import contextlib
import time
from unittest.mock import patch

import pytest
from autogen import ConversableAgent, GroupChat, GroupChatManager, UserProxyAgent, io
from openai import APIConnectionError
from openai.types.chat import ChatCompletion
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice

import mlflow
from mlflow.entities import SpanLogLevel
from mlflow.entities.span import SpanType
from mlflow.tracing.constant import SpanAttributeKey
from mlflow.version import IS_TRACING_SDK_ONLY

from tests.helper_functions import start_mock_openai_server
from tests.tracing.helper import get_traces


@pytest.fixture(scope="module", autouse=True)
def mock_openai():
    with start_mock_openai_server() as base_url:
        yield base_url


@pytest.fixture
def llm_config(mock_openai):
    return {
        "config_list": [
            {
                "model": "gpt-4o-mini",
                "base_url": mock_openai,
                "api_key": "test",
                "max_tokens": 100,
            },
        ]
    }


@contextlib.contextmanager
def mock_user_input(messages: list[str]):
    with patch.object(io.IOStream.get_default(), "input", side_effect=messages):
        yield


def get_simple_agent(llm_config):
    assistant = ConversableAgent("agent", llm_config=llm_config)
    user_proxy = UserProxyAgent("user", code_execution_config=False)
    return assistant, user_proxy


def test_enable_disable_autolog(llm_config):
    mlflow.ag2.autolog()
    with mock_user_input(["Hi", "exit"]):
        assistant, user_proxy = get_simple_agent(llm_config)
        assistant.initiate_chat(user_proxy, message="foo")

    traces = get_traces()
    assert len(traces) == 1

    mlflow.ag2.autolog(disable=True)
    with mock_user_input(["Hi", "exit"]):
        assistant, user_proxy = get_simple_agent(llm_config)
        assistant.initiate_chat(user_proxy, message="foo")

    # No new trace should be created
    traces = get_traces()
    assert len(traces) == 1


def test_tracing_agent(llm_config, mock_litellm_cost):
    mlflow.ag2.autolog()

    with mock_user_input([
        "What is the capital of Tokyo?",
        "How long is it take from San Francisco?",
        "exit",
    ]):
        assistant, user_proxy = get_simple_agent(llm_config)
        response = assistant.initiate_chat(user_proxy, message="How can I help you today?")

    # Check if the initiate_chat method is patched
    traces = get_traces()
    assert len(traces) == 1
    assert traces[0].info.status == "OK"
    assert traces[0].info.execution_time_ms > 0
    # 7 spans are expected:
    # initiate_chat
    #    |-- user
    #    |-- assistant -- chat_completion
    #    |-- user
    #    |-- assistant -- chat_completion
    assert len(traces[0].data.spans) == 7

    session_span = traces[0].data.spans[0]
    assert session_span.name == "initiate_chat"
    assert session_span.span_type == SpanType.UNKNOWN
    # UNKNOWN type → DEBUG default; AGENT children → INFO; LLM child → INFO.
    assert session_span.log_level == SpanLogLevel.DEBUG
    assert session_span.inputs["message"] == "How can I help you today?"
    assert session_span.outputs["chat_history"] == response.chat_history
    user_span = traces[0].data.spans[1]
    assert user_span.name == "user"
    assert user_span.span_type == SpanType.AGENT
    assert user_span.log_level == SpanLogLevel.INFO
    assert user_span.parent_id == session_span.span_id
    assert user_span.inputs["message"] == "How can I help you today?"
    assert user_span.outputs["message"]["content"] == "What is the capital of Tokyo?"
    agent_span = traces[0].data.spans[2]
    assert agent_span.name == "agent"
    assert agent_span.span_type == SpanType.AGENT
    assert agent_span.parent_id == session_span.span_id
    assert agent_span.inputs["message"]["content"] == "What is the capital of Tokyo?"
    assert agent_span.outputs is not None
    llm_span = traces[0].data.spans[3]
    assert llm_span.name == "chat_completion"
    assert llm_span.span_type == SpanType.LLM
    assert llm_span.parent_id == agent_span.span_id
    assert llm_span.inputs["messages"][-1]["content"] == "What is the capital of Tokyo?"
    assert llm_span.outputs is not None
    assert llm_span.attributes["cost"] >= 0
    assert llm_span.model_name == "gpt-4o-mini"
    user_span_2 = traces[0].data.spans[4]
    assert user_span_2.name == "user"
    assert user_span_2.parent_id == session_span.span_id
    agent_span_2 = traces[0].data.spans[5]
    assert agent_span_2.name == "agent"
    assert agent_span_2.parent_id == session_span.span_id
    llm_span_2 = traces[0].data.spans[6]
    assert llm_span_2.name == "chat_completion"
    assert llm_span_2.parent_id == agent_span_2.span_id
    assert llm_span_2.model_name == "gpt-4o-mini"

