import os
import sys
import types
from unittest.mock import patch, MagicMock
import pytest

from crewai.llm import LLM
from crewai.llms.providers.anthropic.completion import AnthropicCompletion
from crewai.crew import Crew
from crewai.agent import Agent
from crewai.task import Task
from crewai.cli.constants import DEFAULT_LLM_MODEL


def test_anthropic_completion_is_used_when_anthropic_provider():
    """
    Test that AnthropicCompletion from completion.py is used when LLM uses provider 'anthropic'
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    assert llm.__class__.__name__ == "AnthropicCompletion"
    assert llm.provider == "anthropic"
    assert llm.model == "claude-3-5-sonnet-20241022"


def test_anthropic_completion_is_used_when_claude_provider():
    """
    Test that AnthropicCompletion is used when provider is 'claude'
    """
    llm = LLM(model="claude/claude-3-5-sonnet-20241022")

    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
    assert isinstance(llm, AnthropicCompletion)
    assert llm.provider == "claude"
    assert llm.model == "claude-3-5-sonnet-20241022"




def test_anthropic_tool_use_conversation_flow():
    """
    Test that the Anthropic completion properly handles tool use conversation flow
    """
    from unittest.mock import Mock, patch
    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
    from anthropic.types.tool_use_block import ToolUseBlock

    # Create AnthropicCompletion instance
    completion = AnthropicCompletion(model="claude-3-5-sonnet-20241022")

    # Mock tool function
    def mock_weather_tool(location: str) -> str:
        return f"The weather in {location} is sunny and 75°F"

    available_functions = {"get_weather": mock_weather_tool}

    # Mock the Anthropic client responses
    with patch.object(completion.client.messages, 'create') as mock_create:
        # Mock initial response with tool use - need to properly mock ToolUseBlock
        mock_tool_use = Mock(spec=ToolUseBlock)
        mock_tool_use.id = "tool_123"
        mock_tool_use.name = "get_weather"
        mock_tool_use.input = {"location": "San Francisco"}

        mock_initial_response = Mock()
        mock_initial_response.content = [mock_tool_use]
        mock_initial_response.usage = Mock()
        mock_initial_response.usage.input_tokens = 100
        mock_initial_response.usage.output_tokens = 50

        # Mock final response after tool result - properly mock text content
        mock_text_block = Mock()
        # Set the text attribute as a string, not another Mock
        mock_text_block.configure_mock(text="Based on the weather data, it's a beautiful day in San Francisco with sunny skies and 75°F temperature.")

        mock_final_response = Mock()
        mock_final_response.content = [mock_text_block]
        mock_final_response.usage = Mock()
        mock_final_response.usage.input_tokens = 150
        mock_final_response.usage.output_tokens = 75

        # Configure mock to return different responses on successive calls
        mock_create.side_effect = [mock_initial_response, mock_final_response]

        # Test the call
        messages = [{"role": "user", "content": "What's the weather like in San Francisco?"}]
        result = completion.call(
            messages=messages,
            available_functions=available_functions
        )

        # Verify the result contains the final response
        assert "beautiful day in San Francisco" in result
        assert "sunny skies" in result
        assert "75°F" in result

        # Verify that two API calls were made (initial + follow-up)
        assert mock_create.call_count == 2

        # Verify the second call includes tool results
        second_call_args = mock_create.call_args_list[1][1]  # kwargs of second call
        messages_in_second_call = second_call_args["messages"]

        # Should have original user message + assistant tool use + user tool result
        assert len(messages_in_second_call) == 3
        assert messages_in_second_call[0]["role"] == "user"
        assert messages_in_second_call[1]["role"] == "assistant"
        assert messages_in_second_call[2]["role"] == "user"

        # Verify tool result format
        tool_result = messages_in_second_call[2]["content"][0]
        assert tool_result["type"] == "tool_result"
        assert tool_result["tool_use_id"] == "tool_123"
        assert "sunny and 75°F" in tool_result["content"]


def test_anthropic_completion_module_is_imported():
    """
    Test that the completion module is properly imported when using Anthropic provider
    """
    module_name = "crewai.llms.providers.anthropic.completion"

    # Remove module from cache if it exists
    if module_name in sys.modules:
        del sys.modules[module_name]

    # Create LLM instance - this should trigger the import
    LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Verify the module was imported
    assert module_name in sys.modules
    completion_mod = sys.modules[module_name]
    assert isinstance(completion_mod, types.ModuleType)

