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,
import json
import torch
import datasets
import requests
import evaluate
import numpy as np
import huggingface_hub
from PIL import Image
import albumentations as A
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
from typing import Tuple, Any
from dataclasses import dataclass
from datasets import load_dataset
import matplotlib.patches as mpatches
from huggingface_hub import hf_hub_download
from torch.utils.data import Dataset, DataLoader
from transformers import (
    MaskFormerImageProcessor,
    AutoImageProcessor,
    MaskFormerForInstanceSegmentation,
)

torch.manual_seed(42)
hf_dataset_id = "segments/sidewalk-semantic"
dataset = load_dataset(hf_dataset_id)

dataset = dataset.shuffle(seed=1)
dataset = dataset["train"].train_test_split(test_size=0.2)
train_ds, test_ds = dataset["train"], dataset["test"]

dataset = train_ds.train_test_split(test_size=0.05)
train_ds, val_ds = dataset["train"], dataset["test"]

filename = "id2label.json"
id2label = json.load(
    open(hf_hub_download(hf_dataset_id, filename, repo_type="dataset"), "r")
)
id2label = {int(k): v for k, v in id2label.items()}
print(id2label)
or
label2id = {c:idx for idx,c in enumerate(labels)}
id2label = {idx:c for idx,c in enumerate(labels)}
def show_samples(dataset: datasets.Dataset, n: int = 5):
    """
    Displays 'n' samples from the dataset.
    ----
    Args:
      - dataset: The dataset which should contain 'pixel_values' and 'label' in its items.
      - n (int): Number of samples to display.

    """
    if n > len(dataset):
        raise ValueError("n is larger than the dataset size")

    fig, axs = plt.subplots(n, 2, figsize=(10, 5 * n))

    for i in range(n):
        sample = dataset[i]
        image, label = np.array(sample["pixel_values"]), sample["label"]

        axs[i, 0].imshow(image)
        axs[i, 0].set_title("Image")
        axs[i, 0].axis("off")

        axs[i, 1].imshow(image)
        axs[i, 1].imshow(label, cmap="nipy_spectral", alpha=0.5)
        axs[i, 1].set_title("Segmentation Map")
        axs[i, 1].axis("off")

    plt.tight_layout()
    plt.show()

preprocessor = MaskFormerImageProcessor(
    ignore_index=0,
    do_reduce_labels=False,
    do_resize=False,
    do_rescale=False,
    do_normalize=False,
)
ade_mean = np.array([123.675, 116.280, 103.530]) / 255
ade_std = np.array([58.395, 57.120, 57.375]) / 255

train_transform = A.Compose(
    [
        A.RandomCrop(width=512, height=512),
        A.HorizontalFlip(p=0.5),
        A.Normalize(mean=ade_mean, std=ade_std),
    ]
)

test_transform = A.Compose(
    [
        A.Resize(width=512, height=512),
        A.Normalize(mean=ade_mean, std=ade_std),
    ]
)


@dataclass
class SegmentationDataInput:
    original_image: np.ndarray
    transformed_image: np.ndarray
    original_segmentation_map: np.ndarray
    transformed_segmentation_map: np.ndarray


class SemanticSegmentationDataset(Dataset):
    def __init__(self, dataset: datasets.Dataset, transform: Any) -> None:
        """
        Dataset for Semantic Segmentation.
        ----
        Args:
          - dataset: A dataset containing images and segmentation maps.
          - transform: A transformation function to apply to the images and segmentation maps.
        """
        self.dataset = dataset
        self.transform = transform

    def __len__(self) -> int:
        return len(self.dataset)

    def __getitem__(self, idx: int) -> Tuple[np.ndarray, np.ndarray]:
        sample = self.dataset[idx]
        original_image = np.array(sample["pixel_values"])
        original_segmentation_map = np.array(sample["label"])

        transformed = self.transform(
            image=original_image, mask=original_segmentation_map
        )
        transformed_image = transformed["image"].transpose(
            2, 0, 1
        )  # Transpose to channel-first format
        transformed_segmentation_map = transformed["mask"]

        return SegmentationDataInput(
            original_image=original_image,
            transformed_image=transformed_image,
            original_segmentation_map=original_segmentation_map,
            transformed_segmentation_map=transformed_segmentation_map,
        )


def collate_fn(batch: SegmentationDataInput) -> dict:
    original_images = [sample.original_image for sample in batch]
    transformed_images = [sample.transformed_image for sample in batch]
    original_segmentation_maps = [sample.original_segmentation_map for sample in batch]
    transformed_segmentation_maps = [
        sample.transformed_segmentation_map for sample in batch
    ]

    preprocessed_batch = preprocessor(
        transformed_images,
        segmentation_maps=transformed_segmentation_maps,
        return_tensors="pt",
    )

    preprocessed_batch["original_images"] = original_images
    preprocessed_batch["original_segmentation_maps"] = original_segmentation_maps

    return preprocessed_batch

train_dataset = SemanticSegmentationDataset(train_ds, transform=train_transform)
val_dataset = SemanticSegmentationDataset(val_ds, transform=train_transform)
test_dataset = SemanticSegmentationDataset(test_ds, transform=test_transform)

