Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.


TASK : write below professionally coding below properly 

"""

This module provides a comprehensive interface for applying various defense mechanisms
from the Adversarial Robustness Toolbox (ART) to input data. Users can select a defense
from a predefined list and apply it to their data to enhance model robustness against
adversarial attacks.

Requirements:
    - adversarial-robustness-toolbox
    - numpy

Usage:
    from defences import DefenceManager
    manager = DefenceManager()
    defended_x = manager.apply_defense('GaussianNoise', x, y)
"""

import logging
from typing import Any, Callable, Dict, List, Optional, Tuple, Type

import numpy as np
from defences import (
    BinaryInputDetector,
    BinaryActivationDetector,
    SubsetScanningDetector,
    ClassLabels,
    GaussianNoise,
    HighConfidence,
    Postprocessor,
    ReverseSigmoid,
    Rounded,
    CutMix,
    CutMixPyTorch,
    CutMixTensorFlowV2,
    Cutout,
    CutoutPyTorch,
    CutoutTensorFlowV2,
    FeatureSqueezing,
    GaussianAugmentation,
    DefenseGAN,
    InverseGAN,
    JpegCompression,
    LabelSmoothing,
    Mixup,
    MixupPyTorch,
    MixupTensorFlowV2,
    Mp3Compression,
    Mp3CompressionPyTorch,
    PixelDefend,
    Preprocessor,
    Resample,
    SpatialSmoothing,
    SpatialSmoothingPyTorch,
    SpatialSmoothingTensorFlowV2,
    ThermometerEncoding,
    TotalVarMin,
    VideoCompression,
    VideoCompressionPyTorch,
    Trainer,
    AdversarialTrainer,
    AdversarialTrainerCertifiedPytorch,
    AdversarialTrainerCertifiedIBPPyTorch,
    AdversarialTrainerMadryPGD,
    AdversarialTrainerFBF,
    AdversarialTrainerFBFPyTorch,
    AdversarialTrainerTRADES,
    AdversarialTrainerTRADESPyTorch,
    AdversarialTrainerAWP,
    AdversarialTrainerAWPPyTorch,
    AdversarialTrainerOAAT,
    AdversarialTrainerOAATPyTorch,
    DPInstaHideTrainer,
)

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)


def get_defence_parameters(defence) -> Dict[str, Any]:
    """Retrieve default parameters for each attack.

    Args:
        attack (defenceType): The defence class.

    Returns:
        Dict[str, Any]: Dictionary of parameters.
    """
    # Define default parameters for each attack
    default_params: Dict[Type[Any], Dict[str, Any]] = {
        'BinaryInputDetector': {'detector': None},                        
        'BinaryActivationDetector': {'classifier': None, 'detector': None, 'layer': None},
        'SubsetScanningDetector': {'classifier': None, 'bgd_data': None, 'layer': None, 'scoring_function': 'BerkJones', 'verbose': True},
        'ClassLabels': {'apply_fit': False, 'apply_predict': True},
        'GaussianNoise': {'scale': 0.2, 'apply_fit': False, 'apply_predict': True}, 
        'HighConfidence': {'cutoff': 0.25, 'apply_fit': False, 'apply_predict': True},
        'Postprocessor': {'is_fitted': False, 'apply_fit': True, 'apply_predict': True}, 
        'ReverseSigmoid': {'beta': 1.0, 'gamma': 0.1, 'apply_fit': False, 'apply_predict': True}, 
        'Rounded': {'decimals': 3, 'apply_fit': False, 'apply_predict': True},
        'CutMix': {'num_classes': None, 'alpha': 1.0, 'probability': 0.5, 'channels_first': False,
                    'apply_fit': True, 'apply_predict': False, 'verbose': False}, 
        'CutMixPyTorch': {'num_classes': None, 'alpha': 1.0, 'probability': 0.5, 
                          'channels_first': False, 'apply_fit': True, 'apply_predict': False, 'device_type': 'gpu', 'verbose': False},
        'CutMixTensorFlowV2': {'num_classes': None, 'alpha': 1.0, 'probability': 0.5, 
                               'channels_first': False, 'apply_fit': True, 'apply_predict': False, 
                               'verbose': False},
        'Cutout': {'length': None, 'channels_first': False, 'apply_fit': True,
                    'apply_predict': False, 'verbose': False}, 
        'CutoutPyTorch': {'length': None, 'channels_first': False, 'apply_fit': True, 'apply_predict': False, 'device_type': 'gpu', 'verbose': False}, 
        'CutoutTensorFlowV2': {'length': None, 'channels_first': False, 'apply_fit': True, 'apply_predict': False, 'verbose': False}, 
        'FeatureSqueezing': {'clip_values': None, 'bit_depth': 8, 'apply_fit': False, 'apply_predict': True}, 
        'GaussianAugmentation': {'sigma': 1.0, 'augmentation': True, 'ratio': 1.0, 'clip_values': None, 'apply_fit': True, 'apply_predict': False}, 
        'DefenseGAN': {'sess': None, 'gan': None}, 
        'InverseGAN': {'sess': None, 'gan': None, 'inverse_gan': None, 'apply_fit': False, 'apply_predict': False}, 
        'JpegCompression': {'clip_values': None, 'quality': 50, 'channels_first': False, 'apply_fit': True, 
                            'apply_predict': True, 'verbose': False}, 
                            'LabelSmoothing': {'max_value': 0.9, 'apply_fit': True, 'apply_predict': False}, 
        'Mixup': {'num_classes': None, 'alpha': 1.0, 'num_mix': 2, 'apply_fit': True, 'apply_predict': False}, 
        'MixupPyTorch': {'num_classes': None, 'alpha': 1.0, 'num_mix': 2, 'apply_fit': True, 'apply_predict': False, 'device_type': 'gpu'}, 
        'MixupTensorFlowV2': {'num_classes': None, 'alpha': 1.0, 'num_mix': 2, 'apply_fit': True, 'apply_predict': False}, 
        'Mp3Compression': {'sample_rate': None, 'channels_first': False, 'apply_fit': False, 'apply_predict': True, 'verbose': False}, 
        'Mp3CompressionPyTorch': {'sample_rate': None, 'channels_first': False, 'apply_fit': False, 'apply_predict': True, 'device_type': 'gpu', 'verbose': False}, 
        'PixelDefend': {'clip_values': (0.0, 1.0), 'eps': 16, 'pixel_cnn': None, 'batch_size': 128, 'apply_fit': False, 'apply_predict': True, 'verbose': False}, 
        'Preprocessor': {'is_fitted': False, 'apply_fit': True, 'apply_predict': True}, 'Resample': {'sr_original': None, 'sr_new': None, 'channels_first': False, 'apply_fit': False, 'apply_predict': True}, 
        'SpatialSmoothing': {'window_size': 3, 'channels_first': False, 'clip_values': None, 'apply_fit': False, 'apply_predict': True}
    
