"""Anthropic Provider 测试"""

import json
from unittest.mock import AsyncMock, MagicMock, patch

import httpx
import pytest
from pytest_httpx import HTTPXMock

from agentkit.core.exceptions import LLMProviderError
from agentkit.llm.protocol import LLMRequest, LLMResponse, StreamChunk, TokenUsage
from agentkit.llm.providers.anthropic import AnthropicProvider


class TestAnthropicMessageConversion:
    """消息格式转换测试"""

    def setup_method(self):
        self.provider = AnthropicProvider(api_key="test-key")

    def test_system_message_extracted_as_top_level(self):
        """system 消息应被提取为顶层 system 参数"""
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello"},
        ]
        system, anthropic_msgs = self.provider._convert_messages(messages)

        assert system == "You are a helpful assistant."
        assert len(anthropic_msgs) == 1
        assert anthropic_msgs[0]["role"] == "user"
        assert anthropic_msgs[0]["content"] == [{"type": "text", "text": "Hello"}]

    def test_text_messages_converted_to_content_blocks(self):
        """普通文本消息应转换为 content blocks"""
        messages = [
            {"role": "user", "content": "Hi"},
            {"role": "assistant", "content": "Hello!"},
            {"role": "user", "content": "How are you?"},
        ]
        system, anthropic_msgs = self.provider._convert_messages(messages)

        assert system is None
        assert len(anthropic_msgs) == 3
        assert anthropic_msgs[0] == {"role": "user", "content": [{"type": "text", "text": "Hi"}]}
        assert anthropic_msgs[1] == {"role": "assistant", "content": [{"type": "text", "text": "Hello!"}]}
        assert anthropic_msgs[2] == {"role": "user", "content": [{"type": "text", "text": "How are you?"}]}

    def test_assistant_tool_calls_converted(self):
        """assistant 的 tool_calls 应转换为 tool_use content blocks"""
        messages = [
            {"role": "user", "content": "What's the weather?"},
            {
                "role": "assistant",
                "content": None,
                "tool_calls": [
                    {
                        "id": "call_123",
                        "type": "function",
                        "function": {
                            "name": "get_weather",
                            "arguments": '{"city": "Beijing"}',
                        },
                    }
                ],
            },
        ]
        system, anthropic_msgs = self.provider._convert_messages(messages)

        assert len(anthropic_msgs) == 2
        assistant_msg = anthropic_msgs[1]
        assert assistant_msg["role"] == "assistant"
        assert len(assistant_msg["content"]) == 1
        assert assistant_msg["content"][0]["type"] == "tool_use"
        assert assistant_msg["content"][0]["id"] == "call_123"
        assert assistant_msg["content"][0]["name"] == "get_weather"
        assert assistant_msg["content"][0]["input"] == {"city": "Beijing"}

    def test_assistant_tool_calls_with_text(self):
        """assistant 同时有文本和 tool_calls"""
        messages = [
            {
                "role": "assistant",
                "content": "Let me check that.",
                "tool_calls": [
                    {
                        "id": "call_456",
                        "type": "function",
                        "function": {
                            "name": "search",
                            "arguments": '{"q": "test"}',
                        },
                    }
                ],
            },
        ]
        _, anthropic_msgs = self.provider._convert_messages(messages)

        content = anthropic_msgs[0]["content"]
        assert len(content) == 2
        assert content[0]["type"] == "text"
        assert content[0]["text"] == "Let me check that."
        assert content[1]["type"] == "tool_use"

    def test_tool_result_converted(self):
        """tool 角色消息应转换为 tool_result content blocks"""
        messages = [
            {
                "role": "tool",
                "tool_call_id": "call_123",
                "content": "Sunny, 25°C",
            },
        ]
        _, anthropic_msgs = self.provider._convert_messages(messages)

        assert len(anthropic_msgs) == 1
        msg = anthropic_msgs[0]
        assert msg["role"] == "user"
        assert len(msg["content"]) == 1
        assert msg["content"][0]["type"] == "tool_result"
        assert msg["content"][0]["tool_use_id"] == "call_123"
        assert msg["content"][0]["content"] == [{"type": "text", "text": "Sunny, 25°C"}]

