"""运行时配置：所有参数从 config.yaml 读取。

CPU dev / GPU prod 仅靠 device / model / compute_type 三项切换，代码完全不变。
默认值与 config.example.yaml 对齐，确保无 yaml 时也能用最小配置启动。
"""

from __future__ import annotations

import os
from functools import lru_cache
from pathlib import Path

import yaml
from pydantic import BaseModel, Field, field_validator

PROJECT_ROOT = Path(__file__).resolve().parent.parent
DEFAULT_CONFIG_PATH = PROJECT_ROOT / "config.yaml"


class ServerConfig(BaseModel):
    host: str = "0.0.0.0"
    port: int = 8000
    workers: int = 1


class StorageConfig(BaseModel):
    upload_dir: str = "/data/uploads"
    work_dir: str = "/data/.work"
    output_dir: str = "/data/outputs"
    chunk_bytes: int = 1024 * 1024
    chunk_session_ttl_seconds: int = 300
    # 缓存保留：超期任务产物（输出字幕 / 中间音频 / 保留的原始视频）连同 DB 记录一并清理
    cache_retention_days: int = 7
    # 定时清理间隔（小时）：启动时跑一次，之后按此间隔循环
    cache_cleanup_interval_hours: int = 24


class ProcessingConfig(BaseModel):
    delete_original_after_extract: bool = True
    keep_audio: bool = False


class AsrConfig(BaseModel):
    model: str = "tiny.en"       # CPU: tiny.en；GPU: large-v3-turbo
    device: str = "cpu"          # cpu | cuda
    compute_type: str = "int8"   # cpu: int8；gpu: float16
    language: str = "en"
    word_timestamps: bool = True
    vad_filter: bool = True
    batch_size: int = 8          # BatchedInferencePipeline 的音频块批大小；GPU 建议 16


class TranslationConfig(BaseModel):
    model: str = "Helsinki-NLP/opus-mt-en-zh"  # CPU: opus-mt（轻量）；GPU: facebook/nllb-200-distilled-1.3B
    device: str = "cpu"          # cpu | cuda
    src_lang: str = "eng_Latn"   # NLLB 语言码：英语
    tgt_lang: str = "zho_Hans"   # NLLB 语言码：简体中文
    batch_size: int = 8          # 排序后单批最大条数；CPU: 8，GPU: 32（显存独占可用大 batch）
    max_length: int = 256        # 单条最大生成 token 数
    sort_by_length: bool = True  # 按句子长度排序后分批，减少批内 padding 浪费（GPU 收益大）


class SegmentationConfig(BaseModel):
    max_words_per_line: int = 14
    max_duration_seconds: float = 7.0
    min_duration_seconds: float = 1.0
    max_chars_per_line: int = 42


class LoggingConfig(BaseModel):
    """日志配置：控制台 + 内存缓冲的最低级别，以及缓冲条数。

    分层语义：
    - debug：进度详情（任务 [status pct%]、转写/翻译逐批统计、ffmpeg 命令）
    - info：任务流转里程碑（音频提取/ASR/翻译 的开始与完成、模型加载与卸载）
    - error：详细错误（完整 traceback，由 logger.exception 自带）
    """

    level: str = "info"            # debug | info | warning | error
    buffer_size: int = 2000        # /logs 页面内存缓冲条数

    @field_validator("level", mode="before")
    @classmethod
    def _normalize_level(cls, v):
        return str(v).lower() if v else "info"


class DocsConfig(BaseModel):
    """/docs Basic Auth 凭据（明文，对齐 server）。"""

    enabled: bool = True
    username: str = "admin"
    password: str = ""
    realm: str = "audio2text docs"

    @field_validator("password", mode="before")
    @classmethod
    def _coerce(cls, v):
        return "" if v is None else str(v)


class Settings(BaseModel):
    server: ServerConfig = ServerConfig()
    storage: StorageConfig = StorageConfig()
    processing: ProcessingConfig = ProcessingConfig()
    asr: AsrConfig = AsrConfig()
    translation: TranslationConfig = TranslationConfig()
    segmentation: SegmentationConfig = SegmentationConfig()
    logging: LoggingConfig = LoggingConfig()
    docs: DocsConfig = DocsConfig()

    def upload_dir(self) -> Path:
        return Path(self.storage.upload_dir)

    def work_dir(self) -> Path:
        return Path(self.storage.work_dir)

    def output_dir(self) -> Path:
        return Path(self.storage.output_dir)


def _load_yaml(path: Path) -> dict:
    if not path.exists():
        raise FileNotFoundError(
            f"未找到配置文件 {path}；请先 cp config.example.yaml config.yaml 并填值。"
        )
    return yaml.safe_load(path.read_text(encoding="utf-8")) or {}


@lru_cache(maxsize=1)
def get_settings() -> Settings:
    path = Path(os.getenv("CONFIG_PATH", str(DEFAULT_CONFIG_PATH)))
    return Settings.model_validate(_load_yaml(path))


def reload_settings() -> Settings:
    """清缓存并重新读取，供脚本与测试使用。"""
    get_settings.cache_clear()
    return get_settings()