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 : in the environment so many error logic index error , all argument i will if you inform 


ith a value
2024-10-24 20:27:56,744 [ERROR] Error during step execution: cannot access local variable 'error_msg' where it is not associated with a value
[INFO] Model checkpoint loaded.
2024-10-24 20:27:56,776 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:27:56,785 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'SignOPTAttack' and Defense 'ReverseSigmoid'
2024-10-24 20:27:56,808 [INFO] Step 1: Applying Attack 'SignOPTAttack' and Defense 'ReverseSigmoid'
2024-10-24 20:27:56,824 [INFO] Initializing attack: SignOPTAttack
2024-10-24 20:27:56,824 [ERROR] Unexpected error during 'SignOPTAttack': 'SignOPTAttack' object has no attribute 'clip_min'
[ERROR] Error during step execution: 'SignOPTAttack' object has no attribute 'clip_min'
2024-10-24 20:27:56,827 [ERROR] Error during step execution: 'SignOPTAttack' object has no attribute 'clip_min'
[INFO] Model checkpoint loaded.
2024-10-24 20:27:56,853 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:27:56,865 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'NewtonFool' and Defense 'SubsetScanningDetector'
2024-10-24 20:27:56,893 [INFO] Step 1: Applying Attack 'NewtonFool' and Defense 'SubsetScanningDetector'
2024-10-24 20:27:56,920 [INFO] Initializing attack: NewtonFool
NewtonFool: 100%|████████████████████████████████████████████████████████| 32/32 [00:50<00:00,  1.57s/it]
2024-10-24 20:28:47,274 [INFO] Attack 'NewtonFool' performed successfully.
2024-10-24 20:28:47,274 [INFO] Initializing defense: SubsetScanningDetector
[ERROR] Error during step execution: cannot access local variable 'error_msg' where it is not associated with a value
2024-10-24 20:28:47,274 [ERROR] Error during step execution: cannot access local variable 'error_msg' where it is not associated with a value
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,296 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,299 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'Wasserstein' and Defense 'AdversarialTrainerTRADESPyTorch'
2024-10-24 20:28:47,330 [INFO] Step 1: Applying Attack 'Wasserstein' and Defense 'AdversarialTrainerTRADESPyTorch'
2024-10-24 20:28:47,336 [INFO] Initializing attack: Wasserstein
2024-10-24 20:28:47,336 [ERROR] Unexpected error during 'Wasserstein': index 441 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,336 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,357 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,379 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'MomentumIterativeMethod' and Defense 'Preprocessor'
2024-10-24 20:28:47,407 [INFO] Step 1: Applying Attack 'MomentumIterativeMethod' and Defense 'Preprocessor'
2024-10-24 20:28:47,411 [INFO] Initializing attack: MomentumIterativeMethod
2024-10-24 20:28:47,412 [INFO] Creating adversarial samples.
2024-10-24 20:28:47,412 [ERROR] Unexpected error during 'MomentumIterativeMethod': index 25 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,413 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,436 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,456 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'AutoConjugateGradient' and Defense 'Mp3Compression'
2024-10-24 20:28:47,486 [INFO] Step 1: Applying Attack 'AutoConjugateGradient' and Defense 'Mp3Compression'
2024-10-24 20:28:47,501 [INFO] Initializing attack: AutoConjugateGradient
2024-10-24 20:28:47,505 [ERROR] Unexpected error during 'AutoConjugateGradient': index 396 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,505 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,531 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,542 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'MomentumIterativeMethod' and Defense 'Mp3Compression'
2024-10-24 20:28:47,568 [INFO] Step 1: Applying Attack 'MomentumIterativeMethod' and Defense 'Mp3Compression'
2024-10-24 20:28:47,586 [INFO] Initializing attack: MomentumIterativeMethod
2024-10-24 20:28:47,587 [INFO] Creating adversarial samples.
2024-10-24 20:28:47,587 [ERROR] Unexpected error during 'MomentumIterativeMethod': index 41 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,587 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,609 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,626 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'HopSkipJump' and Defense 'CutoutTensorFlowV2'
2024-10-24 20:28:47,652 [INFO] Step 1: Applying Attack 'HopSkipJump' and Defense 'CutoutTensorFlowV2'
2024-10-24 20:28:47,667 [INFO] Initializing attack: HopSkipJump
2024-10-24 20:28:47,667 [ERROR] Unexpected error during 'HopSkipJump': index 36 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,667 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,691 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,699 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CarliniLInfMethod' and Defense 'SpatialSmoothing'
2024-10-24 20:28:47,723 [INFO] Step 1: Applying Attack 'CarliniLInfMethod' and Defense 'SpatialSmoothing' 
2024-10-24 20:28:47,741 [INFO] Initializing attack: CarliniLInfMethod
2024-10-24 20:28:47,742 [ERROR] Unexpected error during 'CarliniLInfMethod': index 551 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,742 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,767 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,781 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'AdversarialPatchPyTorch' and Defense 'CutMix'
2024-10-24 20:28:47,810 [INFO] Step 1: Applying Attack 'AdversarialPatchPyTorch' and Defense 'CutMix'     
2024-10-24 20:28:47,821 [INFO] Initializing attack: AdversarialPatchPyTorch
