StableTuner_RunPod_Fix/discriminator.py

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import einops, einops.layers.torch
import diffusers
from diffusers.configuration_utils import ConfigMixin, register_to_config
from typing import Tuple, Optional

import inspect
import os
from functools import partial
from typing import Callable, List, Optional, Tuple, Union

import torch
from torch import Tensor, device



class ModelMixin(torch.nn.Module):
    r"""
    Base class for all models.
    [`ModelMixin`] takes care of storing the configuration of the models and handles methods for loading, downloading
    and saving models.
        - **config_name** ([`str`]) -- A filename under which the model should be stored when calling
          [`~models.ModelMixin.save_pretrained`].
    """
    config_name = "new"
    _automatically_saved_args = ["_diffusers_version", "_class_name", "_name_or_path"]
    _supports_gradient_checkpointing = False

    def __init__(self):
        super().__init__()

    @property
    def is_gradient_checkpointing(self) -> bool:
        """
        Whether gradient checkpointing is activated for this model or not.
        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".
        """
        return any(hasattr(m, "gradient_checkpointing") and m.gradient_checkpointing for m in self.modules())

    def enable_gradient_checkpointing(self):
        """
        Activates gradient checkpointing for the current model.
        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".
        """
        if not self._supports_gradient_checkpointing:
            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
        self.apply(partial(self._set_gradient_checkpointing, value=True))

    def disable_gradient_checkpointing(self):
        """
        Deactivates gradient checkpointing for the current model.
        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".
        """
        if self._supports_gradient_checkpointing:
            self.apply(partial(self._set_gradient_checkpointing, value=False))

    def set_use_memory_efficient_attention_xformers(
        self, valid: bool, attention_op: Optional[Callable] = None
    ) -> None:
        # Recursively walk through all the children.
        # Any children which exposes the set_use_memory_efficient_attention_xformers method
        # gets the message
        def fn_recursive_set_mem_eff(module: torch.nn.Module):
            if hasattr(module, "set_use_memory_efficient_attention_xformers"):
                module.set_use_memory_efficient_attention_xformers(valid, attention_op)

            for child in module.children():
                fn_recursive_set_mem_eff(child)

        for module in self.children():
            if isinstance(module, torch.nn.Module):
                fn_recursive_set_mem_eff(module)

    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
        r"""
        Enable memory efficient attention as implemented in xformers.
        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
        time. Speed up at training time is not guaranteed.
        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
        is used.
        Parameters:
            attention_op (`Callable`, *optional*):
                Override the default `None` operator for use as `op` argument to the
                [`memory_efficient_attention()`](https://facebookresearch.github.io/xformers/components/ops.html#xformers.ops.memory_efficient_attention)
                function of xFormers.
        Examples:
        ```py
        >>> import torch
        >>> from diffusers import UNet2DConditionModel
        >>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp
        >>> model = UNet2DConditionModel.from_pretrained(
        ...     "stabilityai/stable-diffusion-2-1", subfolder="unet", torch_dtype=torch.float16
        ... )
        >>> model = model.to("cuda")
        >>> model.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp)
        ```
        """
        self.set_use_memory_efficient_attention_xformers(True, attention_op)

    def disable_xformers_memory_efficient_attention(self):
        r"""
        Disable memory efficient attention as implemented in xformers.
        """
        self.set_use_memory_efficient_attention_xformers(False)

    def save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
        is_main_process: bool = True,
        save_function: Callable = None,
        safe_serialization: bool = False,
        variant: Optional[str] = None,
    ):
        """
        Save a model and its configuration file to a directory, so that it can be re-loaded using the
        `[`~models.ModelMixin.from_pretrained`]` class method.
        Arguments:
            save_directory (`str` or `os.PathLike`):
                Directory to which to save. Will be created if it doesn't exist.
            is_main_process (`bool`, *optional*, defaults to `True`):
                Whether the process calling this is the main process or not. Useful when in distributed training like
                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
                the main process to avoid race conditions.
            save_function (`Callable`):
                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
                need to replace `torch.save` by another method. Can be configured with the environment variable
                `DIFFUSERS_SAVE_MODE`.
            safe_serialization (`bool`, *optional*, defaults to `False`):
                Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
            variant (`str`, *optional*):
                If specified, weights are saved in the format pytorch_model.<variant>.bin.
        """
        if safe_serialization and not is_safetensors_available():
            raise ImportError("`safe_serialization` requires the `safetensors library: `pip install safetensors`.")