    def test_user_with_tool_call_id_converted(self):
        """user 消息带 tool_call_id 也应转换为 tool_result"""
        messages = [
            {
                "role": "user",
                "tool_call_id": "call_789",
                "content": "Result data",
            },
        ]
        _, anthropic_msgs = self.provider._convert_messages(messages)

        msg = anthropic_msgs[0]
        assert msg["role"] == "user"
        assert msg["content"][0]["type"] == "tool_result"
        assert msg["content"][0]["tool_use_id"] == "call_789"

    def test_no_system_message(self):
        """没有 system 消息时返回 None"""
        messages = [
            {"role": "user", "content": "Hello"},
        ]
        system, _ = self.provider._convert_messages(messages)
        assert system is None


class TestAnthropicToolConversion:
    """工具格式转换测试"""

    def setup_method(self):
        self.provider = AnthropicProvider(api_key="test-key")

    def test_convert_openai_tools_to_anthropic(self):
        """OpenAI function 格式应转换为 Anthropic tool 格式"""
        tools = [
            {
                "type": "function",
                "function": {
                    "name": "get_weather",
                    "description": "Get weather for a city",
                    "parameters": {
                        "type": "object",
                        "properties": {"city": {"type": "string"}},
                    },
                },
            }
        ]
        result = self.provider._convert_tools(tools)

        assert len(result) == 1
        assert result[0]["name"] == "get_weather"
        assert result[0]["description"] == "Get weather for a city"
        assert result[0]["input_schema"] == {
            "type": "object",
            "properties": {"city": {"type": "string"}},
        }

    def test_convert_tool_choice_auto(self):
        """tool_choice=auto 应转换为 Anthropic 格式"""
        result = self.provider._convert_tool_choice("auto")
        assert result == {"type": "auto"}

    def test_convert_tool_choice_required(self):
        """tool_choice=required 应转换为 Anthropic any 格式"""
        result = self.provider._convert_tool_choice("required")
        assert result == {"type": "any"}

    def test_convert_tool_choice_specific_tool(self):
        """指定工具名的 tool_choice 应转换为 Anthropic tool 格式"""
        result = self.provider._convert_tool_choice("get_weather")
        assert result == {"type": "tool", "name": "get_weather"}

    def test_convert_tool_choice_none(self):
        """tool_choice=none 应返回 None"""
        result = self.provider._convert_tool_choice("none")
        assert result is None


class TestAnthropicResponseParsing:
    """响应解析测试"""

    def setup_method(self):
        self.provider = AnthropicProvider(api_key="test-key")

    def test_parse_text_response(self):
        """解析纯文本响应"""
        data = {
            "id": "msg_123",
            "type": "message",
            "role": "assistant",
            "model": "claude-sonnet-4-20250514",
            "content": [
                {"type": "text", "text": "Hello! How can I help?"}
            ],
            "usage": {"input_tokens": 10, "output_tokens": 6},
        }
        response = self.provider._parse_response(data, "claude-sonnet-4-20250514")

        assert isinstance(response, LLMResponse)
        assert response.content == "Hello! How can I help?"
        assert response.model == "claude-sonnet-4-20250514"
        assert response.usage.prompt_tokens == 10
        assert response.usage.completion_tokens == 6
        assert not response.has_tool_calls

    def test_parse_tool_use_response(self):
        """解析包含 tool_use 的响应"""
        data = {
            "id": "msg_456",
            "type": "message",
            "role": "assistant",
            "model": "claude-sonnet-4-20250514",
            "content": [
                {"type": "text", "text": "Let me check the weather."},
                {
                    "type": "tool_use",
                    "id": "toolu_123",
                    "name": "get_weather",
                    "input": {"city": "Beijing"},
                },
            ],
            "usage": {"input_tokens": 20, "output_tokens": 15},
        }
        response = self.provider._parse_response(data, "claude-sonnet-4-20250514")