2024-10-24 20:28:47,821 [ERROR] Type error while initializing 'AdversarialPatchPyTorch': 'NoneType' object is not subscriptable. Check parameter compatibility.
[ERROR] Error during step execution: 'NoneType' object is not subscriptable
2024-10-24 20:28:47,821 [ERROR] Error during step execution: 'NoneType' object is not subscriptable
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,844 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,849 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'SignOPTAttack' and Defense 'AdversarialTrainer'
2024-10-24 20:28:47,877 [INFO] Step 1: Applying Attack 'SignOPTAttack' and Defense 'AdversarialTrainer'   
2024-10-24 20:28:47,886 [INFO] Initializing attack: SignOPTAttack
2024-10-24 20:28:47,886 [ERROR] Unexpected error during 'SignOPTAttack': 'SignOPTAttack' object has no attribute 'clip_min'
[ERROR] Error during step execution: 'SignOPTAttack' object has no attribute 'clip_min'
2024-10-24 20:28:47,886 [ERROR] Error during step execution: 'SignOPTAttack' object has no attribute 'clip_min'
[INFO] Model checkpoint loaded.
2024-10-24 20:28:47,905 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:47,918 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'AutoConjugateGradient' and Defense 'AdversarialTrainerMadryPGD'
2024-10-24 20:28:47,944 [INFO] Step 1: Applying Attack 'AutoConjugateGradient' and Defense 'AdversarialTrainerMadryPGD'
2024-10-24 20:28:47,968 [INFO] Initializing attack: AutoConjugateGradient
2024-10-24 20:28:47,972 [ERROR] Unexpected error during 'AutoConjugateGradient': index 568 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:47,972 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:48,003 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:48,026 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'ElasticNet' and Defense 'SubsetScanningDetector'
2024-10-24 20:28:48,049 [INFO] Step 1: Applying Attack 'ElasticNet' and Defense 'SubsetScanningDetector'
2024-10-24 20:28:48,074 [INFO] Initializing attack: ElasticNet
2024-10-24 20:28:48,074 [ERROR] Unexpected error during 'ElasticNet': index 215 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:48,074 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:48,098 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:48,110 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'SquareAttack' and Defense 'Rounded'
2024-10-24 20:28:48,143 [INFO] Step 1: Applying Attack 'SquareAttack' and Defense 'Rounded'
2024-10-24 20:28:48,155 [INFO] Initializing attack: SquareAttack
2024-10-24 20:28:48,155 [ERROR] Unexpected error during 'SquareAttack': index 301 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:48,156 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:48,191 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:48,201 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CompositeAdversarialAttackPyTorch' and Defense 'DPInstaHideTrainer'
2024-10-24 20:28:48,230 [INFO] Step 1: Applying Attack 'CompositeAdversarialAttackPyTorch' and Defense 'DPInstaHideTrainer'
2024-10-24 20:28:48,242 [INFO] Initializing attack: CompositeAdversarialAttackPyTorch
C:\Users\heman\AppData\Local\Programs\Python\Python312\Lib\site-packages\kornia\feature\lightglue.py:44: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
  @torch.cuda.amp.custom_fwd(cast_inputs=torch.float32)
2024-10-24 20:28:50,890 [ERROR] Unexpected error during 'CompositeAdversarialAttackPyTorch': index 301 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:50,890 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:50,906 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:50,914 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'PixelAttack' and Defense 'AdversarialTrainerFBFPyTorch'
2024-10-24 20:28:50,940 [INFO] Step 1: Applying Attack 'PixelAttack' and Defense 'AdversarialTrainerFBFPyTorch'
2024-10-24 20:28:50,952 [INFO] Initializing attack: PixelAttack
2024-10-24 20:28:50,953 [ERROR] Value error: This attack requires estimator clip values to be defined.    
[ERROR] Error during step execution: This attack requires estimator clip values to be defined.
2024-10-24 20:28:50,953 [ERROR] Error during step execution: This attack requires estimator clip values to be defined.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:50,969 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:50,982 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'AdversarialPatch' and Defense 'HighConfidence'
2024-10-24 20:28:51,010 [INFO] Step 1: Applying Attack 'AdversarialPatch' and Defense 'HighConfidence'
2024-10-24 20:28:51,027 [INFO] Initializing attack: AdversarialPatch
2024-10-24 20:28:51,027 [ERROR] Value error: Adversarial Patch attack requires a classifier with clip_values.
[ERROR] Error during step execution: Adversarial Patch attack requires a classifier with clip_values.     
2024-10-24 20:28:51,027 [ERROR] Error during step execution: Adversarial Patch attack requires a classifier with clip_values.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:51,058 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:51,066 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'BasicIterativeMethod' and Defense 'SpatialSmoothing'
2024-10-24 20:28:51,091 [INFO] Step 1: Applying Attack 'BasicIterativeMethod' and Defense 'SpatialSmoothing'
2024-10-24 20:28:51,104 [INFO] Initializing attack: BasicIterativeMethod
2024-10-24 20:28:51,104 [INFO] Creating adversarial samples.
2024-10-24 20:28:51,105 [ERROR] Unexpected error during 'BasicIterativeMethod': index 441 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:51,105 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:51,123 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:51,130 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedIBPPyTorch'      