        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return

        os.makedirs(save_directory, exist_ok=True)

        model_to_save = self

        # Attach architecture to the config
        # Save the config
        if is_main_process:
            model_to_save.save_config(save_directory)

        # Save the model
        state_dict = model_to_save.state_dict()

        weights_name = SAFETENSORS_WEIGHTS_NAME if safe_serialization else WEIGHTS_NAME
        weights_name = _add_variant(weights_name, variant)

        # Save the model
        if safe_serialization:
            safetensors.torch.save_file(
                state_dict, os.path.join(save_directory, weights_name), metadata={"format": "pt"}
            )
        else:
            torch.save(state_dict, os.path.join(save_directory, weights_name))

        logger.info(f"Model weights saved in {os.path.join(save_directory, weights_name)}")

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
        r"""
        Instantiate a pretrained pytorch model from a pre-trained model configuration.
        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you should first set it back in training mode with `model.train()`.
        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
        task.
        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
        weights are discarded.
        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
                Can be either:
                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                      Valid model ids should have an organization name, like `google/ddpm-celebahq-256`.
                    - A path to a *directory* containing model weights saved using [`~ModelMixin.save_config`], e.g.,
                      `./my_model_directory/`.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            torch_dtype (`str` or `torch.dtype`, *optional*):
                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
                will be automatically derived from the model's weights.
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (i.e., do not try to download the model).
            use_auth_token (`str` or *bool*, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
                when running `diffusers-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.
            from_flax (`bool`, *optional*, defaults to `False`):
                Load the model weights from a Flax checkpoint save file.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo (either remote in
                huggingface.co or downloaded locally), you can specify the folder name here.
            mirror (`str`, *optional*):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                Please refer to the mirror site for more information.
            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be refined to each
                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
                same device.
                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
                Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
                also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
                model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
                setting this argument to `True` will raise an error.
            variant (`str`, *optional*):
                If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
                ignored when using `from_flax`.
            use_safetensors (`bool`, *optional* ):
                If set to `True`, the pipeline will forcibly load the models from `safetensors` weights. If set to
                `None` (the default). The pipeline will load using `safetensors` if safetensors weights are available
                *and* if `safetensors` is installed. If the to `False` the pipeline will *not* use `safetensors`.
        <Tip>
         It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated
         models](https://huggingface.co/docs/hub/models-gated#gated-models).
        </Tip>
        <Tip>
        Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use
        this method in a firewalled environment.
        </Tip>
        """
        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
        force_download = kwargs.pop("force_download", False)
        from_flax = kwargs.pop("from_flax", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        output_loading_info = kwargs.pop("output_loading_info", False)
        local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        torch_dtype = kwargs.pop("torch_dtype", None)
        subfolder = kwargs.pop("subfolder", None)
        device_map = kwargs.pop("device_map", None)
        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
        variant = kwargs.pop("variant", None)
        use_safetensors = kwargs.pop("use_safetensors", None)

        if use_safetensors and not is_safetensors_available():
            raise ValueError(
                "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors"
            )

        allow_pickle = False
        if use_safetensors is None:
            use_safetensors = is_safetensors_available()
            allow_pickle = True

        if low_cpu_mem_usage and not is_accelerate_available():
            low_cpu_mem_usage = False
            logger.warning(
                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
                " install accelerate\n```\n."
            )

        if device_map is not None and not is_accelerate_available():
            raise NotImplementedError(
                "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
                " `device_map=None`. You can install accelerate with `pip install accelerate`."
            )

        # Check if we can handle device_map and dispatching the weights
        if device_map is not None and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `device_map=None`."
            )

        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
            raise NotImplementedError(
                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
                " `low_cpu_mem_usage=False`."
            )

        if low_cpu_mem_usage is False and device_map is not None:
            raise ValueError(
                f"You cannot set `low_cpu_mem_usage` to `False` while using device_map={device_map} for loading and"
                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
            )

        # Load config if we don't provide a configuration
        config_path = pretrained_model_name_or_path

        user_agent = {
            "diffusers": __version__,
            "file_type": "model",
            "framework": "pytorch",
        }

        # load config
        config, unused_kwargs, commit_hash = cls.load_config(
            config_path,
            cache_dir=cache_dir,
            return_unused_kwargs=True,
            return_commit_hash=True,
            force_download=force_download,
            resume_download=resume_download,
            proxies=proxies,
            local_files_only=local_files_only,
            use_auth_token=use_auth_token,
            revision=revision,
            subfolder=subfolder,
            device_map=device_map,
            user_agent=user_agent,
            **kwargs,
        )