        assert response.content == "Let me check the weather."
        assert response.has_tool_calls
        assert len(response.tool_calls) == 1
        assert response.tool_calls[0].id == "toolu_123"
        assert response.tool_calls[0].name == "get_weather"
        assert response.tool_calls[0].arguments == {"city": "Beijing"}

    def test_parse_multiple_tool_uses(self):
        """解析包含多个 tool_use 的响应"""
        data = {
            "id": "msg_789",
            "type": "message",
            "role": "assistant",
            "model": "claude-sonnet-4-20250514",
            "content": [
                {
                    "type": "tool_use",
                    "id": "toolu_1",
                    "name": "get_weather",
                    "input": {"city": "Beijing"},
                },
                {
                    "type": "tool_use",
                    "id": "toolu_2",
                    "name": "get_weather",
                    "input": {"city": "Shanghai"},
                },
            ],
            "usage": {"input_tokens": 25, "output_tokens": 20},
        }
        response = self.provider._parse_response(data, "claude-sonnet-4-20250514")

        assert len(response.tool_calls) == 2
        assert response.tool_calls[0].name == "get_weather"
        assert response.tool_calls[0].arguments == {"city": "Beijing"}
        assert response.tool_calls[1].arguments == {"city": "Shanghai"}


class TestAnthropicChat:
    """chat() 方法集成测试"""

    async def test_chat_returns_llm_response(self, httpx_mock: HTTPXMock):
        """chat 应返回 LLMResponse"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            json={
                "id": "msg_001",
                "type": "message",
                "role": "assistant",
                "model": "claude-sonnet-4-20250514",
                "content": [{"type": "text", "text": "Hello from Claude!"}],
                "usage": {"input_tokens": 10, "output_tokens": 5},
                "stop_reason": "end_turn",
            },
        )

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )
        response = await provider.chat(request)

        assert isinstance(response, LLMResponse)
        assert response.content == "Hello from Claude!"
        assert response.model == "claude-sonnet-4-20250514"
        assert response.usage.prompt_tokens == 10
        assert response.usage.completion_tokens == 5
        assert response.latency_ms > 0

    async def test_chat_with_system_message(self, httpx_mock: HTTPXMock):
        """system 消息应作为顶层参数发送"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            json={
                "id": "msg_002",
                "type": "message",
                "role": "assistant",
                "model": "claude-sonnet-4-20250514",
                "content": [{"type": "text", "text": "I am a helpful assistant."}],
                "usage": {"input_tokens": 15, "output_tokens": 8},
                "stop_reason": "end_turn",
            },
        )

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": "Who are you?"},
            ],
            model="claude-sonnet-4-20250514",
        )
        response = await provider.chat(request)

        assert response.content == "I am a helpful assistant."

        # Verify the request payload
        request_body = json.loads(httpx_mock.get_requests()[-1].content)
        assert "system" in request_body
        assert request_body["system"] == "You are a helpful assistant."
        # System should NOT be in messages
        for msg in request_body["messages"]:
            assert msg["role"] != "system"

    async def test_chat_with_tools(self, httpx_mock: HTTPXMock):
        """带工具的请求应正确转换格式"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            json={
                "id": "msg_003",
                "type": "message",
                "role": "assistant",
                "model": "claude-sonnet-4-20250514",
                "content": [
                    {
                        "type": "tool_use",
                        "id": "toolu_001",
                        "name": "get_weather",
                        "input": {"city": "Tokyo"},
                    }
                ],
                "usage": {"input_tokens": 30, "output_tokens": 20},
                "stop_reason": "tool_use",
            },
        )

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Weather in Tokyo?"}],
            model="claude-sonnet-4-20250514",
            tools=[
                {
                    "type": "function",
                    "function": {
                        "name": "get_weather",
                        "description": "Get weather",
                        "parameters": {
                            "type": "object",
                            "properties": {"city": {"type": "string"}},
                        },
                    },
                }
            ],
        )
        response = await provider.chat(request)

        assert response.has_tool_calls
        assert response.tool_calls[0].name == "get_weather"
        assert response.tool_calls[0].arguments == {"city": "Tokyo"}

