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 : develop only environment and policy and critic and actor nn (keep my api developed )



we develop advanced enviorments 
below user input 
list of attacks arrange high impact to low impact , from select most influence attack ( some statical logic Bayes theories)
original metrics , impact of attack , select attack , 
agents should come best defense 
make metrics analysis best improve 

rewarding robustness of model 

action space, observation space , rewarding , make robustnes to capture all necessary details 
keep in the mind(model based rl)

NOTE : develop only environment end to end with advanced algo all posiblity

step 
reset
close
render
termination
soon







develop already user inputs  to enviroment:
from rlpedata import datapipeline, DatasetConfig
from Imageclassificationmetrics import MetricsCalculator
from attacks import AdversarialAttack,AttackConfig
from defence import   ( GaussianNoiseDefense,
    LabelSmoothingDefense,
    MixupDefense,
    FeatureSqueezingDefense,
    SpatialSmoothingDefense,
    CutoutDefense,
    JpegCompressionDefense,
)
from modelloader import ImageClassificationPipeline, ModelSource
import logging
from torchvision import transforms
from typing import Any, Dict, List, Optional, Union
import torch
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def print_metrics(metrics: Dict[str, Any]) -> None:
    """
    Print the classification metrics.

    Args:
        metrics (Dict[str, Any]): Dictionary of metric names and values.
           we developed already developed 
           metrics = {
            'Accuracy': self.accuracy(),
            'Precision': self.precision(),
            'Recall': self.recall(),
            'F1 Score': self.f1_score(),
            'Confusion Matrix': self.confusion_matrix(),
            'ROC-AUC': self.roc_auc(),
            'PR-AUC': self.pr_auc(),
            'Top-5 Accuracy': self.top_k_accuracy(k=5) if self.preds.ndim > 1 else None,
            'Log Loss': self.log_loss(),
            'Cross Entropy Loss': self.cross_entropy_loss(),
            'Mean Squared Error': self.mean_squared_error(),
            'Kappa Statistic': self.kappa_score(),
            'Matthews Correlation Coefficient': self.matthews_correlation_coefficient(),
            'Specificity': self.specificity(),
            'Jaccard Index': self.jaccard_index(),
            'Balanced Accuracy': self.balanced_accuracy(),
            'True Negative Rate': self.true_negative_rate(),
            'False Positive Rate': self.false_positive_rate(),
            'Dice Coefficient': self.dice_coefficient(),
            'Mean Average Precision': self.mean_average_precision(),
        }
    """
    for metric, value in metrics.items():
        if isinstance(value, torch.Tensor):
            if value.numel() == 1:
                print(f"{metric}: {value.item()}")
            else:
                print(f"{metric}: {value.cpu().numpy()}")
        elif isinstance(value, float):
            print(f"{metric}: {value}")
        else:
            # Handle other possible types (e.g., None should have been removed)
            print(f"{metric}: {value}")


config = DatasetConfig(
        dataset_path="zh-plus/tiny-imagenet",
        split_config={"train": 0.8, "test": 0.2},
        columns=["image", "label"],
        num_samples=1000,
        seed=42,
        kwargs={"cache_dir": "./data_test"},
    )

    # Data transformations
trans = transforms.Compose([
        transforms.Lambda(lambda img: img.convert("RGB")),
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    ])


train_loader, test_loader = datapipeline(
            config=config,
            transforms_fn=trans,
            target_column="image",
            label_column="label"
        )

    # Load model

model_pipeline=ImageClassificationPipeline(
model_name_or_path="google/vit-base-patch16-224", model_source=ModelSource.TRANSFORMERS.value,
        )


model=model_pipeline.model 


    # Initialize AdversarialAttack
adversary = AdversarialAttack(model=model)

list_attack: List[str] = [
        'FGSM',
        'FastGradientMethod',
        'PGD',
        'AutoProjectedGradientDescent',
        'DeepFool',
        'CW_L2',
        'CW_Linf',
        'ElasticNet',
        'BasicIterativeMethod',
        'MomentumIterativeMethod',
        'AutoConjugateGradient',
        'PixelAttack',
        'SquareAttack',
        'SquareAttackBatch',
        'SnowAttack',  # Assuming 'SpatialTransformation' was intended as 'SnowAttack'
        'AdversarialPatch',
        'UniversalPerturbation',
        'ZooAttack',
        'CompositeAdversarialAttackPyTorch',
        'SquareAttackBatch',  # Duplicate removed or kept based on use-case
    ]