        # load model
        model_file = None
        if from_flax:
            model_file = _get_model_file(
                pretrained_model_name_or_path,
                weights_name=FLAX_WEIGHTS_NAME,
                cache_dir=cache_dir,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
                subfolder=subfolder,
                user_agent=user_agent,
                commit_hash=commit_hash,
            )
            model = cls.from_config(config, **unused_kwargs)

            # Convert the weights
            from .modeling_pytorch_flax_utils import load_flax_checkpoint_in_pytorch_model

            model = load_flax_checkpoint_in_pytorch_model(model, model_file)
        else:
            if use_safetensors:
                try:
                    model_file = _get_model_file(
                        pretrained_model_name_or_path,
                        weights_name=_add_variant(SAFETENSORS_WEIGHTS_NAME, variant),
                        cache_dir=cache_dir,
                        force_download=force_download,
                        resume_download=resume_download,
                        proxies=proxies,
                        local_files_only=local_files_only,
                        use_auth_token=use_auth_token,
                        revision=revision,
                        subfolder=subfolder,
                        user_agent=user_agent,
                        commit_hash=commit_hash,
                    )
                except IOError as e:
                    if not allow_pickle:
                        raise e
                    pass
            if model_file is None:
                model_file = _get_model_file(
                    pretrained_model_name_or_path,
                    weights_name=_add_variant(WEIGHTS_NAME, variant),
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    use_auth_token=use_auth_token,
                    revision=revision,
                    subfolder=subfolder,
                    user_agent=user_agent,
                    commit_hash=commit_hash,
                )

            if low_cpu_mem_usage:
                # Instantiate model with empty weights
                with accelerate.init_empty_weights():
                    model = cls.from_config(config, **unused_kwargs)

                # if device_map is None, load the state dict and move the params from meta device to the cpu
                if device_map is None:
                    param_device = "cpu"
                    state_dict = load_state_dict(model_file, variant=variant)
                    # move the params from meta device to cpu
                    missing_keys = set(model.state_dict().keys()) - set(state_dict.keys())
                    if len(missing_keys) > 0:
                        raise ValueError(
                            f"Cannot load {cls} from {pretrained_model_name_or_path} because the following keys are"
                            f" missing: \n {', '.join(missing_keys)}. \n Please make sure to pass"
                            " `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize"
                            " those weights or else make sure your checkpoint file is correct."
                        )

                    empty_state_dict = model.state_dict()
                    for param_name, param in state_dict.items():
                        accepts_dtype = "dtype" in set(
                            inspect.signature(set_module_tensor_to_device).parameters.keys()
                        )

                        if empty_state_dict[param_name].shape != param.shape:
                            raise ValueError(
                                f"Cannot load {pretrained_model_name_or_path} because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example."
                            )

                        if accepts_dtype:
                            set_module_tensor_to_device(
                                model, param_name, param_device, value=param, dtype=torch_dtype
                            )
                        else:
                            set_module_tensor_to_device(model, param_name, param_device, value=param)
                else:  # else let accelerate handle loading and dispatching.
                    # Load weights and dispatch according to the device_map
                    # by default the device_map is None and the weights are loaded on the CPU
                    accelerate.load_checkpoint_and_dispatch(model, model_file, device_map, dtype=torch_dtype)

                loading_info = {
                    "missing_keys": [],
                    "unexpected_keys": [],
                    "mismatched_keys": [],
                    "error_msgs": [],
                }
            else:
                model = cls.from_config(config, **unused_kwargs)

                state_dict = load_state_dict(model_file, variant=variant)

                model, missing_keys, unexpected_keys, mismatched_keys, error_msgs = cls._load_pretrained_model(
                    model,
                    state_dict,
                    model_file,
                    pretrained_model_name_or_path,
                    ignore_mismatched_sizes=ignore_mismatched_sizes,
                )

                loading_info = {
                    "missing_keys": missing_keys,
                    "unexpected_keys": unexpected_keys,
                    "mismatched_keys": mismatched_keys,
                    "error_msgs": error_msgs,
                }

        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
            raise ValueError(
                f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}."
            )
        elif torch_dtype is not None:
            model = model.to(torch_dtype)

        model.register_to_config(_name_or_path=pretrained_model_name_or_path)

        # Set model in evaluation mode to deactivate DropOut modules by default
        model.eval()
        if output_loading_info:
            return model, loading_info

        return model

    @classmethod
    def _load_pretrained_model(
        cls,
        model,
        state_dict,
        resolved_archive_file,
        pretrained_model_name_or_path,
        ignore_mismatched_sizes=False,
    ):
        # Retrieve missing & unexpected_keys
        model_state_dict = model.state_dict()
        loaded_keys = list(state_dict.keys())

        expected_keys = list(model_state_dict.keys())

        original_loaded_keys = loaded_keys

        missing_keys = list(set(expected_keys) - set(loaded_keys))
        unexpected_keys = list(set(loaded_keys) - set(expected_keys))

