"""Core Pipeline Schemas

定义工作流管道的核心 Schema，包括 StepInput、StepOutput、StepResult、
StepStatus、StepRecord、WorkflowState 和 StepDefinition。
使用 Pydantic v2 确保类型安全和自动验证。
"""

from pydantic import BaseModel, Field
from typing import Optional, Dict, Literal, Any, List
from datetime import datetime
from enum import Enum
from pathlib import Path


class StepStatus(str, Enum):
    """步骤执行状态枚举"""
    PENDING = "pending"
    RUNNING = "running"
    COMPLETED = "completed"
    FAILED = "failed"
    SKIPPED = "skipped"


class StepInput(BaseModel):
    """所有步骤输入的基类

    Attributes:
        content_id: 内容唯一标识，如 B站 BV号、知乎回答ID等
        platform: 平台名称，限定为 bilibili 或 zhihu
    """
    content_id: str = Field(..., min_length=1, description="内容唯一标识")
    platform: Literal["bilibili", "zhihu"] = Field(..., description="平台名称")


class StepOutput(BaseModel):
    """所有步骤输出的基类

    Attributes:
        success: 步骤是否成功执行
        error_message: 错误信息，成功时为 None
        completed_at: 完成时间，默认为当前时间
    """
    success: bool = Field(..., description="步骤是否成功")
    error_message: Optional[str] = Field(None, description="错误信息")
    completed_at: datetime = Field(default_factory=datetime.now, description="完成时间")


class StepResult(BaseModel):
    """步骤执行结果

    包含步骤的输入、输出和执行状态。

    Attributes:
        step_name: 步骤名称
        input_data: 步骤输入数据
        output_data: 步骤输出数据，可能为 None
        status: 步骤执行状态
    """
    step_name: str = Field(..., description="步骤名称")
    input_data: StepInput = Field(..., description="步骤输入数据")
    output_data: Optional[StepOutput] = Field(None, description="步骤输出数据")
    status: StepStatus = Field(default=StepStatus.PENDING, description="步骤执行状态")


class StepRecord(BaseModel):
    """单个步骤的执行记录

    记录步骤执行的状态、时间和结果摘要。

    Attributes:
        step_name: 步骤名称
        status: 步骤执行状态
        started_at: 开始时间
        completed_at: 完成时间
        error_message: 错误信息
        output_summary: 输出摘要
    """
    step_name: str = Field(..., description="步骤名称")
    status: StepStatus = Field(..., description="步骤执行状态")
    started_at: Optional[datetime] = Field(None, description="开始时间")
    completed_at: Optional[datetime] = Field(None, description="完成时间")
    error_message: Optional[str] = Field(None, description="错误信息")
    output_summary: Optional[str] = Field(None, description="输出摘要")


class WorkflowState(BaseModel):
    """工作流状态 - 存储在 workflow_state.json

    管理整个工作流的状态，包括内容信息、执行历史和步骤状态。

    Attributes:
        schema_version: Schema 版本号
        content_id: 内容唯一标识
        platform: 平台名称
        source_url: 原始链接
        created_at: 创建时间
        updated_at: 更新时间
        steps: 步骤执行记录字典
    """
    schema_version: str = Field(default="1.0", description="Schema 版本号")
    content_id: str = Field(..., description="内容唯一标识")
    platform: str = Field(..., description="平台名称")
    source_url: str = Field(..., description="原始链接")
    created_at: datetime = Field(default_factory=datetime.now, description="创建时间")
    updated_at: datetime = Field(default_factory=datetime.now, description="更新时间")
    steps: Dict[str, StepRecord] = Field(default_factory=dict, description="步骤执行记录")

    def is_step_completed(self, step_name: str) -> bool:
        """检查指定步骤是否已完成

        Args:
            step_name: 步骤名称

        Returns:
            如果步骤存在且状态为 COMPLETED 返回 True，否则返回 False
        """
        record = self.steps.get(step_name)
        return record is not None and record.status == StepStatus.COMPLETED

    def mark_step_started(self, step_name: str) -> None:
        """标记步骤开始执行

        Args:
            step_name: 步骤名称
        """
        self.steps[step_name] = StepRecord(
            step_name=step_name,
            status=StepStatus.RUNNING,
            started_at=datetime.now()
        )
        self.updated_at = datetime.now()

    def mark_step_completed(self, step_name: str, output_summary: Optional[str] = None) -> None:
        """标记步骤完成

