ((PyLongObject*)op1)->ob_digit;
+        const Py_ssize_t size = Py_SIZE(op1);
+        if (likely(__Pyx_sst_abs(size) <= 1)) {
+            a = likely(size) ? digits[0] : 0;
+            if (size == -1) a = -a;
+        } else {
+            switch (size) {
+                case -2:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
+                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                    CYTHON_FALLTHROUGH;
+                case 2:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
+                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                    CYTHON_FALLTHROUGH;
+                case -3:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
+                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                    CYTHON_FALLTHROUGH;
+                case 3:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
+                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                    CYTHON_FALLTHROUGH;
+                case -4:
+                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
+                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                    CYTHON_FALLTHROUGH;
+                case 4:
+                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
+                        break;
+#ifdef HAVE_LONG_LONG
+                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
+                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
+                        goto long_long;
+#endif
+                    }
+                    CYTHON_FALLTHROUGH;
+                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
+            }
+        }
+                x = a + b;
+            return PyLong_FromLong(x);
+#ifdef HAVE_LONG_LONG
+        long_long:
+                llx = lla + llb;
+            return PyLong_FromLongLong(llx);
+#endif
+        
+        
+    }
+    #endif
+    if (PyFloat_CheckExact(op1)) {
+        const long b = intval;
+        double a = PyFloat_AS_DOUBLE(op1);
+            double result;
+            PyFPE_START_PROTECT("add", return NULL)
+            result = ((double)a) + (double)b;
+            PyFPE_END_PROTECT(result)
+            return PyFloat_FromDouble(result);
+    }
+    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
+}
+#endif
+
+/* None */
+static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
+    long q = a / b;
+    long r = a - q*b;
+    q -= ((r != 0) & ((r ^ b) < 0));
+    return q;
+}
+
+/* ImportFrom */
+static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
+    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
+    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
+        PyErr_Format(PyExc_ImportError,
+        #if PY_MAJOR_VERSION < 3
+            "cannot import name %.230s", PyString_AS_STRING(name));
+        #else
+            "cannot import name %S", name);
+        #endif
+    }
+    return value;
+}
+
+/* HasAttr */
+static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
+    PyObject *r;
+    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
+        PyErr_SetString(PyExc_TypeError,
+                        "hasattr(): attribute name must be string");
+        return -1;
+    }
+    r = __Pyx_GetAttr(o, n);
+    if (unlikely(!r)) {
+        PyErr_Clear();
+        return 0;
+    } else {
+        Py_DECREF(r);
+        return 1;
+    }
+}
+
+/* PyObject_GenericGetAttrNoDict */
+#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
+    PyErr_Format(PyExc_AttributeError,
+#if PY_MAJOR_VERSION >= 3
+                 "'%.50s' object has no attribute '%U'",
+                 tp->tp_name, attr_name);
+#else
+                 "'%.50s' object has no attribute '%.400s'",
+                 tp->tp_name, PyString_AS_STRING(attr_name));
+#endif
+    return NULL;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
+    PyObject *descr;
+    PyTypeObject *tp = Py_TYPE(obj);
+    if (unlikely(!PyString_Check(attr_name))) {
+        return PyObject_GenericGetAttr(obj, attr_name);
+    }
+    assert(!tp->tp_dictoffset);
+    descr = _PyType_Lookup(tp, attr_name);
+    if (unlikely(!descr)) {
+        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
+    }
+    Py_INCREF(descr);
+    #if PY_MAJOR_VERSION < 3
+    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
+    #endif
+    {
+        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
+        if (unlikely(f)) {
+            PyObject *res = f(descr, obj, (PyObject *)tp);
+            Py_DECREF(descr);
+            return res;
+        }
+    }
+    return descr;
+}
+#endif
+
+/* PyObject_GenericGetAttr */
+#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
+static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
+    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
+        return PyObject_GenericGetAttr(obj, attr_name);
+    }
+    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
+}
+#endif
+
+/* SetVTable */
+static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
+#if PY_VERSION_HEX >= 0x02070000
+    PyObject *ob = PyCapsule_New(vtable, 0, 0);
+#else
+    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
+#endif
+    if (!ob)
+        goto bad;
+    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
+        goto bad;
+    Py_DECREF(ob);
+    return 0;
+bad:
+    Py_XDECREF(ob);
+    return -1;
+}
+
+/* PyObjectGetAttrStrNoError */
+static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
+    __Pyx_PyThreadState_declare
+    __Pyx_PyThreadState_assign
+    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
+        __Pyx_PyErr_Clear();
+}
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
+    PyObject *result;
+#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
+        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);
+    }
+#endif
+    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);
+    if (unlikely(!result)) {
+        __Pyx_PyObject_GetAttrStr_ClearAttributeError();
+    }
+    return result;
+}
+
+/* SetupReduce */
+static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
+  int ret;
+  PyObject *name_attr;
+  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);
+  if (likely(name_attr)) {
+      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
+  } else {
+      ret = -1;
+  }
+  if (unlikely(ret < 0)) {
+      PyErr_Clear();
+      ret = 0;
+  }
+  Py_XDECREF(name_attr);
+  return ret;
+}
+static int __Pyx_setup_reduce(PyObject* type_obj) {
+    int ret = 0;
+    PyObject *object_reduce = NULL;
+    PyObject *object_reduce_ex = NULL;
+    PyObject *reduce = NULL;
+    PyObject *reduce_ex = NULL;
+    PyObject *reduce_cython = NULL;
+    PyObject *setstate = NULL;
+    PyObject *setstate_cython = NULL;
+#if CYTHON_USE_PYTYPE_LOOKUP
+    if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
+#else
+    if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
+#endif
+#if CYTHON_USE_PYTYPE_LOOKUP
+    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
+#else
+    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
+#endif
+    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
+    if (reduce_ex == object_reduce_ex) {
+#if CYTHON_USE_PYTYPE_LOOKUP
+        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
+#else
+        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
+#endif
+        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
+        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
+            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
+            if (likely(reduce_cython)) {
+                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
+                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
+            } else if (reduce == object_reduce || PyErr_Occurred()) {
+                goto __PYX_BAD;
+            }
+            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
+            if (!setstate) PyErr_Clear();
+            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
+                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
+                if (likely(setstate_cython)) {
+                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
+                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
+                } else if (!setstate || PyErr_Occurred()) {
+                    goto __PYX_BAD;
+                }
+            }
+            PyType_Modified((PyTypeObject*)type_obj);
+        }
+    }
+    goto __PYX_GOOD;
+__PYX_BAD:
+    if (!PyErr_Occurred())
+        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
+    ret = -1;
+__PYX_GOOD:
+#if !CYTHON_USE_PYTYPE_LOOKUP
+    Py_XDECREF(object_reduce);
+    Py_XDECREF(object_reduce_ex);
+#endif
+    Py_XDECREF(reduce);
+    Py_XDECREF(reduce_ex);
+    Py_XDECREF(reduce_cython);
+    Py_XDECREF(setstate);
+    Py_XDECREF(setstate_cython);
+    return ret;
+}
+
+/* CLineInTraceback */
+#ifndef CYTHON_CLINE_IN_TRACEBACK
+static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
+    PyObject *use_cline;
+    PyObject *ptype, *pvalue, *ptraceback;
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject **cython_runtime_dict;
+#endif
+    if (unlikely(!__pyx_cython_runtime)) {
+        return c_line;
+    }
+    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
+#if CYTHON_COMPILING_IN_CPYTHON
+    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
+    if (likely(cython_runtime_dict)) {
+        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
+            use_cline, *cython_runtime_dict,
+            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
+    } else
+#endif
+    {
+      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
+      if (use_cline_obj) {
+        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
+        Py_DECREF(use_cline_obj);
+      } else {
+        PyErr_Clear();
+        use_cline = NULL;
+      }
+    }
+    if (!use_cline) {
+        c_line = 0;
+        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
+    }
+    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
+        c_line = 0;
+    }
+    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
+    return c_line;
+}
+#endif
+
+/* CodeObjectCache */
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+    int start = 0, mid = 0, end = count - 1;
+    if (end >= 0 && code_line > entries[end].code_line) {
+        return count;
+    }
+    while (start < end) {
+        mid = start + (end - start) / 2;
+        if (code_line < entries[mid].code_line) {
+            end = mid;
+        } else if (code_line > entries[mid].code_line) {
