"""
Training Step Level 4 Integration Tests

This module provides Level 4 validation for Training step builders, focusing on:
- Complete Training step creation and validation
- End-to-end training workflow integration
- Framework-specific training patterns
- Hyperparameter optimization integration
- Data channel and model artifact management
- Training job monitoring and completion
"""

from typing import Dict, Any, List, Optional
import logging

from ..integration_tests import IntegrationTests

logger = logging.getLogger(__name__)


class TrainingIntegrationTests(IntegrationTests):
    """Level 4 Training-specific integration validation tests."""

    def __init__(
        self,
        builder_class,
        step_info: Optional[Dict[str, Any]] = None,
        config=None,
        spec=None,
        contract=None,
        step_name=None,
        verbose: bool = False,
        test_reporter=None,
        **kwargs
    ):
        """
        Initialize Training integration tests.

        Args:
            builder_class: The Training step builder class to test
            step_info: Training-specific step information
            config: Optional config to use
            spec: Optional step specification
            contract: Optional script contract
            step_name: Optional step name
            verbose: Whether to print verbose output
            test_reporter: Optional function to report test results
            **kwargs: Additional arguments
        """
        # Initialize parent with new signature
        super().__init__(
            builder_class=builder_class,
            config=config,
            spec=spec,
            contract=contract,
            step_name=step_name,
            verbose=verbose,
            test_reporter=test_reporter,
            **kwargs
        )
        
        # Store Training-specific step info
        self.step_info = step_info or {}
        self.step_type = "Training"

    def get_step_type_specific_tests(self) -> List[str]:
        """Return Training-specific Level 4 integration tests."""
        return [
            "test_complete_training_step_creation",
            "test_framework_specific_training_workflow",
            "test_hyperparameter_optimization_integration",
            "test_data_channel_integration",
        ]

    def test_complete_training_step_creation(self) -> Dict[str, Any]:
        """Test complete Training step creation and validation."""
        test_name = "test_complete_training_step_creation"
        logger.info(f"Running {test_name}")

        try:
            results = {
                "test_name": test_name,
                "passed": True,
                "details": {},
                "errors": [],
            }

            # Test Training step instantiation
            if hasattr(self.builder_instance, "create_step"):
                step = self.builder_instance.create_step()
                results["details"]["step_created"] = step is not None

                if step is None:
                    results["passed"] = False
                    results["errors"].append("Failed to create Training step")
                else:
                    # Validate step properties
                    step_validation = self._validate_training_step_properties(step)
                    results["details"]["step_validation"] = step_validation

                    if not step_validation["valid"]:
                        results["passed"] = False
                        results["errors"].extend(step_validation["errors"])

            # Test step configuration completeness
            if hasattr(self.builder_instance, "get_step_config"):
                config = self.builder_instance.get_step_config()
                results["details"]["step_config"] = config

                config_validation = self._validate_step_config_completeness(config)
                results["details"]["config_validation"] = config_validation

                if not config_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(config_validation["errors"])

            # Test estimator configuration
            if hasattr(self.builder_instance, "get_estimator_config"):
                estimator_config = self.builder_instance.get_estimator_config()
                results["details"]["estimator_config"] = estimator_config

                estimator_validation = self._validate_estimator_config(estimator_config)
                if not estimator_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(estimator_validation["errors"])

            return results

        except Exception as e:
            logger.error(f"Error in {test_name}: {str(e)}")
            return {
                "test_name": test_name,
                "passed": False,
                "details": {},
                "errors": [f"Test execution failed: {str(e)}"],
            }

    def test_framework_specific_training_workflow(self) -> Dict[str, Any]:
        """Test framework-specific training workflow patterns."""
        test_name = "test_framework_specific_training_workflow"
        logger.info(f"Running {test_name}")

        try:
            results = {
                "test_name": test_name,
                "passed": True,
                "details": {},
                "errors": [],
            }

            framework = self._detect_framework()
            if not framework:
                results["details"]["framework"] = "No framework detected"
                return results

            results["details"]["framework"] = framework

            # Test PyTorch training workflow
            if framework == "pytorch":
                pytorch_workflow = self._test_pytorch_training_workflow()
                results["details"]["pytorch_workflow"] = pytorch_workflow
                if not pytorch_workflow["valid"]:
                    results["passed"] = False
                    results["errors"].extend(pytorch_workflow["errors"])

            # Test XGBoost training workflow
            elif framework == "xgboost":
                xgboost_workflow = self._test_xgboost_training_workflow()
                results["details"]["xgboost_workflow"] = xgboost_workflow
                if not xgboost_workflow["valid"]:
                    results["passed"] = False
                    results["errors"].extend(xgboost_workflow["errors"])

            # Test TensorFlow training workflow
            elif framework == "tensorflow":
                tf_workflow = self._test_tensorflow_training_workflow()
                results["details"]["tensorflow_workflow"] = tf_workflow
                if not tf_workflow["valid"]:
                    results["passed"] = False
                    results["errors"].extend(tf_workflow["errors"])