        Args:
            step_name: 步骤名称
            output_summary: 输出摘要（可选）
        """
        if step_name in self.steps:
            self.steps[step_name].status = StepStatus.COMPLETED
            self.steps[step_name].completed_at = datetime.now()
            self.steps[step_name].output_summary = output_summary
        else:
            self.steps[step_name] = StepRecord(
                step_name=step_name,
                status=StepStatus.COMPLETED,
                completed_at=datetime.now(),
                output_summary=output_summary
            )
        self.updated_at = datetime.now()

    def mark_step_failed(self, step_name: str, error_message: str) -> None:
        """标记步骤失败

        Args:
            step_name: 步骤名称
            error_message: 错误信息
        """
        if step_name in self.steps:
            self.steps[step_name].status = StepStatus.FAILED
            self.steps[step_name].error_message = error_message
            self.steps[step_name].completed_at = datetime.now()
        else:
            self.steps[step_name] = StepRecord(
                step_name=step_name,
                status=StepStatus.FAILED,
                error_message=error_message,
                completed_at=datetime.now()
            )
        self.updated_at = datetime.now()


class StepDefinition(BaseModel):
    """步骤定义 - 描述步骤的前置条件、输出格式、成功标准

    Attributes:
        step_name: 步骤名称
        description: 步骤描述
        input_schema: 输入 Schema 类型
        output_schema: 输出 Schema 类型
        dependencies: 依赖的步骤列表
    """
    step_name: str = Field(..., description="步骤名称")
    description: str = Field(..., description="步骤描述")
    input_schema: type[StepInput] = Field(..., description="输入 Schema 类型")
    output_schema: type[StepOutput] = Field(..., description="输出 Schema 类型")
    dependencies: list[str] = Field(default_factory=list, description="依赖的步骤列表")


# =============================================================================
# ASR Schemas (Phase 3)
# =============================================================================

class ASRInput(StepInput):
    """ASR 步骤输入

    继承 StepInput 的基础字段，添加视频路径和内容类型。

    Attributes:
        video_path: 视频文件路径（可能为空，用于 Whisper 降级）
        content_type: 内容类型，默认为 'video'
        content_dir: 内容目录路径，用于输出字幕文件
    """
    video_path: str = Field(default="", description="视频文件路径（可能为空）")
    content_type: str = Field(default="video", description="内容类型")
    content_dir: str = Field(..., description="内容目录路径")


class ASROutput(StepOutput):
    """ASR 步骤输出

    继承 StepOutput 的基础字段，添加 ASR 特定字段。

    Attributes:
        tool: 使用的工具 (subtitle, whisper, whisper-diarization)
        has_subtitle: 是否有官方字幕
        output_files: 输出文件路径字典 {txt, srt, json}
        segments: 带时间戳的分段列表
        text: 完整文本
        duration_seconds: 音频时长
        speakers: 说话者列表（仅说话者分离时有值）
    """
    tool: Literal["subtitle", "whisper", "whisper-diarization"] = Field(
        ..., description="使用的工具"
    )
    has_subtitle: bool = Field(..., description="是否有官方字幕")
    output_files: Dict[str, str] = Field(
        default_factory=dict, description="输出文件路径 {txt, srt, json}"
    )
    segments: List[Dict[str, Any]] = Field(
        default_factory=list, description="带时间戳的分段列表"
    )
    text: str = Field(default="", description="完整文本")
    duration_seconds: float = Field(default=0.0, description="音频时长")
    speakers: Optional[List[str]] = Field(
        None, description="说话者列表（仅说话者分离时有值）"
    )