        # Make sure we are able to load base models as well as derived models (with heads)
        model_to_load = model

        def _find_mismatched_keys(
            state_dict,
            model_state_dict,
            loaded_keys,
            ignore_mismatched_sizes,
        ):
            mismatched_keys = []
            if ignore_mismatched_sizes:
                for checkpoint_key in loaded_keys:
                    model_key = checkpoint_key

                    if (
                        model_key in model_state_dict
                        and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape
                    ):
                        mismatched_keys.append(
                            (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape)
                        )
                        del state_dict[checkpoint_key]
            return mismatched_keys

        if state_dict is not None:
            # Whole checkpoint
            mismatched_keys = _find_mismatched_keys(
                state_dict,
                model_state_dict,
                original_loaded_keys,
                ignore_mismatched_sizes,
            )
            error_msgs = _load_state_dict_into_model(model_to_load, state_dict)

        if len(error_msgs) > 0:
            error_msg = "\n\t".join(error_msgs)
            if "size mismatch" in error_msg:
                error_msg += (
                    "\n\tYou may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method."
                )
            raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")

        if len(unexpected_keys) > 0:
            logger.warning(
                f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
                f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
                f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task"
                " or with another architecture (e.g. initializing a BertForSequenceClassification model from a"
                " BertForPreTraining model).\n- This IS NOT expected if you are initializing"
                f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly"
                " identical (initializing a BertForSequenceClassification model from a"
                " BertForSequenceClassification model)."
            )
        else:
            logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n")
        if len(missing_keys) > 0:
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably"
                " TRAIN this model on a down-stream task to be able to use it for predictions and inference."
            )
        elif len(mismatched_keys) == 0:
            logger.info(
                f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the"
                f" checkpoint was trained on, you can already use {model.__class__.__name__} for predictions"
                " without further training."
            )
        if len(mismatched_keys) > 0:
            mismatched_warning = "\n".join(
                [
                    f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated"
                    for key, shape1, shape2 in mismatched_keys
                ]
            )
            logger.warning(
                f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at"
                f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not"
                f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be"
                " able to use it for predictions and inference."
            )

        return model, missing_keys, unexpected_keys, mismatched_keys, error_msgs

    @property
    def device(self) -> device:
        """
        `torch.device`: The device on which the module is (assuming that all the module parameters are on the same
        device).
        """
        return get_parameter_device(self)

    @property
    def dtype(self) -> torch.dtype:
        """
        `torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
        """
        return get_parameter_dtype(self)

    def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool = False) -> int:
        """
        Get number of (optionally, trainable or non-embeddings) parameters in the module.
        Args:
            only_trainable (`bool`, *optional*, defaults to `False`):
                Whether or not to return only the number of trainable parameters
            exclude_embeddings (`bool`, *optional*, defaults to `False`):
                Whether or not to return only the number of non-embeddings parameters
        Returns:
            `int`: The number of parameters.
        """

        if exclude_embeddings:
            embedding_param_names = [
                f"{name}.weight"
                for name, module_type in self.named_modules()
                if isinstance(module_type, torch.nn.Embedding)
            ]
            non_embedding_parameters = [
                parameter for name, parameter in self.named_parameters() if name not in embedding_param_names
            ]
            return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable)
        else:
            return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable)

def Downsample(dim, dim_out):
    return nn.Conv2d(dim, dim_out, 4, 2, 1)

class Residual(nn.Sequential):
    def forward(self, input):
        x = input
        for module in self:
            x = module(x)
        return x + input

def ConvLayer(dim, dim_out, *, kernel_size=3, groups=32):
    return nn.Sequential(
        nn.GroupNorm(groups, dim),
        nn.SiLU(),
        nn.Conv2d(dim, dim_out, kernel_size=kernel_size, padding=kernel_size//2),
    )

def ResnetBlock(dim, *, kernel_size=3, groups=32):
    return Residual(
        ConvLayer(dim, dim, kernel_size=kernel_size, groups=groups),
        ConvLayer(dim, dim, kernel_size=kernel_size, groups=groups),
    )
    
class SelfAttention(nn.Module):
    def __init__(self, dim, out_dim, *, heads=8, key_dim=32, value_dim=32):
        super().__init__()
        self.dim = dim
        self.out_dim = dim
        self.heads = heads
        self.key_dim = key_dim