2024-10-24 20:28:51,150 [INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedIBPPyTorch'
2024-10-24 20:28:51,165 [INFO] Initializing attack: CarliniL2Method
2024-10-24 20:28:51,165 [ERROR] Unexpected error during 'CarliniL2Method': index 36 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:51,166 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:51,186 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:51,194 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'UniversalPerturbation' and Defense 'AdversarialTrainerTRADESPyTorch'
2024-10-24 20:28:51,216 [INFO] Step 1: Applying Attack 'UniversalPerturbation' and Defense 'AdversarialTrainerTRADESPyTorch'
2024-10-24 20:28:51,228 [INFO] Initializing attack: UniversalPerturbation
2024-10-24 20:28:51,228 [INFO] Computing universal perturbation based on deepfool attack.
2024-10-24 20:28:51,228 [ERROR] Unexpected error during 'UniversalPerturbation': index 313 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:51,229 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:51,249 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:51,265 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'ElasticNet' and Defense 'AdversarialTrainerAWPPyTorch'
2024-10-24 20:28:51,289 [INFO] Step 1: Applying Attack 'ElasticNet' and Defense 'AdversarialTrainerAWPPyTorch'
2024-10-24 20:28:51,299 [INFO] Initializing attack: ElasticNet
2024-10-24 20:28:51,299 [ERROR] Unexpected error during 'ElasticNet': index 359 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:51,299 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:51,316 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:51,329 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedPytorch'
[INFO] Environment has been reset.
2024-10-24 20:28:51,329 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedPytorch'
2024-10-24 20:28:51,329 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedPytorch'
[INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedPytorch'
2024-10-24 20:28:51,353 [INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'AdversarialTrainerCertifiedPytorch'
rtifiedPytorch'
2024-10-24 20:28:51,369 [INFO] Initializing attack: CarliniL2Method
2024-10-24 20:28:51,370 [ERROR] Unexpected error during 'CarliniL2Method': index 818 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:28:51,370 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:28:51,386 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:28:51,402 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'DeepFool' and Defense 'LabelSmoothing'
2024-10-24 20:28:51,427 [INFO] Step 1: Applying Attack 'DeepFool' and Defense 'LabelSmoothing'
2024-10-24 20:28:51,441 [INFO] Initializing attack: DeepFool
2024-10-24 20:28:51,441 [WARNING] The `clip_values` attribute of the estimator is `None`, therefore this instance of DeepFool will by default generate adversarial perturbations scaled for input values in the range [0, 1] but not clip the adversarial example.
DeepFool: 100%|██████████████████████████████████████████████████████████| 32/32 [01:07<00:00,  2.12s/it] 
2024-10-24 20:29:59,367 [INFO] Attack 'DeepFool' performed successfully.
2024-10-24 20:29:59,367 [INFO] Initializing defense: LabelSmoothing
2024-10-24 20:29:59,367 [INFO] Applying preprocessor defense: LabelSmoothing
[ERROR] Error during step execution: cannot access local variable 'error_msg' where it is not associated with a value
2024-10-24 20:29:59,368 [ERROR] Error during step execution: cannot access local variable 'error_msg' where it is not associated with a value
[INFO] Model checkpoint loaded.
2024-10-24 20:29:59,385 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:29:59,394 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'CutMix'
2024-10-24 20:29:59,417 [INFO] Step 1: Applying Attack 'CarliniL2Method' and Defense 'CutMix'
2024-10-24 20:29:59,434 [INFO] Initializing attack: CarliniL2Method
2024-10-24 20:29:59,434 [ERROR] Unexpected error during 'CarliniL2Method': index 523 is out of bounds for axis 1 with size 10
[ERROR] Invalid action index.
2024-10-24 20:29:59,434 [ERROR] Invalid action index.
[INFO] Model checkpoint loaded.
2024-10-24 20:29:59,455 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
2024-10-24 20:29:59,471 [INFO] Environment has been reset.
[INFO] Step 1: Applying Attack 'SpatialTransformation' and Defense 'GaussianNoise'
2024-10-24 20:29:59,492 [INFO] Step 1: Applying Attack 'SpatialTransformation' and Defense 'GaussianNoise'
2024-10-24 20:29:59,505 [INFO] Initializing attack: SpatialTransformation
2024-10-24 20:29:59,505 [INFO] Computing spatial transformation based on grid search.
Spatial transformation: 100%|██████████████████████████████████████████████| 1/1 [00:00<00:00,  1.83it/s]
2024-10-24 20:30:00,181 [INFO] Success rate of spatial transformation attack: 0.00%
2024-10-24 20:30:00,181 [INFO] Attack-translation in x: 0.00%
2024-10-24 20:30:00,181 [INFO] Attack-translation in y: 0.00%
2024-10-24 20:30:00,182 [INFO] Attack-rotation: 0.00%
2024-10-24 20:30:00,182 [INFO] Attack 'SpatialTransformation' performed successfully.
2024-10-24 20:30:00,182 [INFO] Initializing defense: GaussianNoise
2024-10-24 20:30:00,182 [INFO] Applying postprocessor defense: GaussianNoise
[ERROR] Error during step execution: Input type (double) and bias type (float) should be the same
2024-10-24 20:30:00,229 [ERROR] Error during step execution: Input type (double) and bias type (float) should be the same
[INFO] Model checkpoint loaded.
2024-10-24 20:30:00,252 [INFO] Model checkpoint loaded.
[INFO] Environment has been reset.
=