    }
    
    return default_params.get(defence, {})
class DefenceManager:
    """
    Manages the selection and application of defense mechanisms from ART.

    Attributes:
        defenses (Dict[str, Type[Callable]]): Mapping of defense names to their corresponding classes.
    """

    def __init__(self) -> None:
        """
        Initializes the DefenceManager with a dictionary of available defenses.
        """
        self.defenses: Dict[str, Type[Any]] = {
            # Evasion Detectors
            'BinaryInputDetector': BinaryInputDetector,
            'BinaryActivationDetector': BinaryActivationDetector,
            'SubsetScanningDetector': SubsetScanningDetector,

            # Postprocessors
            'ClassLabels': ClassLabels,
            'GaussianNoise': GaussianNoise,
            'HighConfidence': HighConfidence,
            'Postprocessor': Postprocessor,
            'ReverseSigmoid': ReverseSigmoid,
            'Rounded': Rounded,

            # Preprocessors
            'CutMix': CutMix,
            'CutMixPyTorch': CutMixPyTorch,
            'CutMixTensorFlowV2': CutMixTensorFlowV2,
            'Cutout': Cutout,
            'CutoutPyTorch': CutoutPyTorch,
            'CutoutTensorFlowV2': CutoutTensorFlowV2,
            'FeatureSqueezing': FeatureSqueezing,
            'GaussianAugmentation': GaussianAugmentation,
            'DefenseGAN': DefenseGAN,
            'InverseGAN': InverseGAN,
            'JpegCompression': JpegCompression,
            'LabelSmoothing': LabelSmoothing,
            'Mixup': Mixup,
            'MixupPyTorch': MixupPyTorch,
            'MixupTensorFlowV2': MixupTensorFlowV2,
            'Mp3Compression': Mp3Compression,
            'Mp3CompressionPyTorch': Mp3CompressionPyTorch,
            'PixelDefend': PixelDefend,
            'Preprocessor': Preprocessor,
            'Resample': Resample,
            'SpatialSmoothing': SpatialSmoothing,
            'SpatialSmoothingPyTorch': SpatialSmoothingPyTorch,
            'SpatialSmoothingTensorFlowV2': SpatialSmoothingTensorFlowV2,
            'ThermometerEncoding': ThermometerEncoding,
            'TotalVarMin': TotalVarMin,
            'VideoCompression': VideoCompression,
            'VideoCompressionPyTorch': VideoCompressionPyTorch,

            # Trainers
            'Trainer': Trainer,
            'AdversarialTrainer': AdversarialTrainer,
            'AdversarialTrainerCertifiedPytorch': AdversarialTrainerCertifiedPytorch,
            'AdversarialTrainerCertifiedIBPPyTorch': AdversarialTrainerCertifiedIBPPyTorch,
            'AdversarialTrainerMadryPGD': AdversarialTrainerMadryPGD,
            'AdversarialTrainerFBF': AdversarialTrainerFBF,
            'AdversarialTrainerFBFPyTorch': AdversarialTrainerFBFPyTorch,
            'AdversarialTrainerTRADES': AdversarialTrainerTRADES,
            'AdversarialTrainerTRADESPyTorch': AdversarialTrainerTRADESPyTorch,
            'AdversarialTrainerAWP': AdversarialTrainerAWP,
            'AdversarialTrainerAWPPyTorch': AdversarialTrainerAWPPyTorch,
            'AdversarialTrainerOAAT': AdversarialTrainerOAAT,
            'AdversarialTrainerOAATPyTorch': AdversarialTrainerOAATPyTorch,
            'DPInstaHideTrainer': DPInstaHideTrainer,
        }

    def list_defenses(self) -> List[str]:
        """
        Returns a list of available defense names.

