"""
CreateModel Step Level 4 Integration Tests

This module provides Level 4 validation for CreateModel step builders, focusing on:
- Complete CreateModel step creation and validation
- End-to-end integration testing
- Framework-specific deployment patterns
- Model registry integration workflows
- Multi-container model deployment
- Production deployment readiness
"""

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

from ..integration_tests import IntegrationTests

logger = logging.getLogger(__name__)


class CreateModelIntegrationTests(IntegrationTests):
    """Level 4 CreateModel-specific integration validation tests."""

    def __init__(self, builder_instance, config: Dict[str, Any]):
        super().__init__(builder_instance, config)
        self.step_type = "CreateModel"

    def get_step_type_specific_tests(self) -> List[str]:
        """Return CreateModel-specific Level 4 integration tests."""
        return [
            "test_complete_createmodel_step_creation",
            "test_framework_specific_model_deployment",
            "test_model_registry_integration_workflow",
            "test_multi_container_model_deployment",
            "test_inference_endpoint_preparation",
            "test_production_deployment_readiness",
            "test_model_versioning_integration",
            "test_container_optimization_validation",
            "test_createmodel_dependency_resolution",
        ]

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

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

            # Test CreateModel 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 CreateModel step")
                else:
                    # Validate step properties
                    step_validation = self._validate_createmodel_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 step dependencies resolution
            dependency_validation = self._validate_step_dependencies()
            results["details"]["dependency_validation"] = dependency_validation

            if not dependency_validation["valid"]:
                results["passed"] = False
                results["errors"].extend(dependency_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_model_deployment(self) -> Dict[str, Any]:
        """Test framework-specific model deployment patterns."""
        test_name = "test_framework_specific_model_deployment"
        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 deployment pattern
            if framework == "pytorch":
                pytorch_deployment = self._test_pytorch_deployment_pattern()
                results["details"]["pytorch_deployment"] = pytorch_deployment
                if not pytorch_deployment["valid"]:
                    results["passed"] = False
                    results["errors"].extend(pytorch_deployment["errors"])

            # Test XGBoost deployment pattern
            elif framework == "xgboost":
                xgboost_deployment = self._test_xgboost_deployment_pattern()
                results["details"]["xgboost_deployment"] = xgboost_deployment
                if not xgboost_deployment["valid"]:
                    results["passed"] = False
                    results["errors"].extend(xgboost_deployment["errors"])

            # Test TensorFlow deployment pattern
            elif framework == "tensorflow":
                tf_deployment = self._test_tensorflow_deployment_pattern()
                results["details"]["tensorflow_deployment"] = tf_deployment
                if not tf_deployment["valid"]:
                    results["passed"] = False
                    results["errors"].extend(tf_deployment["errors"])

            # Test SKLearn deployment pattern
            elif framework == "sklearn":
                sklearn_deployment = self._test_sklearn_deployment_pattern()
                results["details"]["sklearn_deployment"] = sklearn_deployment
                if not sklearn_deployment["valid"]:
                    results["passed"] = False
                    results["errors"].extend(sklearn_deployment["errors"])

            # Test custom framework deployment
            else:
                custom_deployment = self._test_custom_deployment_pattern(framework)
                results["details"]["custom_deployment"] = custom_deployment
                if not custom_deployment["valid"]:
                    results["passed"] = False
                    results["errors"].extend(custom_deployment["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_model_registry_integration_workflow(self) -> Dict[str, Any]:
        """Test model registry integration workflow."""
        test_name = "test_model_registry_integration_workflow"
        logger.info(f"Running {test_name}")

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

            # Test model package creation
            if hasattr(self.builder_instance, "create_model_package"):
                package_creation = self.builder_instance.create_model_package()
                results["details"]["model_package_creation"] = package_creation

                if not package_creation.get("success", False):
                    results["passed"] = False
                    results["errors"].append("Model package creation failed")

            # Test model approval workflow
            if hasattr(self.builder_instance, "get_model_approval_status"):
                approval_status = self.builder_instance.get_model_approval_status()
                results["details"]["approval_status"] = approval_status

                if approval_status not in ["Approved", "PendingManualApproval"]:
                    results["passed"] = False
                    results["errors"].append(
                        f"Invalid approval status: {approval_status}"
                    )

            # Test model versioning
            if hasattr(self.builder_instance, "get_model_version_info"):
                version_info = self.builder_instance.get_model_version_info()
                results["details"]["version_info"] = version_info

                version_validation = self._validate_model_version_info(version_info)
                if not version_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(version_validation["errors"])