# =============================================================================
# Parse Schemas (Phase 04)
# =============================================================================


class ParseInput(StepInput):
    """Parse 步骤输入

    继承 StepInput 的基础字段，添加 URL 字段。
    content_id 和 platform 在解析前是空的，解析后会被填充。

    Attributes:
        url: 要解析的 URL
        content_id: 解析后填充的内容 ID（初始为空）
        platform: 解析后填充的平台名称（初始为空）
    """
    url: str = Field(..., min_length=1, description="URL to parse")
    content_id: str = Field(default="", description="Will be populated after parsing")
    platform: str = Field(default="", description="Will be populated after parsing")


class ParseOutput(StepOutput):
    """Parse 步骤输出

    继承 StepOutput 的基础字段，添加解析特定字段。

    Attributes:
        platform: 检测到的平台 (bilibili, zhihu)
        content_type: 内容类型 (video, article, answer, zhuanlan)
        content_id: 提取的内容 ID
        resolved_url: 短链解析后的完整 URL
        original_url: 用户提供的原始 URL
    """
    platform: str = Field(..., description="Detected platform")
    content_type: str = Field(..., description="Content type (video, article, etc.)")
    content_id: str = Field(..., description="Extracted content ID")
    resolved_url: str = Field(..., description="Resolved URL after short link expansion")
    original_url: str = Field(..., description="Original URL provided by user")


# =============================================================================
# Download Schemas (Phase 04)
# =============================================================================


class DownloadInput(StepInput):
    """Download 步骤输入

    继承 StepInput 的基础字段，添加下载特定字段。

    Attributes:
        content_dir: 保存下载内容的目录路径
        content_type: 内容类型 (video, article, answer, zhuanlan)
        source_url: 原始 URL（可选，用于知乎回答等需要完整 URL 的情况）
    """
    content_dir: str = Field(..., min_length=1, description="Directory to save downloaded content")
    content_type: str = Field(..., min_length=1, description="Content type (video, article, answer, zhuanlan)")
    source_url: Optional[str] = Field(None, description="Original source URL")


class DownloadOutput(StepOutput):
    """Download 步骤输出

    继承 StepOutput 的基础字段，添加下载特定字段。

    Attributes:
        metadata_file: 元数据 JSON 文件路径
        content_file: 内容文件路径 (视频/音频或文本)
        title: 内容标题
        author: 内容作者
        duration: 视频内容的时长（秒），仅视频有值
    """
    metadata_file: str = Field(..., description="Path to metadata JSON file")
    content_file: str = Field(..., description="Path to content file (video/audio or text)")
    title: str = Field(..., description="Content title")
    author: str = Field(..., description="Content author")
    duration: Optional[int] = Field(None, description="Duration for video content in seconds")


# =============================================================================
# Analyze Schemas (Phase 04)
# =============================================================================


class AnalyzeInput(StepInput):
    """Analyze 步骤输入

    继承 StepInput 的基础字段，添加分析特定字段。

    Attributes:
        metadata_file: 元数据 JSON 文件路径
        content_file: 内容文件路径（字幕 JSON 或文章内容）
        content_type: 内容类型（video 或 article）
        output_file: 输出分析 Markdown 文件路径
    """
    metadata_file: str = Field(..., min_length=1, description="Path to metadata JSON file")
    content_file: str = Field(..., min_length=1, description="Path to content file (subtitle JSON or article content)")
    content_type: str = Field(..., min_length=1, description="Content type (video or article)")
    output_file: str = Field(..., min_length=1, description="Path to output analysis Markdown file")


class AnalyzeOutput(StepOutput):
    """Analyze 步骤输出

    继承 StepOutput 的基础字段，添加分析特定字段。

    Attributes:
        analysis_file: 生成的分析报告文件路径
        summary: 内容摘要
        main_topics: 提取的主要主题列表
        key_insights: 关键见解列表
        tags: 推荐标签列表
        stats: 分析统计信息字典
    """
    analysis_file: str = Field(..., description="Path to generated analysis report")
    summary: str = Field(default="", description="Content summary")
    main_topics: List[str] = Field(default_factory=list, description="Main topics extracted")
    key_insights: List[str] = Field(default_factory=list, description="Key insights")
    tags: List[str] = Field(default_factory=list, description="Recommended tags")
    stats: Dict[str, Any] = Field(default_factory=dict, description="Statistics about analysis")


