"""Pipeline YAML加载器 - 从YAML文件/字符串加载Pipeline定义，验证DAG，解析变量"""

import logging
import re
from pathlib import Path

import yaml

from .schema import Pipeline, PipelineStage

logger = logging.getLogger(__name__)

# 变量引用正则：匹配 ${var.path} 格式
VARIABLE_PATTERN = re.compile(r"\$\{([^}]+)\}")


class PipelineLoadError(Exception):
    """Pipeline加载错误"""

    def __init__(self, pipeline_name: str, reason: str = ""):
        self.pipeline_name = pipeline_name
        self.reason = reason
        super().__init__(f"Failed to load pipeline '{pipeline_name}': {reason}")


class PipelineCyclicError(PipelineLoadError):
    """Pipeline存在循环依赖"""

    def __init__(self, pipeline_name: str):
        super().__init__(pipeline_name, "Cyclic dependency detected in stages")


class PipelineValidationError(PipelineLoadError):
    """Pipeline验证失败"""

    def __init__(self, pipeline_name: str, reason: str = ""):
        super().__init__(pipeline_name, f"Validation error: {reason}")


class PipelineLoader:
    """从YAML文件或字符串加载Pipeline定义"""

    def __init__(self, pipelines_dir: str | None = None):
        """
        Args:
            pipelines_dir: Pipeline YAML文件所在目录，默认为 backend/pipelines
        """
        if pipelines_dir is None:
            # 默认路径：backend/pipelines（相对于项目根目录）
            pipelines_dir = str(Path(__file__).resolve().parents[3] / "pipelines")
        self.pipelines_dir = Path(pipelines_dir)

    def load(self, pipeline_name: str) -> Pipeline:
        """
        从YAML文件加载Pipeline定义。

        Args:
            pipeline_name: Pipeline名称，对应 {pipelines_dir}/{pipeline_name}.yaml

        Returns:
            Pipeline对象

        Raises:
            PipelineLoadError: 文件不存在或解析失败
            PipelineCyclicError: 存在循环依赖
            PipelineValidationError: 验证失败
        """
        yaml_path = self.pipelines_dir / f"{pipeline_name}.yaml"

        if not yaml_path.exists():
            # 尝试 .yml 后缀
            yml_path = self.pipelines_dir / f"{pipeline_name}.yml"
            if yml_path.exists():
                yaml_path = yml_path
            else:
                raise PipelineLoadError(
                    pipeline_name,
                    f"Pipeline file not found: {yaml_path}",
                )

        try:
            raw_content = yaml_path.read_text(encoding="utf-8")
        except OSError as e:
            raise PipelineLoadError(pipeline_name, f"Failed to read file: {e}")

        return self.load_from_yaml(raw_content, pipeline_name=pipeline_name)

    def load_from_yaml(
        self, yaml_content: str, pipeline_name: str | None = None
    ) -> Pipeline:
        """
        从YAML字符串加载Pipeline定义。

        Args:
            yaml_content: YAML格式字符串
            pipeline_name: 可选的Pipeline名称（用于错误信息），默认从YAML内容中读取

        Returns:
            Pipeline对象

        Raises:
            PipelineLoadError: 解析失败
            PipelineCyclicError: 存在循环依赖
            PipelineValidationError: 验证失败
        """
        try:
            raw_data = yaml.safe_load(yaml_content)
        except yaml.YAMLError as e:
            name = pipeline_name or "<unknown>"
            raise PipelineLoadError(name, f"YAML parse error: {e}")

        if not isinstance(raw_data, dict):
            name = pipeline_name or "<unknown>"
            raise PipelineLoadError(name, "YAML content must be a mapping")

        # 使用Pydantic解析
        try:
            pipeline = Pipeline.model_validate(raw_data)
        except Exception as e:
            name = pipeline_name or raw_data.get("name", "<unknown>")
            raise PipelineLoadError(str(name), f"Schema validation error: {e}")

        # 验证
        name = pipeline.name
        self._validate_stage_names(pipeline)
        self._validate_dependencies(pipeline)

