from dataclasses import asdict, dataclass, field
from typing import Dict, Optional, List
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)


@dataclass
class XTTSAudioConfig:
    """Configuration for audio processing parameters"""
    sample_rate: int = 22050
    output_sample_rate: int = 24000
    mel_channels: int = 80
    hop_length: int = 256
    win_length: int = 1024
    n_fft: int = 1024
    fmin: int = 0
    fmax: int = 8000
    power: float = 1.0
    mel_norms_file: Optional[str] = None


class XTTSGPTConfig(PretrainedConfig):
    """Configuration class for the GPT component of XTTS"""
    model_type = "xtts_gpt"

    def __init__(
            self,
            # Model architecture
            vocab_size: int = 256,
            num_chars: int = 255,

            # GPT parameters
            gpt_batch_size: int = 1,
            gpt_max_audio_tokens: int = 605,
            gpt_max_text_tokens: int = 402,
            gpt_max_prompt_tokens: int = 70,
            gpt_layers: int = 30,
            gpt_n_model_channels: int = 1024,
            gpt_n_heads: int = 16,
            gpt_number_text_tokens: int = 6681,
            gpt_start_text_token: Optional[int] = None,
            gpt_stop_text_token: Optional[int] = None,
            gpt_num_audio_tokens: int = 1026,
            gpt_start_audio_token: int = 1024,
            gpt_stop_audio_token: int = 1025,
            gpt_code_stride_len: int = 1024,
            gpt_use_masking_gt_prompt_approach: bool = True,
            gpt_use_perceiver_resampler: bool = True,
            gpt_checkpointing: bool = False,
            gpt_train_solo_embeddings: bool = False,

            # Training parameters
            enable_redaction: bool = False,
            kv_cache: bool = True,
            perceiver_cond_length_compression: int = 256,
            label_smoothing: float = 0.0,

            # Generation parameters
            temperature: float = 0.75,
            length_penalty: float = 1.0,
            repetition_penalty: float = 5.0,
            top_k: int = 50,
            top_p: float = 0.85,
            gpt_cond_len: int = 30,
            gpt_cond_chunk_len: int = 4,
            max_ref_len: int = 30,
            sound_norm_refs: bool = False,

            # Audio processing
            audio_config: Optional[XTTSAudioConfig] = None,

            # Constants and limits
            duration_const: int = 102400,
            char_limits: Optional[Dict[str, int]] = None,
            languages: Optional[List[str]] = None,
            pad_token_id: Optional[int] = None,
            bos_token_id: Optional[int] = None,
            eos_token_id: Optional[int] = None,
            **kwargs,
    ):
        if char_limits is None:
            char_limits = {
                "en": 250, "de": 253, "fr": 273, "es": 239,
                "it": 213, "pt": 203, "pl": 224, "zh": 82,
                "ar": 166, "cs": 186, "ru": 182, "nl": 251,
                "tr": 226, "ja": 71, "hu": 224, "ko": 95,
            }

        if languages is None:
            languages = [
                "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl",
                "cs", "ar", "zh-cn", "hu", "ko", "ja", "hi"
            ]

        if audio_config is None:
            audio_config = XTTSAudioConfig()

        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs
        )

        self.vocab_size = vocab_size
        self.num_chars = num_chars

        # GPT parameters
        self.gpt_batch_size = gpt_batch_size
        self.gpt_max_audio_tokens = gpt_max_audio_tokens
        self.gpt_max_text_tokens = gpt_max_text_tokens
        self.gpt_max_prompt_tokens = gpt_max_prompt_tokens
        self.gpt_layers = gpt_layers
        self.gpt_n_model_channels = gpt_n_model_channels
        self.gpt_n_heads = gpt_n_heads
        self.gpt_number_text_tokens = gpt_number_text_tokens
        self.gpt_start_text_token = gpt_start_text_token
        self.gpt_stop_text_token = gpt_stop_text_token
        self.gpt_num_audio_tokens = gpt_num_audio_tokens
        self.gpt_start_audio_token = gpt_start_audio_token
        self.gpt_stop_audio_token = gpt_stop_audio_token
        self.gpt_code_stride_len = gpt_code_stride_len
        self.gpt_use_masking_gt_prompt_approach = gpt_use_masking_gt_prompt_approach
        self.gpt_use_perceiver_resampler = gpt_use_perceiver_resampler
        self.gpt_checkpointing = gpt_checkpointing
        self.gpt_train_solo_embeddings = gpt_train_solo_embeddings

        # Training parameters
        self.enable_redaction = enable_redaction
        self.kv_cache = kv_cache
        self.perceiver_cond_length_compression = perceiver_cond_length_compression
        self.label_smoothing = label_smoothing

        # Generation parameters
        self.temperature = temperature
        self.length_penalty = length_penalty
        self.repetition_penalty = repetition_penalty
        self.top_k = top_k
        self.top_p = top_p
        self.gpt_cond_len = gpt_cond_len
        self.gpt_cond_chunk_len = gpt_cond_chunk_len
        self.max_ref_len = max_ref_len
        self.sound_norm_refs = sound_norm_refs

        # Audio processing
        self.audio_config = audio_config

        # Constants and limits
        self.duration_const = duration_const
        self.char_limits = char_limits
        self.languages = languages

    def to_dict(self):
        """Convert config to dictionary"""
        config_dict = super().to_dict()
        config_dict["audio_config"] = asdict(self.audio_config)
        return config_dict

    @classmethod
    def from_dict(cls, config_dict):
        """Create config from dictionary"""
        audio_config = XTTSAudioConfig(**config_dict.pop("audio_config", {}))
        return cls(audio_config=audio_config, **config_dict)

    def update_with_tokenizer(self, tokenizer=None):
        """Update configuration values based on tokenizer"""
        if tokenizer is not None:
            self.gpt_number_text_tokens = tokenizer.get_vocab_size()
            self.gpt_start_text_token = tokenizer.bos_token_id
            self.gpt_stop_text_token = tokenizer.eos_token_id