"""
Test file for LLM Council functionality.

Tests core functionalities of the LLM Council including:
- Initialization (default and custom)
- Running queries
- Batch processing
- Output formatting
"""

import pytest
from loguru import logger
from dotenv import load_dotenv
from swarms.structs.llm_council import LLMCouncil
from swarms.structs.agent import Agent

load_dotenv()


def test_llm_council_default_initialization():
    """Test LLM Council initialization with default council members."""
    try:
        logger.info("Testing LLM Council default initialization...")

        council = LLMCouncil(
            verbose=False, output_type="dict-all-except-first"
        )

        assert council is not None, "Council should be initialized"
        assert (
            council.name == "LLM Council"
        ), "Default name should be 'LLM Council'"
        assert (
            len(council.council_members) > 0
        ), "Should have council members"
        assert (
            council.chairman is not None
        ), "Chairman should be initialized"
        assert (
            council.conversation is not None
        ), "Conversation should be initialized"

        logger.info(
            f"✓ Council initialized with {len(council.council_members)} members"
        )
        logger.info("✓ Default initialization test passed")

    except Exception as e:
        logger.error(f"✗ Default initialization test failed: {e}")
        raise


def test_llm_council_custom_initialization():
    """Test LLM Council initialization with custom council members."""
    try:
        logger.info("Testing LLM Council custom initialization...")

        # Create custom council members with simpler models
        custom_members = [
            Agent(
                agent_name="TestAgent1",
                agent_description="First test agent",
                system_prompt="You are a helpful test agent.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
            Agent(
                agent_name="TestAgent2",
                agent_description="Second test agent",
                system_prompt="You are a helpful test agent.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
        ]

        council = LLMCouncil(
            name="Custom Council",
            council_members=custom_members,
            chairman_model="gpt-5.4",
            verbose=False,
            output_type="string",
        )

        assert council is not None, "Council should be initialized"
        assert (
            council.name == "Custom Council"
        ), "Name should match custom value"
        assert (
            len(council.council_members) == 2
        ), "Should have 2 custom members"
        assert (
            council.council_members[0].agent_name == "TestAgent1"
        ), "First member should match"
        assert (
            council.council_members[1].agent_name == "TestAgent2"
        ), "Second member should match"
        assert (
            council.output_type == "string"
        ), "Output type should be 'string'"

        logger.info("✓ Custom initialization test passed")

    except Exception as e:
        logger.error(f"✗ Custom initialization test failed: {e}")
        raise


def test_llm_council_run():
    """Test LLM Council run method with a simple query."""
    try:
        logger.info("Testing LLM Council run method...")

        # Use simpler models for testing
        custom_members = [
            Agent(
                agent_name="TestAgent1",
                agent_description="First test agent",
                system_prompt="You are a helpful test agent. Provide concise answers.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
            Agent(
                agent_name="TestAgent2",
                agent_description="Second test agent",
                system_prompt="You are a helpful test agent. Provide concise answers.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
        ]

        council = LLMCouncil(
            council_members=custom_members,
            chairman_model="gpt-5.4",
            verbose=False,
            output_type="dict-all-except-first",
        )

        query = "What is 2 + 2? Provide a brief answer."
        result = council.run(query)

        # Basic assertions
        assert result is not None, "Result should not be None"
        assert (
            council.conversation is not None
        ), "Conversation should exist"
        assert (
            len(council.conversation.conversation_history) > 0
        ), "Conversation should have messages"

        # Enhanced assertions to verify workflow steps
        messages = council.conversation.conversation_history

        # Step 1: Verify User query was added
        user_messages = [
            msg for msg in messages if msg.get("role") == "User"
        ]
        assert (
            len(user_messages) > 0
        ), "User query should be in conversation"

        # Step 2: Verify all council members responded
        member_responses = [
            msg
            for msg in messages
            if msg.get("role") in ["TestAgent1", "TestAgent2"]
        ]
        assert len(member_responses) == len(
            custom_members
        ), f"All {len(custom_members)} council members should have responded"

        # Step 3: Verify evaluations were performed
        evaluation_messages = [
            msg
            for msg in messages
            if "-Evaluation" in msg.get("role", "")
        ]
        assert len(evaluation_messages) == len(
            custom_members
        ), f"All {len(custom_members)} members should have evaluated"

        # Step 4: Verify Chairman synthesis occurred
        chairman_messages = [
            msg for msg in messages if msg.get("role") == "Chairman"
        ]
        assert (
            len(chairman_messages) > 0
        ), "Chairman should have synthesized final response"

        logger.info("✓ Run method test passed")
        logger.info(
            f"✓ Verified {len(member_responses)} member responses, {len(evaluation_messages)} evaluations, and {len(chairman_messages)} chairman synthesis"
        )

    except Exception as e:
        logger.error(f"✗ Run method test failed: {e}")
        raise


def test_llm_council_batched_run():
    """Test LLM Council batched_run method with multiple tasks."""
    try:
        logger.info("Testing LLM Council batched_run method...")

        # Use simpler models for testing
        custom_members = [
            Agent(
                agent_name="TestAgent1",
                agent_description="First test agent",
                system_prompt="You are a helpful test agent. Provide concise answers.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
            Agent(
                agent_name="TestAgent2",
                agent_description="Second test agent",
                system_prompt="You are a helpful test agent. Provide concise answers.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
        ]

        council = LLMCouncil(
            council_members=custom_members,
            chairman_model="gpt-5.4",
            verbose=False,
            output_type="dict-all-except-first",
        )

        tasks = [
            "What is 1 + 1?",
            "What is 3 + 3?",
        ]

        results = council.batched_run(tasks)

        assert results is not None, "Results should not be None"
        assert len(results) == len(
            tasks
        ), f"Should have {len(tasks)} results"
        assert all(
            result is not None for result in results
        ), "All results should not be None"

        logger.info(
            f"✓ Batched run test passed with {len(results)} results"
        )

    except Exception as e:
        logger.error(f"✗ Batched run test failed: {e}")
        raise


def test_llm_council_output_types():
    """Test LLM Council with different output types."""
    try:
        logger.info(
            "Testing LLM Council with different output types..."
        )

        # Use simpler models for testing
        custom_members = [
            Agent(
                agent_name="TestAgent1",
                agent_description="First test agent",
                system_prompt="You are a helpful test agent. Provide concise answers.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
            Agent(
                agent_name="TestAgent2",
                agent_description="Second test agent",
                system_prompt="You are a helpful test agent. Provide concise answers.",
                model_name="gpt-5.4",
                max_loops=1,
                verbose=False,
            ),
        ]

        output_types = ["string", "dict-all-except-first", "final"]

        for output_type in output_types:
            logger.info(f"Testing output type: {output_type}")

            council = LLMCouncil(
                council_members=custom_members,
                chairman_model="gpt-5.4",
                verbose=False,
                output_type=output_type,
            )

            query = "What is 5 + 5? Provide a brief answer."
            result = council.run(query)

            assert (
                result is not None
            ), f"Result should not be None for output type {output_type}"
            assert (
                council.output_type == output_type
            ), f"Output type should be {output_type}"

            logger.info(f"✓ Output type '{output_type}' test passed")

        logger.info("✓ All output types test passed")

    except Exception as e:
        logger.error(f"✗ Output types test failed: {e}")
        raise


if __name__ == "__main__":
    pytest.main([__file__, "-v"])