            # Test registry metadata
            if hasattr(self.builder_instance, "get_registry_metadata"):
                metadata = self.builder_instance.get_registry_metadata()
                results["details"]["registry_metadata"] = metadata

                metadata_validation = self._validate_registry_metadata(metadata)
                if not metadata_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(metadata_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_multi_container_model_deployment(self) -> Dict[str, Any]:
        """Test multi-container model deployment patterns."""
        test_name = "test_multi_container_model_deployment"
        logger.info(f"Running {test_name}")

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

            # Check if multi-container deployment is configured
            if hasattr(self.builder_instance, "is_multi_container"):
                is_multi_container = self.builder_instance.is_multi_container()
                results["details"]["is_multi_container"] = is_multi_container

                if not is_multi_container:
                    results["details"]["deployment_type"] = "single_container"
                    return results

            # Test container configuration
            if hasattr(self.builder_instance, "get_container_definitions"):
                containers = self.builder_instance.get_container_definitions()
                results["details"]["container_definitions"] = containers

                # Validate each container
                for i, container in enumerate(containers):
                    container_validation = self._validate_container_definition(
                        container, i
                    )
                    results["details"][
                        f"container_{i}_validation"
                    ] = container_validation

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

            # Test container communication
            if hasattr(self.builder_instance, "get_container_communication_config"):
                comm_config = self.builder_instance.get_container_communication_config()
                results["details"]["communication_config"] = comm_config

                comm_validation = self._validate_container_communication(comm_config)
                if not comm_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(comm_validation["errors"])

            # Test load balancing configuration
            if hasattr(self.builder_instance, "get_load_balancing_config"):
                lb_config = self.builder_instance.get_load_balancing_config()
                results["details"]["load_balancing_config"] = lb_config

                lb_validation = self._validate_load_balancing_config(lb_config)
                if not lb_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(lb_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_inference_endpoint_preparation(self) -> Dict[str, Any]:
        """Test inference endpoint preparation and configuration."""
        test_name = "test_inference_endpoint_preparation"
        logger.info(f"Running {test_name}")

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

            # Test endpoint configuration generation
            if hasattr(self.builder_instance, "generate_endpoint_config"):
                endpoint_config = self.builder_instance.generate_endpoint_config()
                results["details"]["endpoint_config"] = endpoint_config

                config_validation = self._validate_endpoint_config(endpoint_config)
                results["details"]["config_validation"] = config_validation

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

            # Test auto-scaling configuration
            if hasattr(self.builder_instance, "get_autoscaling_config"):
                autoscaling_config = self.builder_instance.get_autoscaling_config()
                results["details"]["autoscaling_config"] = autoscaling_config

                autoscaling_validation = self._validate_autoscaling_config(
                    autoscaling_config
                )
                if not autoscaling_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(autoscaling_validation["errors"])

            # Test data capture configuration
            if hasattr(self.builder_instance, "get_data_capture_config"):
                capture_config = self.builder_instance.get_data_capture_config()
                results["details"]["data_capture_config"] = capture_config

                capture_validation = self._validate_data_capture_config(capture_config)
                if not capture_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(capture_validation["errors"])

            # Test monitoring configuration
            if hasattr(self.builder_instance, "get_monitoring_config"):
                monitoring_config = self.builder_instance.get_monitoring_config()
                results["details"]["monitoring_config"] = monitoring_config

                monitoring_validation = self._validate_monitoring_config(
                    monitoring_config
                )
                if not monitoring_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(monitoring_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_production_deployment_readiness(self) -> Dict[str, Any]:
        """Test production deployment readiness validation."""
        test_name = "test_production_deployment_readiness"
        logger.info(f"Running {test_name}")

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

            # Test security configuration
            security_validation = self._validate_security_configuration()
            results["details"]["security_validation"] = security_validation

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

            # Test performance optimization
            performance_validation = self._validate_performance_optimization()
            results["details"]["performance_validation"] = performance_validation

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

            # Test resource allocation
            resource_validation = self._validate_resource_allocation()
            results["details"]["resource_validation"] = resource_validation

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

            # Test compliance requirements
            compliance_validation = self._validate_compliance_requirements()
            results["details"]["compliance_validation"] = compliance_validation

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

            # Test disaster recovery configuration
            dr_validation = self._validate_disaster_recovery_config()
            results["details"]["disaster_recovery_validation"] = dr_validation

            if not dr_validation["valid"]:
                results["passed"] = False
                results["errors"].extend(dr_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_model_versioning_integration(self) -> Dict[str, Any]:
        """Test model versioning integration and management."""
        test_name = "test_model_versioning_integration"
        logger.info(f"Running {test_name}")

