from pathlib import Path
from mlpipeline.constants import CONFIG_FILE_PATH, AUTOML_CONFIG_FILE_PATH
from mlpipeline.utils.common import read_yaml, create_directories
from mlpipeline.entity.config_entity import (
    DataIngestionConfig,
    DataValidationConfig,
    DataTransformationConfig,
    FeatureEngineeringConfig,
    ModelTrainerConfig,
    ModelEvaluationConfig,
    ModelPusherConfig,
)


class ConfigurationManager:
    def __init__(self, config_filepath=CONFIG_FILE_PATH):
        self.config = read_yaml(config_filepath)
        create_directories([self.config.artifacts_root])

    def get_data_ingestion_config(self) -> DataIngestionConfig:
        config = self.config.data_ingestion
        create_directories([config.root_dir])
        
        return DataIngestionConfig(
            root_dir=Path(config.root_dir),
            source_url=config.source_url,
            local_data_file=Path(config.local_data_file),
            unzip_dir=Path(config.unzip_dir),
        )

    def get_data_validation_config(self) -> DataValidationConfig:
        config = self.config.data_validation
        create_directories([config.root_dir])
        
        return DataValidationConfig(
            root_dir=Path(config.root_dir),
            data_dir=Path(config.data_dir),
            status_file=Path(config.status_file),
            schema_file=Path(config.schema_file),
        )

    def get_data_transformation_config(self) -> DataTransformationConfig:
        config = self.config.data_transformation
        create_directories([config.root_dir])
        
        return DataTransformationConfig(
            root_dir=Path(config.root_dir),
            data_path=Path(config.data_path),
            train_path=Path(config.train_path),
            test_path=Path(config.test_path),
            test_size=config.test_size,
            random_state=config.random_state,
        )

    def get_feature_engineering_config(self) -> FeatureEngineeringConfig:
        config = self.config.feature_engineering
        create_directories([config.root_dir])
        
        return FeatureEngineeringConfig(
            root_dir=Path(config.root_dir),
            train_path=Path(config.train_path),
            test_path=Path(config.test_path),
            output_train_path=Path(config.output_train_path),
            output_test_path=Path(config.output_test_path),
        )

    def get_model_trainer_config(self) -> ModelTrainerConfig:
        config = self.config.model_trainer
        automl_config = read_yaml(Path(AUTOML_CONFIG_FILE_PATH))
        create_directories([config.root_dir])
        
        return ModelTrainerConfig(
            root_dir=Path(config.root_dir),
            train_data_path=Path(config.train_data_path),
            test_data_path=Path(config.test_data_path),
            model_path=Path(config.model_path),
            target_column=config.target_column,
            automl_library=automl_config.automl_library,
        )

    def get_model_evaluation_config(self) -> ModelEvaluationConfig:
        config = self.config.model_evaluation
        automl_config = read_yaml(Path(AUTOML_CONFIG_FILE_PATH))
        create_directories([config.root_dir])
        
        return ModelEvaluationConfig(
            root_dir=Path(config.root_dir),
            model_path=Path(config.model_path),
            test_data_path=Path(config.test_data_path),
            metrics_file=Path(config.metrics_file),
            target_column=config.target_column,
            automl_library=automl_config.automl_library,
        )

    def get_model_pusher_config(self) -> ModelPusherConfig:
        config = self.config.model_pusher
        create_directories([config.root_dir])
        
        return ModelPusherConfig(
            root_dir=Path(config.root_dir),
            model_path=Path(config.model_path),
            model_registry_path=Path(config.model_registry_path),
        )