"""Configuration for Chatterbox TTS Service."""

from pathlib import Path
from typing import Literal

from pydantic import Field, field_validator
from pydantic_settings import SettingsConfigDict

from lilith_service_fastapi_bootstrap import BaseServiceSettings


class ChatterboxSettings(BaseServiceSettings):
    """Configuration settings for Chatterbox TTS Service.

    Extends BaseServiceSettings with Chatterbox-specific options for
    model configuration, GPU management, voice storage, and synthesis defaults.
    """

    # Model configuration
    model_type: Literal["turbo", "original"] = Field(
        default="turbo",
        description="Chatterbox model variant (turbo is faster, original is higher quality)",
    )
    model_cache_dir: Path = Field(
        default=Path.home() / ".cache" / "huggingface",
        description="Directory for cached model files",
    )

    # GPU configuration
    gpu_device_ids: list[int] | None = Field(
        default=None,
        description="GPU device IDs to use (None = auto-detect all)",
    )

    # Performance optimizations
    enable_compile: bool = Field(
        default=False,
        description="Enable torch.compile() (disabled by default - ChatterboxTTS has complex control flow)",
    )
    compile_mode: Literal["default", "reduce-overhead", "max-autotune"] = Field(
        default="reduce-overhead",
        description="torch.compile mode (reduce-overhead is best for inference)",
    )
    use_half_precision: bool = Field(
        default=False,
        description="Use bf16 half precision (disabled by default - ChatterboxTTS has internal dtype conflicts)",
    )
    warmup_on_load: bool = Field(
        default=True,
        description="Run warmup generation on model load to pre-compile CUDA kernels",
    )

    # model-boss coordinator URL
    model_boss_url: str = Field(
        default="http://localhost:8210",
        description="Base URL of the model-boss coordinator service",
    )
    # whisper-http backend URL (STT service delegated to model-boss)
    whisper_http_url: str = Field(
        default="http://localhost:10011",
        description="Base URL of the whisper-http coordinator (faster-whisper via model-boss)",
    )


    # Voice storage
    voices_dir: Path = Field(
        default=Path("voices"),
        description="Directory for storing cloned voice reference audio and conditionals",
    )
    voice_library_dir: Path = Field(
        default=Path.home() / "datasets" / "voices" / "library",
        description="Directory for browsable voice library (auto-discovered voices)",
    )
    max_conditionals_cache: int = Field(
        default=20,
        ge=1,
        le=100,
        description="Maximum number of voice conditionals to keep in memory",
    )

    # Synthesis defaults
    default_exaggeration: float = Field(
        default=0.5,
        ge=0.0,
        le=1.0,
        description="Default emotional expressiveness (0.0=calm, 1.0=dramatic)",
    )
    default_cfg_weight: float = Field(
        default=0.5,
        ge=0.0,
        le=1.0,
        description="Default pacing control (lower=slower, higher=faster)",
    )
    default_temperature: float = Field(
        default=0.8,
        ge=0.0,
        le=2.0,
        description="Default sampling temperature",
    )
    default_top_p: float = Field(
        default=0.95,
        ge=0.0,
        le=1.0,
        description="Default top-p sampling",
    )
    default_repetition_penalty: float = Field(
        default=1.2,
        ge=1.0,
        le=3.0,
        description="Default repetition penalty",
    )
    max_text_length: int = Field(
        default=10000,
        ge=1,
        le=100000,
        description="Maximum input text length in characters",
    )

    # Audio output
    default_format: Literal["wav", "mp3", "opus"] = Field(
        default="wav",
        description="Default output audio format",
    )
    normalize_loudness: bool = Field(
        default=True,
        description="Normalize output loudness by default",
    )
    target_loudness_lufs: float = Field(
        default=-23.0,
        description="Target loudness in LUFS for normalization",
    )

    # Conversation / VAD settings
    vad_speech_threshold: float = Field(
        default=0.5,
        ge=0.0,
        le=1.0,
        description="Silero VAD speech probability threshold (0.0-1.0)",
    )
    vad_echo_aware_threshold: float = Field(
        default=0.7,
        ge=0.0,
        le=1.0,
        description="Raised VAD threshold during AI playback to avoid echo triggers",
    )
    vad_post_speech_silence: float = Field(
        default=0.4,
        ge=0.1,
        le=3.0,
        description="Seconds of silence after speech before emitting speech_end",
    )
    vad_min_speech_duration: float = Field(
        default=0.15,
        ge=0.0,
        le=2.0,
        description="Minimum continuous speech duration before confirming speech_start",
    )
    conversation_stt_model: str = Field(
        default="base",
        description="Default Whisper model for conversation streaming STT",
    )

    # Server configuration
    host: str = Field(
        default="0.0.0.0",
        description="Server host address",
    )
    port: int = Field(
        default=8000,
        ge=1,
        le=65535,
        description="Server port",
    )

    model_config = SettingsConfigDict(
        env_prefix="CHATTERBOX_",
        env_file=".env",
        env_file_encoding="utf-8",
        case_sensitive=False,
        extra="ignore",
    )

    @field_validator("gpu_device_ids", mode="before")
    @classmethod
    def parse_gpu_device_ids(cls, v: str | list[int] | None) -> list[int] | None:
        """Parse GPU device IDs from comma-separated string or list."""
        if v is None:
            return None
        if isinstance(v, str):
            if not v.strip():
                return None
            return [int(x.strip()) for x in v.split(",") if x.strip()]
        return v

    @field_validator("voices_dir", "model_cache_dir", "voice_library_dir", mode="before")
    @classmethod
    def parse_path(cls, v: str | Path) -> Path:
        """Parse path from string and expand ~."""
        if isinstance(v, str):
            return Path(v).expanduser()
        return v.expanduser() if hasattr(v, 'expanduser') else v