from views_pipeline_core.templates.utils import save_python_script
from pathlib import Path
import logging

logger = logging.getLogger(__name__)


def generate(script_path: Path) -> bool:
    """Generates a Python script for a custom model manager template at the specified script_path.
    This function writes a comprehensive template for implementing a custom ForecastingModelManager
    in the views-pipeline-core framework. The generated script includes detailed documentation on 
    the structure and usage of the `self.config` property, as well as step-by-step instructions 
    and placeholders for implementing model training, evaluation, forecasting, and hyperparameter 
    sweep logic.
    Args:
        script_path (Path): The file path where the generated Python script will be saved.
        bool: True if the script was successfully saved, False otherwise.
    """
    
    code = """
from views_pipeline_core.managers.model import ModelPathManager, ForecastingModelManager
from views_pipeline_core.files.utils import read_dataframe
from views_pipeline_core.configs.pipeline import PipelineConfig
import logging

logger = logging.getLogger(__name__)

# =====================================================================================
# SPECIAL NOTE: Understanding self.config
# =====================================================================================
# The self.config property provides a unified configuration dictionary combining
# settings from multiple sources. This is the PRIMARY interface for accessing all
# model parameters and runtime settings.
#
# STRUCTURE:
#   self.config = {
#       **hyperparameters_config,
#       **metadata_config,
#       **deployment_config,
#   }
#
# CONFIGURATION SOURCES:
# -------------------------------------------------------------------------------------
# 1. Hyperparameters (from config_hyperparameters.py)
#    - Model architecture parameters
#    - Training settings
#    - Example keys:
#        'learning_rate': 0.001,          # Float value for optimizer
#        'hidden_layers': [128, 64],       # Network architecture
#        'batch_size': 32,                 # Training batch size
#        'dropout': 0.5                    # Regularization parameter
#
# 2. Metadata (from config_meta.py)
#    - Model identification and core settings
#    - Data specifications
#    - Evaluation metrics
#    - Example keys:
#        'name': 'my_model',               # Model identifier
#        'algorithm': 'Transformer',       # Algorithm type
#        'targets': ['ged_sb'],            # Prediction targets
#        'steps': [1, 2, 3, 6],            # Forecast horizons
#        'metrics': ['RMSLE', 'CRPS']       # Evaluation metrics
#
# 3. Deployment Settings (from config_deployment.py)
#    - Example keys:
#        'deployment_status': 'shadow'
#
# 4. Runtime Properties (automatically added)
#    - Execution context and operational flags
#    - Example keys:
#        'run_type': 'validation',         # Current pipeline stage
#        'sweep': False,                   # Hyperparameter tuning flag
#        'timestamp': '20230621_142510'    # Run identifier (YYYYMMDD_HHMMSS)
#
# KEY USAGE NOTES:
# -------------------------------------------------------------------------------------
# - Access any configuration value directly:
#       lr = self.config['learning_rate']
#
# - For optional parameters, use safe getters:
#       dropout = self.config.get('dropout_rate', 0.1)  # Default 0.1 if missing
#
# - Critical metadata is always present:
#       print(f"Training {self.config['name']} for {self.config['targets']}")
#
# - During hyperparameter sweeps:
#   - WandB overrides hyperparameter values
#   - Original config remain for other parameters
#
# - Configuration is validated automatically for required keys:
#   Required: ['name', 'algorithm', 'targets', 'steps']
#
# TIP: All custom parameters added to your config files will automatically
#      appear in self.config. Use consistent naming conventions.
# =====================================================================================


class ExampleForecastingModelManager(ForecastingModelManager):
    \"""
    Template for building a custom model manager. Follow these steps to implement
    your model's training, evaluation, sweep, and forecasting functionality.
    
    Steps to implement:
    1. Initialize model-specific components in __init__ (if needed)
    2. Implement model training in _train_model_artifact()
    3. Implement model evaluation in _evaluate_model_artifact()
    4. Implement forecasting in _forecast_model_artifact()
    5. Implement sweep evaluation in _evaluate_sweep()
    
    Common variables available:
    - self._model_path: Path manager for model directories
    - self.config: Combined configuration dictionary
    - self._data_loader: Data loader with partition information
    \"""

    def __init__(
        self, 
        model_path: ModelPathManager, 
        wandb_notifications: bool = True,
        use_prediction_store: bool = False
    ) -> None:
        \"""
        Initialize your custom model manager.
        
        USER IMPLEMENTATION:
        - Add model-specific initialization here
        - Call super() first to inherit base functionality
        
        EXAMPLE:
        super().__init__(model_path, wandb_notifications, use_prediction_store)
        self.special_component = YourComponent()
        \"""
        super().__init__(model_path, wandb_notifications, use_prediction_store)
        
        # Add your custom initialization below
        logger.info("Initializing CustomModelManager")
        # YOUR CODE HERE

    def _train_model_artifact(self) -> any:
        \"""
        Train and save your model artifact.
        