# environment.py
import sys
import gymnasium as gym
import numpy as np
import torch
from torch import nn
from typing import Tuple, Dict, Any, Optional, Callable, List
from art.estimators.classification import PyTorchClassifier
import logging
from collections import deque
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader
import tkinter as tk
from tkinter import ttk, scrolledtext



from attacks import apply_attack, list_attack
from defences import DefenseManager



logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[logging.StreamHandler(sys.stdout)],
)
logger = logging.getLogger(__name__)


class RobustModelEnvironment(gym.Env):
    """
    Environment for training a robust model against various adversarial attacks.
    """

    metadata = {'render.modes': ['human']}

    def __init__(
        self,
        model: nn.Module,
        dataset: torch.utils.data.Dataset,
        batch_size: int = 32,
        device: Optional[torch.device] = None,  # Changed to torch.device
        memory_efficient: bool = True,
        max_steps: int = 1000,
        num_classes: Optional[int] = None  # Optional parameter to specify number of classes
    ) -> None:
        """
        Initialize the RobustModelEnvironment.

        Args:
            model (nn.Module): The PyTorch model to evaluate and train.
            dataset (torch.utils.data.Dataset): The dataset for training and evaluation.
            batch_size (int, optional): Batch size for data loading. Defaults to 32.
            device (Optional[torch.device], optional): Device to run computations on. Defaults to 'cuda' if available.
            memory_efficient (bool, optional): Flag for memory-efficient operations. Defaults to True.
            max_steps (int, optional): Maximum number of steps per episode. Defaults to 1000.
            num_classes (Optional[int], optional): Number of distinct classes. If not provided, deduced from dataset.
        """
        super(RobustModelEnvironment, self).__init__()
        self.model = model.to(device or ('cuda' if torch.cuda.is_available() else 'cpu'))
        self.dataset = dataset
        self.batch_size = batch_size
        self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # Ensure torch.device
        self.memory_efficient = memory_efficient
        self.max_steps = max_steps
        self.steps = 0

        # Initialize logger first
        self.logger = self._setup_logger()

        self.initial_checkpoint = self._get_model_checkpoint()

        if num_classes is not None:
            self.num_classes = num_classes
            self.logger.info(f"Number of classes provided: {self.num_classes}")
        else:
            self.num_classes = self._get_num_classes()

        self.classifier = self._setup_classifier()
        self.original_accuracy = self._evaluate_accuracy()

        self.attacks = self._setup_attacks()
        self.defenses_manager = DefenseManager()
        self.defenses = self._setup_defenses()

        self.action_space = gym.spaces.Discrete(len(self.attacks) * len(self.defenses))
        self.observation_space = self._setup_observation_space()

        self.performance_history = deque(maxlen=100)
        self.performance_history.append(self.original_accuracy)

    def _setup_logger(self) -> logging.Logger:
        """
        Set up the logger for the environment.

        Returns:
            logging.Logger: Configured logger.
        """
        logger_env = logging.getLogger('RobustModelEnvironment')
        logger_env.setLevel(logging.INFO)
        if not logger_env.handlers:
            handler = logging.StreamHandler(sys.stdout)
            formatter = logging.Formatter('[%(levelname)s] %(message)s')
            handler.setFormatter(formatter)
            logger_env.addHandler(handler)
        return logger_env

    def _get_num_classes(self) -> int:
        """
        Get the number of classes from the dataset.

        Returns:
            int: Number of distinct classes.
        """
        try:
            if isinstance(self.dataset, TensorDataset):
                _, labels = self.dataset.tensors
                labels = labels.tolist()
            else:
                labels = [label for _, label in self.dataset]

            labels = [label if isinstance(label, int) else label.item() for label in labels]

            num_classes = len(set(labels))
            self.logger.info(f"Detected number of classes: {num_classes}")
            return num_classes
        except Exception as e:
            self.logger.error(f"Failed to get number of classes: {e}")
            raise

    def _setup_classifier(self) -> PyTorchClassifier:
        """
        Set up the PyTorch classifier using ART.