+             start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    if (code_line <= entries[mid].code_line) {
+        return mid;
+    } else {
+        return mid + 1;
+    }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+    PyCodeObject* code_object;
+    int pos;
+    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+        return NULL;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+        return NULL;
+    }
+    code_object = __pyx_code_cache.entries[pos].code_object;
+    Py_INCREF(code_object);
+    return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+    int pos, i;
+    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+    if (unlikely(!code_line)) {
+        return;
+    }
+    if (unlikely(!entries)) {
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (likely(entries)) {
+            __pyx_code_cache.entries = entries;
+            __pyx_code_cache.max_count = 64;
+            __pyx_code_cache.count = 1;
+            entries[0].code_line = code_line;
+            entries[0].code_object = code_object;
+            Py_INCREF(code_object);
+        }
+        return;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+        PyCodeObject* tmp = entries[pos].code_object;
+        entries[pos].code_object = code_object;
+        Py_DECREF(tmp);
+        return;
+    }
+    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+        int new_max = __pyx_code_cache.max_count + 64;
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
+        if (unlikely(!entries)) {
+            return;
+        }
+        __pyx_code_cache.entries = entries;
+        __pyx_code_cache.max_count = new_max;
+    }
+    for (i=__pyx_code_cache.count; i>pos; i--) {
+        entries[i] = entries[i-1];
+    }
+    entries[pos].code_line = code_line;
+    entries[pos].code_object = code_object;
+    __pyx_code_cache.count++;
+    Py_INCREF(code_object);
+}
+
+/* AddTraceback */
+#include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+            const char *funcname, int c_line,
+            int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    #if PY_MAJOR_VERSION < 3
+    py_srcfile = PyString_FromString(filename);
+    #else
+    py_srcfile = PyUnicode_FromString(filename);
+    #endif
+    if (!py_srcfile) goto bad;
+    if (c_line) {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #else
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #endif
+    }
+    else {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromString(funcname);
+        #else
+        py_funcname = PyUnicode_FromString(funcname);
+        #endif
+    }
+    if (!py_funcname) goto bad;
+    py_code = __Pyx_PyCode_New(
+        0,
+        0,
+        0,
+        0,
+        0,
+        __pyx_empty_bytes, /*PyObject *code,*/
+        __pyx_empty_tuple, /*PyObject *consts,*/
+        __pyx_empty_tuple, /*PyObject *names,*/
+        __pyx_empty_tuple, /*PyObject *varnames,*/
+        __pyx_empty_tuple, /*PyObject *freevars,*/
+        __pyx_empty_tuple, /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        py_line,
+        __pyx_empty_bytes  /*PyObject *lnotab*/
+    );
+    Py_DECREF(py_srcfile);
+    Py_DECREF(py_funcname);
+    return py_code;
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyFrameObject *py_frame = 0;
+    PyThreadState *tstate = __Pyx_PyThreadState_Current;
+    if (c_line) {
+        c_line = __Pyx_CLineForTraceback(tstate, c_line);
+    }
+    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
+    if (!py_code) {
+        py_code = __Pyx_CreateCodeObjectForTraceback(
+            funcname, c_line, py_line, filename);
+        if (!py_code) goto bad;
+        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
+    }
+    py_frame = PyFrame_New(
+        tstate,            /*PyThreadState *tstate,*/
+        py_code,           /*PyCodeObject *code,*/
+        __pyx_d,    /*PyObject *globals,*/
+        0                  /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
+
+#if PY_MAJOR_VERSION < 3
+static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
+    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
+        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
+        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
+    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
+    return -1;
+}
+static void __Pyx_ReleaseBuffer(Py_buffer *view) {
+    PyObject *obj = view->obj;
+    if (!obj) return;
+    if (PyObject_CheckBuffer(obj)) {
+        PyBuffer_Release(view);
+        return;
+    }
+    if ((0)) {}
+    view->obj = NULL;
+    Py_DECREF(obj);
+}
+#endif
+
+
+/* MemviewSliceIsContig */
+static int
+__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
+{
+    int i, index, step, start;
+    Py_ssize_t itemsize = mvs.memview->view.itemsize;
+    if (order == 'F') {
+        step = 1;
+        start = 0;
+    } else {
+        step = -1;
+        start = ndim - 1;
+    }
+    for (i = 0; i < ndim; i++) {
+        index = start + step * i;
+        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
+            return 0;
+        itemsize *= mvs.shape[index];
+    }
+    return 1;
+}
+
+/* OverlappingSlices */
+static void
+__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
+                               void **out_start, void **out_end,
+                               int ndim, size_t itemsize)
+{
+    char *start, *end;
+    int i;
+    start = end = slice->data;
+    for (i = 0; i < ndim; i++) {
+        Py_ssize_t stride = slice->strides[i];
+        Py_ssize_t extent = slice->shape[i];
+        if (extent == 0) {
+            *out_start = *out_end = start;
+            return;
+        } else {
+            if (stride > 0)
+                end += stride * (extent - 1);
+            else
+                start += stride * (extent - 1);
+        }
+    }
+    *out_start = start;
+    *out_end = end + itemsize;
+}
+static int
+__pyx_slices_overlap(__Pyx_memviewslice *slice1,
+                     __Pyx_memviewslice *slice2,
+                     int ndim, size_t itemsize)
+{
+    void *start1, *end1, *start2, *end2;
+    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
+    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
+    return (start1 < end2) && (start2 < end1);
+}
+
+/* Capsule */
+static CYTHON_INLINE PyObject *
+__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
+{
+    PyObject *cobj;
+#if PY_VERSION_HEX >= 0x02070000
+    cobj = PyCapsule_New(p, sig, NULL);
+#else
+    cobj = PyCObject_FromVoidPtr(p, NULL);
+#endif
+    return cobj;
+}
+
+/* IsLittleEndian */
+static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
+{
+  union {
+    uint32_t u32;
+    uint8_t u8[4];
+  } S;
+  S.u32 = 0x01020304;
+  return S.u8[0] == 4;
+}
+
+/* BufferFormatCheck */
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type) {
+  stack[0].field = &ctx->root;
+  stack[0].parent_offset = 0;
+  ctx->root.type = type;
+  ctx->root.name = "buffer dtype";
+  ctx->root.offset = 0;
+  ctx->head = stack;
+  ctx->head->field = &ctx->root;
+  ctx->fmt_offset = 0;
+  ctx->head->parent_offset = 0;
+  ctx->new_packmode = '@';
+  ctx->enc_packmode = '@';
+  ctx->new_count = 1;
+  ctx->enc_count = 0;
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  ctx->is_valid_array = 0;
+  ctx->struct_alignment = 0;
+  while (type->typegroup == 'S') {
+    ++ctx->head;
+    ctx->head->field = type->fields;
+    ctx->head->parent_offset = 0;
+    type = type->fields->type;
+  }
+}
+static int __Pyx_BufFmt_ParseNumber(const char** ts) {
+    int count;
+    const char* t = *ts;
+    if (*t < '0' || *t > '9') {
+      return -1;
+    } else {
+        count = *t++ - '0';
+        while (*t >= '0' && *t <= '9') {
+            count *= 10;
+            count += *t++ - '0';
+        }
+    }
+    *ts = t;
+    return count;
+}
+static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
+    int number = __Pyx_BufFmt_ParseNumber(ts);
+    if (number == -1)
+        PyErr_Format(PyExc_ValueError,\
+                     "Does not understand character buffer dtype format string ('%c')", **ts);
+    return number;
+}
+static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
+  PyErr_Format(PyExc_ValueError,
+               "Unexpected format string character: '%c'", ch);
+}
+static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
+  switch (ch) {
+    case '?': return "'bool'";
+    case 'c': return "'char'";
+    case 'b': return "'signed char'";
+    case 'B': return "'unsigned char'";
+    case 'h': return "'short'";
+    case 'H': return "'unsigned short'";
+    case 'i': return "'int'";
+    case 'I': return "'unsigned int'";
+    case 'l': return "'long'";
+    case 'L': return "'unsigned long'";
+    case 'q': return "'long long'";
+    case 'Q': return "'unsigned long long'";
+    case 'f': return (is_complex ? "'complex float'" : "'float'");
+    case 'd': return (is_complex ? "'complex double'" : "'double'");
+    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
+    case 'T': return "a struct";
+    case 'O': return "Python object";
+    case 'P': return "a pointer";
+    case 's': case 'p': return "a string";
+    case 0: return "end";
+    default: return "unparseable format string";
+  }
+}
+static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return 2;
+    case 'i': case 'I': case 'l': case 'L': return 4;
+    case 'q': case 'Q': return 8;
+    case 'f': return (is_complex ? 8 : 4);
+    case 'd': return (is_complex ? 16 : 8);
+    case 'g': {
+      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
+      return 0;
+    }
+    case 'O': case 'P': return sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(short);
+    case 'i': case 'I': return sizeof(int);
+    case 'l': case 'L': return sizeof(long);
+    #ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
+    #endif
+    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
+    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
+    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
+    case 'O': case 'P': return sizeof(void*);
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+typedef struct { char c; short x; } __Pyx_st_short;
+typedef struct { char c; int x; } __Pyx_st_int;
+typedef struct { char c; long x; } __Pyx_st_long;
+typedef struct { char c; float x; } __Pyx_st_float;
+typedef struct { char c; double x; } __Pyx_st_double;
+typedef struct { char c; long double x; } __Pyx_st_longdouble;
+typedef struct { char c; void *x; } __Pyx_st_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+/* These are for computing the padding at the end of the struct to align
+   on the first member of the struct. This will probably the same as above,
+   but we don't have any guarantees.
