#  Copyright 2026 zhaoxi826
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIChatModel
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.models.gemini import GeminiModel
from pydantic_ai.providers.openai import OpenAIProvider
from pydantic_ai.providers.anthropic import AnthropicProvider
from pydantic_ai.providers.deepseek import DeepSeekProvider
from pydantic_ai.providers.google import GoogleProvider
from kilostar.core.global_state_machine.model_provider import Provider
from kilostar.utils.agent_model import ResponseModel, DepsModel
from kilostar.utils.error import ModelNotExistError


class AgentFactory:
    """AgentFactory 核心组件类。
    这是一个领域数据模型或功能封装类，承载了 AgentFactory 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""

    def __init__(self):
        self._models_mapping = {
            "openai": {
                "model_class": OpenAIChatModel,
                "provider_class": OpenAIProvider,
                "provider_kwargs": {"base_url": True, "api_key": True},
            },
            "claude": {
                "model_class": AnthropicModel,
                "provider_class": AnthropicProvider,
                "provider_kwargs": {"api_key": True},
            },
            "deepseek": {
                "model_class": OpenAIChatModel,
                "provider_class": DeepSeekProvider,
                "provider_kwargs": {"api_key": True},
            },
            "gemini": {
                "model_class": GeminiModel,
                "provider_class": GoogleProvider,
                "provider_kwargs": {"api_key": True},
            },
        }

    def create_agent(
        self,
        provider: Provider,
        model_id: str,
        output_type: ResponseModel,
        system_prompt: str,
        deps_type: DepsModel,
        agent_name: str,
        tools: list = None,
    ) -> Agent:
        """
            create_agent方法，将输入的provider对象实例化为一个pydantic-ai的agent对象

        Args:
            provider: Provider对象，从global_state_machine中获取
            model_id: 模型名
            output_type: 输出格式
            system_prompt: 系统提示词
            deps_type: 依赖类型，在agent运行时动态输入的格式化消息
            agent_name: agent的名字
            tools: 工具列表

        Returns:
            返回被实例化的pydantic-ai的Agent对象
        """
        if model_id not in provider.provider_models:
            raise ModelNotExistError("模型不存在")
        if provider.provider_type not in self._models_mapping:
            raise ValueError(f"不支持的协议类型: {provider.provider_type}")

        config = self._models_mapping[provider.provider_type]
        model_class = config["model_class"]
        provider_class = config["provider_class"]
        provider_kwargs = config["provider_kwargs"]

        # 构建 provider 实例化参数
        init_kwargs = {}
        if provider_kwargs.get("api_key"):
            init_kwargs["api_key"] = provider.provider_apikey
        if provider_kwargs.get("base_url"):
            init_kwargs["base_url"] = provider.provider_url

        model_provider = provider_class(**init_kwargs)

        # 对于 Gemini，provider 需要传递给 model
        if provider.provider_type == "gemini":
            model = model_class(
                model_name=model_id,
                provider=model_provider,
            )
        else:
            model = model_class(model_id, provider=model_provider)

        # 创建 Agent
        agent = Agent(
            model=model,
            name=agent_name,
            system_prompt=system_prompt,
            output_type=output_type,
            deps_type=deps_type,
            tools=tools,
        )
        return agent