            # Test SKLearn training workflow
            elif framework == "sklearn":
                sklearn_workflow = self._test_sklearn_training_workflow()
                results["details"]["sklearn_workflow"] = sklearn_workflow
                if not sklearn_workflow["valid"]:
                    results["passed"] = False
                    results["errors"].extend(sklearn_workflow["errors"])

            # Test custom framework workflow
            else:
                custom_workflow = self._test_custom_training_workflow(framework)
                results["details"]["custom_workflow"] = custom_workflow
                if not custom_workflow["valid"]:
                    results["passed"] = False
                    results["errors"].extend(custom_workflow["errors"])

            return results

        except Exception as e:
            logger.error(f"Error in {test_name}: {str(e)}")
            return {
                "test_name": test_name,
                "passed": False,
                "details": {},
                "errors": [f"Test execution failed: {str(e)}"],
            }

    def test_hyperparameter_optimization_integration(self) -> Dict[str, Any]:
        """Test hyperparameter optimization integration."""
        test_name = "test_hyperparameter_optimization_integration"
        logger.info(f"Running {test_name}")

        try:
            results = {
                "test_name": test_name,
                "passed": True,
                "details": {},
                "errors": [],
            }

            # Test hyperparameter configuration
            if hasattr(self.builder_instance, "get_hyperparameters"):
                hyperparams = self.builder_instance.get_hyperparameters()
                results["details"]["hyperparameters"] = hyperparams

                hyperparam_validation = self._validate_hyperparameters(hyperparams)
                results["details"]["hyperparam_validation"] = hyperparam_validation

                if not hyperparam_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(hyperparam_validation["errors"])

            # Test hyperparameter tuning configuration
            if hasattr(self.builder_instance, "get_tuning_config"):
                tuning_config = self.builder_instance.get_tuning_config()
                results["details"]["tuning_config"] = tuning_config

                tuning_validation = self._validate_tuning_config(tuning_config)
                if not tuning_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(tuning_validation["errors"])

            # Test automatic model tuning integration
            if hasattr(self.builder_instance, "get_auto_ml_config"):
                automl_config = self.builder_instance.get_auto_ml_config()
                results["details"]["automl_config"] = automl_config

                automl_validation = self._validate_automl_config(automl_config)
                if not automl_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(automl_validation["errors"])

            return results

        except Exception as e:
            logger.error(f"Error in {test_name}: {str(e)}")
            return {
                "test_name": test_name,
                "passed": False,
                "details": {},
                "errors": [f"Test execution failed: {str(e)}"],
            }

    def test_data_channel_integration(self) -> Dict[str, Any]:
        """Test data channel integration and management."""
        test_name = "test_data_channel_integration"
        logger.info(f"Running {test_name}")

        try:
            results = {
                "test_name": test_name,
                "passed": True,
                "details": {},
                "errors": [],
            }

            # Test training data channels
            if hasattr(self.builder_instance, "get_training_inputs"):
                training_inputs = self.builder_instance.get_training_inputs()
                results["details"]["training_inputs"] = training_inputs

                for i, input_config in enumerate(training_inputs):
                    input_validation = self._validate_training_input(input_config, i)
                    results["details"][f"input_{i}_validation"] = input_validation

                    if not input_validation["valid"]:
                        results["passed"] = False
                        results["errors"].extend(input_validation["errors"])

            # Test data distribution strategies
            if hasattr(self.builder_instance, "get_data_distribution_config"):
                distribution_config = (
                    self.builder_instance.get_data_distribution_config()
                )
                results["details"]["data_distribution"] = distribution_config

                distribution_validation = self._validate_data_distribution(
                    distribution_config
                )
                if not distribution_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(distribution_validation["errors"])

            # Test data preprocessing integration
            if hasattr(self.builder_instance, "get_preprocessing_config"):
                preprocessing_config = self.builder_instance.get_preprocessing_config()
                results["details"]["preprocessing_config"] = preprocessing_config

                preprocessing_validation = self._validate_preprocessing_config(
                    preprocessing_config
                )
                if not preprocessing_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(preprocessing_validation["errors"])

            return results

        except Exception as e:
            logger.error(f"Error in {test_name}: {str(e)}")
            return {
                "test_name": test_name,
                "passed": False,
                "details": {},
                "errors": [f"Test execution failed: {str(e)}"],
            }


    # Helper methods for Training-specific validations

    def _validate_training_step_properties(self, step) -> Dict[str, Any]:
        """Validate Training step properties."""
        validation = {"valid": True, "errors": []}

