import re
import json
from typing import Type, TypeVar, Any, Generic
from pydantic import BaseModel, ValidationError
from pydantic_ai import Agent, RunContext
from pydantic_ai.run import AgentRunResult

T = TypeVar('T', bound=BaseModel)

class AgentRunResultProxy:
    def __init__(self, original, parsed):
        self._original = original
        self._parsed = parsed
    def __getattr__(self, name):
        if name == 'data':
            return self._parsed
        if name == 'output':
            return self._parsed
        return getattr(self._original, name)

class DeepSeekReasonerAgent(Generic[T]):
    """
    专为 DeepSeek-V4/R1 设计的适配器。
    将结构化输出降级为文本解析模式，并支持重试逻辑以确保系统兼容性。
    """

    def __init__(
            self,
            model,
            name,
            output_type: Any = str,
            system_prompt: str = "",
            deps_type: Type[Any] = None,
            tools: list = None,
            retries: int = 3,
            **kwargs
    ):
        self.output_schema = output_type
        self.has_custom_output = output_type is not str and output_type is not None
        self.tools = tools or []
        self.retries = retries

        format_instruction = ""
        if self.has_custom_output:
            try:
                from pydantic import TypeAdapter
                schema_dict = TypeAdapter(self.output_schema).json_schema()
                schema_str = json.dumps(schema_dict, ensure_ascii=False)
                format_instruction = (
                    f"\n\nCRITICAL: 你必须输出且只能输出一段纯 JSON 格式的数据，"
                    f"并包裹在 ```json 和 ``` 之间。格式必须符合以下 JSON Schema 结构（或对应数据类型）：\n"
                    f"{schema_str}"
                )
            except Exception:
                pass

        tool_instruction = ""
        if self.tools:
            tool_descs = []
            for t in self.tools:
                desc = getattr(t, '__name__', str(t))
                if hasattr(t, '__doc__') and t.__doc__:
                    desc += f": {t.__doc__.strip()}"
                tool_descs.append(f"- {desc}")
            tool_instruction = (
                "\n\n系统为您提供了以下工具。由于当前处于结构化降级模式，无法原生调用。"
                "但如果您在思考过程中判断必须使用这些工具，请在返回的结构中（或如果是自由文本）注明意图，由外层逻辑进行调度：\n" +
                "\n".join(tool_descs)
            )

        self.agent = Agent(
            model=model,
            name=name,
            output_type=str,  # Force native agent to return str to disable function calling
            system_prompt=system_prompt + format_instruction + tool_instruction,
            deps_type=deps_type,
            **kwargs
        )

    def _parse_output(self, text: str) -> Any:
        if not self.has_custom_output:
            return text

        match = re.search(r'```json\s*(.*?)\s*```', text, re.DOTALL)
        json_str = match.group(1).strip() if match else text

        if not json_str.startswith('{') and not json_str.startswith('['):
            start_obj = json_str.find('{')
            start_arr = json_str.find('[')
            start = -1
            end = -1
            if start_obj != -1 and (start_arr == -1 or start_obj < start_arr):
                start = start_obj
                end = json_str.rfind('}')
            elif start_arr != -1:
                start = start_arr
                end = json_str.rfind(']')

            if start != -1 and end != -1 and end > start:
                json_str = json_str[start:end+1]

        if not json_str:
            raise ValueError("未找到有效的 JSON 块。请将结果包装在 ```json 中。")

        try:
            from pydantic import TypeAdapter
            adapter = TypeAdapter(self.output_schema)
            return adapter.validate_json(json_str)
        except ValidationError as e:
            raise ValueError(f"返回的 JSON 无法匹配所需结构：{e}")
        except json.JSONDecodeError as e:
            raise ValueError(f"返回的不是合法的 JSON：{e}")


    def __getattr__(self, item):
        # Delegate any unknown attributes (like .system_prompt, .tool) to the underlying pydantic_ai Agent
        return getattr(self.agent, item)

    async def run(self, user_prompt: str, deps: Any = None, message_history: list = None, **kwargs) -> Any:
        # Custom retry loop
        current_history = message_history or []
        last_exception = None

        for attempt in range(self.retries + 1):
            result = await self.agent.run(
                user_prompt,
                deps=deps,
                message_history=current_history,
                **kwargs
            )

            raw_text = result.data if hasattr(result, 'data') else getattr(result, 'output', str(result))

            try:
                parsed_data = self._parse_output(raw_text)

                # Proxy the result to inject the parsed data seamlessly
                return AgentRunResultProxy(result, parsed_data)

            except ValueError as e:
                last_exception = e
                # Prepare retry prompt
                user_prompt = f"你的上一次输出解析失败，错误原因是: {e}\n请修正格式后重新输出。"

                # We need to maintain history manually so the model sees what it did wrong
                # Actually, pydantic-ai manages history inside the result. Let's use the all_messages from result
                if hasattr(result, 'all_messages'):
                    current_history = result.all_messages()

        raise ValueError(f"Exceeded maximum retries ({self.retries}) for output validation. Last error: {last_exception}")