+ */
+typedef struct { short x; char c; } __Pyx_pad_short;
+typedef struct { int x; char c; } __Pyx_pad_int;
+typedef struct { long x; char c; } __Pyx_pad_long;
+typedef struct { float x; char c; } __Pyx_pad_float;
+typedef struct { double x; char c; } __Pyx_pad_double;
+typedef struct { long double x; char c; } __Pyx_pad_longdouble;
+typedef struct { void *x; char c; } __Pyx_pad_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
+  switch (ch) {
+    case 'c':
+        return 'H';
+    case 'b': case 'h': case 'i':
+    case 'l': case 'q': case 's': case 'p':
+        return 'I';
+    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
+        return 'U';
+    case 'f': case 'd': case 'g':
+        return (is_complex ? 'C' : 'R');
+    case 'O':
+        return 'O';
+    case 'P':
+        return 'P';
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
+  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
+    const char* expected;
+    const char* quote;
+    if (ctx->head == NULL) {
+      expected = "end";
+      quote = "";
+    } else {
+      expected = ctx->head->field->type->name;
+      quote = "'";
+    }
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected %s%s%s but got %s",
+                 quote, expected, quote,
+                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
+  } else {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_StructField* parent = (ctx->head - 1)->field;
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
+                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
+                 parent->type->name, field->name);
+  }
+}
+static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
+  char group;
+  size_t size, offset, arraysize = 1;
+  if (ctx->enc_type == 0) return 0;
+  if (ctx->head->field->type->arraysize[0]) {
+    int i, ndim = 0;
+    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
+        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
+        ndim = 1;
+        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
+            PyErr_Format(PyExc_ValueError,
+                         "Expected a dimension of size %zu, got %zu",
+                         ctx->head->field->type->arraysize[0], ctx->enc_count);
+            return -1;
+        }
+    }
+    if (!ctx->is_valid_array) {
+      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
+                   ctx->head->field->type->ndim, ndim);
+      return -1;
+    }
+    for (i = 0; i < ctx->head->field->type->ndim; i++) {
+      arraysize *= ctx->head->field->type->arraysize[i];
+    }
+    ctx->is_valid_array = 0;
+    ctx->enc_count = 1;
+  }
+  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
+  do {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_TypeInfo* type = field->type;
+    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
+      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
+    } else {
+      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
+    }
+    if (ctx->enc_packmode == '@') {
+      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
+      size_t align_mod_offset;
+      if (align_at == 0) return -1;
+      align_mod_offset = ctx->fmt_offset % align_at;
+      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
+      if (ctx->struct_alignment == 0)
+          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
+                                                                 ctx->is_complex);
+    }
+    if (type->size != size || type->typegroup != group) {
+      if (type->typegroup == 'C' && type->fields != NULL) {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        ++ctx->head;
+        ctx->head->field = type->fields;
+        ctx->head->parent_offset = parent_offset;
+        continue;
+      }
+      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
+      } else {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+      }
+    }
+    offset = ctx->head->parent_offset + field->offset;
+    if (ctx->fmt_offset != offset) {
+      PyErr_Format(PyExc_ValueError,
+                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
+                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
+      return -1;
+    }
+    ctx->fmt_offset += size;
+    if (arraysize)
+      ctx->fmt_offset += (arraysize - 1) * size;
+    --ctx->enc_count;
+    while (1) {
+      if (field == &ctx->root) {
+        ctx->head = NULL;
+        if (ctx->enc_count != 0) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+        }
+        break;
+      }
+      ctx->head->field = ++field;
+      if (field->type == NULL) {
+        --ctx->head;
+        field = ctx->head->field;
+        continue;
+      } else if (field->type->typegroup == 'S') {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        if (field->type->fields->type == NULL) continue;
+        field = field->type->fields;
+        ++ctx->head;
+        ctx->head->field = field;
+        ctx->head->parent_offset = parent_offset;
+        break;
+      } else {
+        break;
+      }
+    }
+  } while (ctx->enc_count);
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  return 0;
+}
+static PyObject *
+__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
+{
+    const char *ts = *tsp;
+    int i = 0, number, ndim;
+    ++ts;
+    if (ctx->new_count != 1) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Cannot handle repeated arrays in format string");
+        return NULL;
+    }
+    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+    ndim = ctx->head->field->type->ndim;
+    while (*ts && *ts != ')') {
+        switch (*ts) {
+            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
+            default:  break;
+        }
+        number = __Pyx_BufFmt_ExpectNumber(&ts);
+        if (number == -1) return NULL;
+        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
+            return PyErr_Format(PyExc_ValueError,
+                        "Expected a dimension of size %zu, got %d",
+                        ctx->head->field->type->arraysize[i], number);
+        if (*ts != ',' && *ts != ')')
+            return PyErr_Format(PyExc_ValueError,
+                                "Expected a comma in format string, got '%c'", *ts);
+        if (*ts == ',') ts++;
+        i++;
+    }
+    if (i != ndim)
+        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
+                            ctx->head->field->type->ndim, i);
+    if (!*ts) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Unexpected end of format string, expected ')'");
+        return NULL;
+    }
+    ctx->is_valid_array = 1;
+    ctx->new_count = 1;
+    *tsp = ++ts;
+    return Py_None;
+}
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
+  int got_Z = 0;
+  while (1) {
+    switch(*ts) {
+      case 0:
+        if (ctx->enc_type != 0 && ctx->head == NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        if (ctx->head != NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        return ts;
+      case ' ':
+      case '\r':
+      case '\n':
+        ++ts;
+        break;
+      case '<':
+        if (!__Pyx_Is_Little_Endian()) {
+          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '>':
+      case '!':
+        if (__Pyx_Is_Little_Endian()) {
+          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '=':
+      case '@':
+      case '^':
+        ctx->new_packmode = *ts++;
+        break;
+      case 'T':
+        {
+          const char* ts_after_sub;
+          size_t i, struct_count = ctx->new_count;
+          size_t struct_alignment = ctx->struct_alignment;
+          ctx->new_count = 1;
+          ++ts;
+          if (*ts != '{') {
+            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
+            return NULL;
+          }
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          ctx->enc_count = 0;
+          ctx->struct_alignment = 0;
+          ++ts;
+          ts_after_sub = ts;
+          for (i = 0; i != struct_count; ++i) {
+            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
+            if (!ts_after_sub) return NULL;
+          }
+          ts = ts_after_sub;
+          if (struct_alignment) ctx->struct_alignment = struct_alignment;
+        }
+        break;
+      case '}':
+        {
+          size_t alignment = ctx->struct_alignment;
+          ++ts;
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0;
+          if (alignment && ctx->fmt_offset % alignment) {
+            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
+          }
+        }
+        return ts;
+      case 'x':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->fmt_offset += ctx->new_count;
+        ctx->new_count = 1;
+        ctx->enc_count = 0;
+        ctx->enc_type = 0;
+        ctx->enc_packmode = ctx->new_packmode;
+        ++ts;
+        break;
+      case 'Z':
+        got_Z = 1;
+        ++ts;
+        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
+          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
+          return NULL;
+        }
+        CYTHON_FALLTHROUGH;
+      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
+      case 'l': case 'L': case 'q': case 'Q':
+      case 'f': case 'd': case 'g':
+      case 'O': case 'p':
+        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
+            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
+          ctx->enc_count += ctx->new_count;
+          ctx->new_count = 1;
+          got_Z = 0;
+          ++ts;
+          break;
+        }
+        CYTHON_FALLTHROUGH;
+      case 's':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->enc_count = ctx->new_count;
+        ctx->enc_packmode = ctx->new_packmode;
+        ctx->enc_type = *ts;
+        ctx->is_complex = got_Z;
+        ++ts;
+        ctx->new_count = 1;
+        got_Z = 0;
+        break;
+      case ':':
+        ++ts;
+        while(*ts != ':') ++ts;
+        ++ts;
+        break;
+      case '(':
+        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
+        break;
+      default:
+        {
+          int number = __Pyx_BufFmt_ExpectNumber(&ts);
+          if (number == -1) return NULL;
+          ctx->new_count = (size_t)number;
+        }
+    }
+  }
+}
+
+/* TypeInfoCompare */
+  static int
+__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
+{
+    int i;
+    if (!a || !b)
+        return 0;
+    if (a == b)
+        return 1;
+    if (a->size != b->size || a->typegroup != b->typegroup ||
+            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
+        if (a->typegroup == 'H' || b->typegroup == 'H') {
+            return a->size == b->size;
+        } else {
+            return 0;
+        }
+    }
+    if (a->ndim) {
+        for (i = 0; i < a->ndim; i++)
+            if (a->arraysize[i] != b->arraysize[i])
+                return 0;
+    }
+    if (a->typegroup == 'S') {
+        if (a->flags != b->flags)
+            return 0;
+        if (a->fields || b->fields) {
+            if (!(a->fields && b->fields))
+                return 0;
+            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
+                __Pyx_StructField *field_a = a->fields + i;
+                __Pyx_StructField *field_b = b->fields + i;
+                if (field_a->offset != field_b->offset ||
+                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
+                    return 0;
+            }
+            return !a->fields[i].type && !b->fields[i].type;
+        }
+    }
+    return 1;
+}
+
+/* MemviewSliceValidateAndInit */
+  static int
+__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
+{
+    if (buf->shape[dim] <= 1)
+        return 1;
+    if (buf->strides) {
+        if (spec & __Pyx_MEMVIEW_CONTIG) {
+            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
+                if (unlikely(buf->strides[dim] != sizeof(void *))) {
+                    PyErr_Format(PyExc_ValueError,
+                                 "Buffer is not indirectly contiguous "
+                                 "in dimension %d.", dim);
+                    goto fail;
+                }
+            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
+                PyErr_SetString(PyExc_ValueError,
+                                "Buffer and memoryview are not contiguous "
+                                "in the same dimension.");
+                goto fail;
+            }
+        }
+        if (spec & __Pyx_MEMVIEW_FOLLOW) {
+            Py_ssize_t stride = buf->strides[dim];
+            if (stride < 0)
+                stride = -stride;
+            if (unlikely(stride < buf->itemsize)) {
+                PyErr_SetString(PyExc_ValueError,
+                                "Buffer and memoryview are not contiguous "
+                                "in the same dimension.");
+                goto fail;
+            }
+        }
+    } else {
+        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
+            PyErr_Format(PyExc_ValueError,
+                         "C-contiguous buffer is not contiguous in "
+                         "dimension %d", dim);
+            goto fail;
+        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
+            PyErr_Format(PyExc_ValueError,
+                         "C-contiguous buffer is not indirect in "
+                         "dimension %d", dim);
+            goto fail;
+        } else if (unlikely(buf->suboffsets)) {
+            PyErr_SetString(PyExc_ValueError,
+                            "Buffer exposes suboffsets but no strides");
+            goto fail;
+        }
+    }
+    return 1;
+fail:
+    return 0;
+}
+static int
+__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
+{
+    if (spec & __Pyx_MEMVIEW_DIRECT) {
+        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
+            PyErr_Format(PyExc_ValueError,
+                         "Buffer not compatible with direct access "
+                         "in dimension %d.", dim);
+            goto fail;
+        }
+    }
+    if (spec & __Pyx_MEMVIEW_PTR) {
+        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
+            PyErr_Format(PyExc_ValueError,
+                         "Buffer is not indirectly accessible "
+                         "in dimension %d.", dim);
+            goto fail;
+        }
+    }
+    return 1;
+fail:
+    return 0;
+}
+static int
+__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
+{
+    int i;
+    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
+        Py_ssize_t stride = 1;
+        for (i = 0; i < ndim; i++) {
+            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
