import pytest
from unittest.mock import MagicMock, patch
from models import Ticket, Track, WorkerContext

# These tests define the expected interface for multi_agent_conductor.py
# which will be implemented in the next phase of TDD.

def test_conductor_engine_initialization():
    """
    Test that ConductorEngine can be initialized with a Track.
    """
    track = Track(id="test_track", description="Test Track")
    from multi_agent_conductor import ConductorEngine
    engine = ConductorEngine(track=track)
    assert engine.track == track

def test_conductor_engine_run_linear_executes_tickets_in_order():
    """
    Test that run_linear iterates through executable tickets and calls the worker lifecycle.
    """
    ticket1 = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
    ticket2 = Ticket(id="T2", description="Task 2", status="todo", assigned_to="worker2", depends_on=["T1"])
    track = Track(id="track1", description="Track 1", tickets=[ticket1, ticket2])
    
    from multi_agent_conductor import ConductorEngine
    engine = ConductorEngine(track=track)
    
    # We mock run_worker_lifecycle as it is expected to be in the same module
    with patch("multi_agent_conductor.run_worker_lifecycle") as mock_lifecycle:
        # Mocking lifecycle to mark ticket as complete so dependencies can be resolved
        def side_effect(ticket, context):
            ticket.mark_complete()
            return "Success"
        mock_lifecycle.side_effect = side_effect
        
        engine.run_linear()
        
        # Track.get_executable_tickets() should be called repeatedly until all are done
        # T1 should run first, then T2.
        assert mock_lifecycle.call_count == 2
        assert ticket1.status == "completed"
        assert ticket2.status == "completed"
        
        # Verify sequence: T1 before T2
        calls = mock_lifecycle.call_args_list
        assert calls[0][0][0].id == "T1"
        assert calls[1][0][0].id == "T2"

def test_run_worker_lifecycle_calls_ai_client_send():
    """
    Test that run_worker_lifecycle triggers the AI client and updates ticket status on success.
    """
    ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
    context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
    
    from multi_agent_conductor import run_worker_lifecycle
    
    with patch("ai_client.send") as mock_send:
        mock_send.return_value = "Task complete. I have updated the file."
        
        result = run_worker_lifecycle(ticket, context)
        
        assert result == "Task complete. I have updated the file."
        assert ticket.status == "completed"
        mock_send.assert_called_once()
        
        # Check if description was passed to send()
        args, kwargs = mock_send.call_args
        # user_message is passed as a keyword argument
        assert ticket.description in kwargs["user_message"]

def test_run_worker_lifecycle_handles_blocked_response():
    """
    Test that run_worker_lifecycle marks the ticket as blocked if the AI indicates it cannot proceed.
    """
    ticket = Ticket(id="T1", description="Task 1", status="todo", assigned_to="worker1")
    context = WorkerContext(ticket_id="T1", model_name="test-model", messages=[])
    
    from multi_agent_conductor import run_worker_lifecycle
    
    with patch("ai_client.send") as mock_send:
        # Simulate a response indicating a block
        mock_send.return_value = "I am BLOCKED because I don't have enough information."
        
        run_worker_lifecycle(ticket, context)
        
        assert ticket.status == "blocked"
        assert "BLOCKED" in ticket.blocked_reason