"""
Unit tests for database-driven AI workflows.

Tests database-sourced training, inference, and pipeline orchestration.
All tests use mock providers and mock database operations.
"""

import pytest
from unittest.mock import Mock, MagicMock
from dataclasses import dataclass

from kaizen.integrations.dataflow.db_driven_ai import (
    DBTrainingPipeline,
    InferencePipeline,
    PipelineOrchestrator,
    TrainingPipelineSignature,
    InferenceSignature,
    PipelineOrchestrationSignature
)


@dataclass
class MockConfig:
    """Mock configuration for testing."""
    llm_provider: str = "mock"
    model: str = "mock-model"
    temperature: float = 0.3


class TestDBTrainingPipeline:
    """Test database-driven training pipeline."""

    def test_db_training_pipeline_creation(self):
        """Test creating a training pipeline with database connection."""
        config = MockConfig()
        mock_db = Mock()

        pipeline = DBTrainingPipeline(config=config, db=mock_db)

        assert pipeline is not None
        assert pipeline.db_connection is not None
        assert hasattr(pipeline.db_connection, 'db')  # Has wrapped db
        assert isinstance(pipeline.signature, TrainingPipelineSignature)
        assert hasattr(pipeline, 'trained_models')

    def test_feature_extraction_from_db(self):
        """Test extracting features from database tables."""
        config = MockConfig()
        mock_db = Mock()

        # Mock database query response
        training_data = [
            {"age": 25, "income": 50000, "purchases": 5, "churn_risk": "low"},
            {"age": 45, "income": 80000, "purchases": 12, "churn_risk": "high"},
            {"age": 35, "income": 65000, "purchases": 8, "churn_risk": "medium"}
        ]

        pipeline = DBTrainingPipeline(config=config, db=mock_db)
        pipeline.query_database = Mock(return_value=training_data)
        pipeline.db_connection.get_table_schema = Mock(return_value={
            "columns": ["age", "income", "purchases", "churn_risk"],
            "types": {"age": "int", "income": "float", "purchases": "int", "churn_risk": "str"}
        })

        # Mock LLM response
        pipeline.run = Mock(return_value={
            "feature_columns": ["age", "income", "purchases"],
            "target_column": "churn_risk",
            "preprocessing_steps": ["normalize_numeric", "encode_categorical"],
            "model_config": {"algorithm": "random_forest", "n_estimators": 100}
        })

        # Mock model training
        mock_model = Mock()
        mock_model.accuracy = 0.85
        pipeline._train_model = Mock(return_value=mock_model)
        pipeline._save_model_metadata = Mock(return_value={
            "id": "model-123",
            "source_table": "customers",
            "features": ["age", "income", "purchases"],
            "accuracy": 0.85
        })

        result = pipeline.train_from_database(
            table="customers",
            model_objective="Predict customer churn risk",
            validation_split=0.2
        )

        assert result["model_id"] == "model-123"
        assert result["accuracy"] == 0.85
        assert "age" in result["features"]
        assert "metadata" in result

    def test_model_metadata_storage(self):
        """Test storing model metadata in database."""
        config = MockConfig()
        mock_db = Mock()
        mock_db_connection = Mock()

        pipeline = DBTrainingPipeline(config=config, db=mock_db)
        pipeline.db_connection = mock_db_connection
        pipeline.insert_database = Mock(return_value={"id": "model-456"})

        mock_model = Mock()
        mock_model.accuracy = 0.92

        training_plan = {
            "feature_columns": ["feature1", "feature2"],
            "target_column": "target",
            "model_config": {"algorithm": "xgboost"}
        }

        metadata = pipeline._save_model_metadata(
            model=mock_model,
            table="test_table",
            training_plan=training_plan
        )

        assert metadata["source_table"] == "test_table"
        assert metadata["features"] == ["feature1", "feature2"]
        assert metadata["target"] == "target"
        assert metadata["accuracy"] == 0.92
        assert "id" in metadata

    def test_batch_inference_from_db(self):
        """Test batch inference on database data."""
        config = MockConfig()
        mock_db = Mock()

        pipeline = DBTrainingPipeline(config=config, db=mock_db)