    # Verify the class exists in the module
    assert hasattr(completion_mod, 'AnthropicCompletion')


def test_fallback_to_litellm_when_native_anthropic_fails():
    """
    Test that LLM falls back to LiteLLM when native Anthropic completion fails
    """
    # Mock the _get_native_provider to return a failing class
    with patch('crewai.llm.LLM._get_native_provider') as mock_get_provider:

        class FailingCompletion:
            def __init__(self, *args, **kwargs):
                raise Exception("Native Anthropic SDK failed")

        mock_get_provider.return_value = FailingCompletion

        # This should fall back to LiteLLM
        llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

        # Check that it's using LiteLLM
        assert hasattr(llm, 'is_litellm')
        assert llm.is_litellm == True


def test_anthropic_completion_initialization_parameters():
    """
    Test that AnthropicCompletion is initialized with correct parameters
    """
    llm = LLM(
        model="anthropic/claude-3-5-sonnet-20241022",
        temperature=0.7,
        max_tokens=2000,
        top_p=0.9,
        api_key="test-key"
    )

    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
    assert isinstance(llm, AnthropicCompletion)
    assert llm.model == "claude-3-5-sonnet-20241022"
    assert llm.temperature == 0.7
    assert llm.max_tokens == 2000
    assert llm.top_p == 0.9


def test_anthropic_specific_parameters():
    """
    Test Anthropic-specific parameters like stop_sequences and streaming
    """
    llm = LLM(
        model="anthropic/claude-3-5-sonnet-20241022",
        stop_sequences=["Human:", "Assistant:"],
        stream=True,
        max_retries=5,
        timeout=60
    )

    from crewai.llms.providers.anthropic.completion import AnthropicCompletion
    assert isinstance(llm, AnthropicCompletion)
    assert llm.stop_sequences == ["Human:", "Assistant:"]
    assert llm.stream == True
    assert llm.client.max_retries == 5
    assert llm.client.timeout == 60


def test_anthropic_completion_call():
    """
    Test that AnthropicCompletion call method works
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock the call method on the instance
    with patch.object(llm, 'call', return_value="Hello! I'm Claude, ready to help.") as mock_call:
        result = llm.call("Hello, how are you?")

        assert result == "Hello! I'm Claude, ready to help."
        mock_call.assert_called_once_with("Hello, how are you?")


def test_anthropic_completion_called_during_crew_execution():
    """
    Test that AnthropicCompletion.call is actually invoked when running a crew
    """
    # Create the LLM instance first
    anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock the call method on the specific instance
    with patch.object(anthropic_llm, 'call', return_value="Tokyo has 14 million people.") as mock_call:

        # Create agent with explicit LLM configuration
        agent = Agent(
            role="Research Assistant",
            goal="Find population info",
            backstory="You research populations.",
            llm=anthropic_llm,
        )

        task = Task(
            description="Find Tokyo population",
            expected_output="Population number",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task])
        result = crew.kickoff()

        # Verify mock was called
        assert mock_call.called
        assert "14 million" in str(result)


def test_anthropic_completion_call_arguments():
    """
    Test that AnthropicCompletion.call is invoked with correct arguments
    """
    # Create LLM instance first
    anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock the instance method
    with patch.object(anthropic_llm, 'call') as mock_call:
        mock_call.return_value = "Task completed successfully."

        agent = Agent(
            role="Test Agent",
            goal="Complete a simple task",
            backstory="You are a test agent.",
            llm=anthropic_llm  # Use same instance
        )

        task = Task(
            description="Say hello world",
            expected_output="Hello world",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task])
        crew.kickoff()

        # Verify call was made
        assert mock_call.called

        # Check the arguments passed to the call method
        call_args = mock_call.call_args
        assert call_args is not None

        # The first argument should be the messages
        messages = call_args[0][0]  # First positional argument
        assert isinstance(messages, (str, list))

        # Verify that the task description appears in the messages
        if isinstance(messages, str):
            assert "hello world" in messages.lower()
        elif isinstance(messages, list):
            message_content = str(messages).lower()
            assert "hello world" in message_content


def test_multiple_anthropic_calls_in_crew():
    """
    Test that AnthropicCompletion.call is invoked multiple times for multiple tasks
    """
    # Create LLM instance first
    anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock the instance method
    with patch.object(anthropic_llm, 'call') as mock_call:
        mock_call.return_value = "Task completed."

        agent = Agent(
            role="Multi-task Agent",
            goal="Complete multiple tasks",
            backstory="You can handle multiple tasks.",
            llm=anthropic_llm  # Use same instance
        )

        task1 = Task(
            description="First task",
            expected_output="First result",
            agent=agent,
        )

        task2 = Task(
            description="Second task",
            expected_output="Second result",
            agent=agent,
        )

        crew = Crew(
            agents=[agent],
            tasks=[task1, task2]
        )
        crew.kickoff()