        Steps:
        1. Load training data
        2. Preprocess data
        3. Initialize model
        4. Train model
        5. Save artifact
        
        USER IMPLEMENTATION:
        - Implement steps 2-4 with your model-specific logic
        - Save artifact in step 5 using provided paths
        
        Returns:
        Trained model object (used in sweeps)
        \"""
        # Common paths and data loading (provided)
        path_raw = self._model_path.data_raw # Path to raw data
        path_artifacts = self._model_path.artifacts # Path to save model artifacts
        run_type = self.config["run_type"] # e.g., "calibration", "validation", "forecasting"
        df_viewser = read_dataframe(
            path_raw / f"{run_type}_viewser_df{PipelineConfig.dataframe_format}"
        ) # Dataframe obtained from viewser
        partitioner_dict = self._data_loader.partition_dict # Partition dict from ViewsDataLoader

        # --- USER IMPLEMENTATION STARTS HERE ---
        # 1. Preprocessing
        logger.info("Preprocessing data")
        # YOUR PREPROCESSING CODE HERE
        
        # 2. Model initialization
        logger.info(f"Initializing model with config: {self.config}")
        # YOUR MODEL INITIALIZATION CODE HERE
        # Example: model = MyModel(**self.config['hyperparameters'])
        
        # 3. Model training
        logger.info("Training model")
        # YOUR TRAINING CODE HERE
        # Example: model.fit(train_data)
        
        # 4. Save artifact (if not in sweep)
        if not self.config["sweep"]:
            model_filename = self.generate_model_file_name(run_type, ".pkl")
            logger.info(f"Saving model artifact: {model_filename}")
            # YOUR SAVING CODE HERE
            # Example: model.save(path_artifacts / model_filename)
        
        return model  # Return trained model for sweep evaluation
        # --- USER IMPLEMENTATION ENDS HERE ---

    def _evaluate_model_artifact(
        self, 
        eval_type: str, 
        artifact_name: str = None
    ) -> list:
        \"""
        Evaluate trained model artifact.
        
        Steps:
        1. Locate model artifact
        2. Load model
        3. Load evaluation data
        4. Generate predictions
        5. Return predictions
        
        USER IMPLEMENTATION:
        - Implement steps 2 and 4 with model-specific logic
        \"""
        # Common setup (provided)
        path_raw = self._model_path.data_raw
        path_artifacts = self._model_path.artifacts
        run_type = self.config["run_type"]

        # Resolve artifact path
        if artifact_name:
            path_artifact = path_artifacts / artifact_name
        else:
            path_artifact = self._model_path.get_latest_model_artifact_path(run_type)
        
        self.config["timestamp"] = path_artifact.stem[-15:]
        df_viewser = read_dataframe(
            path_raw / f"{run_type}_viewser_df{PipelineConfig.dataframe_format}"
        )

        # --- USER IMPLEMENTATION STARTS HERE ---
        # 1. Load model
        logger.info(f"Loading model artifact: {path_artifact}")
        # YOUR MODEL LOADING CODE HERE
        # Example: model = MyModel.load(path_artifact)
        
        # 2. Generate predictions
        # The expected format of your prediction dataframe can be found here: 
        # https://github.com/views-platform/views-pipeline-core/tree/main/views_pipeline_core/managers#dataframe-structures-for-evaluation-and-forecast-methods

        logger.info(f"Generating predictions for {eval_type} evaluation")
        predictions = []
        
        # Determine evaluation length
        sequence_numbers = self._resolve_evaluation_sequence_number(eval_type)
        for seq_num in range(sequence_numbers):
            # YOUR PREDICTION CODE HERE
            # Example: preds = model.predict(seq_num, steps=self.config['steps'])
            predictions.append(preds)  # Append predictions for each sequence
        
        return predictions
        # --- USER IMPLEMENTATION ENDS HERE ---

    def _forecast_model_artifact(self, artifact_name: str = None) -> pd.DataFrame:
        \"""
        Generate forecasts using trained model artifact.
        
        Steps:
        1. Locate model artifact
        2. Load model
        3. Load forecasting data
        4. Generate forecasts
        5. Return forecasts
        
        USER IMPLEMENTATION:
        - Implement steps 2 and 4 with model-specific logic
        \"""
        # Common setup (provided)
        path_raw = self._model_path.data_raw
        path_artifacts = self._model_path.artifacts
        run_type = self.config["run_type"]

        # Resolve artifact path
        if artifact_name:
            path_artifact = path_artifacts / artifact_name
        else:
            path_artifact = self._model_path.get_latest_model_artifact_path(run_type)
        
        self.config["timestamp"] = path_artifact.stem[-15:]
        df_viewser = read_dataframe(
            path_raw / f"{run_type}_viewser_df{PipelineConfig.dataframe_format}"
        )

        # --- USER IMPLEMENTATION STARTS HERE ---
        # 1. Load model
        logger.info(f"Loading model for forecasting: {path_artifact}")
        # YOUR MODEL LOADING CODE HERE
        
        # 2. Generate forecasts
        logger.info("Generating forecasts")
        # YOUR FORECASTING CODE HERE
        # The expected format of your prediction dataframe can be found here: 
        # https://github.com/views-platform/views-pipeline-core/tree/main/views_pipeline_core/managers#dataframe-structures-for-evaluation-and-forecast-methods
        # Example: forecasts = model.forecast(steps=self.config['steps'])
        
        return forecasts
        # --- USER IMPLEMENTATION ENDS HERE ---

    def _evaluate_sweep(self, eval_type: str, model: any) -> list:
        \"""
        Evaluate model during hyperparameter sweep (in-memory).
        
        USER IMPLEMENTATION:
        - Implement evaluation using in-memory model
        - Same prediction logic as _evaluate_model_artifact but without loading from disk
        \"""
        # Common setup (provided)
        path_raw = self._model_path.data_raw
        run_type = self.config["run_type"]
        df_viewser = read_dataframe(
            path_raw / f"{run_type}_viewser_df{PipelineConfig.dataframe_format}"
        )

        # --- USER IMPLEMENTATION STARTS HERE ---
        logger.info(f"Evaluating sweep model for {eval_type}")
        predictions = []
        sequence_numbers = self._resolve_evaluation_sequence_number(eval_type)
        
        for seq_num in range(sequence_numbers):
            # YOUR PREDICTION CODE HERE
            # Example: preds = model.predict(seq_num, steps=self.config['steps'])
            predictions.append(preds)
            
        return predictions
        # --- USER IMPLEMENTATION ENDS HERE ---
"""
    return save_python_script(script_path, code)