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

            # Test version tracking
            if hasattr(self.builder_instance, "get_version_tracking_info"):
                version_tracking = self.builder_instance.get_version_tracking_info()
                results["details"]["version_tracking"] = version_tracking

                tracking_validation = self._validate_version_tracking(version_tracking)
                if not tracking_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(tracking_validation["errors"])

            # Test version comparison
            if hasattr(self.builder_instance, "compare_model_versions"):
                version_comparison = self.builder_instance.compare_model_versions()
                results["details"]["version_comparison"] = version_comparison

                comparison_validation = self._validate_version_comparison(
                    version_comparison
                )
                if not comparison_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(comparison_validation["errors"])

            # Test rollback capability
            if hasattr(self.builder_instance, "test_rollback_capability"):
                rollback_test = self.builder_instance.test_rollback_capability()
                results["details"]["rollback_capability"] = rollback_test

                if not rollback_test.get("supported", False):
                    results["passed"] = False
                    results["errors"].append("Model rollback capability not supported")

            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_container_optimization_validation(self) -> Dict[str, Any]:
        """Test container optimization and performance validation."""
        test_name = "test_container_optimization_validation"
        logger.info(f"Running {test_name}")

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

            # Test container size optimization
            if hasattr(self.builder_instance, "get_container_size_info"):
                size_info = self.builder_instance.get_container_size_info()
                results["details"]["container_size_info"] = size_info

                size_validation = self._validate_container_size(size_info)
                if not size_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(size_validation["errors"])

            # Test startup time optimization
            if hasattr(self.builder_instance, "get_startup_optimization_info"):
                startup_info = self.builder_instance.get_startup_optimization_info()
                results["details"]["startup_optimization"] = startup_info

                startup_validation = self._validate_startup_optimization(startup_info)
                if not startup_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(startup_validation["errors"])

            # Test memory optimization
            if hasattr(self.builder_instance, "get_memory_optimization_info"):
                memory_info = self.builder_instance.get_memory_optimization_info()
                results["details"]["memory_optimization"] = memory_info

                memory_validation = self._validate_memory_optimization(memory_info)
                if not memory_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(memory_validation["errors"])

            # Test inference latency optimization
            if hasattr(self.builder_instance, "get_latency_optimization_info"):
                latency_info = self.builder_instance.get_latency_optimization_info()
                results["details"]["latency_optimization"] = latency_info

                latency_validation = self._validate_latency_optimization(latency_info)
                if not latency_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(latency_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_createmodel_dependency_resolution(self) -> Dict[str, Any]:
        """Test CreateModel step dependency resolution."""
        test_name = "test_createmodel_dependency_resolution"
        logger.info(f"Running {test_name}")

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

            # Test training step dependency
            if hasattr(self.builder_instance, "get_training_dependency"):
                training_dep = self.builder_instance.get_training_dependency()
                results["details"]["training_dependency"] = training_dep

                if training_dep:
                    dep_validation = self._validate_training_dependency(training_dep)
                    if not dep_validation["valid"]:
                        results["passed"] = False
                        results["errors"].extend(dep_validation["errors"])

            # Test model artifact dependencies
            if hasattr(self.builder_instance, "get_artifact_dependencies"):
                artifact_deps = self.builder_instance.get_artifact_dependencies()
                results["details"]["artifact_dependencies"] = artifact_deps

                for dep in artifact_deps:
                    dep_validation = self._validate_artifact_dependency(dep)
                    if not dep_validation["valid"]:
                        results["passed"] = False
                        results["errors"].extend(dep_validation["errors"])

            # Test external dependencies
            if hasattr(self.builder_instance, "get_external_dependencies"):
                external_deps = self.builder_instance.get_external_dependencies()
                results["details"]["external_dependencies"] = external_deps

                for dep in external_deps:
                    dep_validation = self._validate_external_dependency(dep)
                    if not dep_validation["valid"]:
                        results["passed"] = False
                        results["errors"].extend(dep_validation["errors"])

            # Test dependency ordering
            if hasattr(self.builder_instance, "get_dependency_order"):
                dep_order = self.builder_instance.get_dependency_order()
                results["details"]["dependency_order"] = dep_order

                order_validation = self._validate_dependency_order(dep_order)
                if not order_validation["valid"]:
                    results["passed"] = False
                    results["errors"].extend(order_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 CreateModel-specific validations