        # Verify multiple calls were made
        assert mock_call.call_count >= 2  # At least one call per task

        # Verify each call had proper arguments
        for call in mock_call.call_args_list:
            assert len(call[0]) > 0  # Has positional arguments
            messages = call[0][0]
            assert messages is not None


def test_anthropic_completion_with_tools():
    """
    Test that AnthropicCompletion.call is invoked with tools when agent has tools
    """
    from crewai.tools import tool

    @tool
    def sample_tool(query: str) -> str:
        """A sample tool for testing"""
        return f"Tool result for: {query}"

    # Create LLM instance first
    anthropic_llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock the instance method
    with patch.object(anthropic_llm, 'call') as mock_call:
        mock_call.return_value = "Task completed with tools."

        agent = Agent(
            role="Tool User",
            goal="Use tools to complete tasks",
            backstory="You can use tools.",
            llm=anthropic_llm,  # Use same instance
            tools=[sample_tool]
        )

        task = Task(
            description="Use the sample tool",
            expected_output="Tool usage result",
            agent=agent,
        )

        crew = Crew(agents=[agent], tasks=[task])
        crew.kickoff()

        assert mock_call.called

        call_args = mock_call.call_args
        call_kwargs = call_args[1] if len(call_args) > 1 else {}

        if 'tools' in call_kwargs:
            assert call_kwargs['tools'] is not None
            assert len(call_kwargs['tools']) > 0


def test_anthropic_raises_error_when_model_not_supported():
    """Test that AnthropicCompletion raises ValueError when model not supported"""

    # Mock the Anthropic client to raise an error
    with patch('crewai.llms.providers.anthropic.completion.Anthropic') as mock_anthropic_class:
        mock_client = MagicMock()
        mock_anthropic_class.return_value = mock_client

        # Mock the error that Anthropic would raise for unsupported models
        from anthropic import NotFoundError
        mock_client.messages.create.side_effect = NotFoundError(
            message="The model `model-doesnt-exist` does not exist",
            response=MagicMock(),
            body={}
        )

        llm = LLM(model="anthropic/model-doesnt-exist")

        with pytest.raises(Exception):  # Should raise some error for unsupported model
            llm.call("Hello")


def test_anthropic_client_params_setup():
    """
    Test that client_params are properly merged with default client parameters
    """
    # Use only valid Anthropic client parameters
    custom_client_params = {
        "default_headers": {"X-Custom-Header": "test-value"},
    }

    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
        llm = LLM(
            model="anthropic/claude-3-5-sonnet-20241022",
            api_key="test-key",
            base_url="https://custom-api.com",
            timeout=45,
            max_retries=5,
            client_params=custom_client_params
        )

        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
        assert isinstance(llm, AnthropicCompletion)

        assert llm.client_params == custom_client_params

        merged_params = llm._get_client_params()

        assert merged_params["api_key"] == "test-key"
        assert merged_params["base_url"] == "https://custom-api.com"
        assert merged_params["timeout"] == 45
        assert merged_params["max_retries"] == 5

        assert merged_params["default_headers"] == {"X-Custom-Header": "test-value"}


def test_anthropic_client_params_override_defaults():
    """
    Test that client_params can override default client parameters
    """
    override_client_params = {
        "timeout": 120,  # Override the timeout parameter
        "max_retries": 10,  # Override the max_retries parameter
        "default_headers": {"X-Override": "true"}  # Valid custom parameter
    }

    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
        llm = LLM(
            model="anthropic/claude-3-5-sonnet-20241022",
            api_key="test-key",
            timeout=30,
            max_retries=3,
            client_params=override_client_params
        )

        # Verify this is actually AnthropicCompletion, not LiteLLM fallback
        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
        assert isinstance(llm, AnthropicCompletion)

        merged_params = llm._get_client_params()

        # client_params should override the individual parameters
        assert merged_params["timeout"] == 120
        assert merged_params["max_retries"] == 10
        assert merged_params["default_headers"] == {"X-Override": "true"}


def test_anthropic_client_params_none():
    """
    Test that client_params=None works correctly (no additional parameters)
    """
    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
        llm = LLM(
            model="anthropic/claude-3-5-sonnet-20241022",
            api_key="test-key",
            base_url="https://api.anthropic.com",
            timeout=60,
            max_retries=2,
            client_params=None
        )

        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
        assert isinstance(llm, AnthropicCompletion)

        assert llm.client_params is None

        merged_params = llm._get_client_params()

        expected_keys = {"api_key", "base_url", "timeout", "max_retries"}
        assert set(merged_params.keys()) == expected_keys