    assert llm_span.get_attribute(SpanAttributeKey.CHAT_USAGE) == {
        "input_tokens": 9,
        "output_tokens": 12,
        "total_tokens": 21,
    }
    assert llm_span.get_attribute(SpanAttributeKey.MESSAGE_FORMAT) == "ag2"
    if not IS_TRACING_SDK_ONLY:
        # Verify cost is calculated (9 input tokens * 1.0 + 12 output tokens * 2.0)
        assert llm_span.llm_cost == {
            "input_cost": 9.0,
            "output_cost": 24.0,
            "total_cost": 33.0,
        }

    assert llm_span_2.get_attribute(SpanAttributeKey.CHAT_USAGE) == {
        "input_tokens": 9,
        "output_tokens": 12,
        "total_tokens": 21,
    }
    assert llm_span_2.get_attribute(SpanAttributeKey.MESSAGE_FORMAT) == "ag2"
    if not IS_TRACING_SDK_ONLY:
        # Verify cost is calculated (9 input tokens * 1.0 + 12 output tokens * 2.0)
        assert llm_span_2.llm_cost == {
            "input_cost": 9.0,
            "output_cost": 24.0,
            "total_cost": 33.0,
        }

    assert traces[0].info.token_usage == {
        "input_tokens": 18,
        "output_tokens": 24,
        "total_tokens": 42,
    }


def test_tracing_agent_with_error():
    mlflow.ag2.autolog()

    invalid_llm_config = {
        "config_list": [
            {
                "model": "gpt-4o-mini",
                "base_url": "invalid_url",
                "api_key": "invalid",
            }
        ]
    }
    assistant = ConversableAgent("agent", llm_config=invalid_llm_config)
    user_proxy = UserProxyAgent("user", code_execution_config=False)

    with mock_user_input(["What is the capital of Tokyo?", "exit"]):
        with pytest.raises(APIConnectionError, match="Connection error"):
            assistant.initiate_chat(user_proxy, message="How can I help you today?")

    traces = get_traces()
    assert len(traces) == 1
    assert traces[0].info.status == "ERROR"
    assert traces[0].info.execution_time_ms > 0
    assert traces[0].data.spans[0].status.status_code == "ERROR"
    assert traces[0].data.spans[0].status.description == "Connection error."


def test_tracing_agent_multiple_chat_sessions(llm_config):
    mlflow.ag2.autolog()

    with mock_user_input(["Hi", "exit", "Hello", "exit", "Hola", "exit"]):
        assistant, user_proxy = get_simple_agent(llm_config)
        assistant.initiate_chat(user_proxy, message="foo")
        assistant.initiate_chat(user_proxy, message="bar")
        assistant.initiate_chat(user_proxy, message="baz")

    # Traces should be created for each chat session
    traces = get_traces()
    assert len(traces) == 3

    assert traces[0].info.token_usage == {
        "input_tokens": 9,
        "output_tokens": 12,
        "total_tokens": 21,
    }


def test_tracing_agent_with_function_calling(llm_config):
    mlflow.ag2.autolog()

    # Define a simple tool and register it with the assistant agent
    def sum(a: int, b: int) -> int:
        time.sleep(1)
        return a + b

    assistant = ConversableAgent(
        name="assistant",
        system_message="You are a helpful AI assistant. "
        "You can help with simple calculations. "
        "Return 'TERMINATE' when the task is done.",
        llm_config=llm_config,
    )
    user_proxy = ConversableAgent(
        name="tool_agent",
        llm_config=False,
        is_termination_msg=lambda msg: (
            msg.get("content") is not None and "TERMINATE" in msg["content"]
        ),
        human_input_mode="NEVER",
    )
    assistant.register_for_llm(name="sum", description="A simple sum calculator")(sum)
    user_proxy.register_for_execution(name="sum")(sum)

    # Start a chat session. We mock OpenAI response to simulate function calling response.
    with patch(
        "autogen.oai.client.OpenAIClient.create",
        side_effect=[
            ChatCompletion(
                id="chat_1",
                created=0,
                object="chat.completion",
                model="gpt-4o-mini",
                choices=[
                    Choice(
                        index=1,
                        finish_reason="stop",
                        message=ChatCompletionMessage(
                            role="assistant",
                            tool_calls=[
                                {
                                    "id": "call_1",
                                    "function": {"arguments": '{"a": 1, "b": 1}', "name": "sum"},
                                    "type": "function",
                                },
                            ],
                        ),
                    ),
                ],
            ),
            ChatCompletion(
                id="chat_2",
                created=0,
                object="chat.completion",
                model="gpt-4o-mini",
                choices=[
                    Choice(
                        index=2,
                        finish_reason="stop",
                        message=ChatCompletionMessage(
                            role="assistant",
                            content="The result of the calculation is 2. \n\nTERMINATE",
                        ),
                    ),
                ],
            ),
        ],
    ):
        response = user_proxy.initiate_chat(assistant, message="What is 1 + 1?")

