code: def main():
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
  
      parser = HfArgumentParser(
          (ModelArguments, DataTrainingArguments, TrainingArguments)
      )
      if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          model_args, data_args, training_args = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1])
          )
      else:
          (
              model_args,
              data_args,
              training_args,
          ) = parser.parse_args_into_dataclasses()
  
      if model_args.use_auth_token is not None:
          warnings.warn(
              "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
              FutureWarning,
          )
          if model_args.token is not None:
              raise ValueError(
                  "`token` and `use_auth_token` are both specified. Please set only the argument `token`."
              )
          model_args.token = model_args.use_auth_token
  
      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry(
          "run_semantic_segmentation", model_args, data_args
      )
  
      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
  
      if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()
  
      log_level = training_args.get_process_log_level()
      logger.setLevel(log_level)
      transformers.utils.logging.set_verbosity(log_level)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
  
      # Log on each process the small summary:
      logger.warning(
          f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
          + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
      )
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # Detecting last checkpoint.
      last_checkpoint = None
      if (
          os.path.isdir(training_args.output_dir)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
          last_checkpoint = get_last_checkpoint(
              training_args.output_dir
          )
          if (
              last_checkpoint is None
              and len(os.listdir(training_args.output_dir)) > 0
          ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                  "Use --overwrite_output_dir to overcome."
              )
          elif (
              last_checkpoint is not None
              and training_args.resume_from_checkpoint is None
          ):
              logger.info(
                  f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                  "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
              )
  
      # Load dataset
      # In distributed training, the load_dataset function guarantees that only one local process can concurrently
      # download the dataset.
      # TODO support datasets from local folders
      dataset = load_dataset(
          data_args.dataset_name, cache_dir=model_args.cache_dir
      )
  
      # Rename column names to standardized names (only "image" and "label" need to be present)
      if "pixel_values" in dataset["train"].column_names:
          dataset = dataset.rename_columns({"pixel_values": "image"})
      if "annotation" in dataset["train"].column_names:
          dataset = dataset.rename_columns({"annotation": "label"})
  
      # If we don't have a validation split, split off a percentage of train as validation.
      data_args.train_val_split = (
          None
          if "validation" in dataset.keys()
          else data_args.train_val_split
      )
      if (
          isinstance(data_args.train_val_split, float)
          and data_args.train_val_split > 0.0
      ):
          split = dataset["train"].train_test_split(
              data_args.train_val_split
          )
          dataset["train"] = split["train"]
          dataset["validation"] = split["test"]
  
      # Prepare label mappings.
      # We'll include these in the model's config to get human readable labels in the Inference API.
      if data_args.dataset_name == "scene_parse_150":
          repo_id = "huggingface/label-files"
          filename = "ade20k-id2label.json"
      else:
          repo_id = data_args.dataset_name
          filename = "id2label.json"
      id2label = json.load(
          open(
              hf_hub_download(repo_id, filename, repo_type="dataset"),
              "r",
          )
      )
      id2label = {int(k): v for k, v in id2label.items()}
      label2id = {v: str(k) for k, v in id2label.items()}
  
      # Load the mean IoU metric from the datasets package
      metric = evaluate.load("mean_iou", cache_dir=model_args.cache_dir)
  
      # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      @torch.no_grad()
      def compute_metrics(eval_pred):
          logits, labels = eval_pred
          logits_tensor = torch.from_numpy(logits)
          # scale the logits to the size of the label
          logits_tensor = nn.functional.interpolate(
              logits_tensor,
              size=labels.shape[-2:],
              mode="bilinear",
              align_corners=False,
          ).argmax(dim=1)
  
          pred_labels = logits_tensor.detach().cpu().numpy()
          metrics = metric.compute(
              predictions=pred_labels,
              references=labels,
              num_labels=len(id2label),
              ignore_index=0,
              reduce_labels=image_processor.do_reduce_labels,
          )
          # add per category metrics as individual key-value pairs
          per_category_accuracy = metrics.pop(
              "per_category_accuracy"
          ).tolist()
          per_category_iou = metrics.pop("per_category_iou").tolist()
  
          metrics.update(
              {
                  f"accuracy_{id2label[i]}": v
                  for i, v in enumerate(per_category_accuracy)
              }
          )
          metrics.update(
              {
                  f"iou_{id2label[i]}": v
                  for i, v in enumerate(per_category_iou)
              }
          )
  
          return metrics
  
      config = AutoConfig.from_pretrained(
          model_args.config_name or model_args.model_name_or_path,
          label2id=label2id,
          id2label=id2label,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
      model = AutoModelForSemanticSegmentation.from_pretrained(
          model_args.model_name_or_path,
          from_tf=bool(".ckpt" in model_args.model_name_or_path),
          config=config,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
      image_processor = AutoImageProcessor.from_pretrained(
          model_args.image_processor_name
          or model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          token=model_args.token,
          trust_remote_code=model_args.trust_remote_code,
      )
  
