import pandas as pd
import numpy as np

from typing import Optional, List, Any

from aboba.base import BaseDataProcessor

class RegressionProcessor(BaseDataProcessor):
    """
    Performs regression-based transformation for CUPED or other adjustments.
    
    This processor uses linear regression to adjust target metrics based on
    covariates. It can be used for CUPED transformation or other regression-based
    adjustments.
    
    Examples:
        ```python
        import pandas as pd
        import numpy as np
        from aboba.processing.regression_processor import RegressionProcessor

        # Create sample data
        np.random.seed(42)
        n = 1000
        covariate1 = np.random.normal(0, 1, n)
        covariate2 = np.random.normal(0, 1, n)
        # Target variable with some relationship to covariates
        target = 2 * covariate1 + 1.5 * covariate2 + np.random.normal(0, 0.5, n)
        # Add group column
        group = ['control'] * (n//2) + ['test'] * (n//2)
        # Add treatment effect
        target[n//2:] += 2

        data = pd.DataFrame({
            'target': target,
            'covariate1': covariate1,
            'covariate2': covariate2,
            'group': group
        })

        # Apply regression adjustment
        processor = RegressionProcessor(
            value_column='target',
            covariate_columns=['covariate1', 'covariate2'],
            result_column='adjusted_target',
            group_column='group',
            group_test='test',
            group_control='control'
        )
        processed_data, _ = processor.transform(data)
        print(f"Original mean: {data['target'].mean():.2f}")
        print(f"Adjusted mean: {processed_data['adjusted_target'].mean():.2f}")
        ```
    """

    def __init__(
        self,
        value_column: str,
        covariate_columns: List[str],
        result_column: str,
        group_column: Optional[str] = None,
        group_test: Any = None,
        group_control: Any = None,
    ):
        """
        Initialize the RegressionProcessor.
        
        Args:
            value_column (str): Name of the column with the target values.
            covariate_columns (List[str]): List of column names with covariate values.
            result_column (str): Name of the column to store the adjusted values.
            group_column (Optional[str]): Name of the column with group labels.
                If None, all data is used for regression.
            group_test (Any): Value in group_column that identifies the test group.
            group_control (Any): Value in group_column that identifies the control group.
        """

        raise NotImplementedError("Because of infixed bugs")

        super().__init__()
        self.value_column = value_column
        self.covariate_columns = covariate_columns
        self.result_column = result_column

        if group_column is not None:
            assert group_test is not None
            assert group_control is not None

        self.group_column = group_column
        self.group_test = group_test
        self.group_control = group_control

    def transform(self, data: pd.DataFrame):
        """
        Apply regression transformation to the data.
        
        Args:
            data (pd.DataFrame): DataFrame to transform.
            
        Returns:
            Tuple[pd.DataFrame, Dict]: Transformed DataFrame and empty artifacts dict.
        """
        # This implementation needs to be fixed - the current code has undefined variables
        # and doesn't match the class purpose. A proper implementation would:
        # 1. Fit a regression model on control group data
        # 2. Use the model to predict values for all data
        # 3. Adjust the target values based on the predictions
        #
        # For now, we'll return the data unchanged with a warning
        import warnings
        warnings.warn("RegressionProcessor.transform() is not properly implemented")
        return data, dict()
