ollowing 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 : below bring simple api like below str, x, y control all defence 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 (Any): 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.
            TypeError: If the defense type is not supported.
            RuntimeError: If an unexpected error occurs during defense application.
        """
        if defense_name not in self.defenses:
            available = self.list_defenses()
            error_msg = (
                f"Defense '{defense_name}' is not available. "
                f"Available defenses: {available}"
            )
            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 default and provided parameters
            params = get_defense_parameters(defense_name)
            params.update(kwargs)
            defense_instance = defense_class(**params)
            logger.debug(
                f"Defense '{defense_name}' initialized with parameters: {params}"
            )

            # 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}")
                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}")
                # Example: defense_instance.fit(x, y)
                # Assuming detection and handling adversarial samples
                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}")
                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


from art.defences.postprocessor import (
    ClassLabels,
    GaussianNoise,
    HighConfidence,
    Postprocessor,
    ReverseSigmoid,
    Rounded,
)
from art.defences.preprocessor import (
    CutMix,
    CutMixPyTorch,
    CutMixTensorFlowV2,
    Cutout,
    CutoutPyTorch,
    CutoutTensorFlowV2,
    FeatureSqueezing,
    GaussianAugmentation,
    DefenseGAN,
    InverseGAN,
    JpegCompression,
    LabelSmoothing,
    Mixup,
    MixupPyTorch,
    MixupTensorFlowV2,
    Mp3Compression,
    Mp3CompressionPyTorch,
    PixelDefend,
    Preprocessor,
    Resample,
    SpatialSmoothing,
)

from art.defences.trainer import (
AdversarialTrainer,
 AdversarialTrainerCertifiedPytorch,
 AdversarialTrainerCertifiedIBPPyTorch,
 AdversarialTrainerMadryPGD,
 AdversarialTrainerFBF,
  AdversarialTrainerFBFPyTorch,
  AdversarialTrainerTRADESPyTorch,
   AdversarialTrainerTRADES,
   AdversarialTrainerTRADESPyTorch,
   AdversarialTrainerAWP,
   AdversarialTrainerAWPPyTorch,
 AdversarialTrainerOAAT,
 AdversarialTrainerOAATPyTorch,
 DPInstaHideTrainer,
)



parameters :
{'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}}

KEEP THIS IN THE mind:

from __future__ import absolute_import, division, print_function, unicode_literals, annotations

import abc

import numpy as np


class Postprocessor(abc.ABC):
    """
    Abstract base class for postprocessing defences. Postprocessing defences are not included in the loss function
    evaluation for loss gradients or the calculation of class gradients.
    """

    params: list[str] = []

    def __init__(self, is_fitted: bool = False, apply_fit: bool = True, apply_predict: bool = True) -> None:
        """
        Create a postprocessing object.

        Optionally, set attributes.
        """
        self._is_fitted = bool(is_fitted)
        self._apply_fit = bool(apply_fit)
        self._apply_predict = bool(apply_predict)
        Postprocessor._check_params(self)

    @property
    def is_fitted(self) -> bool:
        """
        Return the state of the postprocessing object.

        :return: `True` if the postprocessing model has been fitted (if this applies).
        """
        return self._is_fitted

    @property
    def apply_fit(self) -> bool:
        """
        Property of the defence indicating if it should be applied at training time.

        :return: `True` if the defence should be applied when fitting a model, `False` otherwise.
        """
        return self._apply_fit

    @property
    def apply_predict(self) -> bool:
        """
        Property of the defence indicating if it should be applied at test time.

        :return: `True` if the defence should be applied at prediction time, `False` otherwise.
        """
        return self._apply_predict

    @abc.abstractmethod
    def __call__(self, preds: np.ndarray) -> np.ndarray:
        """
        Perform model postprocessing and return postprocessed output.

        :param preds: model output to be postprocessed.
        :return: Postprocessed model output.
        """
        raise NotImplementedError

    def fit(self, preds: np.ndarray, **kwargs) -> None:
        """
        Fit the parameters of the postprocessor if it has any.