      # Define torchvision transforms to be applied to each image + target.
      # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
      # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
      if "shortest_edge" in image_processor.size:
          # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
          size = (
              image_processor.size["shortest_edge"],
              image_processor.size["shortest_edge"],
          )
      else:
          size = (
              image_processor.size["height"],
              image_processor.size["width"],
          )
      train_transforms = Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              RandomCrop(size=size),
              RandomHorizontalFlip(flip_prob=0.5),
              PILToTensor(),
              ConvertImageDtype(torch.float),
              Normalize(
                  mean=image_processor.image_mean,
                  std=image_processor.image_std,
              ),
          ]
      )
      # Define torchvision transform to be applied to each image.
      # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
      val_transforms = Compose(
          [
              ReduceLabels() if data_args.reduce_labels else Identity(),
              Resize(size=size),
              PILToTensor(),
              ConvertImageDtype(torch.float),
              Normalize(
                  mean=image_processor.image_mean,
                  std=image_processor.image_std,
              ),
          ]
      )
  
      def preprocess_train(example_batch):
          pixel_values = []
          labels = []
          for image, target in zip(
              example_batch["image"], example_batch["label"]
          ):
              image, target = train_transforms(
                  image.convert("RGB"), target
              )
              pixel_values.append(image)
              labels.append(target)
  
          encoding = {}
          encoding["pixel_values"] = torch.stack(pixel_values)
          encoding["labels"] = torch.stack(labels)
  
          return encoding
  
      def preprocess_val(example_batch):
          pixel_values = []
          labels = []
          for image, target in zip(
              example_batch["image"], example_batch["label"]
          ):
              image, target = val_transforms(
                  image.convert("RGB"), target
              )
              pixel_values.append(image)
              labels.append(target)
  
          encoding = {}
          encoding["pixel_values"] = torch.stack(pixel_values)
          encoding["labels"] = torch.stack(labels)
  
          return encoding
  
      if training_args.do_train:
          if "train" not in dataset:
              raise ValueError("--do_train requires a train dataset")
          if data_args.max_train_samples is not None:
              dataset["train"] = (
                  dataset["train"]
                  .shuffle(seed=training_args.seed)
                  .select(range(data_args.max_train_samples))
              )
          # Set the training transforms
          dataset["train"].set_transform(preprocess_train)
  
      if training_args.do_eval:
          if "validation" not in dataset:
              raise ValueError(
                  "--do_eval requires a validation dataset"
              )
          if data_args.max_eval_samples is not None:
              dataset["validation"] = (
                  dataset["validation"]
                  .shuffle(seed=training_args.seed)
                  .select(range(data_args.max_eval_samples))
              )
          # Set the validation transforms
          dataset["validation"].set_transform(preprocess_val)
  
      # Initalize our trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=dataset["train"]
          if training_args.do_train
          else None,
          eval_dataset=dataset["validation"]
          if training_args.do_eval
          else None,
          compute_metrics=compute_metrics,
          tokenizer=image_processor,
          data_collator=default_data_collator,
      )
  
      # Training
      if training_args.do_train:
          checkpoint = None
          if training_args.resume_from_checkpoint is not None:
              checkpoint = training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
              checkpoint = last_checkpoint
          train_result = trainer.train(
              resume_from_checkpoint=checkpoint
          )
          trainer.save_model()
          trainer.log_metrics("train", train_result.metrics)
          trainer.save_metrics("train", train_result.metrics)
          trainer.save_state()
  
      # Evaluation
      if training_args.do_eval:
          metrics = trainer.evaluate()
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      # Write model card and (optionally) push to hub
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "dataset": data_args.dataset_name,
          "tags": ["image-segmentation", "vision"],
      }
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
imports: import json
  import logging
  import os
  import random
  import sys
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional
  import evaluate
  import numpy
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  from torch import nn
  from torchvision import transforms
  from torchvision.transforms import functional
  import transformers
  from transformers import AutoConfig, AutoImageProcessor, AutoModelForSemanticSegmentation, HfArgumentParser, Trainer, TrainingArguments, default_data_collator
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: metrics, encoding, encoding