# Prepare Dataloaders
train_dataloader = DataLoader(
    train_dataset, batch_size=2, shuffle=True, collate_fn=collate_fn
)
val_dataloader = DataLoader(
    val_dataset, batch_size=2, shuffle=True, collate_fn=collate_fn
)
test_dataloader = DataLoader(
    test_dataset, batch_size=2, shuffle=False, collate_fn=collate_fn
)
def denormalize_image(image, mean, std):
    """
    Denormalizes a normalized image.
    ----
    Args:
     - image (numpy.ndarray): The normalized image.
     - mean (list or numpy.ndarray): The mean used for normalization.
     - std (list or numpy.ndarray): The standard deviation used for normalization.

    """
    unnormalized_image = (image * std[:, None, None]) + mean[:, None, None]
    unnormalized_image = (unnormalized_image * 255).numpy().astype(np.uint8)
    unnormalized_image = np.moveaxis(unnormalized_image, 0, -1)
    return unnormalized_image


denormalized_image = denormalize_image(sample["pixel_values"][0], ade_mean, ade_std)
pil_image = Image.fromarray(denormalized_image)
pil_image
labels = [id2label[label] for label in sample["class_labels"][0].tolist()]
print(labels)
def visualize_mask(sample, labels, label_name):
    print(f"Category: {label_name}")
    idx = labels.index(label_name)

    visual_mask = (sample["mask_labels"][0][idx].bool().numpy() * 255).astype(np.uint8)
    return Image.fromarray(visual_mask)
processor = AutoImageProcessor.from_pretrained("facebook/maskformer-swin-base-coco")
model = MaskFormerForInstanceSegmentation.from_pretrained(
    "facebook/maskformer-swin-base-coco"
)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)

predicted_semantic_map = processor.post_process_semantic_segmentation(
    outputs, target_sizes=[image.size[::-1]]
)[0]
predicted_semantic_map

num_classes = len(np.unique(predicted_semantic_map))
cmap = plt.cm.get_cmap("hsv", num_classes)

overlay = np.zeros(
    (predicted_semantic_map.shape[0], predicted_semantic_map.shape[1], 4)
)

for i, unique_value in enumerate(np.unique(predicted_semantic_map)):
    overlay[predicted_semantic_map == unique_value, :3] = cmap(i)[:3]
    overlay[predicted_semantic_map == unique_value, 3] = 0.5  # 50% transparency

fig, ax = plt.subplots(figsize=(8, 6))
ax.imshow(image)
ax.imshow(overlay, interpolation="nearest", alpha=0.9)
plt.axis("off")

plt.show()
predicted_semantic_map = processor.post_process_panoptic_segmentation(
    outputs, target_sizes=[image.size[::-1]]
)[0]
predicted_semantic_map
num_instances = len(predicted_semantic_map["segments_info"])
colors = plt.cm.get_cmap("viridis", num_instances)
overlay = np.zeros((np.array(image).shape[0], np.array(image).shape[1], 4))

for i, info in enumerate(predicted_semantic_map["segments_info"]):
    mask = predicted_semantic_map["segmentation"] == info["id"]
    color = colors(i)
    overlay[mask, :3] = color[:3]  # RGB channels
    overlay[mask, 3] = 0.5  # Alpha channel for transparency

fig, ax = plt.subplots(figsize=(8, 6))
ax.imshow(np.array(image))
ax.imshow(overlay, interpolation="nearest", alpha=0.9)
plt.axis("off")

handles = [
    mpatches.Patch(
        color=colors(i)[:3], label=f"{model.config.id2label[info['label_id']]}-{i}"
    )
    for i, info in enumerate(predicted_semantic_map["segments_info"])
]
plt.legend(handles=handles, bbox_to_anchor=(1.05, 1), loc="upper left")

plt.show()
model = MaskFormerForInstanceSegmentation.from_pretrained(
    "facebook/maskformer-swin-base-ade", id2label=id2label, ignore_mismatched_sizes=True
)
# pixel level module contains both the backbone and the pixel decoder
for param in model.model.pixel_level_module.parameters():
    param.requires_grad = False