        # Verify request format
        request_body = json.loads(httpx_mock.get_requests()[-1].content)
        assert "tools" in request_body
        assert request_body["tools"][0]["name"] == "get_weather"
        assert "input_schema" in request_body["tools"][0]
        assert "tool_choice" in request_body
        assert request_body["tool_choice"] == {"type": "auto"}

    async def test_chat_sends_correct_headers(self, httpx_mock: HTTPXMock):
        """验证请求头包含正确的 Anthropic 认证信息"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            json={
                "id": "msg_004",
                "type": "message",
                "role": "assistant",
                "model": "claude-sonnet-4-20250514",
                "content": [{"type": "text", "text": "OK"}],
                "usage": {"input_tokens": 5, "output_tokens": 2},
                "stop_reason": "end_turn",
            },
        )

        provider = AnthropicProvider(api_key="sk-ant-test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )
        await provider.chat(request)

        sent_request = httpx_mock.get_requests()[-1]
        assert sent_request.headers.get("x-api-key") == "sk-ant-test-key"
        assert sent_request.headers.get("anthropic-version") == "2023-06-01"
        assert sent_request.headers.get("content-type") == "application/json"

    async def test_chat_with_custom_base_url(self, httpx_mock: HTTPXMock):
        """自定义 base_url 应正确使用"""
        httpx_mock.add_response(
            url="https://custom-proxy.example.com/v1/messages",
            json={
                "id": "msg_005",
                "type": "message",
                "role": "assistant",
                "model": "claude-sonnet-4-20250514",
                "content": [{"type": "text", "text": "Proxy response"}],
                "usage": {"input_tokens": 5, "output_tokens": 3},
                "stop_reason": "end_turn",
            },
        )

        provider = AnthropicProvider(
            api_key="test-key",
            base_url="https://custom-proxy.example.com",
        )
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )
        response = await provider.chat(request)

        assert response.content == "Proxy response"


class TestAnthropicStreaming:
    """chat_stream() 方法测试"""

    def _make_stream_response(self, sse_lines: list[str]):
        """Create a mock httpx streaming response context manager."""
        response = MagicMock()
        response.status_code = 200

        async def aiter_lines():
            for line in sse_lines:
                yield line

        response.aiter_lines = aiter_lines
        response.aread = AsyncMock(return_value=b"")

        # Create async context manager
        context = MagicMock()
        context.__aenter__ = AsyncMock(return_value=response)
        context.__aexit__ = AsyncMock(return_value=False)
        return context

    async def test_stream_text_response(self):
        """流式文本响应应正确解析"""
        sse_lines = [
            'event: message_start',
            'data: {"type":"message_start","message":{"id":"msg_s1","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[]}}',
            '',
            'event: content_block_start',
            'data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}',
            '',
            'event: content_block_delta',
            'data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}',
            '',
            'event: content_block_delta',
            'data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" world"}}',
            '',
            'event: content_block_stop',
            'data: {"type":"content_block_stop","index":0}',
            '',
            'event: message_delta',
            'data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"input_tokens":10,"output_tokens":5}}',
            '',
            'event: message_stop',
            'data: {"type":"message_stop"}',
            '',
        ]

        mock_client = MagicMock()
        mock_client.stream = MagicMock(return_value=self._make_stream_response(sse_lines))

        provider = AnthropicProvider(api_key="test-key")
        provider._client = mock_client

        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        chunks = []
        async for chunk in provider.chat_stream(request):
            chunks.append(chunk)

        # Should have text chunks + final chunk
        text_chunks = [c for c in chunks if c.content]
        assert len(text_chunks) == 2
        assert text_chunks[0].content == "Hello"
        assert text_chunks[1].content == " world"