        :param preds: Training set to fit the postprocessor.
        :param kwargs: Other parameters.
        """
        pass

    def set_params(self, **kwargs) -> None:
        """
        Take in a dictionary of parameters and apply checks before saving them as attributes.
        """
        for key, value in kwargs.items():
            if key in self.params:
                setattr(self, key, value)
        self._check_params()

    def _check_params(self) -> None:
        pass


from __future__ import absolute_import, division, print_function, unicode_literals, annotations

import abc
from typing import Any, TYPE_CHECKING

import numpy as np

from art import config

if TYPE_CHECKING:
    import torch
    import tensorflow as tf


class Preprocessor(abc.ABC):
    """
    Abstract base class for preprocessing defences.

    By default, the gradient is estimated using BPDA with the identity function.
        To modify, override `estimate_gradient`
    """

    params: list[str] = []

    def __init__(self, is_fitted: bool = False, apply_fit: bool = True, apply_predict: bool = True) -> None:
        """
        Create a preprocessing object.

        Optionally, set attributes.
        """
        self._is_fitted = bool(is_fitted)
        self._apply_fit = bool(apply_fit)
        self._apply_predict = bool(apply_predict)

    @property
    def is_fitted(self) -> bool:
        """
        Return the state of the preprocessing object.

        :return: `True` if the preprocessing model has been fitted (if this applies).
        """
        return self._is_fitted

    @property
    def apply_fit(self) -> bool:
        """
        Property of the defence indicating if it should be applied at training time.

        :return: `True` if the defence should be applied when fitting a model, `False` otherwise.
        """
        return self._apply_fit

    @property
    def apply_predict(self) -> bool:
        """
        Property of the defence indicating if it should be applied at test time.

        :return: `True` if the defence should be applied at prediction time, `False` otherwise.
        """
        return self._apply_predict

    @abc.abstractmethod
    def __call__(self, x: np.ndarray, y: Any | None = None) -> tuple[np.ndarray, Any | None]:
        """
        Perform data preprocessing and return preprocessed data as tuple.

        :param x: Dataset to be preprocessed.
        :param y: Labels to be preprocessed.
        :return: Preprocessed data.
        """
        raise NotImplementedError

    def fit(self, x: np.ndarray, y: np.ndarray | None = None, **kwargs) -> None:
        """
        Fit the parameters of the data preprocessor if it has any.

        :param x: Training set to fit the preprocessor.
        :param y: Labels for the training set.
        :param kwargs: Other parameters.
        """
        pass

    def estimate_gradient(self, x: np.ndarray, grad: np.ndarray) -> np.ndarray:
        """
        Provide an estimate of the gradients of the defence for the backward pass. If the defence is not differentiable,
        this is an estimate of the gradient, most often replacing the computation performed by the defence with the
        identity function (the default).

        :param x: Input data for which the gradient is estimated. First dimension is the batch size.
        :param grad: Gradient value so far.
        :return: The gradient (estimate) of the defence.
        """
        return grad

    def set_params(self, **kwargs) -> None:  # pragma: no cover
        """
        Take in a dictionary of parameters and apply checks before saving them as attributes.
        """
        for key, value in kwargs.items():
            if key in self.params:
                setattr(self, key, value)
        self._check_params()

    def _check_params(self) -> None:  # pragma: no cover
        pass

    def forward(self, x: Any, y: Any = None) -> tuple[Any, Any]:
        """
        Perform data preprocessing and return preprocessed data.

        :param x: Dataset to be preprocessed.
        :param y: Labels to be preprocessed.
        :return: Preprocessed data.
        """
        raise NotImplementedError





"""
This module implements the abstract base class for defences that adversarially train models.
"""
from __future__ import absolute_import, division, print_function, unicode_literals, annotations

import abc
from typing import TYPE_CHECKING

import numpy as np

if TYPE_CHECKING:
    from art.utils import CLASSIFIER_LOSS_GRADIENTS_TYPE


class Trainer(abc.ABC):
    """
    Abstract base class for training defences.
    """

    def __init__(self, classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE") -> None:
        """
        Create a adversarial training object
        """
        self._classifier = classifier

    @abc.abstractmethod
    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
        """
        Train the model.

        :param x: Training data.
        :param y: Labels for the training data.
        :param kwargs: Other parameters.
        """
        raise NotImplementedError

    @property
    def classifier(self) -> "CLASSIFIER_LOSS_GRADIENTS_TYPE":
        """
        Access function to get the classifier.

        :return: The classifier.
        """
        return self._classifier

    def get_classifier(self) -> "CLASSIFIER_LOSS_GRADIENTS_TYPE":
        """
        Return the classifier trained via adversarial training.

        :return: The classifier.
        """
        return self._classifier