        # Mock batch data
        batch_data = [
            {"age": 30, "income": 60000, "purchases": 7},
            {"age": 50, "income": 90000, "purchases": 15},
            {"age": 28, "income": 45000, "purchases": 3}
        ]

        pipeline.query_database = Mock(return_value=batch_data)

        # Verify batch data can be fetched
        result = pipeline.query_database(table="customers", limit=100)
        assert len(result) == 3
        assert result[0]["age"] == 30


class TestInferencePipeline:
    """Test real-time inference pipeline."""

    def test_inference_with_db_lookup(self):
        """Test inference using database context."""
        config = MockConfig()
        mock_db = Mock()

        pipeline = InferencePipeline(config=config, db=mock_db)

        # Mock model metadata retrieval
        model_metadata = {
            "id": "model-789",
            "features": ["age", "income", "purchases"],
            "target": "churn_risk",
            "accuracy": 0.88
        }

        pipeline.query_database = Mock(return_value=[model_metadata])
        pipeline._fetch_context_data = Mock(return_value={
            "historical_data": [{"previous_churn": False}],
            "related_entities": []
        })

        # Mock LLM inference
        pipeline.run = Mock(return_value={
            "prediction": "medium",
            "confidence": 0.75,
            "explanation": "Based on age and purchase history, moderate churn risk"
        })

        pipeline._store_inference_result = Mock()

        input_data = {"age": 35, "income": 75000, "purchases": 12}

        result = pipeline.infer_with_db_context(
            model_id="model-789",
            input_data=input_data,
            store_result=True
        )

        assert result["prediction"] == "medium"
        assert result["confidence"] == 0.75
        assert "explanation" in result
        pipeline._store_inference_result.assert_called_once()

    def test_context_enrichment_from_db(self):
        """Test enriching input data with database context."""
        config = MockConfig()
        mock_db = Mock()

        pipeline = InferencePipeline(config=config, db=mock_db)
        pipeline.query_database = Mock(return_value=[
            {"user_id": 123, "historical_purchases": 50, "avg_rating": 4.5}
        ])

        input_data = {"user_id": 123, "current_action": "browse"}
        model_metadata = {"features": ["user_id", "historical_purchases"]}

        context = pipeline._fetch_context_data(input_data, model_metadata)

        assert isinstance(context, dict)
        assert "historical_data" in context or "related_entities" in context

    def test_inference_result_storage(self):
        """Test storing inference results in database."""
        config = MockConfig()
        mock_db = Mock()

        pipeline = InferencePipeline(config=config, db=mock_db)
        pipeline.db_connection = Mock()
        pipeline.insert_database = Mock(return_value={"id": "inference-001"})

        result = {
            "prediction": "high",
            "confidence": 0.92,
            "explanation": "Strong indicators of churn"
        }

        pipeline._store_inference_result(
            model_id="model-123",
            input_data={"age": 55},
            result=result
        )

        pipeline.insert_database.assert_called_once()
        call_args = pipeline.insert_database.call_args
        assert call_args[1]["table"] == "inference_results"
        assert "model_id" in call_args[1]["data"]


class TestPipelineOrchestrator:
    """Test automated pipeline orchestration."""

    def test_automated_pipeline_execution(self):
        """Test end-to-end pipeline automation."""
        config = MockConfig()
        mock_db = Mock()

        orchestrator = PipelineOrchestrator(config=config, db=mock_db)
        orchestrator.db_connection = Mock()
        orchestrator.db_connection.get_table_schema = Mock(return_value={
            "columns": ["id", "name", "value"],
            "types": {"id": "int", "name": "str", "value": "float"}
        })

