import os
from collections.abc import Mapping
from dataclasses import dataclass
from typing import Any, Protocol

import httpx
from sqlalchemy import select

from app.core.errors import (
    DependencyUnavailableError,
    RecoverableAgentError,
    UpstreamTimeoutError,
)
from app.infrastructure.db import SessionFactory
from app.model.configuration import ModelEndpointConfig


class ModelGateway(Protocol):
    async def generate(self, *, endpoint_code: str, prompt: str, timeout_ms: int) -> str: ...

    async def embed(self, *, endpoint_code: str, text: str, timeout_ms: int) -> list[float]: ...


class EndpointSettings(Protocol):
    endpoint_code: str
    base_url: str
    model_name: str
    secret_ref: str


class EnvironmentSecretResolver:
    def resolve(self, secret_ref: str) -> str:
        if not secret_ref.startswith("env:"):
            raise RecoverableAgentError("模型密钥必须使用 env: 引用")
        name = secret_ref.removeprefix("env:")
        value = os.getenv(name)
        if not value:
            raise RecoverableAgentError("模型密钥未配置")
        return value


class OpenAICompatibleGateway:
    """供应商无关的 Chat Completions Adapter；密钥只从 secret_ref 解析。"""

    def __init__(
        self,
        endpoints: Mapping[str, EndpointSettings],
        *,
        secret_resolver: EnvironmentSecretResolver | None = None,
        client: httpx.AsyncClient | None = None,
    ) -> None:
        self.endpoints = endpoints
        self.secret_resolver = secret_resolver or EnvironmentSecretResolver()
        self.client = client
        self._owns_client = client is None

    async def generate(self, *, endpoint_code: str, prompt: str, timeout_ms: int) -> str:
        endpoint = self.endpoints.get(endpoint_code)
        if endpoint is None:
            raise RecoverableAgentError("模型端点未注册")
        token = self.secret_resolver.resolve(endpoint.secret_ref)
        url = endpoint.base_url.rstrip("/") + "/chat/completions"
        payload = {
            "model": endpoint.model_name,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0,
        }
        client = self.client or httpx.AsyncClient()
        try:
            response = await client.post(
                url,
                headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
                json=payload,
                timeout=httpx.Timeout(timeout_ms / 1000),
            )
            response.raise_for_status()
            body: Any = response.json()
            content = body.get("choices", [{}])[0].get("message", {}).get("content")
            if not isinstance(content, str) or not content.strip():
                raise DependencyUnavailableError("模型响应缺少文本")
            return content
        except httpx.TimeoutException as exc:
            raise UpstreamTimeoutError("模型端点调用超时") from exc
        except httpx.HTTPError as exc:
            raise DependencyUnavailableError("模型端点调用失败") from exc
        finally:
            if self._owns_client:
                await client.aclose()

    async def embed(self, *, endpoint_code: str, text: str, timeout_ms: int) -> list[float]:
        """OpenAI-compatible Embeddings Adapter；密钥同样只从 secret_ref 解析。"""
        endpoint = self.endpoints.get(endpoint_code)
        if endpoint is None:
            raise RecoverableAgentError("模型端点未注册")
        token = self.secret_resolver.resolve(endpoint.secret_ref)
        url = endpoint.base_url.rstrip("/") + "/embeddings"
        payload = {"model": endpoint.model_name, "input": text}
        client = self.client or httpx.AsyncClient()
        try:
            response = await client.post(
                url,
                headers={"Authorization": f"Bearer {token}", "Content-Type": "application/json"},
                json=payload,
                timeout=httpx.Timeout(timeout_ms / 1000),
            )
            response.raise_for_status()
            body: Any = response.json()
            vector = _first_embedding(body)
            if vector is None:
                raise DependencyUnavailableError("向量响应缺少 embedding")
            return vector
        except httpx.TimeoutException as exc:
            raise UpstreamTimeoutError("向量端点调用超时") from exc
        except httpx.HTTPError as exc:
            raise DependencyUnavailableError("向量端点调用失败") from exc
        finally:
            if self._owns_client:
                await client.aclose()


def _first_embedding(body: Any) -> list[float] | None:
    """取 OpenAI-compatible 响应的首条向量；结构不符时返回 None，由调用方失败关闭。"""
    data = body.get("data") if isinstance(body, dict) else None
    if not isinstance(data, list) or not data:
        return None
    entry = data[0]
    raw = entry.get("embedding") if isinstance(entry, dict) else None
    if not isinstance(raw, list) or not raw:
        return None
    try:
        return [float(value) for value in raw]
    except (TypeError, ValueError):
        return None


class DatabaseModelGateway:
    """从当前数据库端点配置动态构造已批准的 OpenAI-compatible Adapter。"""

    async def _endpoint(self, endpoint_code: str) -> ModelEndpointConfig:
        async with SessionFactory() as session:
            endpoint = await session.scalar(select(ModelEndpointConfig).where(
                ModelEndpointConfig.endpoint_code == endpoint_code,
                ModelEndpointConfig.status == "active",
            ))
        if endpoint is None:
            raise RecoverableAgentError("模型端点未注册或未激活")
        return endpoint

    async def generate(self, *, endpoint_code: str, prompt: str, timeout_ms: int) -> str:
        endpoint = await self._endpoint(endpoint_code)
        adapter = OpenAICompatibleGateway({endpoint.endpoint_code: endpoint})
        return await adapter.generate(
            endpoint_code=endpoint.endpoint_code, prompt=prompt, timeout_ms=timeout_ms
        )

    async def embed(self, *, endpoint_code: str, text: str, timeout_ms: int) -> list[float]:
        endpoint = await self._endpoint(endpoint_code)
        adapter = OpenAICompatibleGateway({endpoint.endpoint_code: endpoint})
        return await adapter.embed(
            endpoint_code=endpoint.endpoint_code, text=text, timeout_ms=timeout_ms
        )


