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

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

from dataclasses import dataclass
from unittest.mock import Mock

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


@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"])
