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 :make below pipeline generalised, training,evaluation,inference,plots and weights save in the ouput folder 


from datasets import load_dataset

dataset=load_dataset("anindya64/hardhat")
train_dataset=dataset['train"]
test_dataset=dataset["test"]


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()

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
from transformers import AutoImageProcessor

checkpoint = "facebook/detr-resnet-50-dc5"
image_processor = AutoImageProcessor.from_pretrained(checkpoint)

# 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")

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



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,
    cache_dir='./cache'
)






# 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=False,
)

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()
# delete this model, since it is already been uploaded to hub

del model
torch.cuda.synchronize()

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
try 
with 
wget subprocess
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
plot_results(image, results)
def predict(image, pipeline, threshold=0.7):
    results = pipeline(image)
    return plot_results(image, results, threshold)
img = test_dataset[0]['image']
predict(img, obj_detector)
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))
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,
)

for name,p in model.named_parameters():
    if not 'bbox_predictor' in name or not name.startswith('class_label'):
        p.requires_grad = False