    assert response.summary.startswith("The result of the calculation is 2.")

    traces = get_traces()
    assert len(traces) == 1
    assert traces[0].info.status == "OK"
    assistant_span = traces[0].data.spans[1]
    assert assistant_span.span_type == SpanType.AGENT
    tool_agent_span = traces[0].data.spans[3]
    assert tool_agent_span.span_type == SpanType.AGENT
    tool_span = traces[0].data.spans[4]
    assert tool_span.span_type == SpanType.TOOL
    assert tool_span.parent_id == tool_agent_span.span_id
    assert tool_span.inputs["a"] == 1
    assert tool_span.inputs["b"] == 1
    assert tool_span.outputs == "2"
    assert tool_span.end_time_ns - tool_span.start_time_ns >= 1e9  # 1 second


@pytest.fixture
def tokyo_timezone(monkeypatch):
    # Set the timezone to Tokyo
    monkeypatch.setenv("TZ", "Asia/Tokyo")
    time.tzset()

    yield

    # Reset the timezone
    monkeypatch.delenv("TZ")
    time.tzset()


def test_tracing_llm_completion_duration_timezone(llm_config, tokyo_timezone):
    # Test if the duration calculation for LLM completion is robust to timezone changes.
    mlflow.ag2.autolog()

    with mock_user_input([
        "What is the capital of Tokyo?",
        "How long is it take from San Francisco?",
        "exit",
    ]):
        assistant, user_proxy = get_simple_agent(llm_config)
        assistant.initiate_chat(user_proxy, message="How can I help you today?")

    # Check if the initiate_chat method is patched
    traces = get_traces()
    span_name_to_dict = {span.name: span for span in traces[0].data.spans}
    llm_span = span_name_to_dict["chat_completion"]

    # We mock OpenAI LLM call so it should not take too long e.g. > 10 seconds. If it does,
    # it most likely a bug such as incorrect timezone handling.
    assert 0 < llm_span.end_time_ns - llm_span.start_time_ns <= 10e9
    assert llm_span.model_name == "gpt-4o-mini"

    # Check if the start time is in reasonable range
    root_span = span_name_to_dict["initiate_chat"]
    assert 0 < llm_span.start_time_ns - root_span.start_time_ns <= 1e9

    assert traces[0].info.token_usage == {
        "input_tokens": 18,
        "output_tokens": 24,
        "total_tokens": 42,
    }


def test_tracing_composite_agent(llm_config):
    # Composite agent can call initiate_chat() or generate_reply() method of its sub-agents.
    # This test is to ensure that won't create a new trace for the sub-agent's method call.
    mlflow.ag2.autolog()

    agent_1 = ConversableAgent("agent_1", llm_config=llm_config)
    agent_2 = ConversableAgent("agent_2", llm_config=llm_config)
    group_chat = GroupChat(
        agents=[agent_1, agent_2],
        messages=[],
        max_round=3,
        speaker_selection_method="round_robin",
    )
    group_chat_manager = GroupChatManager(
        groupchat=group_chat,
        llm_config=llm_config,
    )
    agent_1.initiate_chat(group_chat_manager, message="Hello")

    traces = get_traces()
    assert len(traces) == 1
    assert traces[0].info.status == "OK"
    spans = traces[0].data.spans
    # 1 for the root initiate_chat, 2 for the messages and 2 for the corresponding LLM calls.
    assert len(spans) == 5
    span_names = {span.name for span in spans}
    assert span_names == {
        "initiate_chat",
        "agent_1",
        "agent_2",
        "chat_completion",
    }

    assert traces[0].info.token_usage == {
        "input_tokens": 18,
        "output_tokens": 24,
        "total_tokens": 42,
    }


def test_autogen_logger_catch_exception(llm_config):
    # Error from the logger should not affect the main execution
    mlflow.ag2.autolog()

    with patch(
        "mlflow.tracing.provider.start_detached_span", side_effect=Exception("error")
    ) as mock_start_span:
        with mock_user_input(["Hi", "exit"]):
            assistant, user_proxy = get_simple_agent(llm_config)
            assistant.initiate_chat(user_proxy, message="foo")

    assert mock_start_span.call_count == 1