        # DAG无环检测
        if not self.validate_dag(pipeline.stages):
            raise PipelineCyclicError(name)

        logger.info(f"Pipeline '{name}' loaded successfully ({len(pipeline.stages)} stages)")
        return pipeline

    def _validate_stage_names(self, pipeline: Pipeline) -> None:
        """验证stage名称唯一性"""
        names = [s.name for s in pipeline.stages]
        duplicates = [n for n in names if names.count(n) > 1]
        if duplicates:
            unique_dups = sorted(set(duplicates))
            raise PipelineValidationError(
                pipeline.name,
                f"Duplicate stage names: {unique_dups}",
            )

    def _validate_dependencies(self, pipeline: Pipeline) -> None:
        """验证depends_on引用的stage存在"""
        stage_names = {s.name for s in pipeline.stages}
        for stage in pipeline.stages:
            for dep in stage.depends_on:
                if dep not in stage_names:
                    raise PipelineValidationError(
                        pipeline.name,
                        f"Stage '{stage.name}' depends on unknown stage '{dep}'",
                    )

    @staticmethod
    def validate_dag(stages: list[PipelineStage]) -> bool:
        """
        验证stages的依赖关系是有向无环图（DAG）。
        使用Kahn算法进行拓扑排序检测环。

        Args:
            stages: PipelineStage列表

        Returns:
            True如果是DAG，False如果存在环
        """
        stage_names = {s.name for s in stages}
        name_to_stage = {s.name: s for s in stages}

        # 构建邻接表和入度表
        in_degree: dict[str, int] = {name: 0 for name in stage_names}
        adj: dict[str, list[str]] = {name: [] for name in stage_names}

        for stage in stages:
            for dep in stage.depends_on:
                if dep in stage_names:
                    adj[dep].append(stage.name)
                    in_degree[stage.name] += 1

        # Kahn算法：入度为0的节点入队
        queue: list[str] = [name for name, deg in in_degree.items() if deg == 0]
        visited_count = 0

        while queue:
            node = queue.pop(0)
            visited_count += 1
            for neighbor in adj[node]:
                in_degree[neighbor] -= 1
                if in_degree[neighbor] == 0:
                    queue.append(neighbor)

        # 如果所有节点都被访问，说明无环
        return visited_count == len(stage_names)

    @staticmethod
    def resolve_variables(template: str | dict | list, context: dict) -> str | dict | list | int | float | bool | None:
        """
        解析${var.path}格式的变量引用。

        支持嵌套路径访问，例如：
        - ${stages.step1.outputs.result} → context["stages"]["step1"]["outputs"]["result"]
        - ${brand_name} → context["brand_name"]

        对于字符串模板：替换其中的变量引用
        对于dict/list：递归处理其中的变量引用
        对于其他类型：原样返回

        Args:
            template: 待解析的模板（可以是str/dict/list/其他）
            context: 变量上下文

        Returns:
            解析后的值
        """
        if isinstance(template, str):
            return PipelineLoader._resolve_string(template, context)
        elif isinstance(template, dict):
            return {
                k: PipelineLoader.resolve_variables(v, context)
                for k, v in template.items()
            }
        elif isinstance(template, list):
            return [PipelineLoader.resolve_variables(item, context) for item in template]
        else:
            # int, float, bool, None 等原样返回
            return template

    @staticmethod
    def _resolve_string(template: str, context: dict) -> str | dict | list | int | float | bool | None:
        """
        解析字符串中的变量引用。

        如果整个字符串就是一个变量引用（如"${brand_name}"），
        则直接返回变量值（保留原始类型）。
        如果字符串中包含多个变量引用或混合文本，
        则替换为字符串。
        """
        matches = list(VARIABLE_PATTERN.finditer(template))

        if not matches:
            return template

        # 整个字符串就是一个变量引用 → 保留原始类型
        if len(matches) == 1 and matches[0].group(0) == template:
            path = matches[0].group(1).strip()
            return PipelineLoader._resolve_path(path, context)

        # 混合文本 → 替换为字符串
        def replacer(match: re.Match) -> str:
            path = match.group(1).strip()
            value = PipelineLoader._resolve_path(path, context)
            return str(value)

        return VARIABLE_PATTERN.sub(replacer, template)

    @staticmethod
    def _resolve_path(path: str, context: dict) -> str | dict | list | int | float | bool | None:
        """
        解析点分路径，从context中获取值。

        例如：stages.step1.outputs.result
        → context["stages"]["step1"]["outputs"]["result"]
        """
        parts = path.split(".")
        current: str | dict | list | int | float | bool | None = context

        for part in parts:
            if isinstance(current, dict):
                if part in current:
                    current = current[part]
                else:
                    logger.warning(f"Variable path '{path}' not found in context (missing key: '{part}')")
                    # 返回原始占位符
                    return f"${{{path}}}"
            else:
                logger.warning(f"Variable path '{path}' cannot be resolved (non-dict at '{part}')")
                return f"${{{path}}}"

        return current