        # Check required properties
        required_props = [
            "TrainingJobName",
            "AlgorithmSpecification",
            "RoleArn",
            "InputDataConfig",
            "OutputDataConfig",
        ]
        for prop in required_props:
            if not hasattr(step, prop):
                validation["valid"] = False
                validation["errors"].append(f"Missing required property: {prop}")

        return validation

    def _validate_step_config_completeness(
        self, config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate step configuration completeness."""
        validation = {"valid": True, "errors": []}

        required_config = [
            "training_job_name",
            "algorithm_specification",
            "role",
            "input_data_config",
        ]
        for key in required_config:
            if key not in config:
                validation["valid"] = False
                validation["errors"].append(f"Missing required config: {key}")

        return validation

    def _validate_estimator_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate estimator configuration."""
        validation = {"valid": True, "errors": []}

        required_fields = ["image_uri", "role", "instance_type"]
        for field in required_fields:
            if field not in config:
                validation["valid"] = False
                validation["errors"].append(f"Missing estimator config field: {field}")

        return validation

    def _detect_framework(self) -> Optional[str]:
        """Detect the ML framework being used."""
        if hasattr(self.builder_instance, "framework"):
            return self.builder_instance.framework
        return None

    def _test_pytorch_training_workflow(self) -> Dict[str, Any]:
        """Test PyTorch-specific training workflow."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": "pytorch", "workflow_type": "pytorch_training"},
        }

    def _test_xgboost_training_workflow(self) -> Dict[str, Any]:
        """Test XGBoost-specific training workflow."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": "xgboost", "workflow_type": "xgboost_training"},
        }

    def _test_tensorflow_training_workflow(self) -> Dict[str, Any]:
        """Test TensorFlow-specific training workflow."""
        return {
            "valid": True,
            "errors": [],
            "details": {
                "framework": "tensorflow",
                "workflow_type": "tensorflow_training",
            },
        }

    def _test_sklearn_training_workflow(self) -> Dict[str, Any]:
        """Test SKLearn-specific training workflow."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": "sklearn", "workflow_type": "sklearn_training"},
        }

    def _test_custom_training_workflow(self, framework: str) -> Dict[str, Any]:
        """Test custom training workflow."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": framework, "workflow_type": "custom_training"},
        }

    def _validate_hyperparameters(self, hyperparams: Dict[str, Any]) -> Dict[str, Any]:
        """Validate hyperparameter configuration."""
        return {"valid": True, "errors": []}

    def _validate_tuning_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate hyperparameter tuning configuration."""
        return {"valid": True, "errors": []}

    def _validate_automl_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate AutoML configuration."""
        return {"valid": True, "errors": []}

    def _validate_training_input(
        self, input_config: Dict[str, Any], index: int
    ) -> Dict[str, Any]:
        """Validate training input configuration."""
        validation = {"valid": True, "errors": []}

        required_fields = ["DataSource", "ContentType"]
        for field in required_fields:
            if field not in input_config:
                validation["valid"] = False
                validation["errors"].append(
                    f"Training input {index} missing field: {field}"
                )

        return validation

    def _validate_data_distribution(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate data distribution configuration."""
        return {"valid": True, "errors": []}

    def _validate_preprocessing_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate preprocessing configuration."""
        return {"valid": True, "errors": []}

    def _validate_model_output_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate model output configuration."""
        validation = {"valid": True, "errors": []}

        required_fields = ["S3OutputPath"]
        for field in required_fields:
            if field not in config:
                validation["valid"] = False
                validation["errors"].append(f"Missing model output field: {field}")

        return validation

    def _validate_expected_artifacts(self, artifacts: List[str]) -> Dict[str, Any]:
        """Validate expected model artifacts."""
        return {"valid": True, "errors": []}

    def _validate_serialization_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate model serialization configuration."""
        return {"valid": True, "errors": []}

    def _validate_metrics_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate training metrics configuration."""
        return {"valid": True, "errors": []}

    def _validate_logging_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate logging configuration."""
        return {"valid": True, "errors": []}

    def _validate_checkpoint_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate checkpoint configuration."""
        return {"valid": True, "errors": []}

    def _validate_distributed_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate distributed training configuration."""
        validation = {"valid": True, "errors": []}

        required_fields = ["InstanceType", "InstanceCount"]
        for field in required_fields:
            if field not in config:
                validation["valid"] = False
                validation["errors"].append(
                    f"Missing distributed config field: {field}"
                )

        return validation

    def _validate_cluster_config(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate cluster configuration."""
        return {"valid": True, "errors": []}

    def _validate_resource_optimization(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate resource optimization configuration."""
        return {"valid": True, "errors": []}

    def _validate_gpu_optimization(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate GPU optimization configuration."""
        return {"valid": True, "errors": []}

    def _validate_memory_optimization(self, config: Dict[str, Any]) -> Dict[str, Any]:
        """Validate memory optimization configuration."""
        return {"valid": True, "errors": []}

    def _validate_processing_dependency(self, dep: Dict[str, Any]) -> Dict[str, Any]:
        """Validate processing step dependency."""
        return {"valid": True, "errors": []}

    def _validate_data_dependency(self, dep: Dict[str, Any]) -> Dict[str, Any]:
        """Validate data dependency."""
        return {"valid": True, "errors": []}

    def _validate_external_dependency(self, dep: Dict[str, Any]) -> Dict[str, Any]:
        """Validate external dependency."""
        return {"valid": True, "errors": []}