for batch in train_loader:
    images, labels = batch["images"], batch["labels"]
    images = images.to(adversary.device)
    labels = labels.to(adversary.device)
    # Iterate over each attack
    for attack_name in list_attack:
        try:
            config_attack: Optional[AttackConfig] = None
            # Define specific configurations for each attack if needed
            if attack_name == 'FGSM':
                config_attack = {'epsilon': 0.007}
            elif attack_name == 'FastGradientMethod':
                config_attack = {'epsilon': 0.007}
            elif attack_name == 'PGD':
                config_attack = {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
            elif attack_name == 'AutoProjectedGradientDescent':
                config_attack = {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
            elif attack_name == 'DeepFool':
                config_attack = {'max_iter': 50, 'overshoot': 0.02}
            elif attack_name == 'CW_L2':
                config_attack = {'c': 1e-4, 'max_iter': 1000}
            elif attack_name == 'CW_Linf':
                config_attack = {'c': 1e-4, 'max_iter': 1000}
            elif attack_name == 'ElasticNet':
                config_attack = {'alpha': 1.0, 'beta': 0.01, 'iters': 1000}
            elif attack_name == 'BasicIterativeMethod':
                config_attack = {'epsilon': 0.03, 'alpha': 0.005, 'iters': 10}
            elif attack_name == 'MomentumIterativeMethod':
                config_attack = {'alpha': 0.01, 'momentum': 1.0, 'epsilon': 0.03, 'iters': 40}
            elif attack_name == 'AutoConjugateGradient':
                config_attack = {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
            elif attack_name == 'PixelAttack':
                config_attack = {'num_pixels': 100, 'epsilon': 0.02, 'iters': 1000}
            elif attack_name == 'SquareAttack':
                config_attack = {'n_queries': 1000, 'p_init': 0.05, 'p_max': 0.4,
                                 'p_min': 0.001, 's_init': 0.5, 's_min': 0.05, 's_max': 0.9}
            elif attack_name == 'SquareAttackBatch':
                config_attack = {'n_queries': 1000, 'p_init': 0.05, 'p_max': 0.4,
                                 'p_min': 0.001, 's_init': 0.5, 's_min': 0.05, 's_max': 0.9}
            elif attack_name == 'AdversarialPatch':
                config_attack = {'patch_size': 50, 'epsilon': 0.03, 'iters': 100}
            elif attack_name == 'UniversalPerturbation':
                config_attack = {'xi': 0.03, 'delta': 0.2, 'max_iter_uni': 10, 'max_iter_df': 50}
            elif attack_name == 'ZooAttack':
                config_attack = {'n_queries': 1000, 'epsilon': 0.007}
            elif attack_name == 'CompositeAdversarialAttackPyTorch':
                # Example composite: FGSM followed by PGD
                config_attack = {
                    'attacks': ['FGSM', 'PGD'],
                    'configs': [
                        {'epsilon': 0.007},
                        {'epsilon': 0.03, 'alpha': 0.007, 'iters': 40}
                    ]
                }
            elif attack_name == 'SpatialTransformation':
                config_attack = {'alpha': 0.1, 'beta': 0.1}
            else:
                logger.warning(f"No configuration defined for attack '{attack_name}'. Using defaults.")
            adv_images = adversary.attack(attack_name, images, labels, config_attack)
            # Forward pass with adversarial examples
            model_output = model(adv_images).logits  # Extract logits
            metrics_calc = MetricsCalculator(preds=model_output, labels=labels, num_classes=1000)
            metrics = metrics_calc.get_all_metrics()
            print(f"\nAttack: {attack_name}")
            print_metrics(metrics)
        except ValueError as ve:
            logger.warning(f"Skipping unsupported attack '{attack_name}': {ve}")
        except NotImplementedError as nie:
            logger.warning(f"Attack '{attack_name}' is not implemented: {nie}")
        except RuntimeError as re:
            logger.error(f"Attack '{attack_name}' failed: {re}")

x = torch.randn(32, 3, 224, 224)
y = torch.randint(0, 100, (32,))  # Corrected line
# Initialize defenses
gaussian_defense = GaussianNoiseDefense(sigma=0.02)
label_smoothing_defense = LabelSmoothingDefense(alpha=0.1, num_classes=3)
mixup_defense = MixupDefense(alpha=0.4)
feature_squeezing_defense = FeatureSqueezingDefense(bit_depth=4)
spatial_smoothing_defense = SpatialSmoothingDefense(kernel_size=5)
cutout_defense = CutoutDefense(mask_size=50)

# Ensure PIL is installed for JpegCompressionDefense
try:
    from PIL import Image
    jpeg_defense = JpegCompressionDefense(quality=50)
except ImportError:
    logger.error("PIL is required for JpegCompressionDefense. Please install it using `pip install Pillow`.")
    jpeg_defense = None
# Apply defenses
x_noisy = gaussian_defense.apply(x)
y_smooth = label_smoothing_defense.apply(y)
x_mixup, y_mixup = mixup_defense.apply(x, y)
x_squeezed = feature_squeezing_defense.apply(x)
x_smooth = spatial_smoothing_defense.apply(x)
x_cutout = cutout_defense.apply(x)
print(x_cutout.shape)

if jpeg_defense:
    x_compressed = jpeg_defense.apply(x)
    print(x_compressed.shape)