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

        # Check required properties
        required_props = ["ModelName", "PrimaryContainer", "ExecutionRoleArn"]
        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 = ["model_name", "image_uri", "model_data_url", "role"]
        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_step_dependencies(self) -> Dict[str, Any]:
        """Validate step dependencies."""
        return {"valid": True, "errors": []}

    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_deployment_pattern(self) -> Dict[str, Any]:
        """Test PyTorch-specific deployment pattern."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": "pytorch", "deployment_type": "torchserve"},
        }

    def _test_xgboost_deployment_pattern(self) -> Dict[str, Any]:
        """Test XGBoost-specific deployment pattern."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": "xgboost", "deployment_type": "sagemaker_xgboost"},
        }

    def _test_tensorflow_deployment_pattern(self) -> Dict[str, Any]:
        """Test TensorFlow-specific deployment pattern."""
        return {
            "valid": True,
            "errors": [],
            "details": {
                "framework": "tensorflow",
                "deployment_type": "tensorflow_serving",
            },
        }

    def _test_sklearn_deployment_pattern(self) -> Dict[str, Any]:
        """Test SKLearn-specific deployment pattern."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": "sklearn", "deployment_type": "sagemaker_sklearn"},
        }

    def _test_custom_deployment_pattern(self, framework: str) -> Dict[str, Any]:
        """Test custom deployment pattern."""
        return {
            "valid": True,
            "errors": [],
            "details": {"framework": framework, "deployment_type": "custom"},
        }

    def _validate_model_version_info(
        self, version_info: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate model version information."""
        validation = {"valid": True, "errors": []}

        required_fields = ["version", "created_by", "creation_time"]
        for field in required_fields:
            if field not in version_info:
                validation["valid"] = False
                validation["errors"].append(f"Missing version field: {field}")

        return validation

    def _validate_registry_metadata(self, metadata: Dict[str, Any]) -> Dict[str, Any]:
        """Validate registry metadata."""
        validation = {"valid": True, "errors": []}

        required_metadata = ["model_package_group_name", "model_approval_status"]
        for field in required_metadata:
            if field not in metadata:
                validation["valid"] = False
                validation["errors"].append(f"Missing metadata field: {field}")

        return validation

    def _validate_container_definition(
        self, container: Dict[str, Any], index: int
    ) -> Dict[str, Any]:
        """Validate container definition."""
        validation = {"valid": True, "errors": []}

        required_fields = ["Image", "ModelDataUrl"]
        for field in required_fields:
            if field not in container:
                validation["valid"] = False
                validation["errors"].append(f"Container {index} missing field: {field}")

        return validation

    def _validate_container_communication(
        self, comm_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate container communication configuration."""
        return {"valid": True, "errors": []}

    def _validate_load_balancing_config(
        self, lb_config: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate load balancing configuration."""
        return {"valid": True, "errors": []}

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

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

        return validation

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

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

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

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

    def _validate_performance_optimization(self) -> Dict[str, Any]:
        """Validate performance optimization."""
        return {"valid": True, "errors": []}

    def _validate_resource_allocation(self) -> Dict[str, Any]:
        """Validate resource allocation."""
        return {"valid": True, "errors": []}

    def _validate_compliance_requirements(self) -> Dict[str, Any]:
        """Validate compliance requirements."""
        return {"valid": True, "errors": []}

    def _validate_disaster_recovery_config(self) -> Dict[str, Any]:
        """Validate disaster recovery configuration."""
        return {"valid": True, "errors": []}

    def _validate_version_tracking(
        self, tracking_info: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate version tracking information."""
        return {"valid": True, "errors": []}

    def _validate_version_comparison(
        self, comparison: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate version comparison."""
        return {"valid": True, "errors": []}

    def _validate_container_size(self, size_info: Dict[str, Any]) -> Dict[str, Any]:
        """Validate container size optimization."""
        return {"valid": True, "errors": []}

    def _validate_startup_optimization(
        self, startup_info: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate startup optimization."""
        return {"valid": True, "errors": []}

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

    def _validate_latency_optimization(
        self, latency_info: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate latency optimization."""
        return {"valid": True, "errors": []}

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

    def _validate_artifact_dependency(
        self, artifact_dep: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Validate artifact dependency."""
        return {"valid": True, "errors": []}

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

    def _validate_dependency_order(self, dep_order: List[str]) -> Dict[str, Any]:
        """Validate dependency order."""
        return {"valid": True, "errors": []}