        self.to_k = nn.Linear(dim, key_dim)
        self.to_v = nn.Linear(dim, value_dim)
        self.to_q = nn.Linear(dim, key_dim * heads)
        self.to_out = nn.Linear(value_dim * heads, out_dim)

    def forward(self, x):
        shape = x.shape
        x = einops.rearrange(x, 'b c ... -> b (...) c')

        k = self.to_k(x)
        v = self.to_v(x)
        q = self.to_q(x)
        q = einops.rearrange(q, 'b n (h c) -> b (n h) c', h=self.heads)
        if hasattr(nn.functional, "scaled_dot_product_attention"):
            result = F.scaled_dot_product_attention(q, k, v)
        else:
            attention_scores = torch.bmm(q, k.transpose(-2, -1))
            attention_probs = torch.softmax(attention_scores.float() / math.sqrt(self.key_dim), dim=-1).type(attention_scores.dtype)
            result = torch.bmm(attention_probs, v)
        result = einops.rearrange(result, 'b (n h) c -> b n (h c)', h=self.heads)
        out = self.to_out(result)

        out = einops.rearrange(out, 'b n c -> b c n')
        out = torch.reshape(out, (shape[0], self.out_dim, *shape[2:]))
        return out

def SelfAttentionBlock(dim, attention_dim, *, heads=8, groups=32):
    if not attention_dim:
        attention_dim = dim // heads
    return Residual(
        nn.GroupNorm(groups, dim),
        SelfAttention(dim, dim, heads=heads, key_dim=attention_dim, value_dim=attention_dim),
    )
    
class Discriminator2D(ModelMixin, ConfigMixin):
    @register_to_config
    def __init__(
        self, 
        in_channels: int = 8,
        out_channels: int = 1,
        block_out_channels: Tuple[int] = (128, 256, 512, 1024, 1024, 1024),
        block_repeats: Tuple[int] = (2, 2, 2, 2, 2),
        downsample_blocks: Tuple[int] = (0, 1, 2),
        attention_blocks: Tuple[int] = (1, 2, 3, 4),
        mlp_hidden_channels: Tuple[int] = (2048, 2048, 2048),
        mlp_uses_norm: bool = True,
        attention_dim: Optional[int] = None,
        attention_heads: int = 8,
        groups: int = 32,
        embedding_dim: int = 768,
    ):
        super().__init__()
        
        self.blocks = nn.ModuleList([])
        
        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], 7, padding=3)
        
        for i in range(0, len(block_out_channels) - 1):
            block_in = block_out_channels[i]
            block_out = block_out_channels[i + 1]
            block = nn.Sequential()
            for j in range(0, block_repeats[i]):
                if i in attention_blocks:
                    block.append(SelfAttentionBlock(block_in, attention_dim, heads=attention_heads, groups=groups))
                block.append(ResnetBlock(block_in, groups=groups))
            if i in downsample_blocks:
                block.append(Downsample(block_in, block_out))
            elif block_in != block_out:
                block.append(nn.Conv2d(block_in, block_out, 1))
            self.blocks.append(block)

        # A simple MLP to make the final decision based on statistics from
        # the output of every block
        self.to_out = nn.Sequential()
        d_channels = 2 * sum(block_out_channels[1:]) + embedding_dim
        for c in mlp_hidden_channels:
            self.to_out.append(nn.Linear(d_channels, c))
            if mlp_uses_norm:
                self.to_out.append(nn.GroupNorm(groups, c))
            self.to_out.append(nn.SiLU())
            d_channels = c
        self.to_out.append(nn.Linear(d_channels, out_channels))
        
        self.gradient_checkpointing = False
    
    def enable_gradient_checkpointing(self):
        self.gradient_checkpointing = True
        
    def disable_gradient_checkpointing(self):
        self.gradient_checkpointing = False
        
    def forward(self, x, encoder_hidden_states):
        x = self.conv_in(x)
        if self.config.embedding_dim != 0:
            d = einops.reduce(encoder_hidden_states, 'b n c -> b c', 'mean')
        else:
            d = torch.zeros([x.shape[0], 0], device=x.device, dtype=x.dtype)
        for block in self.blocks:
            if self.gradient_checkpointing:
                x = torch.utils.checkpoint.checkpoint(block, x)
            else:
                x = block(x)
            x_mean = einops.reduce(x, 'b c ... -> b c', 'mean')
            x_max = einops.reduce(x, 'b c ... -> b c', 'max')
            d = torch.cat([d, x_mean, x_max], dim=-1)
        return self.to_out(d)