        Returns:
            PyTorchClassifier: Configured classifier.
        """
        try:
            # device_type = 'cuda' if self.device.type == 'cuda' else 'cpu'
            return PyTorchClassifier(
                model=self.model,
                loss=nn.CrossEntropyLoss(),
                optimizer=optim.Adam(self.model.parameters()),
                input_shape=self._get_input_shape(),
                nb_classes=self.num_classes,
                device_type="cpu"
            )
        except Exception as e:
            self.logger.error(f"Failed to set up classifier: {e}")
            raise

    def _get_input_shape(self) -> Tuple[int, ...]:
        """
        Get the input shape from the dataset.

        Returns:
            Tuple[int, ...]: Shape of a single input sample.
        """
        try:
            sample_input, _ = self.dataset[0]
            return sample_input.shape
        except Exception as e:
            self.logger.error(f"Failed to get input shape: {e}")
            raise

    def _setup_attacks(self) -> List[str]:
        """
        Retrieve the list of available attacks.

        Returns:
            List[str]: List of attack names.
        """
        try:
            attacks = list_attack
            self.logger.info(f"Available attacks: {attacks}")
            return attacks
        except Exception as e:
            self.logger.error(f"Failed to set up attacks: {e}")
            raise

    def _setup_defenses(self) -> List[str]:
        """
        Retrieve the list of available defenses from DefenseManager.

        Returns:
            List[str]: List of defense names.
        """
        try:
            defenses = self.defenses_manager.list_defenses()
            return defenses
        except Exception as e:
            self.logger.error(f"Failed to set up defenses: {e}")
            raise

    def _setup_observation_space(self) -> gym.spaces.Dict:
        """
        Set up the observation space.

        Returns:
            gym.spaces.Dict: Observation space definition.
        """
        return gym.spaces.Dict({
            'model_state': gym.spaces.Box(low=-np.inf, high=np.inf, shape=(1,), dtype=np.float32),
            'attack_impact': gym.spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
            'current_accuracy': gym.spaces.Box(low=0.0, high=1.0, shape=(1,), dtype=np.float32),
            'step': gym.spaces.Box(low=0, high=self.max_steps, shape=(1,), dtype=np.int32)
        })

    def step(self, action: int) -> Tuple[Dict[str, np.ndarray], float, bool, bool, Dict[str, Any]]:
        """
        Execute one step in the environment.

        Args:
            action (int): Combined index for attack and defense.

        Returns:
            Tuple[Dict[str, np.ndarray], float, bool, bool, Dict[str, Any]]:
                - observation
                - reward
                - done
                - truncated
                - info
        """
        self.steps += 1
        attack_idx = action // len(self.defenses)
        defense_idx = action % len(self.defenses)

        try:
            attack_name = self.attacks[attack_idx]
            defense_name = self.defenses[defense_idx]
            self.logger.info(f"Step {self.steps}: Applying Attack '{attack_name}' and Defense '{defense_name}'")

            data_loader = DataLoader(self.dataset, batch_size=self.batch_size, shuffle=True)
            x_test, y_test = next(iter(data_loader))
            x_test, y_test = x_test.to(self.device), y_test.to(self.device)

            x_test_np = x_test.cpu().numpy()
            y_test_np = y_test.cpu().numpy()

            # Apply attack
            adversarial_x = apply_attack(
                attack_name=attack_name,
                classifier=self.classifier,
                x=x_test_np,
                y=y_test_np,
                # params=None  # Add parameters if needed
            )

            # Apply defense
            defended_x = self.defenses_manager.apply_defense(
                defense_name=defense_name,
                # classifier=self.classifier,
                x=adversarial_x,
                y=y_test_np,
                # params=None  # Add parameters if needed
            )

            # Evaluate model accuracy on adversarial examples
            y_pred_adv = self.classifier.predict(adversarial_x)
            if y_pred_adv.ndim > 1 and y_pred_adv.shape[1] > 1:
                y_pred_adv = np.argmax(y_pred_adv, axis=1)
            attack_accuracy = np.mean(y_pred_adv == y_test_np)
            attack_impact = self.original_accuracy - attack_accuracy

            # Evaluate model accuracy on defended examples
            y_pred_defended = self.classifier.predict(defended_x)
            if y_pred_defended.ndim > 1 and y_pred_defended.shape[1] > 1:
                y_pred_defended = np.argmax(y_pred_defended, axis=1)
            new_accuracy = np.mean(y_pred_defended == y_test_np)

            self.logger.info(f"Attack Accuracy: {attack_accuracy:.4f}, New Accuracy after Defense: {new_accuracy:.4f}")

            reward = self._calculate_reward(new_accuracy, attack_impact)
            done = self.steps >= self.max_steps
            observation = self._get_observation(new_accuracy, attack_impact)

            self.performance_history.append(new_accuracy)