# task_type → 端点必须声明的能力名。
#
# 为什么需要一张映射表而不是把 task_type 直接当能力名用：`memory_extraction` 并不是
# 任何端点的能力名（deepseek 声明的是 text_generation/json_output/intent_classification），
# 它需要的是「能生成结构化文本」的端点。若按同名筛选会得到空集，把记忆抽取打成
# 失败关闭——这是修复端点筛选时最容易引入的回归。
TASK_CAPABILITY: dict[str, str] = {
    "embedding": "embedding",
    "intent_classification": "intent_classification",
    "memory_extraction": "text_generation",
    "text_generation": "text_generation",
}


class DatabaseModelEndpointResolver:
    """按任务类型筛选当前已激活的模型端点。

    修复的缺陷：原实现 `del agent_type, task_type` 后返回**全部** active 端点，而
    `ModelDispatchService` 只按顺序尝试前 `max_attempts`（默认 2）个端点。两者叠加的
    后果是「能不能选到支持该任务的端点」取决于端点在表里的顺序——实测表现为每次
    embedding 都先拿文本生成端点失败一次再落到真正的向量端点。

    筛选策略保持保守：只在确实筛到端点时收窄，未映射的 task_type 与「一个都没声明
    该能力」的配置缺口都退回全部端点，让故障表现为调用失败而不是解析为空。
    """

    async def resolve(self, *, agent_type: str, task_type: str) -> list[ModelEndpointConfig]:
        del agent_type  # 按 agent_type 分配端点的路由仍由发布配置与 ModelRouterService 扩展。
        async with SessionFactory() as session:
            endpoints = list(await session.scalars(select(ModelEndpointConfig).where(
                ModelEndpointConfig.status == "active"
            )))
        capability = TASK_CAPABILITY.get(task_type)
        if capability is None:
            return endpoints
        matched = [
            endpoint for endpoint in endpoints
            if isinstance(endpoint.capabilities, list) and capability in endpoint.capabilities
        ]
        return matched or endpoints


@dataclass(frozen=True)
class ModelExecution:
    endpoint_code: str
    text: str
    attempts: int
    degraded: bool = False


@dataclass(frozen=True)
class ModelEmbedding:
    endpoint_code: str
    vector: list[float]
    attempts: int
    degraded: bool = False


class ModelDispatchService:
    """Executes only router-approved endpoints and falls back in declared order."""

    def __init__(self, gateway: ModelGateway) -> None:
        self.gateway = gateway

    async def generate(
        self,
        endpoints: list[Any],
        prompt: str,
        *,
        max_attempts: int = 2,
    ) -> ModelExecution:
        last_error: Exception | None = None
        attempts = 0
        for endpoint in endpoints[: max(1, max_attempts)]:
            attempts += 1
            try:
                text = await self.gateway.generate(
                    endpoint_code=endpoint.endpoint_code,
                    prompt=prompt,
                    timeout_ms=endpoint.timeout_ms,
                )
                return ModelExecution(endpoint.endpoint_code, text, attempts, attempts > 1)
            except Exception as exc:
                last_error = exc
        raise RecoverableAgentError("所有已批准模型端点调用失败") from last_error

    async def embed(
        self, endpoints: list[Any], text: str, *, max_attempts: int = 2
    ) -> ModelEmbedding:
        """与文本生成同族的受控降级：按声明顺序尝试，成功即返回并标记是否降级。"""
        last_error: Exception | None = None
        attempts = 0
        for endpoint in endpoints[: max(1, max_attempts)]:
            attempts += 1
            try:
                vector = await self.gateway.embed(
                    endpoint_code=endpoint.endpoint_code,
                    text=text,
                    timeout_ms=endpoint.timeout_ms,
                )
                return ModelEmbedding(endpoint.endpoint_code, vector, attempts, attempts > 1)
            except Exception as exc:
                last_error = exc
        raise RecoverableAgentError("所有已批准向量端点调用失败") from last_error


class ModelGenerationService:
    """业务 Agent 的唯一模型生成入口。路由结果必须先由 ModelRouterService 给出。"""

    def __init__(self, dispatch: ModelDispatchService) -> None:
        self.dispatch = dispatch

    async def generate(
        self, endpoints: list[Any], prompt: str, *, max_attempts: int = 2
    ) -> ModelExecution:
        if not endpoints:
            raise RecoverableAgentError("没有可用的已批准模型端点")
        return await self.dispatch.generate(endpoints, prompt, max_attempts=max_attempts)


class ModelEmbeddingService:
    """文本向量化入口（记忆语义召回等只读用途），与生成同样必须先经过端点解析。"""

    def __init__(self, dispatch: ModelDispatchService) -> None:
        self.dispatch = dispatch

    async def embed(
        self, endpoints: list[Any], text: str, *, max_attempts: int = 2
    ) -> ModelEmbedding:
        if not endpoints:
            raise RecoverableAgentError("没有可用的已批准向量端点")
        return await self.dispatch.embed(endpoints, text, max_attempts=max_attempts)