# =============================================================================
# Pipeline Schemas (Phase 04-04)
# =============================================================================


class PipelineInput(BaseModel):
    """Pipeline input - URL to process

    Attributes:
        url: URL to process (required)
        output_dir: Optional output directory for content
        skip_completed: Skip already completed steps (default True)
    """
    url: str = Field(..., min_length=1, description="URL to process")
    output_dir: Optional[Path] = Field(None, description="Output directory for content")
    skip_completed: bool = Field(default=True, description="Skip already completed steps")


class PipelineOutput(BaseModel):
    """Pipeline output - results from all steps

    Attributes:
        success: Whether the pipeline completed successfully
        error_message: Error message if pipeline failed
        results: Dictionary of results from each step
        content_dir: Path to the content directory
    """
    success: bool = Field(..., description="Whether the pipeline completed successfully")
    error_message: Optional[str] = Field(None, description="Error message if pipeline failed")
    results: Dict[str, Any] = Field(default_factory=dict, description="Results from each step")
    content_dir: Optional[Path] = Field(None, description="Path to the content directory")


class PipelineConfig(BaseModel):
    """Pipeline configuration

    Attributes:
        steps: Steps to execute in order (default: parse, download, asr, analyze, archive)
        output_dir: Base output directory
        skip_completed: Skip completed steps (default True)
        max_retries: Max retries per step (default 3)
    """
    steps: List[str] = Field(
        default_factory=lambda: ["parse", "download", "asr", "analyze", "archive"],
        description="Steps to execute in order"
    )
    output_dir: Optional[Path] = Field(None, description="Base output directory")
    skip_completed: bool = Field(default=True, description="Skip completed steps")
    max_retries: int = Field(default=3, description="Max retries per step")


# =============================================================================
# Archive Schemas (Phase 5)
# =============================================================================


class ArchiveInput(StepInput):
    """Archive 步骤输入

    继承 StepInput 的基础字段，添加归档特定字段。

    Attributes:
        content_dir: 内容目录路径（中间目录）
        analysis_file: 分析报告 md 文件路径
        content_type: 内容类型
    """
    content_dir: str = Field(..., min_length=1, description="Content directory path (intermediate)")
    analysis_file: str = Field(..., min_length=1, description="Path to analysis Markdown file")
    content_type: str = Field(..., min_length=1, description="Content type (video, article, etc.)")


class ArchiveOutput(StepOutput):
    """Archive 步骤输出

    继承 StepOutput 的基础字段，添加归档特定字段。

    Attributes:
        archive_file: 归档后的文件路径
        skipped: 是否跳过（已有相同文件）
        platform: 平台名称
    """
    archive_file: str = Field(default="", description="Path to archived file")
    skipped: bool = Field(default=False, description="Whether archiving was skipped")
    platform: str = Field(default="", description="Platform name")


# =============================================================================
# SRT Schemas (Phase 12)
# =============================================================================


class SRTInput(StepInput):
    """SRT 步骤输入

    用于 SRTProcessor 处理 B站字幕。

    Attributes:
        srt_file: SRT 字幕文件路径
        metadata_file: 元数据 JSON 文件路径
        output_file: 输出 Markdown 文件路径
        content_type: 内容类型（默认 video）
    """
    srt_file: str = Field(..., min_length=1, description="Path to SRT subtitle file")
    metadata_file: str = Field(..., min_length=1, description="Path to metadata JSON file")
    output_file: str = Field(..., min_length=1, description="Path to output Markdown file")
    content_type: str = Field(default="video", description="Content type")


class SRTOutput(StepOutput):
    """SRT 步骤输出

    继承 StepOutput 的基础字段，添加 SRT 处理特定字段。

    Attributes:
        output_file: 生成的 Markdown 文件路径
        paragraphs: 生成的段落列表
        tags: 提取的标签列表
        corrected: 是否进行了 ASR 纠错
    """
    output_file: str = Field(..., description="Path to generated Markdown file")
    paragraphs: List[str] = Field(default_factory=list, description="Generated paragraphs")
    tags: List[str] = Field(default_factory=list, description="Extracted tags")
    corrected: bool = Field(default=False, description="Whether ASR correction was applied")