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

dataset = load_dataset("anindya64/hardhat")
dataset
import numpy as np
from PIL import Image, ImageDraw

def draw_image_from_idx(dataset, idx):
    sample = dataset[idx]
    image = sample["image"]
    annotations = sample['objects']
    draw = ImageDraw.Draw(image)
    width, height = sample['width'], sample['height']

    for i in range(len(annotations["id"])):
        box = annotations["bbox"][i]
        class_idx = annotations["id"][i]
        x, y, w, h = tuple(box)
        if max(box) > 1.0:
            x1, y1 = int(x), int(y)
            x2, y2 = int(x + w), int(y + h)
        else:
            x1 = int(x * width)
            y1 = int(y * height)
            x2 = int((x + w) * width)
            y2 = int((y + h) * height)
        draw.rectangle((x1, y1, x2, y2), outline="red", width=1)
        draw.text((x1, y1), annotations["category"][i], fill="white")
    return image


draw_image_from_idx(dataset=train_dataset, idx=10)
# Now let's make a simple function on plotting multiple images

import matplotlib.pyplot as plt

def plot_images(dataset, indices):
    """
    Plot images and their annotations.
    """
    num_rows = len(indices) // 3
    num_cols = 3
    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 10))

    for i, idx in enumerate(indices):
        row = i // num_cols
        col = i % num_cols

        # Draw image
        image = draw_image_from_idx(dataset, idx)

        # Display image on the corresponding subplot
        axes[row, col].imshow(image)
        axes[row, col].axis('off')

    plt.tight_layout()
    plt.show()
from transformers import AutoImageProcessor

checkpoint = "facebook/detr-resnet-50-dc5"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
# now creating random augmentations using albumentations

import albumentations
import numpy as np
import torch

transform = albumentations.Compose(
    [
        albumentations.Resize(480, 480),
        albumentations.HorizontalFlip(p=1.0),
        albumentations.RandomBrightnessContrast(p=1.0),
    ],
    bbox_params=albumentations.BboxParams(format="coco", label_fields=["category"]),
)
def formatted_anns(image_id, category, area, bbox):
    annotations = []
    for i in range(0, len(category)):
        new_ann = {
            "image_id": image_id,
            "category_id": category[i],
            "isCrowd": 0,
            "area": area[i],
            "bbox": list(bbox[i]),
        }
        annotations.append(new_ann)

    return annotations
# transforming a batch

def transform_aug_ann(examples):
    image_ids = examples["image_id"]
    images, bboxes, area, categories = [], [], [], []
    for image, objects in zip(examples["image"], examples["objects"]):
        image = np.array(image.convert("RGB"))[:, :, ::-1]
        out = transform(image=image, bboxes=objects["bbox"], category=objects["id"])

        area.append(objects["area"])
        images.append(out["image"])
        bboxes.append(out["bboxes"])
        categories.append(out["category"])

    targets = [
        {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)}
        for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
    ]

    return image_processor(images=images, annotations=targets, return_tensors="pt")
# Apply transformations for both train and test dataset

train_dataset_transformed = train_dataset.with_transform(transform_aug_ann)
test_dataset_transformed = test_dataset.with_transform(transform_aug_ann)
def collate_fn(batch):
    pixel_values = [item["pixel_values"] for item in batch]
    encoding = image_processor.pad(pixel_values, return_tensors="pt")
    labels = [item["labels"] for item in batch]
    batch = {}
    batch["pixel_values"] = encoding["pixel_values"]
    batch["pixel_mask"] = encoding["pixel_mask"]
    batch["labels"] = labels
    return batch

example :
from transformers import AutoModelForObjectDetection

id2label = {0:'head', 1:'helmet', 2:'person'}
label2id = {v: k for k, v in id2label.items()}


model = AutoModelForObjectDetection.from_pretrained(
    checkpoint,
    id2label=id2label,
    label2id=label2id,
    ignore_mismatched_sizes=True,
)
# Define the training Arguments

from transformers import TrainingArguments

training_args = TrainingArguments(
    output_dir="detr-resnet-50-hardhat-finetuned",
    per_device_train_batch_size=8,
    num_train_epochs=3,
    max_steps=1000,
    fp16=True,
    save_steps=10,
    logging_steps=30,
    learning_rate=1e-5,
    weight_decay=1e-4,
    save_total_limit=2,
    remove_unused_columns=False,
    push_to_hub=True,
)
from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=collate_fn,
    train_dataset=train_dataset_transformed,
    eval_dataset=test_dataset_transformed,
    tokenizer=image_processor,
)

trainer.train()
import requests

# download a sample image

url = "https://huggingface.co/datasets/hf-vision/course-assets/blob/main/test-helmet-object-detection.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image
from transformers import pipeline

# make the object detection pipeline

obj_detector = pipeline("object-detection", model="anindya64/detr-resnet-50-dc5-hardhat-finetuned")
results = obj_detector(train_dataset[0]['image'])

results
def plot_results(image, results, threshold = 0.7):
    image = Image.fromarray(np.uint8(image))
    draw = ImageDraw.Draw(image)
    for result in results:
        score = result['score']
        label = result['label']
        box = list(result['box'].values())
        if score > threshold:
            x, y, x2, y2 = tuple(box)
            draw.rectangle((x, y, x2, y2), outline="red", width=1)
            draw.text((x, y), label, fill="white")
            draw.text((x+0.5, y-0.5), text=str(score), fill='green' if score > 0.7 else 'red')
    return image
def predict(image, pipeline, threshold=0.7):
    results = pipeline(image)
    return plot_results(image, results, threshold)
from tqdm.auto import tqdm

def plot_images(dataset, indices):
    """
    Plot images and their annotations.
    """
    num_rows = len(indices) // 3
    num_cols = 3
    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 10))

    for i, idx in tqdm(enumerate(indices), total=len(indices)):
        row = i // num_cols
        col = i % num_cols

        # Draw image
        image = predict(dataset[idx]['image'], obj_detector)

        # Display image on the corresponding subplot
        axes[row, col].imshow(image)
        axes[row, col].axis('off')

    plt.tight_layout()
    plt.show()


plot_images(test_dataset, range(6))