+                PyErr_SetString(PyExc_ValueError,
+                    "Buffer not fortran contiguous.");
+                goto fail;
+            }
+            stride = stride * buf->shape[i];
+        }
+    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
+        Py_ssize_t stride = 1;
+        for (i = ndim - 1; i >- 1; i--) {
+            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
+                PyErr_SetString(PyExc_ValueError,
+                    "Buffer not C contiguous.");
+                goto fail;
+            }
+            stride = stride * buf->shape[i];
+        }
+    }
+    return 1;
+fail:
+    return 0;
+}
+static int __Pyx_ValidateAndInit_memviewslice(
+                int *axes_specs,
+                int c_or_f_flag,
+                int buf_flags,
+                int ndim,
+                __Pyx_TypeInfo *dtype,
+                __Pyx_BufFmt_StackElem stack[],
+                __Pyx_memviewslice *memviewslice,
+                PyObject *original_obj)
+{
+    struct __pyx_memoryview_obj *memview, *new_memview;
+    __Pyx_RefNannyDeclarations
+    Py_buffer *buf;
+    int i, spec = 0, retval = -1;
+    __Pyx_BufFmt_Context ctx;
+    int from_memoryview = __pyx_memoryview_check(original_obj);
+    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
+    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
+                                                            original_obj)->typeinfo)) {
+        memview = (struct __pyx_memoryview_obj *) original_obj;
+        new_memview = NULL;
+    } else {
+        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
+                                            original_obj, buf_flags, 0, dtype);
+        new_memview = memview;
+        if (unlikely(!memview))
+            goto fail;
+    }
+    buf = &memview->view;
+    if (unlikely(buf->ndim != ndim)) {
+        PyErr_Format(PyExc_ValueError,
+                "Buffer has wrong number of dimensions (expected %d, got %d)",
+                ndim, buf->ndim);
+        goto fail;
+    }
+    if (new_memview) {
+        __Pyx_BufFmt_Init(&ctx, stack, dtype);
+        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
+    }
+    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
+        PyErr_Format(PyExc_ValueError,
+                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
+                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
+                     buf->itemsize,
+                     (buf->itemsize > 1) ? "s" : "",
+                     dtype->name,
+                     dtype->size,
+                     (dtype->size > 1) ? "s" : "");
+        goto fail;
+    }
+    if (buf->len > 0) {
+        for (i = 0; i < ndim; i++) {
+            spec = axes_specs[i];
+            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
+                goto fail;
+            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
+                goto fail;
+        }
+        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
+            goto fail;
+    }
+    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
+                                         new_memview != NULL) == -1)) {
+        goto fail;
+    }
+    retval = 0;
+    goto no_fail;
+fail:
+    Py_XDECREF(new_memview);
+    retval = -1;
+no_fail:
+    __Pyx_RefNannyFinishContext();
+    return retval;
+}
+
+/* ObjectToMemviewSlice */
+  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
+    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
+    __Pyx_BufFmt_StackElem stack[1];
+    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
+    int retcode;
+    if (obj == Py_None) {
+        result.memview = (struct __pyx_memoryview_obj *) Py_None;
+        return result;
+    }
+    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
+                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
+                                                 &__Pyx_TypeInfo_int, stack,
+                                                 &result, obj);
+    if (unlikely(retcode == -1))
+        goto __pyx_fail;
+    return result;
+__pyx_fail:
+    result.memview = NULL;
+    result.data = NULL;
+    return result;
+}
+
+/* ObjectToMemviewSlice */
+  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
+    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
+    __Pyx_BufFmt_StackElem stack[1];
+    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
+    int retcode;
+    if (obj == Py_None) {
+        result.memview = (struct __pyx_memoryview_obj *) Py_None;
+        return result;
+    }
+    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
+                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
+                                                 &__Pyx_TypeInfo_float, stack,
+                                                 &result, obj);
+    if (unlikely(retcode == -1))
+        goto __pyx_fail;
+    return result;
+__pyx_fail:
+    result.memview = NULL;
+    result.data = NULL;
+    return result;
+}
+
+/* ObjectToMemviewSlice */
+  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
+    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
+    __Pyx_BufFmt_StackElem stack[1];
+    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
+    int retcode;
+    if (obj == Py_None) {
+        result.memview = (struct __pyx_memoryview_obj *) Py_None;
+        return result;
+    }
+    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
+                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
+                                                 &__Pyx_TypeInfo_int, stack,
+                                                 &result, obj);
+    if (unlikely(retcode == -1))
+        goto __pyx_fail;
+    return result;
+__pyx_fail:
+    result.memview = NULL;
+    result.data = NULL;
+    return result;
+}
+
+/* CIntToPy */
+  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
+    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(int) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(int) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(int) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(int),
+                                     little, !is_unsigned);
+    }
+}
+
+/* CIntFromPyVerify */
+  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
+    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
+#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
+    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
+#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
+    {\
+        func_type value = func_value;\
+        if (sizeof(target_type) < sizeof(func_type)) {\
+            if (unlikely(value != (func_type) (target_type) value)) {\
+                func_type zero = 0;\
+                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
+                    return (target_type) -1;\
+                if (is_unsigned && unlikely(value < zero))\
+                    goto raise_neg_overflow;\
+                else\
+                    goto raise_overflow;\
+            }\
+        }\
+        return (target_type) value;\
+    }
+
+/* CIntToPy */
+  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
+#endif
+        }
+    } else {
+        if (sizeof(long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+#ifdef HAVE_LONG_LONG
+        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+            return PyLong_FromLongLong((PY_LONG_LONG) value);
+#endif
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(long),
+                                     little, !is_unsigned);
+    }
+}
+
+/* MemviewSliceCopyTemplate */
+  static __Pyx_memviewslice
+__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
+                                 const char *mode, int ndim,
+                                 size_t sizeof_dtype, int contig_flag,
+                                 int dtype_is_object)
+{
+    __Pyx_RefNannyDeclarations
+    int i;
+    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
+    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
+    Py_buffer *buf = &from_memview->view;
+    PyObject *shape_tuple = NULL;
+    PyObject *temp_int = NULL;
+    struct __pyx_array_obj *array_obj = NULL;
+    struct __pyx_memoryview_obj *memview_obj = NULL;
+    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
+    for (i = 0; i < ndim; i++) {
+        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
+            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
+                                           "indirect dimensions (axis %d)", i);
+            goto fail;
+        }
+    }
+    shape_tuple = PyTuple_New(ndim);
+    if (unlikely(!shape_tuple)) {
+        goto fail;
+    }
+    __Pyx_GOTREF(shape_tuple);
+    for(i = 0; i < ndim; i++) {
+        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
+        if(unlikely(!temp_int)) {
+            goto fail;
+        } else {
+            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
+            temp_int = NULL;
+        }
+    }
+    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
+    if (unlikely(!array_obj)) {
+        goto fail;
+    }
+    __Pyx_GOTREF(array_obj);
+    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
+                                    (PyObject *) array_obj, contig_flag,
+                                    dtype_is_object,
+                                    from_mvs->memview->typeinfo);
+    if (unlikely(!memview_obj))
+        goto fail;
+    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
+        goto fail;
+    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
+                                                dtype_is_object) < 0))
+        goto fail;
+    goto no_fail;
+fail:
+    __Pyx_XDECREF(new_mvs.memview);
+    new_mvs.memview = NULL;
+    new_mvs.data = NULL;
+no_fail:
+    __Pyx_XDECREF(shape_tuple);
+    __Pyx_XDECREF(temp_int);
+    __Pyx_XDECREF(array_obj);
+    __Pyx_RefNannyFinishContext();
+    return new_mvs;
+}
+
+/* CIntFromPy */
+  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(int) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (int) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (int) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
+                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
+                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
+                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (int) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(int) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (int) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
+                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            int val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (int) -1;
+        }
+    } else {
+        int val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (int) -1;
+        val = __Pyx_PyInt_As_int(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to int");
+    return (int) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to int");
+    return (int) -1;
+}
+
+/* CIntFromPy */
+  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (long) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (long) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
+                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
+                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
+                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (long) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (long) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
+                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            long val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (long) -1;
+        }
+    } else {
+        long val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (long) -1;
+        val = __Pyx_PyInt_As_long(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to long");
+    return (long) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to long");
+    return (long) -1;
+}
+
+/* CIntFromPy */
+  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
+    const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(char) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                goto raise_neg_overflow;
+            }
+            return (char) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (char) 0;
+                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
+                case 2:
+                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
+                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
+                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
+                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
+                        }
+                    }
+                    break;
+            }
+#endif
+#if CYTHON_COMPILING_IN_CPYTHON
+            if (unlikely(Py_SIZE(x) < 0)) {
+                goto raise_neg_overflow;
+            }
+#else
+            {
+                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
+                if (unlikely(result < 0))
+                    return (char) -1;
+                if (unlikely(result == 1))
+                    goto raise_neg_overflow;
+            }
+#endif
+            if (sizeof(char) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
+#endif
+            }
+        } else {
+#if CYTHON_USE_PYLONG_INTERNALS
+            const digit* digits = ((PyLongObject*)x)->ob_digit;
+            switch (Py_SIZE(x)) {
+                case  0: return (char) 0;
+                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
+                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
+                case -2:
+                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
+                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case 2:
+                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
+                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case -3:
+                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
+                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case 3:
+                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
+                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case -4:
+                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
+                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+                case 4:
+                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
+                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
+                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
+                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
+                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
+                        }
+                    }
+                    break;
+            }
+#endif
+            if (sizeof(char) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
+#ifdef HAVE_LONG_LONG
+            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
+                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
+#endif
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            char val;
+            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (char) -1;
+        }
+    } else {
+        char val;
+        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
+        if (!tmp) return (char) -1;
+        val = __Pyx_PyInt_As_char(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+raise_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "value too large to convert to char");
+    return (char) -1;
+raise_neg_overflow:
+    PyErr_SetString(PyExc_OverflowError,
+        "can't convert negative value to char");
+    return (char) -1;
+}
+
+/* CheckBinaryVersion */
+  static int __Pyx_check_binary_version(void) {
+    char ctversion[4], rtversion[4];
+    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
+    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
+    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
+        char message[200];
+        PyOS_snprintf(message, sizeof(message),
+                      "compiletime version %s of module '%.100s' "
+                      "does not match runtime version %s",
+                      ctversion, __Pyx_MODULE_NAME, rtversion);
+        return PyErr_WarnEx(NULL, message, 1);
+    }
+    return 0;
+}
+
+/* InitStrings */
+  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        #if PY_MAJOR_VERSION < 3
+        if (t->is_unicode) {
+            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+        } else if (t->intern) {
+            *t->p = PyString_InternFromString(t->s);
+        } else {
+            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        }
+        #else
+        if (t->is_unicode | t->is_str) {
+            if (t->intern) {
+                *t->p = PyUnicode_InternFromString(t->s);
+            } else if (t->encoding) {
+                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+            } else {
+                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+            }
+        } else {
+            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+        }
+        #endif
+        if (!*t->p)
+            return -1;
+        if (PyObject_Hash(*t->p) == -1)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
+    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
+}
+static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
+    Py_ssize_t ignore;
+    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
+}
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+#if !CYTHON_PEP393_ENABLED
+static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+    char* defenc_c;
+    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+    if (!defenc) return NULL;
+    defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+    {
+        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+        char* c;
+        for (c = defenc_c; c < end; c++) {
+            if ((unsigned char) (*c) >= 128) {
+                PyUnicode_AsASCIIString(o);
+                return NULL;
+            }
+        }
+    }
+#endif
+    *length = PyBytes_GET_SIZE(defenc);
+    return defenc_c;
+}
+#else
+static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+    if (likely(PyUnicode_IS_ASCII(o))) {
+        *length = PyUnicode_GET_LENGTH(o);
+        return PyUnicode_AsUTF8(o);
+    } else {
+        PyUnicode_AsASCIIString(o);
+        return NULL;
+    }
+#else
+    return PyUnicode_AsUTF8AndSize(o, length);
+#endif
+}
+#endif
+#endif
+static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+    if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+            __Pyx_sys_getdefaultencoding_not_ascii &&
+#endif
+            PyUnicode_Check(o)) {
+        return __Pyx_PyUnicode_AsStringAndSize(o, length);
+    } else
+#endif
+#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
+    if (PyByteArray_Check(o)) {
+        *length = PyByteArray_GET_SIZE(o);
+        return PyByteArray_AS_STRING(o);
+    } else
+#endif
+    {
+        char* result;
+        int r = PyBytes_AsStringAndSize(o, &result, length);
+        if (unlikely(r < 0)) {
+            return NULL;
+        } else {
+            return result;
+        }
+    }
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+   int is_true = x == Py_True;
+   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
+   else return PyObject_IsTrue(x);
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
+    int retval;
+    if (unlikely(!x)) return -1;
+    retval = __Pyx_PyObject_IsTrue(x);
+    Py_DECREF(x);
+    return retval;
+}
+static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
+#if PY_MAJOR_VERSION >= 3
+    if (PyLong_Check(result)) {
+        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
+                "__int__ returned non-int (type %.200s).  "
+                "The ability to return an instance of a strict subclass of int "
+                "is deprecated, and may be removed in a future version of Python.",
+                Py_TYPE(result)->tp_name)) {
+            Py_DECREF(result);
+            return NULL;
+        }
+        return result;
+    }
+#endif
+    PyErr_Format(PyExc_TypeError,
+                 "__%.4s__ returned non-%.4s (type %.200s)",
+                 type_name, type_name, Py_TYPE(result)->tp_name);
+    Py_DECREF(result);
+    return NULL;
+}
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
+#if CYTHON_USE_TYPE_SLOTS
+  PyNumberMethods *m;
+#endif
+  const char *name = NULL;
+  PyObject *res = NULL;
+#if PY_MAJOR_VERSION < 3
+  if (likely(PyInt_Check(x) || PyLong_Check(x)))
+#else
+  if (likely(PyLong_Check(x)))
+#endif
+    return __Pyx_NewRef(x);
+#if CYTHON_USE_TYPE_SLOTS
+  m = Py_TYPE(x)->tp_as_number;
+  #if PY_MAJOR_VERSION < 3
+  if (m && m->nb_int) {
+    name = "int";
+    res = m->nb_int(x);
+  }
+  else if (m && m->nb_long) {
+    name = "long";
+    res = m->nb_long(x);
+  }
+  #else
+  if (likely(m && m->nb_int)) {
+    name = "int";
+    res = m->nb_int(x);
+  }
+  #endif
+#else
+  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
+    res = PyNumber_Int(x);
+  }
+#endif
+  if (likely(res)) {
+#if PY_MAJOR_VERSION < 3
+    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
+#else
+    if (unlikely(!PyLong_CheckExact(res))) {
+#endif
+        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
+    }
+  }
+  else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_TypeError,
+                    "an integer is required");
+  }
+  return res;
+}
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+  Py_ssize_t ival;
+  PyObject *x;
+#if PY_MAJOR_VERSION < 3
+  if (likely(PyInt_CheckExact(b))) {
+    if (sizeof(Py_ssize_t) >= sizeof(long))
+        return PyInt_AS_LONG(b);
+    else
+        return PyInt_AsSsize_t(b);
+  }
+#endif
+  if (likely(PyLong_CheckExact(b))) {
+    #if CYTHON_USE_PYLONG_INTERNALS
+    const digit* digits = ((PyLongObject*)b)->ob_digit;
+    const Py_ssize_t size = Py_SIZE(b);
+    if (likely(__Pyx_sst_abs(size) <= 1)) {
+        ival = likely(size) ? digits[0] : 0;
+        if (size == -1) ival = -ival;
+        return ival;
+    } else {
+      switch (size) {
+         case 2:
+           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
+             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case -2:
+           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
+             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case 3:
+           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
+             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case -3:
+           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
+             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case 4:
+           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
+             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+         case -4:
+           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
+             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
+           }
+           break;
+      }
+    }
+    #endif
+    return PyLong_AsSsize_t(b);
+  }
+  x = PyNumber_Index(b);
+  if (!x) return -1;
+  ival = PyInt_AsSsize_t(x);
+  Py_DECREF(x);
+  return ival;
+}
+static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
+  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
+}
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+    return PyInt_FromSize_t(ival);
+}
+
+
+#endif /* Py_PYTHON_H */
diff --git a/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so b/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so
new file mode 100755
index 0000000000000000000000000000000000000000..867feaaff6cf61b9bdf17f495a55e6c6fe8b74a5
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diff --git a/vits/monotonic_align/core.pyx b/vits/monotonic_align/core.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..bfaabd4d21c2299cdd978f0cc0caefa20ad186e5
--- /dev/null
+++ b/vits/monotonic_align/core.pyx
@@ -0,0 +1,42 @@
+cimport cython
+from cython.parallel import prange
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:
+  cdef int x
+  cdef int y
+  cdef float v_prev
+  cdef float v_cur
+  cdef float tmp
+  cdef int index = t_x - 1
+
+  for y in range(t_y):
+    for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
+      if x == y:
+        v_cur = max_neg_val
+      else:
+        v_cur = value[y-1, x]
+      if x == 0:
+        if y == 0:
+          v_prev = 0.
+        else:
+          v_prev = max_neg_val
+      else:
+        v_prev = value[y-1, x-1]
+      value[y, x] += max(v_prev, v_cur)
+
+  for y in range(t_y - 1, -1, -1):
+    path[y, index] = 1
+    if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
+      index = index - 1
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
+  cdef int b = paths.shape[0]
+  cdef int i
+  for i in prange(b, nogil=True):
+    maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
diff --git a/vits/monotonic_align/install.txt b/vits/monotonic_align/install.txt
new file mode 100644
index 0000000000000000000000000000000000000000..92612811b7d4e7b462bee54acc73e89e35f74793
--- /dev/null
+++ b/vits/monotonic_align/install.txt
@@ -0,0 +1,3 @@
+mkdir -p vits/monotonic_align/
+python setup.py build_ext --inplace
+mv vits/monotonic_align/* .
diff --git a/vits/monotonic_align/setup.py b/vits/monotonic_align/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e5d82b588b1ac8c002b3846fc090326be62f74d
--- /dev/null
+++ b/vits/monotonic_align/setup.py
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59514d2318206a53b1af687400b66e17fcfab061d1bfce2c13608ccabef9eaed
+size 206
diff --git a/vits/monotonic_align/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so b/vits/monotonic_align/vits/monotonic_align/core.cpython-310-x86_64-linux-gnu.so
new file mode 100755
index 0000000000000000000000000000000000000000..867feaaff6cf61b9bdf17f495a55e6c6fe8b74a5
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diff --git a/vits/text/LICENSE b/vits/text/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..4ad4ed1d5e34d95c8380768ec16405d789cc6de4
--- /dev/null
+++ b/vits/text/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2017 Keith Ito
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/vits/text/__init__.py b/vits/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..43d013185c214d3d3e459a3fd895ab0891c91611
--- /dev/null
+++ b/vits/text/__init__.py
@@ -0,0 +1,57 @@
+""" from https://github.com/keithito/tacotron """
+from vits.text import cleaners
+from vits.text.symbols import symbols
+
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+
+def text_to_sequence(text, symbols, cleaner_names):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
+  sequence = []
+
+  clean_text = _clean_text(text, cleaner_names)
+  for symbol in clean_text:
+    if symbol not in _symbol_to_id.keys():
+      continue
+    symbol_id = _symbol_to_id[symbol]
+    sequence += [symbol_id]
+  return sequence, clean_text
+
+
+def cleaned_text_to_sequence(cleaned_text):
+  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+  '''
+  sequence = [_symbol_to_id[symbol] for symbol in cleaned_text if symbol in _symbol_to_id.keys()]
+  return sequence
+
+
+def sequence_to_text(sequence):
+  '''Converts a sequence of IDs back to a string'''
+  result = ''
+  for symbol_id in sequence:
+    s = _id_to_symbol[symbol_id]
+    result += s
+  return result
+
+
+def _clean_text(text, cleaner_names):
+  for name in cleaner_names:
+    cleaner = getattr(cleaners, name)
+    if not cleaner:
+      raise Exception('Unknown cleaner: %s' % name)
+    text = cleaner(text)
+  return text
diff --git a/vits/text/__pycache__/__init__.cpython-310.pyc b/vits/text/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..bf313ac4b766582a84f8525e608c7979b91e999f
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diff --git a/vits/text/__pycache__/cleaners.cpython-310.pyc b/vits/text/__pycache__/cleaners.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..25972ca25e64ccfa06566e5ccf40a7f10d3d1750
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diff --git a/vits/text/__pycache__/symbols.cpython-310.pyc b/vits/text/__pycache__/symbols.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..172e7e116a9849efa58f56f29d48fda8d935b8b7
Binary files /dev/null and b/vits/text/__pycache__/symbols.cpython-310.pyc differ
diff --git a/vits/text/cleaners.py b/vits/text/cleaners.py
new file mode 100644
index 0000000000000000000000000000000000000000..0482fd38c1f85a3df9e23746b82da4b8013e23c2
--- /dev/null
+++ b/vits/text/cleaners.py
@@ -0,0 +1,475 @@
+""" from https://github.com/keithito/tacotron """
+
+'''
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+'''
+
+import re
+from unidecode import unidecode
+import pyopenjtalk
+from jamo import h2j, j2hcj
+from pypinyin import lazy_pinyin, BOPOMOFO
+import jieba, cn2an
+
+
+# This is a list of Korean classifiers preceded by pure Korean numerals.