        # Final chunk with usage
        final_chunks = [c for c in chunks if c.is_final]
        assert len(final_chunks) == 1
        assert final_chunks[0].usage is not None
        assert final_chunks[0].usage.prompt_tokens == 10
        assert final_chunks[0].usage.completion_tokens == 5

    async def test_stream_tool_use_response(self):
        """流式 tool_use 响应应正确解析"""
        sse_lines = [
            'event: message_start',
            'data: {"type":"message_start","message":{"id":"msg_s2","type":"message","role":"assistant","model":"claude-sonnet-4-20250514","content":[]}}',
            '',
            'event: content_block_start',
            'data: {"type":"content_block_start","index":0,"content_block":{"type":"tool_use","id":"toolu_s1","name":"get_weather"}}',
            '',
            'event: content_block_delta',
            'data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"{\\"cit"}}',
            '',
            'event: content_block_delta',
            'data: {"type":"content_block_delta","index":0,"delta":{"type":"input_json_delta","partial_json":"y\\":\\"Paris\\"}"}}',
            '',
            'event: content_block_stop',
            'data: {"type":"content_block_stop","index":0}',
            '',
            'event: message_delta',
            'data: {"type":"message_delta","delta":{"stop_reason":"tool_use"},"usage":{"input_tokens":20,"output_tokens":15}}',
            '',
            'event: message_stop',
            'data: {"type":"message_stop"}',
            '',
        ]

        mock_client = MagicMock()
        mock_client.stream = MagicMock(return_value=self._make_stream_response(sse_lines))

        provider = AnthropicProvider(api_key="test-key")
        provider._client = mock_client

        request = LLMRequest(
            messages=[{"role": "user", "content": "Weather in Paris?"}],
            model="claude-sonnet-4-20250514",
            tools=[
                {
                    "type": "function",
                    "function": {
                        "name": "get_weather",
                        "description": "Get weather",
                        "parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
                    },
                }
            ],
        )

        chunks = []
        async for chunk in provider.chat_stream(request):
            chunks.append(chunk)

        # Final chunk should have tool calls
        final_chunks = [c for c in chunks if c.is_final]
        assert len(final_chunks) == 1
        assert len(final_chunks[0].tool_calls) == 1
        assert final_chunks[0].tool_calls[0].id == "toolu_s1"
        assert final_chunks[0].tool_calls[0].name == "get_weather"
        assert final_chunks[0].tool_calls[0].arguments == {"city": "Paris"}

    async def test_stream_error_event(self):
        """流式 error 事件应抛出 LLMProviderError"""
        sse_lines = [
            'event: error',
            'data: {"type":"error","error":{"type":"overloaded_error","message":"Server is overloaded"}}',
            '',
        ]

        mock_client = MagicMock()
        mock_client.stream = MagicMock(return_value=self._make_stream_response(sse_lines))

        provider = AnthropicProvider(api_key="test-key")
        provider._client = mock_client

        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError) as exc_info:
            async for _ in provider.chat_stream(request):
                pass

        assert "overloaded" in str(exc_info.value).lower()

    async def test_stream_non_200_status(self):
        """流式请求非 200 状态应抛出 LLMProviderError"""
        response = MagicMock()
        response.status_code = 429
        response.aread = AsyncMock(return_value=b'{"type":"error","error":{"type":"rate_limit_error","message":"Rate limit"}}')

        context = MagicMock()
        context.__aenter__ = AsyncMock(return_value=response)
        context.__aexit__ = AsyncMock(return_value=False)

        mock_client = MagicMock()
        mock_client.stream = MagicMock(return_value=context)

        provider = AnthropicProvider(api_key="test-key")
        provider._client = mock_client