        # Fixed assertions - all should be inside the with block and use correct values
        assert merged_params["api_key"] == "test-key"  # Not "test-anthropic-key"
        assert merged_params["base_url"] == "https://api.anthropic.com"
        assert merged_params["timeout"] == 60
        assert merged_params["max_retries"] == 2


def test_anthropic_client_params_empty_dict():
    """
    Test that client_params={} works correctly (empty additional parameters)
    """
    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
        llm = LLM(
            model="anthropic/claude-3-5-sonnet-20241022",
            api_key="test-key",
            client_params={}
        )

        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
        assert isinstance(llm, AnthropicCompletion)

        assert llm.client_params == {}

        merged_params = llm._get_client_params()

        assert "api_key" in merged_params
        assert merged_params["api_key"] == "test-key"


def test_anthropic_model_detection():
    """
    Test that various Anthropic model formats are properly detected
    """
    # Test Anthropic model naming patterns that actually work with provider detection
    anthropic_test_cases = [
        "anthropic/claude-3-5-sonnet-20241022",
        "claude/claude-3-5-sonnet-20241022"
    ]

    for model_name in anthropic_test_cases:
        llm = LLM(model=model_name)
        from crewai.llms.providers.anthropic.completion import AnthropicCompletion
        assert isinstance(llm, AnthropicCompletion), f"Failed for model: {model_name}"


def test_anthropic_supports_stop_words():
    """
    Test that Anthropic models support stop sequences
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
    assert llm.supports_stop_words() == True


def test_anthropic_context_window_size():
    """
    Test that Anthropic models return correct context window sizes
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")
    context_size = llm.get_context_window_size()

    # Should return a reasonable context window size (Claude 3.5 has 200k tokens)
    assert context_size > 100000  # Should be substantial
    assert context_size <= 200000  # But not exceed the actual limit


def test_anthropic_message_formatting():
    """
    Test that messages are properly formatted for Anthropic API
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Test message formatting
    test_messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello"},
        {"role": "assistant", "content": "Hi there!"},
        {"role": "user", "content": "How are you?"}
    ]

    formatted_messages, system_message = llm._format_messages_for_anthropic(test_messages)

    # System message should be extracted
    assert system_message == "You are a helpful assistant."

    # Remaining messages should start with user
    assert formatted_messages[0]["role"] == "user"
    assert len(formatted_messages) >= 3  # Should have user, assistant, user messages


def test_anthropic_streaming_parameter():
    """
    Test that streaming parameter is properly handled
    """
    # Test non-streaming
    llm_no_stream = LLM(model="anthropic/claude-3-5-sonnet-20241022", stream=False)
    assert llm_no_stream.stream == False

    # Test streaming
    llm_stream = LLM(model="anthropic/claude-3-5-sonnet-20241022", stream=True)
    assert llm_stream.stream == True


def test_anthropic_tool_conversion():
    """
    Test that tools are properly converted to Anthropic format
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock tool in CrewAI format
    crewai_tools = [{
        "type": "function",
        "function": {
            "name": "test_tool",
            "description": "A test tool",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string", "description": "Search query"}
                },
                "required": ["query"]
            }
        }
    }]

    # Test tool conversion
    anthropic_tools = llm._convert_tools_for_interference(crewai_tools)

    assert len(anthropic_tools) == 1
    assert anthropic_tools[0]["name"] == "test_tool"
    assert anthropic_tools[0]["description"] == "A test tool"
    assert "input_schema" in anthropic_tools[0]


def test_anthropic_environment_variable_api_key():
    """
    Test that Anthropic API key is properly loaded from environment
    """
    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-anthropic-key"}):
        llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

        assert llm.client is not None
        assert hasattr(llm.client, 'messages')


def test_anthropic_token_usage_tracking():
    """
    Test that token usage is properly tracked for Anthropic responses
    """
    llm = LLM(model="anthropic/claude-3-5-sonnet-20241022")

    # Mock the Anthropic response with usage information
    with patch.object(llm.client.messages, 'create') as mock_create:
        mock_response = MagicMock()
        mock_response.content = [MagicMock(text="test response")]
        mock_response.usage = MagicMock(input_tokens=50, output_tokens=25)
        mock_create.return_value = mock_response

        result = llm.call("Hello")

        # Verify the response
        assert result == "test response"

        # Verify token usage was extracted
        usage = llm._extract_anthropic_token_usage(mock_response)
        assert usage["input_tokens"] == 50
        assert usage["output_tokens"] == 25
        assert usage["total_tokens"] == 75