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.


NOTE:only develop environment using import gymnasium as gym  module 

good 
from gymnasium.spaces.box import Box
from gymnasium.spaces.dict import Dict
from gymnasium.spaces.discrete import Discrete
from gymnasium.spaces.graph import Graph, GraphInstance
from gymnasium.spaces.multi_binary import MultiBinary
from gymnasium.spaces.multi_discrete import MultiDiscrete
from gymnasium.spaces.oneof import OneOf
from gymnasium.spaces.sequence import Sequence
from gymnasium.spaces.space import Space
from gymnasium.spaces.text import Text
from gymnasium.spaces.tuple import Tuple
from gymnasium.spaces.utils import flatdim, flatten, flatten_space, unflatten


TASK : we have develop advanced Reinforcementlearning policy engline 
AIM with minimal time we have find for given attack and what is best defences , along with most robustness weights have to save with can withstand all attacks



environment :

observation space:
1) top three  metrics details we have 20 metrics we develop with return # gives  Dict[str, Any] they are (
already implemented
'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(),

)
   
2) what is attack also include 
3) what is impact of attack 
4) what robustness previously/originally


action space : given observation , and previous history 

what is best defenvce I can select



rewards: 1) agent find best defence and 2) how robustness is increased 



below all api already implemented 

     





from torchvision import transforms
import torch
import torch.optim as optim
from torch import nn
from art.estimators.classification import PyTorchClassifier

from rldata import (
DatasetConfig,
datapipeline,
)
from model import ImageClassificationPipeline,ModelSource
from attacks import apply_attack,LIST_OF_ATTACTS
from defences import DefenseManager
from metrics import MetricsCalculator,print_metrics

#=====================Data====================
data_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",}
)

transforms_fnc=transforms.Compose([
    transforms.Lambda(lambda img: img.convert("RGB")),  # Convert to RGB first
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],  # Use three values for three channels
                       std=[0.229, 0.224, 0.225])
])

#=====================Data processing====================
train_loader, test_loader= datapipeline(
    config=data_config,
    transforms_fn=transforms_fnc,
    target_column='image',
    label_column='label',
    
)

#=====================Model====================





class WrappedViTModel(nn.Module):
    def __init__(self, original_model):
        super(WrappedViTModel, self).__init__()
        self.original_model = original_model

    def forward(self, x):
        # Obtain the logits from the HuggingFace model's output
        output = self.original_model(x)
        return output.logits


# Initialize the pipeline
model_pipeline = ImageClassificationPipeline(
    model_name_or_path="google/vit-base-patch16-224-in21k",
    model_source=ModelSource.TRANSFORMERS.value,
    cache_dir="./data",
)

# Extract the model from the pipeline
original_model = model_pipeline.model

# Modify the classifier as you've done
original_model.classifier = torch.nn.Linear(
    in_features=original_model.classifier.in_features,
    out_features=1000, 
    bias=True
)

wrapped_model = WrappedViTModel(original_model)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
wrapped_model.to(device)
wrapped_model.eval() 



from art.estimators.classification import PyTorchClassifier
import torch.optim as optim
import torch.nn as nn

classifier = PyTorchClassifier(
    model=wrapped_model,
    loss=nn.CrossEntropyLoss(),
    optimizer=optim.Adam(wrapped_model.parameters()),
    input_shape=(3, 224, 224),
    nb_classes=1000,
    clip_values=(0.0, 1.0),  # Ensure these values match your data preprocessing
    device_type="cuda" if torch.cuda.is_available() else "cpu"
)

for batch in train_loader:
    images, labels = batch["images"].to(device), batch["labels"].to(device)
    defence_generator = DefenseManager()
    list_of_defences = defence_generator.list_defenses()
    print(list_of_defences)

    for attack in LIST_OF_ATTACTS:
        images = apply_attack(attack_name=attack, classifier=classifier, x=images, y=labels)
        model_output = wrapped_model(images)
        print(model_output.shape)
        metrics_pipeline = MetricsCalculator(preds=model_output, labels=labels, num_classes=1000)
        metrics = metrics_pipeline.get_all_metrics()
        

    for defence in list_of_defences:
        defence_pred_or_image=defence_generator.apply_defense(
            classifier=classifier, 
            x=images,
            y=labels
        )
        metrics_pipeline = MetricsCalculator(preds=defence_pred_or_image, labels=labels, num_classes=1000)
        metrics = metrics_pipeline.get_all_metrics() # gives  Dict[str, Any]