        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError) as exc_info:
            async for _ in provider.chat_stream(request):
                pass

        assert "429" in str(exc_info.value)


class TestAnthropicErrors:
    """错误处理测试"""

    async def test_401_invalid_api_key(self, httpx_mock: HTTPXMock):
        """401 错误应抛出 LLMProviderError"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            status_code=401,
            json={
                "type": "error",
                "error": {"type": "authentication_error", "message": "invalid x-api-key"},
            },
        )

        provider = AnthropicProvider(api_key="bad-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError) as exc_info:
            await provider.chat(request)

        assert "anthropic" in str(exc_info.value)
        assert "401" in str(exc_info.value)

    async def test_429_rate_limit(self, httpx_mock: HTTPXMock):
        """429 错误应抛出 LLMProviderError"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            status_code=429,
            json={
                "type": "error",
                "error": {"type": "rate_limit_error", "message": "Rate limit exceeded"},
            },
        )

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError) as exc_info:
            await provider.chat(request)

        assert "429" in str(exc_info.value)

    async def test_529_overloaded(self, httpx_mock: HTTPXMock):
        """529 错误应抛出 LLMProviderError"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            status_code=529,
            json={
                "type": "error",
                "error": {"type": "overloaded_error", "message": "Overloaded"},
            },
        )

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError) as exc_info:
            await provider.chat(request)

        assert "529" in str(exc_info.value)

    async def test_500_server_error(self, httpx_mock: HTTPXMock):
        """500 错误应抛出 LLMProviderError"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            status_code=500,
            json={
                "type": "error",
                "error": {"type": "api_error", "message": "Internal server error"},
            },
        )

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError):
            await provider.chat(request)

    async def test_network_error(self, httpx_mock: HTTPXMock):
        """网络错误应抛出 LLMProviderError"""
        httpx_mock.add_exception(httpx.ConnectError("Connection refused"))

        provider = AnthropicProvider(api_key="test-key")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError):
            await provider.chat(request)

    async def test_error_does_not_expose_api_key(self, httpx_mock: HTTPXMock):
        """错误消息不应暴露 API Key"""
        httpx_mock.add_response(
            url="https://api.anthropic.com/v1/messages",
            status_code=401,
            json={
                "type": "error",
                "error": {"type": "authentication_error", "message": "invalid x-api-key"},
            },
        )

        provider = AnthropicProvider(api_key="sk-ant-secret-key-12345")
        request = LLMRequest(
            messages=[{"role": "user", "content": "Hi"}],
            model="claude-sonnet-4-20250514",
        )

        with pytest.raises(LLMProviderError) as exc_info:
            await provider.chat(request)

        assert "sk-ant-secret-key-12345" not in str(exc_info.value)


class TestAnthropicGetModelInfo:
    """get_model_info() 测试"""

    def test_returns_provider_and_model_info(self):
        provider = AnthropicProvider(
            api_key="test-key",
            model="claude-sonnet-4-20250514",
            max_tokens=8192,
        )
        info = provider.get_model_info()

        assert info["provider"] == "anthropic"
        assert info["model"] == "claude-sonnet-4-20250514"
        assert info["max_tokens"] == 8192
        assert info["thinking_enabled"] is False

    def test_thinking_enabled_flag(self):
        provider = AnthropicProvider(
            api_key="test-key",
            thinking_enabled=True,
        )
        info = provider.get_model_info()

        assert info["thinking_enabled"] is True


class TestAnthropicLazyClient:
    """Lazy client 初始化测试"""

    def test_client_not_created_on_init(self):
        """初始化时不应创建 HTTP 客户端"""
        provider = AnthropicProvider(api_key="test-key")
        assert provider._client is None

    def test_client_created_on_first_use(self):
        """首次使用时应创建 HTTP 客户端"""
        provider = AnthropicProvider(api_key="test-key")
        client = provider._get_client()
        assert client is not None
        assert provider._client is not None

    def test_client_reused(self):
        """多次调用应复用同一客户端"""
        provider = AnthropicProvider(api_key="test-key")
        client1 = provider._get_client()
        client2 = provider._get_client()
        assert client1 is client2

    async def test_close_resets_client(self):
        """close 后客户端应被重置"""
        provider = AnthropicProvider(api_key="test-key")
        _ = provider._get_client()
        assert provider._client is not None

        await provider.close()
        assert provider._client is None