            # Simulate model weight degradation
            self._degrade_model_weights(attack_impact)

            return observation, reward, done, False, {
                'attack_name': attack_name,
                'defense_name': defense_name,
                'attack_accuracy': attack_accuracy,
                'new_accuracy': new_accuracy
            }

        except StopIteration:
            self.logger.warning("End of DataLoader reached. Resetting environment.")
            return self.reset()
        except IndexError:
            self.logger.error("Invalid action index.")
            return self._get_observation(self.original_accuracy, 0.0), 0.0, True, False, {}
        except Exception as e:
            self.logger.error(f"Error during step execution: {e}")
            return self._get_observation(self.original_accuracy, 0.0), 0.0, True, False, {}

    def reset(
        self,
        seed: Optional[int] = None,
        options: Optional[Dict[str, Any]] = None
    ) -> Tuple[Dict[str, np.ndarray], Dict[str, Any]]:
        """
        Reset the environment to its initial state.

        Args:
            seed (Optional[int], optional): Random seed. Defaults to None.
            options (Optional[Dict[str, Any]], optional): Additional options. Defaults to None.

        Returns:
            Tuple[Dict[str, np.ndarray], Dict[str, Any]]: Initial observation and info.
        """
        super().reset(seed=seed)
        self.steps = 0
        self._load_model_checkpoint(self.initial_checkpoint)
        self.performance_history = deque(maxlen=100)
        self.performance_history.append(self.original_accuracy)
        observation = self._get_observation(self.original_accuracy, 0.0)
        self.logger.info("Environment has been reset.")
        return observation, {}

    def _calculate_reward(self, new_accuracy: float, attack_impact: float) -> float:
        """
        Calculate the reward based on accuracy improvement and attack impact.

        Args:
            new_accuracy (float): Current model accuracy post-defense.
            attack_impact (float): Impact of the attack on the model.

        Returns:
            float: Calculated reward.
        """
        robustness_improvement = new_accuracy - self.original_accuracy
        reward = robustness_improvement * 100 + (1 - attack_impact) * 50
        self.logger.debug(f"Calculated Reward: {reward}")
        return reward

    def _get_model_checkpoint(self) -> Dict[str, torch.Tensor]:
        """
        Get a checkpoint of the model's current state.

        Returns:
            Dict[str, torch.Tensor]: Dictionary of model parameters.
        """
        return {name: param.data.clone() for name, param in self.model.named_parameters()}

    def _update_model_checkpoint(self) -> None:
        """
        Update the initial checkpoint with the current model state.
        """
        self.initial_checkpoint = self._get_model_checkpoint()

    def _load_model_checkpoint(self, checkpoint: Dict[str, torch.Tensor]) -> None:
        """
        Load a model checkpoint.

        Args:
            checkpoint (Dict[str, torch.Tensor]): Model parameters to load.
        """
        with torch.no_grad():
            for name, param in self.model.named_parameters():
                if name in checkpoint:
                    param.copy_(checkpoint[name])
        self.logger.info("Model checkpoint loaded.")

    def _evaluate_accuracy(self) -> float:
        """
        Evaluate the current accuracy of the model.

        Returns:
            float: Model accuracy.
        """
        self.model.eval()
        correct = 0
        total = 0
        try:
            data_loader = DataLoader(self.dataset, batch_size=self.batch_size, shuffle=False)
            with torch.no_grad():
                for inputs, labels in data_loader:
                    inputs, labels = inputs.to(self.device), labels.to(self.device)
                    outputs = self.model(inputs)
                    _, predicted = torch.max(outputs.data, 1)
                    total += labels.size(0)
                    correct += (predicted == labels).sum().item()
            accuracy = correct / total
            self.logger.info(f"Initial Model Accuracy: {accuracy:.4f}")
            return accuracy
        except Exception as e:
            self.logger.error(f"Failed to evaluate accuracy: {e}")
            return 0.0

    def _compute_accuracy_tensor(self, outputs: torch.Tensor, labels: torch.Tensor) -> float:
        """
        Compute accuracy given model outputs and labels.

        Args:
            outputs (torch.Tensor): Model outputs.
            labels (torch.Tensor): Ground truth labels.

        Returns:
            float: Computed accuracy.
        """
        try:
            _, predicted = torch.max(outputs.data, 1)
            correct = (predicted == labels).sum().item()
            accuracy = correct / labels.size(0)
            return accuracy
        except Exception as e:
            self.logger.error(f"Failed to compute accuracy: {e}")
            return 0.0

    def _get_observation(self, accuracy: float, attack_impact: float) -> Dict[str, np.ndarray]:
        """
        Get the current observation of the environment.

        Args:
            accuracy (float): Current model accuracy.
            attack_impact (float): Impact of the latest attack.

        Returns:
            Dict[str, np.ndarray]: Observation dictionary.
        """
        model_state_sum = sum(p.sum().item() for p in self.model.parameters())
        return {
            'model_state': np.array([model_state_sum], dtype=np.float32),
            'attack_impact': np.array([attack_impact], dtype=np.float32),
            'current_accuracy': np.array([accuracy], dtype=np.float32),
            'step': np.array([self.steps], dtype=np.int32)
        }

    def _degrade_model_weights(self, attack_impact: float) -> None:
        """
        Simulate model weight degradation based on attack impact.

