import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from typing import Tuple, Generator, List, Optional
from utils.config_model import DataConfig, FeatureConfig, TrainingConfig, EvaluationConfig

# --- Data Loading ---
def load_raw_data(config: DataConfig) -> pd.DataFrame:
    """
    Load and preprocess raw data from CSV.
    """
    # TODO: Implement CSV loading and datetime parsing
    pass

# --- Feature Engineering ---
def engineer_features(df: pd.DataFrame, target_col: str, feature_config: FeatureConfig) -> pd.DataFrame:
    """
    Create features from the target column and datetime index.
    """
    # TODO: Implement feature engineering (lags, rolling stats, time features, wavelets)
    pass

# --- Cross Validation ---
class TimeSeriesCrossValidationSplitter:
    def __init__(self, config: CrossValidationConfig, n_samples: int):
        self.config = config
        self.n_samples = n_samples

    def split(self) -> Generator[Tuple[np.ndarray, np.ndarray, np.ndarray], None, None]:
        """
        Generate train/val/test splits using expanding window approach.
        """
        # TODO: Implement expanding window CV splitter
        pass

# --- Dataset Class ---
class TimeSeriesDataset(Dataset):
    def __init__(self, data_array: np.ndarray, sequence_length: int, forecast_horizon: int):
        self.data = data_array
        self.sequence_length = sequence_length
        self.forecast_horizon = forecast_horizon

    def __len__(self) -> int:
        # TODO: Implement length calculation
        pass

    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
        # TODO: Implement sequence extraction
        pass

# --- Data Preparation ---
def prepare_fold_data_and_loaders(
    full_df: pd.DataFrame,
    train_idx: np.ndarray,
    val_idx: np.ndarray,
    test_idx: np.ndarray,
    feature_config: FeatureConfig,
    train_config: TrainingConfig,
    eval_config: EvaluationConfig
) -> Tuple[DataLoader, DataLoader, DataLoader, object, int]:
    """
    Prepare data loaders for a single fold.
    """
    # TODO: Implement data preparation pipeline
    pass