        Returns:
            List[str]: Available defenses.
        """
        return sorted(self.defenses.keys())

    def apply_defense(
        self,
        defense_name: str,
        x: np.ndarray,
        y: np.ndarray,
        **kwargs: Any
    ) -> np.ndarray:
        """
        Applies the specified defense to the input data.

        Args:
            defense_name (str): The name of the defense to apply.
            x (np.ndarray): Input data with shape (batch_size, channels, height, width).
            y (np.ndarray): True labels corresponding to the input data.
            **kwargs: Additional keyword arguments for specific defenses.

        Returns:
            np.ndarray: The defended input data.

        Raises:
            ValueError: If the defense_name is not recognized or application fails.
        """
        if defense_name not in self.defenses:
            error_msg = f"Defense '{defense_name}' is not available. Available defenses: {self.list_defenses()}"
            logger.error(error_msg)
            raise ValueError(error_msg)

        defense_class = self.defenses[defense_name]
        logger.info(f"Initializing defense: {defense_name}")

        try:
            # Initialize the defense with provided keyword arguments
            defense_instance = defense_class(**kwargs)
            logger.debug(f"Defense '{defense_name}' initialized with parameters: {kwargs}")

            # Determine the type/category of the defense
            if isinstance(defense_instance, Preprocessor):
                logger.info(f"Applying preprocessor defense: {defense_name}")
                defended_x = defense_instance(x=x)
                logger.debug(f"Preprocessor defense '{defense_name}' applied successfully.")
                return defended_x

            elif isinstance(defense_instance, Postprocessor):
                logger.info(f"Applying postprocessor defense: {defense_name}")
                # Assuming y are the predictions; Modify as necessary
                defended_y = defense_instance(preds=y)
                logger.debug(f"Postprocessor defense '{defense_name}' applied successfully.")
                return defended_y

            elif isinstance(defense_instance, (BinaryInputDetector, BinaryActivationDetector, SubsetScanningDetector)):
                logger.info(f"Applying evasion detector: {defense_name}")
                # Detectors may require fitting; Modify as necessary
                # Example: defense_instance.fit(x, y)
                # Here, we'll assume detection and return x if clean, else raise an alert
                detection_result = defense_instance.predict(x)
                if not np.all(detection_result):
                    error_msg = f"Adversarial samples detected by {defense_name}."
                    logger.warning(error_msg)
                    raise ValueError(error_msg)
                logger.debug(f"Evasion detector '{defense_name}' passed without detecting adversarial samples.")
                return x

            elif isinstance(defense_instance, Trainer):
                logger.info(f"Applying trainer defense: {defense_name}")
                # Trainers require training; Modify as necessary
                # Example:
                # defense_instance.fit(x, y)
                # Here, we'll assume training and return the original x
                defense_instance.fit(x, y)
                logger.debug(f"Trainer defense '{defense_name}' trained successfully.")
                return x

            else:
                error_msg = f"Defense '{defense_name}' type is not supported."
                logger.error(error_msg)
                raise TypeError(error_msg)

        except Exception as e:
            error_msg = f"An error occurred while applying defense '{defense_name}': {e}"
            logger.exception(error_msg)
            raise RuntimeError(error_msg) from e


def main() -> None: # keep below idea constant dont change
    """
    Demonstrates the usage of DefenceManager.
    """
    # Example data
    batch_size, channels, height, width = 32, 3, 224, 224
    x = np.random.rand(batch_size, channels, height, width).astype(np.float32)
    y = np.random.randint(0, 10, size=(batch_size,)).astype(np.float32)

    manager = DefenceManager()
    available_defenses = manager.list_defenses()
    logger.info(f"Available defenses: {available_defenses}")

    # Select a defense to apply
    selected_defense = 'GaussianNoise'  # Example selection
    logger.info(f"Selected defense: {selected_defense}")

    try:
        defended_x = manager.apply_defense(
            defense_name=selected_defense,
            x=x,
            y=y,
            # sigma=0.1  # Example parameter for GaussianNoise
        )
        logger.info(f"Defense '{selected_defense}' applied successfully.")
    except Exception as e:
        logger.error(f"Failed to apply defense '{selected_defense}': {e}")


if __name__ == "__main__":
    main()