        Args:
            attack_impact (float): Impact of the attack on the model.
        """
        try:
            degradation_factor = attack_impact * 0.01  # Scale factor for degradation
            with torch.no_grad():
                for param in self.model.parameters():
                    noise = torch.randn(param.size()).to(self.device) * degradation_factor
                    param.add_(noise)
            self.logger.debug(f"Model weights degraded by factor: {degradation_factor:.4f}")
        except Exception as e:
            self.logger.error(f"Failed to degrade model weights: {e}")

    def render(self, mode: str = 'human') -> None:
        """
        Render the current state of the environment.

        Args:
            mode (str, optional): Mode to render with. Defaults to 'human'.
        """
        if mode == 'human':
            current_accuracy = self.performance_history[-1]
            self.logger.info(f"Step: {self.steps}, Current Accuracy: {current_accuracy:.4f}")

    def close(self) -> None:
        """
        Clean up resources used by the environment.
        """
        self.logger.info("Closing environment.")
        pass


# [Rest of environment.py content remains unchanged]

class EnvironmentGUI:
    """
    Advanced GUI for visualizing the RobustModelEnvironment.
    """

    def __init__(self, env: RobustModelEnvironment) -> None:
        """
        Initialize the GUI with the given environment.

        Args:
            env (RobustModelEnvironment): The environment to visualize.
        """
        self.env = env
        self.root = tk.Tk()
        self.root.title("Robust Model Environment Visualization")
        self._setup_gui_elements()
        self._update_gui()

    def _setup_gui_elements(self) -> None:
        """
        Set up GUI components.
        """
        # Frame for Metrics
        metrics_frame = ttk.LabelFrame(self.root, text="Metrics")
        metrics_frame.grid(column=0, row=0, padx=10, pady=10, sticky="nsew")

        # Metrics Labels
        self.step_var = tk.StringVar(value="Step: 0")
        self.accuracy_var = tk.StringVar(value="Accuracy: 0.0")
        self.attack_impact_var = tk.StringVar(value="Attack Impact: 0.0")

        ttk.Label(metrics_frame, textvariable=self.step_var).grid(column=0, row=0, sticky="w", padx=5, pady=5)
        ttk.Label(metrics_frame, textvariable=self.accuracy_var).grid(column=0, row=1, sticky="w", padx=5, pady=5)
        ttk.Label(metrics_frame, textvariable=self.attack_impact_var).grid(column=0, row=2, sticky="w", padx=5, pady=5)

        # Frame for Controls
        controls_frame = ttk.LabelFrame(self.root, text="Controls")
        controls_frame.grid(column=0, row=1, padx=10, pady=10, sticky="nsew")

        # Control Buttons
        self.start_button = ttk.Button(controls_frame, text="Start", command=self.start_environment)
        self.start_button.grid(column=0, row=0, padx=5, pady=5)

        self.pause_button = ttk.Button(controls_frame, text="Pause", command=self.pause_environment)
        self.pause_button.grid(column=1, row=0, padx=5, pady=5)

        self.reset_button = ttk.Button(controls_frame, text="Reset", command=self.reset_environment)
        self.reset_button.grid(column=2, row=0, padx=5, pady=5)

        # Frame for Logs
        logs_frame = ttk.LabelFrame(self.root, text="Logs")
        logs_frame.grid(column=0, row=2, padx=10, pady=10, sticky="nsew")

        # Scrolled Text for Logs
        self.log_text = scrolledtext.ScrolledText(logs_frame, width=80, height=20, state='disabled')
        self.log_text.grid(column=0, row=0, padx=5, pady=5)

        # Configure Grid Weights
        self.root.columnconfigure(0, weight=1)
        self.root.rowconfigure(2, weight=1)
        metrics_frame.columnconfigure(0, weight=1)
        controls_frame.columnconfigure((0, 1, 2), weight=1)
        logs_frame.columnconfigure(0, weight=1)

        # Redirect logger to GUI
        self._redirect_logging()

    def _redirect_logging(self) -> None:
        """
        Redirect logging output to the GUI log text widget.
        """
        class TextHandler(logging.Handler):
            def __init__(self, text_widget: scrolledtext.ScrolledText) -> None:
                super().__init__()
                self.text_widget = text_widget

            def emit(self, record: logging.LogRecord) -> None:
                msg = self.format(record) + '\n'
                self.text_widget.configure(state='normal')
                self.text_widget.insert(tk.END, msg)
                self.text_widget.configure(state='disabled')
                self.text_widget.yview(tk.END)

        handler = TextHandler(self.log_text)
        handler.setFormatter(logging.Formatter('[%(levelname)s] %(message)s'))
        self.env.logger.addHandler(handler)

    def start_environment(self) -> None:
        """
        Start the environment's training loop.
        """
        if not getattr(self, 'running', False):
            self.env.logger.info("Starting environment...")
            self.running = True
            self._run_steps()