        # Mock pipeline plan generation
        orchestrator.run = Mock(return_value={
            "execution_plan": [
                {"step": "extract", "source": "customers"},
                {"step": "transform", "operation": "normalize"},
                {"step": "train", "algorithm": "random_forest"}
            ],
            "resource_requirements": {"memory": "2GB", "cpu": "4 cores"},
            "monitoring_metrics": ["accuracy", "latency", "throughput"]
        })

        orchestrator._execute_pipeline = Mock(return_value={
            "extract": {"rows": 1000},
            "transform": {"processed": 1000},
            "train": {"accuracy": 0.89}
        })

        orchestrator._collect_metrics = Mock(return_value={
            "accuracy": 0.89,
            "execution_time": 45.2,
            "rows_processed": 1000
        })

        orchestrator._store_pipeline_metrics = Mock()

        result = orchestrator.create_pipeline(
            pipeline_name="customer_churn_pipeline",
            data_sources=["customers", "transactions"],
            objective="Predict customer churn"
        )

        assert result["pipeline_name"] == "customer_churn_pipeline"
        assert result["steps_executed"] == 3
        assert "metrics" in result
        orchestrator._store_pipeline_metrics.assert_called_once()

    def test_pipeline_performance_monitoring(self):
        """Test monitoring pipeline metrics."""
        config = MockConfig()
        mock_db = Mock()

        orchestrator = PipelineOrchestrator(config=config, db=mock_db)

        pipeline_results = {
            "extract": {"rows": 5000, "time": 2.5},
            "transform": {"processed": 5000, "time": 8.3},
            "train": {"accuracy": 0.91, "time": 34.2}
        }

        metrics = orchestrator._collect_metrics(pipeline_results)

        assert isinstance(metrics, dict)
        # Verify basic metric collection structure
        assert metrics is not None

    def test_model_versioning_in_db(self):
        """Test version control for AI models in database."""
        config = MockConfig()
        mock_db = Mock()

        pipeline = DBTrainingPipeline(config=config, db=mock_db)
        pipeline.db_connection = Mock()
        pipeline.insert_database = Mock(return_value={"id": "model-v2"})

        mock_model = Mock()
        mock_model.accuracy = 0.94

        training_plan = {
            "feature_columns": ["f1", "f2", "f3"],
            "target_column": "target",
            "model_config": {"version": "2.0.0"}
        }

        metadata = pipeline._save_model_metadata(
            model=mock_model,
            table="customers",
            training_plan=training_plan
        )

        assert "version" in metadata
        assert metadata["version"] in ["1.0.0", "2.0.0"]  # Flexible for implementation


class TestSignatures:
    """Test signature definitions."""

    def test_training_pipeline_signature(self):
        """Test TrainingPipelineSignature structure."""
        sig = TrainingPipelineSignature()

        # Verify input fields exist
        assert hasattr(sig, 'training_data_sample') or 'training_data_sample' in str(sig)
        assert hasattr(sig, 'table_schema') or 'table_schema' in str(sig)
        assert hasattr(sig, 'model_objective') or 'model_objective' in str(sig)

        # Verify output fields exist
        assert hasattr(sig, 'feature_columns') or 'feature_columns' in str(sig)
        assert hasattr(sig, 'target_column') or 'target_column' in str(sig)

    def test_inference_signature(self):
        """Test InferenceSignature structure."""
        sig = InferenceSignature()

        # Verify input fields
        assert hasattr(sig, 'input_data') or 'input_data' in str(sig)
        assert hasattr(sig, 'model_metadata') or 'model_metadata' in str(sig)

        # Verify output fields
        assert hasattr(sig, 'prediction') or 'prediction' in str(sig)
        assert hasattr(sig, 'confidence') or 'confidence' in str(sig)

    def test_orchestration_signature(self):
        """Test PipelineOrchestrationSignature structure."""
        sig = PipelineOrchestrationSignature()

        # Verify input fields
        assert hasattr(sig, 'pipeline_config') or 'pipeline_config' in str(sig)
        assert hasattr(sig, 'data_sources') or 'data_sources' in str(sig)

        # Verify output fields
        assert hasattr(sig, 'execution_plan') or 'execution_plan' in str(sig)


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