+_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
+
+# Regular expression matching whitespace:
+_whitespace_re = re.compile(r'\s+')
+
+# Regular expression matching Japanese without punctuation marks:
+_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
+
+# Regular expression matching non-Japanese characters or punctuation marks:
+_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('mrs', 'misess'),
+  ('mr', 'mister'),
+  ('dr', 'doctor'),
+  ('st', 'saint'),
+  ('co', 'company'),
+  ('jr', 'junior'),
+  ('maj', 'major'),
+  ('gen', 'general'),
+  ('drs', 'doctors'),
+  ('rev', 'reverend'),
+  ('lt', 'lieutenant'),
+  ('hon', 'honorable'),
+  ('sgt', 'sergeant'),
+  ('capt', 'captain'),
+  ('esq', 'esquire'),
+  ('ltd', 'limited'),
+  ('col', 'colonel'),
+  ('ft', 'fort'),
+]]
+
+# List of (hangul, hangul divided) pairs:
+_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
+  ('ㄳ', 'ㄱㅅ'),
+  ('ㄵ', 'ㄴㅈ'),
+  ('ㄶ', 'ㄴㅎ'),
+  ('ㄺ', 'ㄹㄱ'),
+  ('ㄻ', 'ㄹㅁ'),
+  ('ㄼ', 'ㄹㅂ'),
+  ('ㄽ', 'ㄹㅅ'),
+  ('ㄾ', 'ㄹㅌ'),
+  ('ㄿ', 'ㄹㅍ'),
+  ('ㅀ', 'ㄹㅎ'),
+  ('ㅄ', 'ㅂㅅ'),
+  ('ㅘ', 'ㅗㅏ'),
+  ('ㅙ', 'ㅗㅐ'),
+  ('ㅚ', 'ㅗㅣ'),
+  ('ㅝ', 'ㅜㅓ'),
+  ('ㅞ', 'ㅜㅔ'),
+  ('ㅟ', 'ㅜㅣ'),
+  ('ㅢ', 'ㅡㅣ'),
+  ('ㅑ', 'ㅣㅏ'),
+  ('ㅒ', 'ㅣㅐ'),
+  ('ㅕ', 'ㅣㅓ'),
+  ('ㅖ', 'ㅣㅔ'),
+  ('ㅛ', 'ㅣㅗ'),
+  ('ㅠ', 'ㅣㅜ')
+]]
+
+# List of (Latin alphabet, hangul) pairs:
+_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('a', '에이'),
+  ('b', '비'),
+  ('c', '시'),
+  ('d', '디'),
+  ('e', '이'),
+  ('f', '에프'),
+  ('g', '지'),
+  ('h', '에이치'),
+  ('i', '아이'),
+  ('j', '제이'),
+  ('k', '케이'),
+  ('l', '엘'),
+  ('m', '엠'),
+  ('n', '엔'),
+  ('o', '오'),
+  ('p', '피'),
+  ('q', '큐'),
+  ('r', '아르'),
+  ('s', '에스'),
+  ('t', '티'),
+  ('u', '유'),
+  ('v', '브이'),
+  ('w', '더블유'),
+  ('x', '엑스'),
+  ('y', '와이'),
+  ('z', '제트')
+]]
+
+# List of (Latin alphabet, bopomofo) pairs:
+_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('a', 'ㄟˉ'),
+  ('b', 'ㄅㄧˋ'),
+  ('c', 'ㄙㄧˉ'),
+  ('d', 'ㄉㄧˋ'),
+  ('e', 'ㄧˋ'),
+  ('f', 'ㄝˊㄈㄨˋ'),
+  ('g', 'ㄐㄧˋ'),
+  ('h', 'ㄝˇㄑㄩˋ'),
+  ('i', 'ㄞˋ'),
+  ('j', 'ㄐㄟˋ'),
+  ('k', 'ㄎㄟˋ'),
+  ('l', 'ㄝˊㄛˋ'),
+  ('m', 'ㄝˊㄇㄨˋ'),
+  ('n', 'ㄣˉ'),
+  ('o', 'ㄡˉ'),
+  ('p', 'ㄆㄧˉ'),
+  ('q', 'ㄎㄧㄡˉ'),
+  ('r', 'ㄚˋ'),
+  ('s', 'ㄝˊㄙˋ'),
+  ('t', 'ㄊㄧˋ'),
+  ('u', 'ㄧㄡˉ'),
+  ('v', 'ㄨㄧˉ'),
+  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
+  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
+  ('y', 'ㄨㄞˋ'),
+  ('z', 'ㄗㄟˋ')
+]]
+
+
+# List of (bopomofo, romaji) pairs:
+_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('ㄅㄛ', 'p⁼wo'),
+  ('ㄆㄛ', 'pʰwo'),
+  ('ㄇㄛ', 'mwo'),
+  ('ㄈㄛ', 'fwo'),
+  ('ㄅ', 'p⁼'),
+  ('ㄆ', 'pʰ'),
+  ('ㄇ', 'm'),
+  ('ㄈ', 'f'),
+  ('ㄉ', 't⁼'),
+  ('ㄊ', 'tʰ'),
+  ('ㄋ', 'n'),
+  ('ㄌ', 'l'),
+  ('ㄍ', 'k⁼'),
+  ('ㄎ', 'kʰ'),
+  ('ㄏ', 'h'),
+  ('ㄐ', 'ʧ⁼'),
+  ('ㄑ', 'ʧʰ'),
+  ('ㄒ', 'ʃ'),
+  ('ㄓ', 'ʦ`⁼'),
+  ('ㄔ', 'ʦ`ʰ'),
+  ('ㄕ', 's`'),
+  ('ㄖ', 'ɹ`'),
+  ('ㄗ', 'ʦ⁼'),
+  ('ㄘ', 'ʦʰ'),
+  ('ㄙ', 's'),
+  ('ㄚ', 'a'),
+  ('ㄛ', 'o'),
+  ('ㄜ', 'ə'),
+  ('ㄝ', 'e'),
+  ('ㄞ', 'ai'),
+  ('ㄟ', 'ei'),
+  ('ㄠ', 'au'),
+  ('ㄡ', 'ou'),
+  ('ㄧㄢ', 'yeNN'),
+  ('ㄢ', 'aNN'),
+  ('ㄧㄣ', 'iNN'),
+  ('ㄣ', 'əNN'),
+  ('ㄤ', 'aNg'),
+  ('ㄧㄥ', 'iNg'),
+  ('ㄨㄥ', 'uNg'),
+  ('ㄩㄥ', 'yuNg'),
+  ('ㄥ', 'əNg'),
+  ('ㄦ', 'əɻ'),
+  ('ㄧ', 'i'),
+  ('ㄨ', 'u'),
+  ('ㄩ', 'ɥ'),
+  ('ˉ', '→'),
+  ('ˊ', '↑'),
+  ('ˇ', '↓↑'),
+  ('ˋ', '↓'),
+  ('˙', ''),
+  ('，', ','),
+  ('。', '.'),
+  ('！', '!'),
+  ('？', '?'),
+  ('—', '-')
+]]
+
+
+def expand_abbreviations(text):
+  for regex, replacement in _abbreviations:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def lowercase(text):
+  return text.lower()
+
+
+def collapse_whitespace(text):
+  return re.sub(_whitespace_re, ' ', text)
+
+
+def convert_to_ascii(text):
+  return unidecode(text)
+
+
+def japanese_to_romaji_with_accent(text):
+  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
+  sentences = re.split(_japanese_marks, text)
+  marks = re.findall(_japanese_marks, text)
+  text = ''
+  for i, sentence in enumerate(sentences):
+    if re.match(_japanese_characters, sentence):
+      if text!='':
+        text+=' '
+      labels = pyopenjtalk.extract_fullcontext(sentence)
+      for n, label in enumerate(labels):
+        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
+        if phoneme not in ['sil','pau']:
+          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
+        else:
+          continue
+        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
+        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
+        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
+        a3 = int(re.search(r"\+(\d+)/", label).group(1))
+        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
+          a2_next=-1
+        else:
+          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
+        # Accent phrase boundary
+        if a3 == 1 and a2_next == 1:
+          text += ' '
+        # Falling
+        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
+          text += '↓'
+        # Rising
+        elif a2 == 1 and a2_next == 2:
+          text += '↑'
+    if i<len(marks):
+      text += unidecode(marks[i]).replace(' ','')
+  return text
+
+
+def latin_to_hangul(text):
+  for regex, replacement in _latin_to_hangul:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def divide_hangul(text):
+  for regex, replacement in _hangul_divided:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def hangul_number(num, sino=True):
+  '''Reference https://github.com/Kyubyong/g2pK'''
+  num = re.sub(',', '', num)
+
+  if num == '0':
+      return '영'
+  if not sino and num == '20':
+      return '스무'
+
+  digits = '123456789'
+  names = '일이삼사오육칠팔구'
+  digit2name = {d: n for d, n in zip(digits, names)}
+
+  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
+  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
+  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
+  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
+
+  spelledout = []
+  for i, digit in enumerate(num):
+    i = len(num) - i - 1
+    if sino:
+      if i == 0:
+        name = digit2name.get(digit, '')
+      elif i == 1:
+        name = digit2name.get(digit, '') + '십'
+        name = name.replace('일십', '십')
+    else:
+      if i == 0:
+        name = digit2mod.get(digit, '')
+      elif i == 1:
+        name = digit2dec.get(digit, '')
+    if digit == '0':
+      if i % 4 == 0:
+        last_three = spelledout[-min(3, len(spelledout)):]
+        if ''.join(last_three) == '':
+          spelledout.append('')
+          continue
+      else:
+        spelledout.append('')
+        continue
+    if i == 2:
+      name = digit2name.get(digit, '') + '백'
+      name = name.replace('일백', '백')
+    elif i == 3:
+      name = digit2name.get(digit, '') + '천'
+      name = name.replace('일천', '천')
+    elif i == 4:
+      name = digit2name.get(digit, '') + '만'
+      name = name.replace('일만', '만')
+    elif i == 5:
+      name = digit2name.get(digit, '') + '십'
+      name = name.replace('일십', '십')
+    elif i == 6:
+      name = digit2name.get(digit, '') + '백'
+      name = name.replace('일백', '백')
+    elif i == 7:
+      name = digit2name.get(digit, '') + '천'
+      name = name.replace('일천', '천')
+    elif i == 8:
+      name = digit2name.get(digit, '') + '억'
+    elif i == 9:
+      name = digit2name.get(digit, '') + '십'
+    elif i == 10:
+      name = digit2name.get(digit, '') + '백'
+    elif i == 11:
+      name = digit2name.get(digit, '') + '천'
+    elif i == 12:
+      name = digit2name.get(digit, '') + '조'
+    elif i == 13:
+      name = digit2name.get(digit, '') + '십'
+    elif i == 14:
+      name = digit2name.get(digit, '') + '백'
+    elif i == 15:
+      name = digit2name.get(digit, '') + '천'
+    spelledout.append(name)
+  return ''.join(elem for elem in spelledout)
+
+
+def number_to_hangul(text):
+  '''Reference https://github.com/Kyubyong/g2pK'''
+  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
+  for token in tokens:
+    num, classifier = token
+    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
+      spelledout = hangul_number(num, sino=False)
+    else:
+      spelledout = hangul_number(num, sino=True)
+    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
+  # digit by digit for remaining digits
+  digits = '0123456789'
+  names = '영일이삼사오육칠팔구'
+  for d, n in zip(digits, names):
+    text = text.replace(d, n)
+  return text
+
+
+def number_to_chinese(text):