    def pause_environment(self) -> None:
        """
        Pause the environment's training loop.
        """
        if getattr(self, 'running', False):
            self.env.logger.info("Pausing environment...")
            self.running = False

    def reset_environment(self) -> None:
        """
        Reset the environment.
        """
        self.env.reset()
        self.env.logger.info("Environment has been reset.")
        self._update_gui()

    def _run_steps(self) -> None:
        """
        Run environment steps in a loop.
        """
        if self.running and self.env.steps < self.env.max_steps:
            action = self._select_action()
            observation, reward, done, _, info = self.env.step(action)
            self._update_gui(observation)
            if done:
                self.running = False
                self.env.logger.info("Episode finished.")
                return
            # Schedule next step
            self.root.after(100, self._run_steps)

    def _select_action(self) -> int:
        """
        Select an action for the agent. Placeholder for agent logic.

        Returns:
            int: Selected action index.
        """
        # Placeholder: Select random action
        attack_length = len(self.env.attacks)
        defense_length = len(self.env.defenses)
        total_actions = attack_length * defense_length
        return np.random.randint(0, total_actions)

    def _update_gui(self, observation: Optional[Dict[str, np.ndarray]] = None) -> None:
        """
        Update GUI elements with the latest observation.

        Args:
            observation (Optional[Dict[str, np.ndarray]], optional): Latest observation. Defaults to None.
        """
        if observation:
            self.step_var.set(f"Step: {observation['step'][0]}")
            self.accuracy_var.set(f"Accuracy: {observation['current_accuracy'][0]:.4f}")
            self.attack_impact_var.set(f"Attack Impact: {observation['attack_impact'][0]:.4f}")
        else:
            self.step_var.set(f"Step: {self.env.steps}")
            if self.env.performance_history:
                self.accuracy_var.set(f"Accuracy: {self.env.performance_history[-1]:.4f}")
                attack_impact = self.env.original_accuracy - self.env.performance_history[-1]
                self.attack_impact_var.set(f"Attack Impact: {attack_impact:.4f}")

    def run(self) -> None:
        """
        Start the GUI event loop.
        """
        self.running = False
        self.root.mainloop()


class SimpleCNN(nn.Module):
    """A simple convolutional neural network."""

    def __init__(self, num_classes: int) -> None:
        """Initialize the CNN architecture.

        Args:
            num_classes (int): Number of output classes.
        """
        super(SimpleCNN, self).__init__()
        self.conv1: nn.Conv2d = nn.Conv2d(
            in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1
        )
        self.conv2: nn.Conv2d = nn.Conv2d(
            in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1
        )
        self.adaptive_pool: nn.AdaptiveAvgPool2d = nn.AdaptiveAvgPool2d((32, 32))
        self.fc1: nn.Linear = nn.Linear(64 * 32 * 32, 128)
        self.fc2: nn.Linear = nn.Linear(128, num_classes)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Define the forward pass.

        Args:
            x (torch.Tensor): Input tensor.

        Returns:
            torch.Tensor: Output logits.
        """
        x = torch.relu(self.conv1(x))
        x = torch.relu(self.conv2(x))
        x = self.adaptive_pool(x)
        x = torch.flatten(x, 1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x





def initialize_model(num_classes: int) -> nn.Module:
    """
    Initialize the neural network model.

    Args:
        num_classes (int): Number of output classes.

    Returns:
        nn.Module: Initialized model.
    """
    input_size = 3 * 32 * 32  # Example for CIFAR-10
    model = SimpleCNN(num_classes)
    logger.info("Model initialized.")
    return model


def initialize_classifier(
    model: nn.Module,
    input_shape: Tuple[int, ...],
    nb_classes: int,
    clip_values: Tuple[float, float]
) -> PyTorchClassifier:
    """
    Initialize the ART PyTorch classifier.

    Args:
        model (nn.Module): The PyTorch model.
        input_shape (Tuple[int, ...]): Shape of input data.
        nb_classes (int): Number of output classes.
        clip_values (Tuple[float, float]): Min and max values for input data.

    Returns:
        PyTorchClassifier: Initialized classifier.
    """
    classifier = PyTorchClassifier(
        model=model,
        loss=nn.CrossEntropyLoss(),
        optimizer=optim.Adam(model.parameters()),
        input_shape=input_shape,
        nb_classes=nb_classes,
        clip_values=clip_values,
        device_type='cuda' if torch.cuda.is_available() else 'cpu'
    )
    logger.info("Classifier initialized.")
    return classifier


def generate_dummy_data(batch_size: int) -> Tuple[Any, Any]:
    """
    Generate dummy data for testing purposes.

    Args:
        batch_size (int): Number of samples to generate.

    Returns:
        Tuple[Any, Any]: Inputs and labels.
    """
    inputs = torch.randn(batch_size, 3, 32, 32)
    labels = torch.randint(0, 1000, (batch_size,))
    logger.info("Dummy data generated.")
    return inputs, labels