from dataclasses import dataclass, field
from typing import Literal


@dataclass
class SimpleGLADecoderConfig:
    name: str = "simple_gla"
    dim: int = 512
    use_short_conv = True
    expand_k: float = 0.5
    expand_v: float = 1.0
    num_heads: int = 4
    num_layers: int = 12
    ffn_expansion_factor: int = 4
    conv_layers: list[int] | None = field(default_factory=lambda: [0, 2, 4, 6, 8, 10])
    blind_crossatt: bool = True
    listen_read_crossatt: dict[int, list[Literal["listen", "read"]]] | None = None
    crossatt_num_heads: int = 1
    crossatt_dropout: float = 0.1
    crossatt_layer_idx: list[int] = field(default_factory=lambda: [5])


@dataclass
class TransformerEncoderConfig:
    name: str = "sa_transformer"
    dim: int = 512
    num_heads: int = 8
    num_layers: int = 6
    ffn_expansion_factor: int = 4


@dataclass
class ConvFormerEncoderConfig:
    name: str = "convformer_encoder"
    dim: int = 512
    num_heads: int = 8
    num_transformer_layers: int = 6
    num_conv_layers: int = 3
    ffn_expansion_factor: int = 4


@dataclass
class TransformerDecoderConfig:
    name: str = "sa_transformer"
    dim: int = 512
    num_heads: int = 8
    num_layers: int = 12
    conv_layers: list[int] | None = field(default_factory=lambda: [0, 2, 4, 6, 8, 10])
    ffn_expansion_factor: int = 4
    crossatt_dropout: float = 0.1
    crossatt_num_heads: int = 1
    crossatt_layer_idx: list[int] = field(default_factory=lambda: [5, 6])


DecoderConfig = TransformerDecoderConfig | SimpleGLADecoderConfig
EncoderConfig = TransformerEncoderConfig | ConvFormerEncoderConfig


@dataclass
class TTSConfig:
    dim: int = 512
    text_vocab_size: int = 256 + 3
    audio_vocab_size: int = 4096 + 3
    audio_embed_size: int = 16
    audio_input_type: Literal["continuous", "discrete"] = "continuous"
    diffusion_head_num_layers: int = 3
    encoder_cfg: EncoderConfig = field(default_factory=TransformerEncoderConfig)
    decoder_cfg: DecoderConfig = field(default_factory=TransformerDecoderConfig)
    stop_prediction_head: bool = True
    multi_stop_prediction_head: bool = False
    multi_stop_num_tokens: int = 8
    multi_stop_padding_idx: int | None = None
    multi_stop_tie_embd: bool = True
    stop_token_embd: bool = False
    text_stop_token_embd: bool = False
    continuous_diffusion: bool = True
    num_sink_tokens: int = 0
    disabled_crossatt_head_idx: list[tuple[int, int]] | None = None
    patchvae_path: str | None = None
    text_tokenizer_path: str | None = None
    eos: bool = False
    bos: bool = False


@dataclass
class PlayHeadConfig:
    selected_cross_attention_heads: list[tuple[int, int]]
    dim: int = 256
    num_layers: int = 6
    num_frame_lag: int = 2
    num_sink_tokens: int = 4
    cycle_len: int = 8
    logits_head: bool = True
    target_lag: int = 0
    avg_pool_stride: int = 3
    circular_head: bool = False


@dataclass
class QueryVCConfig:
    dim: int = 512
    semantic_dim: int = 512
    num_layers: int = 12
    lag: int = 4
    audio_embed_size: int = 8
    diffusion_head_num_layers: int = 3