"""
Unit tests for TrainingManager that don't require cyborgdb_core.

These tests focus on the pure logic without requiring the actual CyborgDB modules.
"""

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

# Mock the cyborgdb modules before importing
sys.modules["cyborgdb_core"] = MagicMock()

# Now import after mocking
from cyborgdb_service.core.training_manager import TrainingManager, TrainingJob


class TestTrainingManagerLogic:
    """Test the TrainingManager logic without actual CyborgDB dependencies."""

    def test_should_retrain_logic(self):
        """Test the retraining threshold logic."""
        with patch.object(TrainingManager, "_initialize_training_client"):
            manager = TrainingManager()
            manager._retrain_threshold = 10000

            # Test untrained index (effective n_lists = 1)
            # Threshold = 1 * 10000 = 10000
            assert not manager.should_retrain("test_index", 9999, 128, False, "ivfflat")
            assert manager.should_retrain("test_index", 10001, 128, False, "ivfflat")
            assert manager.should_retrain("test_index", 20000, 128, False, "ivfflat")

            # Test trained index with n_lists=128
            # Threshold = 128 * 10000 = 1,280,000
            assert not manager.should_retrain(
                "test_index", 1_000_000, 128, True, "ivfflat"
            )
            assert manager.should_retrain("test_index", 1_280_001, 128, True, "ivfflat")
            assert manager.should_retrain("test_index", 2_000_000, 128, True, "ivfflat")

    def test_should_retrain_various_n_lists(self):
        """Test retraining logic with various n_lists values."""
        with patch.object(TrainingManager, "_initialize_training_client"):
            manager = TrainingManager()
            manager._retrain_threshold = 10000

            # n_lists = 64, trained
            # Threshold = 64 * 10000 = 640,000
            assert not manager.should_retrain(
                "test_index", 639_999, 64, True, "ivfflat"
            )
            assert manager.should_retrain("test_index", 640_001, 64, True, "ivfflat")

            # n_lists = 256, trained
            # Threshold = 256 * 10000 = 2,560,000
            assert not manager.should_retrain(
                "test_index", 2_559_999, 256, True, "ivfflat"
            )
            assert manager.should_retrain("test_index", 2_560_001, 256, True, "ivfflat")

    def test_is_training_tracking(self):
        """Test tracking of indexes being trained."""
        with patch.object(TrainingManager, "_initialize_training_client"):
            manager = TrainingManager()

            # Initially no indexes are being trained
            assert not manager.is_training("index1")
            assert not manager.is_training("index2")

            # Set index as currently training
            with manager._lock:
                manager._currently_training = "index1"

            assert manager.is_training("index1")
            assert not manager.is_training("index2")

            # Clear currently training
            with manager._lock:
                manager._currently_training = None

            assert not manager.is_training("index1")

            # Add index to queue
            job = TrainingJob(index_name="index2", index_key_hex="0" * 64)
            manager._queue.put(job)

            assert not manager.is_training("index1")
            assert manager.is_training("index2")  # In queue

    def test_get_training_status(self):
        """Test getting the training status."""
        with patch.object(TrainingManager, "_initialize_training_client"):
            manager = TrainingManager()
            manager._retrain_threshold = 10000

            # Set currently training
            with manager._lock:
                manager._currently_training = "index1"

            # Add to queue
            job = TrainingJob(index_name="index2", index_key_hex="0" * 64)
            manager._queue.put(job)

            status = manager.get_training_status()

            assert status["currently_training"] == "index1"
            assert status["queued_indexes"] == ["index2"]
            assert status["training_indexes"] == ["index1", "index2"]
            assert status["retrain_threshold"] == 10000
            assert "worker_running" in status

    def test_custom_retrain_threshold(self):
        """Test custom retrain threshold from environment variable."""
        with patch.dict("os.environ", {"RETRAIN_THRESHOLD": "5000"}):
            with patch.object(TrainingManager, "_initialize_training_client"):
                manager = TrainingManager()
                assert manager._retrain_threshold == 5000

                # Test with custom threshold
                # Threshold = 1 * 5000 = 5000 for untrained
                assert not manager.should_retrain(
                    "test_index", 4999, 128, False, "ivfflat"
                )
                assert manager.should_retrain("test_index", 5001, 128, False, "ivfflat")

    def test_get_queue_position(self):
        """Test getting queue position for an index."""
        with patch.object(TrainingManager, "_initialize_training_client"):
            manager = TrainingManager()

            # Not in queue
            assert manager.get_queue_position("index1") is None

            # Currently training = position 0
            with manager._lock:
                manager._currently_training = "index1"
            assert manager.get_queue_position("index1") == 0

            # In queue
            job2 = TrainingJob(index_name="index2", index_key_hex="0" * 64)
            job3 = TrainingJob(index_name="index3", index_key_hex="0" * 64)
            manager._queue.put(job2)
            manager._queue.put(job3)

            assert manager.get_queue_position("index2") == 1
            assert manager.get_queue_position("index3") == 2
            assert manager.get_queue_position("index4") is None


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