# Confirm that the parameters are correctly frozen
for name, param in model.model.pixel_level_module.named_parameters():
    assert not param.requires_grad

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
metric = evaluate.load("mean_iou")


def evaluate_model(
    model: MaskFormerForInstanceSegmentation,
    dataloader: DataLoader,
    preprocessor: AutoImageProcessor,
    metric: Any,
    id2label: dict,
    max_batches=None,
):
    """
    Evaluates the given model using the specified dataloader and computes the mean Intersection over Union (IoU).
    ----
    Args:
      - model (MaskFormerForInstanceSegmentation): The trained model to be evaluated.
      - dataloader (DataLoader): DataLoader containing the dataset for evaluation.
      - preprocessor (AutoImageProcessor): The preprocessor used for post-processing the model outputs.
      - metric (Any): Metric instance used for calculating IoU.
      - id2label (dict): Dictionary mapping class ids to their corresponding labels.
      - max_batches (int, optional): Maximum number of batches to evaluate. If None, evaluates on the entire validation dataset.

    Returns:
    float: The mean IoU calculated over the specified number of batches.
    """
    model.eval()
    running_iou = 0
    num_batches = 0
    with torch.no_grad():
        for idx, batch in enumerate(tqdm(dataloader)):
            if max_batches and idx >= max_batches:
                break

            pixel_values = batch["pixel_values"].to(device)
            outputs = model(pixel_values=pixel_values)

            original_images = batch["original_images"]
            target_sizes = [
                (image.shape[0], image.shape[1]) for image in original_images
            ]

            predicted_segmentation_maps = (
                preprocessor.post_process_semantic_segmentation(
                    outputs, target_sizes=target_sizes
                )
            )

            ground_truth_segmentation_maps = batch["original_segmentation_maps"]
            metric.add_batch(
                references=ground_truth_segmentation_maps,
                predictions=predicted_segmentation_maps,
            )

            running_iou += metric.compute(num_labels=len(id2label), ignore_index=0)[
                "mean_iou"
            ]
            num_batches += 1

    mean_iou = running_iou / num_batches
    return mean_iou


def train_model(
    model: MaskFormerForInstanceSegmentation,
    train_dataloader: DataLoader,
    val_dataloader: DataLoader,
    preprocessor: AutoImageProcessor,
    metric: AutoImageProcessor,
    id2label: dict,
    num_epochs=100,
    learning_rate=5e-5,
    log_interval=100,
):
    """
    Trains the MaskFormer model for semantic segmentation over a specified number of epochs and evaluates it on a validation set.
    ----
    Args:
      - model (MaskFormerForInstanceSegmentation): The model to be trained.
      - train_dataloader (DataLoader): DataLoader for the training data.
      - val_dataloader (DataLoader): DataLoader for the validation data.
      - preprocessor (AutoImageProcessor): The preprocessor used for preparing the data.
      - metric (Any): Metric instance used for calculating performance metrics.
      - id2label (dict): Dictionary mapping class IDs to their corresponding labels.
      - num_epochs (int): Number of epochs to train the model.
      - learning_rate (float): Learning rate for the optimizer.
      - log_interval (int): Interval (in number of batches) at which to log training progress.

    """
    model.to(device)

    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

    for epoch in range(num_epochs):
        print(f"Current epoch: {epoch+1}/{num_epochs}")
        model.train()

        running_loss = 0.0
        num_samples = 0

        for idx, batch in enumerate(tqdm(train_dataloader)):
            optimizer.zero_grad()

            outputs = model(
                pixel_values=batch["pixel_values"].to(device),
                mask_labels=[labels.to(device) for labels in batch["mask_labels"]],
                class_labels=[labels.to(device) for labels in batch["class_labels"]],
            )

            loss = outputs.loss
            loss.backward()

            batch_size = batch["pixel_values"].size(0)
            running_loss += loss.item()
            num_samples += batch_size

            if idx % log_interval == 0 and idx > 0:
                print(f"Iteration {idx} - loss: {running_loss/num_samples}")

            optimizer.step()
        val_mean_iou = evaluate_model(
            model, val_dataloader, preprocessor, metric, id2label, max_batches=6
        )
        print(f"Validation Mean IoU: {val_mean_iou}")
train_model(
    model,
    train_dataloader,
    val_dataloader,
    preprocessor,
    metric,
    id2label,
    num_epochs=2,
    log_interval=100,
)
train_model(
    model,
    train_dataloader,
    val_dataloader,
    preprocessor,
    metric,
    id2label,
    num_epochs=2,
    log_interval=100,
)