+  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
+  for number in numbers:
+    text = text.replace(number, cn2an.an2cn(number),1)
+  return text
+
+
+def chinese_to_bopomofo(text):
+  text=text.replace('、','，').replace('；','，').replace('：','，')
+  words=jieba.lcut(text,cut_all=False)
+  text=''
+  for word in words:
+    bopomofos=lazy_pinyin(word,BOPOMOFO)
+    if not re.search('[\u4e00-\u9fff]',word):
+      text+=word
+      continue
+    for i in range(len(bopomofos)):
+      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
+        bopomofos[i]+='ˉ'
+    if text!='':
+      text+=' '
+    text+=''.join(bopomofos)
+  return text
+
+
+def latin_to_bopomofo(text):
+  for regex, replacement in _latin_to_bopomofo:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def bopomofo_to_romaji(text):
+  for regex, replacement in _bopomofo_to_romaji:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+def basic_cleaners(text):
+  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def transliteration_cleaners(text):
+  '''Pipeline for non-English text that transliterates to ASCII.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def japanese_cleaners(text):
+  text=japanese_to_romaji_with_accent(text)
+  if re.match('[A-Za-z]',text[-1]):
+    text += '.'
+  return text
+
+
+def japanese_cleaners2(text):
+  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
+
+
+def korean_cleaners(text):
+  '''Pipeline for Korean text'''
+  text = latin_to_hangul(text)
+  text = number_to_hangul(text)
+  text = j2hcj(h2j(text))
+  text = divide_hangul(text)
+  if re.match('[\u3131-\u3163]',text[-1]):
+    text += '.'
+  return text
+
+
+def chinese_cleaners(text):
+  '''Pipeline for Chinese text'''
+  text=number_to_chinese(text)
+  text=chinese_to_bopomofo(text)
+  text=latin_to_bopomofo(text)
+  if re.match('[ˉˊˇˋ˙]',text[-1]):
+    text += '。'
+  return text
+
+
+def zh_ja_mixture_cleaners(text):
+  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
+  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
+  for chinese_text in chinese_texts:
+    cleaned_text=number_to_chinese(chinese_text[4:-4])
+    cleaned_text=chinese_to_bopomofo(cleaned_text)
+    cleaned_text=latin_to_bopomofo(cleaned_text)
+    cleaned_text=bopomofo_to_romaji(cleaned_text)
+    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
+    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
+    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
+    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
+    text = text.replace(chinese_text,cleaned_text+' ',1)
+  for japanese_text in japanese_texts:
+    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
+    text = text.replace(japanese_text,cleaned_text+' ',1)
+  text=text[:-1]
+  if len(text) > 0 and re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
+    text += '.'
+  return text
diff --git a/vits/text/symbols.py b/vits/text/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..edfbd24247be8c757275ce80b9ec27a0ffa808f3
--- /dev/null
+++ b/vits/text/symbols.py
@@ -0,0 +1,39 @@
+'''
+Defines the set of symbols used in text input to the model.
+'''
+
+'''# japanese_cleaners
+_pad        = '_'
+_punctuation = ',.!?-'
+_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
+'''
+
+'''# japanese_cleaners2
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
+'''
+
+'''# korean_cleaners
+_pad        = '_'
+_punctuation = ',.!?…~'
+_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
+'''
+
+'''# chinese_cleaners
+_pad        = '_'
+_punctuation = '，。！？—…'
+_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
+'''
+
+# zh_ja_mixture_cleaners
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
+
+
+# Export all symbols:
+symbols = [_pad] + list(_punctuation) + list(_letters)
+
+# Special symbol ids
+SPACE_ID = symbols.index(" ")
\ No newline at end of file
diff --git a/vits/utils.py b/vits/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1de37df268eb85dcbf636db950b4e1692245bddc
--- /dev/null
+++ b/vits/utils.py
@@ -0,0 +1,436 @@
+import os
+import glob
+import sys
+import argparse
+import logging
+import json
+import subprocess
+import numpy as np
+from scipy.io.wavfile import read
+import torch
+import regex as re
+
+MATPLOTLIB_FLAG = False
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+logger = logging
+
+zh_pattern = re.compile(r'[\u4e00-\u9fa5]')
+en_pattern = re.compile(r'[a-zA-Z]')
+jp_pattern = re.compile(r'[\u3040-\u30ff\u31f0-\u31ff]')
+kr_pattern = re.compile(r'[\uac00-\ud7af\u1100-\u11ff\u3130-\u318f\ua960-\ua97f]')
+num_pattern = re.compile(r'[0-9]')
+comma = r"(?<=[.。!！?？；;，,、:：'\"‘“”’()（）《》「」~——])"  # 向前匹配但固定长度
+tags = {'ZH': '[ZH]', 'EN': '[EN]', 'JP': '[JA]', 'KR': '[KR]'}
+
+
+def tag_cjke(text):
+    '''为中英日韩加tag,中日正则分不开，故先分句分离中日再识别，以应对大部分情况'''
+    sentences = re.split(r"([.。!！?？；;，,、:：'\"‘“”’()（）【】《》「」~——]+ *(?![0-9]))", text)  # 分句，排除小数点
+    sentences.append("")
+    sentences = ["".join(i) for i in zip(sentences[0::2], sentences[1::2])]
+    # print(sentences)
+    prev_lang = None
+    tagged_text = ""
+    for s in sentences:
+        # 全为符号跳过
+        nu = re.sub(r'[\s\p{P}]+', '', s, flags=re.U).strip()
+        if len(nu) == 0:
+            continue
+        s = re.sub(r'[()（）《》「」【】‘“”’]+', '', s)
+        jp = re.findall(jp_pattern, s)
+        # 本句含日语字符判断为日语
+        if len(jp) > 0:
+            prev_lang, tagged_jke = tag_jke(s, prev_lang)
+            tagged_text += tagged_jke
+        else:
+            prev_lang, tagged_cke = tag_cke(s, prev_lang)
+            tagged_text += tagged_cke
+    return tagged_text
+
+
+def tag_jke(text, prev_sentence=None):
+    '''为英日韩加tag'''
+    # 初始化标记变量
+    tagged_text = ""
+    prev_lang = None
+    tagged = 0
+    # 遍历文本
+    for char in text:
+        # 判断当前字符属于哪种语言
+        if jp_pattern.match(char):
+            lang = "JP"
+        elif zh_pattern.match(char):
+            lang = "JP"
+        elif kr_pattern.match(char):
+            lang = "KR"
+        elif en_pattern.match(char):
+            lang = "EN"
+        # elif num_pattern.match(char):
+        #     lang = prev_sentence
+        else:
+            lang = None
+            tagged_text += char
+            continue
+        # 如果当前语言与上一个语言不同，就添加标记
+        if lang != prev_lang:
+            tagged = 1
+            if prev_lang == None:  # 开头
+                tagged_text = tags[lang] + tagged_text
+            else:
+                tagged_text = tagged_text + tags[prev_lang] + tags[lang]
+
+            # 重置标记变量
+            prev_lang = lang
+
+        # 添加当前字符到标记文本中
+        tagged_text += char
+
+    # 在最后一个语言的结尾添加对应的标记
+    if prev_lang:
+        tagged_text += tags[prev_lang]
+    if not tagged:
+        prev_lang = prev_sentence
+        tagged_text = tags[prev_lang] + tagged_text + tags[prev_lang]
+
+    return prev_lang, tagged_text
+
+
+def tag_cke(text, prev_sentence=None):
+    '''为中英韩加tag'''
+    # 初始化标记变量
+    tagged_text = ""
+    prev_lang = None
+    # 是否全略过未标签
+    tagged = 0
+
+    # 遍历文本
+    for char in text:
+        # 判断当前字符属于哪种语言
+        if zh_pattern.match(char):
+            lang = "ZH"
+        elif kr_pattern.match(char):
+            lang = "KR"
+        elif en_pattern.match(char):
+            lang = "EN"
+        # elif num_pattern.match(char):
+        #     lang = prev_sentence
+        else:
+            # 略过
+            lang = None
+            tagged_text += char
+            continue
+
+        # 如果当前语言与上一个语言不同，添加标记
+        if lang != prev_lang:
+            tagged = 1
+            if prev_lang == None:  # 开头
+                tagged_text = tags[lang] + tagged_text
+            else:
+                tagged_text = tagged_text + tags[prev_lang] + tags[lang]
+
+            # 重置标记变量
+            prev_lang = lang
+
+        # 添加当前字符到标记文本中
+        tagged_text += char
+
+    # 在最后一个语言的结尾添加对应的标记
+    if prev_lang:
+        tagged_text += tags[prev_lang]
+    # 未标签则继承上一句标签
+    if tagged == 0:
+        prev_lang = prev_sentence
+        tagged_text = tags[prev_lang] + tagged_text + tags[prev_lang]
+    return prev_lang, tagged_text
+
+
+def load_checkpoint(checkpoint_path, model, optimizer=None, drop_speaker_emb=False):
+    assert os.path.isfile(checkpoint_path)
+    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
+    iteration = checkpoint_dict['iteration']
+    learning_rate = checkpoint_dict['learning_rate']
+    if optimizer is not None:
+        optimizer.load_state_dict(checkpoint_dict['optimizer'])
+    saved_state_dict = checkpoint_dict['model']
+    if hasattr(model, 'module'):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    new_state_dict = {}
+    for k, v in state_dict.items():
+        try:
+            if k == 'emb_g.weight':
+                if drop_speaker_emb:
+                    new_state_dict[k] = v
+                    continue
+                v[:saved_state_dict[k].shape[0], :] = saved_state_dict[k]
+                new_state_dict[k] = v
+            else:
+                new_state_dict[k] = saved_state_dict[k]
+        except:
+            logger.info("%s is not in the checkpoint" % k)
+            new_state_dict[k] = v
+    if hasattr(model, 'module'):
+        model.module.load_state_dict(new_state_dict)
+    else:
+        model.load_state_dict(new_state_dict)
+    logger.info("Loaded checkpoint '{}' (iteration {})".format(
+        checkpoint_path, iteration))
+    return model, optimizer, learning_rate, iteration
+
+
+def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
+    logger.info("Saving model and optimizer state at iteration {} to {}".format(
+        iteration, checkpoint_path))
+    if hasattr(model, 'module'):
+        state_dict = model.module.state_dict()
+    else:
+        state_dict = model.state_dict()
+    torch.save({'model': state_dict,
+                'iteration': iteration,
+                'optimizer': optimizer.state_dict() if optimizer is not None else None,
+                'learning_rate': learning_rate}, checkpoint_path)
+
+
+def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
+    for k, v in scalars.items():
+        writer.add_scalar(k, v, global_step)
+    for k, v in histograms.items():
+        writer.add_histogram(k, v, global_step)
+    for k, v in images.items():
+        writer.add_image(k, v, global_step, dataformats='HWC')
+    for k, v in audios.items():
+        writer.add_audio(k, v, global_step, audio_sampling_rate)
+
+
+def extract_digits(f):
+    digits = "".join(filter(str.isdigit, f))
+    return int(digits) if digits else -1
+
+
+def latest_checkpoint_path(dir_path, regex="G_[0-9]*.pth"):
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    f_list.sort(key=lambda f: extract_digits(f))
+    x = f_list[-1]
+    print(f"latest_checkpoint_path:{x}")
+    return x
+
+
+def oldest_checkpoint_path(dir_path, regex="G_[0-9]*.pth", preserved=4):
+    f_list = glob.glob(os.path.join(dir_path, regex))
+    f_list.sort(key=lambda f: extract_digits(f))
+    if len(f_list) > preserved:
+        x = f_list[0]
+        print(f"oldest_checkpoint_path:{x}")
+        return x
+    return ""
+
+
+def plot_spectrogram_to_numpy(spectrogram):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger('matplotlib')
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(10, 2))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower",
+                   interpolation='none')
+    plt.colorbar(im, ax=ax)
+    plt.xlabel("Frames")
+    plt.ylabel("Channels")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def plot_alignment_to_numpy(alignment, info=None):
+    global MATPLOTLIB_FLAG
+    if not MATPLOTLIB_FLAG:
+        import matplotlib
+        matplotlib.use("Agg")
+        MATPLOTLIB_FLAG = True
+        mpl_logger = logging.getLogger('matplotlib')
+        mpl_logger.setLevel(logging.WARNING)
+    import matplotlib.pylab as plt
+    import numpy as np
+
+    fig, ax = plt.subplots(figsize=(6, 4))
+    im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
+                   interpolation='none')
+    fig.colorbar(im, ax=ax)
+    xlabel = 'Decoder timestep'
+    if info is not None:
+        xlabel += '\n\n' + info
+    plt.xlabel(xlabel)
+    plt.ylabel('Encoder timestep')
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    plt.close()
+    return data
+
+
+def load_wav_to_torch(full_path):
+    sampling_rate, data = read(full_path)
+    return torch.FloatTensor(data.astype(np.float32)), sampling_rate
+
+
+def load_filepaths_and_text(filename, split="|"):
+    with open(filename, encoding='utf-8') as f:
+        filepaths_and_text = [line.strip().split(split) for line in f]
+    return filepaths_and_text
+
+
+def str2bool(v):
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+
+def get_hparams(init=True):
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, default="./configs/modified_finetune_speaker.json",
+                        help='JSON file for configuration')
+    parser.add_argument('-m', '--model', type=str, default="pretrained_models",
+                        help='Model name')
+    parser.add_argument('-n', '--max_epochs', type=int, default=50,
+                        help='finetune epochs')
+    parser.add_argument('--cont', type=str2bool, default=False,
+                        help='whether to continue training on the latest checkpoint')
+    parser.add_argument('--drop_speaker_embed', type=str2bool, default=False,
+                        help='whether to drop existing characters')
+    parser.add_argument('--train_with_pretrained_model', type=str2bool, default=True,
+                        help='whether to train with pretrained model')
+    parser.add_argument('--preserved', type=int, default=4,
+                        help='Number of preserved models')
+
+    args = parser.parse_args()
+    model_dir = os.path.join("./", args.model)
+
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+
+    config_path = args.config
+    config_save_path = os.path.join(model_dir, "config.json")
+    if init:
+        with open(config_path, "r") as f:
+            data = f.read()
+        with open(config_save_path, "w") as f:
+            f.write(data)
+    else:
+        with open(config_save_path, "r") as f:
+            data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    hparams.max_epochs = args.max_epochs
+    hparams.cont = args.cont
+    hparams.drop_speaker_embed = args.drop_speaker_embed
+    hparams.train_with_pretrained_model = args.train_with_pretrained_model
+    hparams.preserved = args.preserved
+    return hparams
+
+
+def get_hparams_from_dir(model_dir):
+    config_save_path = os.path.join(model_dir, "config.json")
+    with open(config_save_path, "r") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    hparams.model_dir = model_dir
+    return hparams
+
+
+def get_hparams_from_file(config_path):
+    with open(config_path, "r", encoding="utf-8") as f:
+        data = f.read()
+    config = json.loads(data)
+
+    hparams = HParams(**config)
+    return hparams
+
+
+def check_git_hash(model_dir):
+    source_dir = os.path.dirname(os.path.realpath(__file__))
+    if not os.path.exists(os.path.join(source_dir, ".git")):
+        logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
+            source_dir
+        ))
+        return
+
+    cur_hash = subprocess.getoutput("git rev-parse HEAD")
+
+    path = os.path.join(model_dir, "githash")
+    if os.path.exists(path):
+        saved_hash = open(path).read()
+        if saved_hash != cur_hash:
+            logger.warn("git hash values are different. {}(saved) != {}(current)".format(
+                saved_hash[:8], cur_hash[:8]))
+    else:
+        open(path, "w").write(cur_hash)
+
+
+def get_logger(model_dir, filename="train.log"):
+    global logger
+    logger = logging.getLogger(os.path.basename(model_dir))
+    logger.setLevel(logging.DEBUG)
+
+    formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
+    if not os.path.exists(model_dir):
+        os.makedirs(model_dir)
+    h = logging.FileHandler(os.path.join(model_dir, filename))
+    h.setLevel(logging.DEBUG)
+    h.setFormatter(formatter)
+    logger.addHandler(h)
+    return logger
+
+
+class HParams():
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            if type(v) == dict:
+                v = HParams(**v)
+            self[k] = v
+
+    def keys(self):
+        return self.__dict__.keys()
+
+    def items(self):
+        return self.__dict__.items()
+
+    def values(self):
+        return self.__dict__.values()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __getitem__(self, key):
+        return getattr(self, key)
+
+    def __setitem__(self, key, value):
+        return setattr(self, key, value)
+
+    def __contains__(self, key):
+        return key in self.__dict__
+
+    def __repr__(self):
+        return self.__dict__.__repr__()