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.
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TASK: we have generalised below code , best generalised , scalable, robustness,

from datasets import load_dataset, DatasetDict, Dataset
from transformers import AutoImageProcessor, AutoModelForImageClassification, ViTConfig, ViTForImageClassification
from transformers import ViTImageProcessor, ViTConfig, ViTForImageClassification
from transformers import TrainingArguments, Trainer
import evaluate
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt 
dataset = load_dataset("pcuenq/oxford-pets")
id2label = {int_label: str_label for int_label, str_label in enumerate(sorted(list(set(dataset['train']['label']))))}
label2id = {str_label: int_label for int_label, str_label in enumerate(sorted(list(set(dataset['train']['label']))))}
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
teacher_model_name = "asusevski/vit-base-patch16-224-oxford-pets"
teacher_model = AutoModelForImageClassification.from_pretrained(teacher_model_name).to(device)
teacher_model.eval()
processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224")
accuracy = evaluate.load("accuracy")
inputs = processor(dataset["train"][0]["image"], return_tensors="pt") # Here, using default processor on the image
inputs = {key: value.to(device) for key, value in inputs.items()} # Converting to a dictionary
model_logits = teacher_model(**inputs).logits
model_prediction = torch.argmax(model_logits, dim=1).item()
print(id2label[model_prediction])
temperature_no_change = 1
low_temperature = 2
high_temperature = 8

teacher_distribution = F.softmax(model_logits / temperature_no_change, dim=-1).cpu().detach().numpy().reshape(-1)
teacher_distribution_low_temp = F.softmax(model_logits / low_temperature, dim=-1).cpu().detach().numpy().reshape(-1)
teacher_distribution_high_temp = F.softmax(model_logits / high_temperature, dim=-1).cpu().detach().numpy().reshape(-1)


fig, axs = plt.subplots(1, 2, figsize=(10, 4))
axs[0].bar(id2label.keys(), teacher_distribution, width = 0.4)
axs[0].set_title("Teacher probability over all classes")
axs[0].set_ylabel('Teacher Probability')
axs[0].set_xlabel("Class ID")

axs[1].bar(np.fromiter(id2label.keys(), dtype=float) - 0.4, teacher_distribution_low_temp, width = 0.4, label='Temperature=2')
axs[1].bar(np.fromiter(id2label.keys(), dtype=float) + 0.4, teacher_distribution_high_temp, width = 0.4, label='Temperature=8')
axs[1].set_title("Teacher probability over all classes, Temperature = 2 and 8")
axs[1].set_ylabel('Teacher Probability')
axs[1].set_xlabel("Class ID")
axs[1].legend()
plt.tight_layout()
plt.show()

def transforms(example):
    # Convert to RGB - there are some example in the Oxford Pets dataset that are RGBA, and even at least one gif
    example['image'] = example['image'].convert('RGB')

    # Feed image into ViT image processor
    inputs = processor(example['image'], return_tensors='pt')

    # Add to example
    example['pixel_values'] = inputs['pixel_values'].squeeze()
    example['label'] = label2id[example['label']]
    return example
transformed_dataset = dataset.map(transforms)
transformed_dataset.set_format("pt", columns=["pixel_values"], output_all_columns=True)

def collate_fn(batch):
    return {
        'pixel_values': torch.cat([x['pixel_values'].unsqueeze(dim=0) for x in batch], dim=0),
        'labels': torch.tensor([x['label'] for x in batch])
    }

train_test_dataset = transformed_dataset['train'].train_test_split(test_size=0.2)
train_val_dataset = train_test_dataset['train'].train_test_split(test_size=(0.1/0.8))
train_test_valid_dataset = DatasetDict({
    'train': train_val_dataset['train'],
    'valid': train_val_dataset['test'],
    'test': train_test_dataset['test']
})

config = ViTForImageClassification.from_pretrained("WinKawaks/vit-tiny-patch16-224").config
config.id2label = id2label
config.label2id = label2id
config.num_labels=len(id2label)
base_model = ViTForImageClassification(config).to(device)

training_args = TrainingArguments(
    output_dir="oxford-pets-vit-from-scratch",
    per_device_train_batch_size=48,
    per_device_eval_batch_size=48,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    logging_steps=100,
    num_train_epochs=10,
    learning_rate=3e-4,
    push_to_hub=True,
    load_best_model_at_end=True,
)

accuracy = evaluate.load("accuracy")

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    acc = accuracy.compute(references=labels, predictions=np.argmax(predictions, axis=1))
    return {"accuracy": acc["accuracy"]}
trainer = Trainer(
    model=base_model,
    args=training_args,
    data_collator=collate_fn,
    compute_metrics=compute_metrics,
    train_dataset=train_test_valid_dataset["train"],
    eval_dataset=train_test_valid_dataset["test"],
    tokenizer=processor
)
trainer.train()
trainer.evaluate(train_test_valid_dataset["test"])


class ImageDistilTrainer(Trainer):
    def __init__(self, teacher_model=None, student_model=None, temperature=None, lambda_param=None,  *args, **kwargs):
        super().__init__(model=student_model, *args, **kwargs)
        self.teacher = teacher_model
        self.student = student_model
        self.loss_function = nn.KLDivLoss(reduction="batchmean")
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.teacher.to(device)
        self.teacher.eval()
        self.temperature = temperature
        self.lambda_param = lambda_param

    def compute_loss(self, inputs, return_outputs=False):
        student_output = self.student(**inputs)

        with torch.no_grad():
            teacher_output = self.teacher(**inputs)

        # Compute soft targets for teacher and student
        soft_teacher = F.softmax(teacher_output.logits / self.temperature, dim=-1)
        soft_student = F.log_softmax(student_output.logits / self.temperature, dim=-1)

        # Compute the loss
        distillation_loss = self.loss_function(soft_student, soft_teacher) * (self.temperature ** 2)

        # Compute the true label loss
        student_target_loss = student_output.loss

        # Calculate final loss
        loss = (1. - self.lambda_param) * student_target_loss + self.lambda_param * distillation_loss
        return (loss, student_output) if return_outputs else loss

training_args = TrainingArguments(
    output_dir="oxford-pets-vit-with-kd",
    per_device_train_batch_size=48,
    per_device_eval_batch_size=48,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    logging_steps=100,
    num_train_epochs=10,
    learning_rate=3e-4,
    push_to_hub=True,
    load_best_model_at_end=True,
)

teacher_model.eval()
trainer = ImageDistilTrainer(
    student_model=student_model,
    teacher_model=teacher_model,
    args=training_args,
    data_collator=collate_fn,
    compute_metrics=compute_metrics,
    train_dataset=train_test_valid_dataset["train"],
    eval_dataset=train_test_valid_dataset["test"],
    tokenizer=processor,
    temperature=5,
    lambda_param=0.9
)

trainer.train()
trainer.evaluate(train_test_valid_dataset["test"])