code: def main():
      # 1. Parse input arguments
      # 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()
  
      # 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_clip", model_args, data_args)
  
      # 2. 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)],
      )
  
      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: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
      )
      logger.info(f"Training/evaluation parameters {training_args}")
  
      # 3. Detecting last checkpoint and eventualy continue from 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."
              )
  
      # 4. Load dataset
      # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
      # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
      # (the dataset will be downloaded automatically from the datasets Hub).
      #
      # For CSV/JSON files this script will use the first column for the full image path and the second column for the
      # captions (unless you specify column names for this with the `image_column` and `caption_column` arguments).
      #
      if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          dataset = load_dataset(
              data_args.dataset_name,
              data_args.dataset_config_name,
              cache_dir=model_args.cache_dir,
              keep_in_memory=False,
              data_dir=data_args.data_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      else:
          data_files = {}
          if data_args.train_file is not None:
              data_files["train"] = data_args.train_file
              extension = data_args.train_file.split(".")[-1]
          if data_args.validation_file is not None:
              data_files["validation"] = data_args.validation_file
              extension = data_args.validation_file.split(".")[-1]
          if data_args.test_file is not None:
              data_files["test"] = data_args.test_file
              extension = data_args.test_file.split(".")[-1]
          dataset = load_dataset(
              extension,
              data_files=data_files,
              cache_dir=model_args.cache_dir,
              use_auth_token=True
              if model_args.use_auth_token
              else None,
          )
      # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
      # https://huggingface.co/docs/datasets/loading_datasets.html.
  
      # 5. Load pretrained model, tokenizer, and feature extractor
      if model_args.tokenizer_name:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.tokenizer_name,
              cache_dir=model_args.cache_dir,
              use_fast=model_args.use_fast_tokenizer,
          )
      elif model_args.model_name_or_path:
          tokenizer = AutoTokenizer.from_pretrained(
              model_args.model_name_or_path,
              cache_dir=model_args.cache_dir,
              use_fast=model_args.use_fast_tokenizer,
          )
      else:
          raise ValueError(
              "You are instantiating a new tokenizer from scratch. This is not supported by this script."
              "You can do it from another script, save it, and load it from here, using --tokenizer_name."
          )
  
      # Load feature_extractor, in this script we only use this to get the mean and std for normalization.
      feature_extractor = AutoFeatureExtractor.from_pretrained(
          model_args.feature_extractor_name
          or model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
  
      model = AutoModel.from_pretrained(
          model_args.model_name_or_path,
          cache_dir=model_args.cache_dir,
          revision=model_args.model_revision,
          use_auth_token=True if model_args.use_auth_token else None,
      )
      config = model.config
  
      def _freeze_params(module):
          for param in module.parameters():
              param.requires_grad = False
  
      if model_args.freeze_vision_model:
          _freeze_params(model.vision_model)
  
      if model_args.freeze_text_model:
          _freeze_params(model.text_model)
  
      # set seed for torch dataloaders
      set_seed(training_args.seed)
  
      # Preprocessing the datasets.
      # We need to tokenize inputs and targets.
      if training_args.do_train:
          column_names = dataset["train"].column_names
      elif training_args.do_eval:
          column_names = dataset["validation"].column_names
      elif training_args.do_predict:
          column_names = dataset["test"].column_names
      else:
          logger.info(
              "There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`."
          )
          return
  
      # 6. Get the column names for input/target.
      dataset_columns = dataset_name_mapping.get(
          data_args.dataset_name, None
      )
      if data_args.image_column is None:
          image_column = (
              dataset_columns[0]
              if dataset_columns is not None
              else column_names[0]
          )
      else:
          image_column = data_args.image_column
          if image_column not in column_names:
              raise ValueError(
                  f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}"
              )
      if data_args.caption_column is None:
          caption_column = (
              dataset_columns[1]
              if dataset_columns is not None
              else column_names[1]
          )
      else:
          caption_column = data_args.caption_column
          if caption_column not in column_names:
              raise ValueError(
                  f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}"
              )
  
      # 7. Preprocessing the datasets.
      # Initialize torchvision transforms and jit it for faster processing.
      image_transformations = Transform(
          config.vision_config.image_size,
          feature_extractor.image_mean,
          feature_extractor.image_std,
      )
      image_transformations = torch.jit.script(image_transformations)
  
      # Preprocessing the datasets.
      # We need to tokenize input captions and transform the images.
      def tokenize_captions(examples):
          captions = [caption for caption in examples[caption_column]]
          text_inputs = tokenizer(
              captions,
              max_length=data_args.max_seq_length,
              padding="max_length",
              truncation=True,
          )
          examples["input_ids"] = text_inputs.input_ids
          examples["attention_mask"] = text_inputs.attention_mask
          return examples
  
      def transform_images(examples):
          images = [
              read_image(image_file, mode=ImageReadMode.RGB)
              for image_file in examples[image_column]
          ]
          examples["pixel_values"] = [
              image_transformations(image) for image in images
          ]
          return examples
  
      def filter_corrupt_images(examples):
          """remove problematic images"""
          valid_images = []
          for image_file in examples[image_column]:
              try:
                  Image.open(image_file)
                  valid_images.append(True)
              except Exception:
                  valid_images.append(False)
          return valid_images
  
      if training_args.do_train:
          if "train" not in dataset:
              raise ValueError("--do_train requires a train dataset")
          train_dataset = dataset["train"]
          if data_args.max_train_samples is not None:
              max_train_samples = min(
                  len(train_dataset), data_args.max_train_samples
              )
              train_dataset = train_dataset.select(
                  range(max_train_samples)
              )
  
          train_dataset = train_dataset.filter(
              filter_corrupt_images,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
          )
          train_dataset = train_dataset.map(
              function=tokenize_captions,
              batched=True,
              remove_columns=[
                  col for col in column_names if col != image_column
              ],
              num_proc=data_args.preprocessing_num_workers,
              load_from_cache_file=not data_args.overwrite_cache,
              desc="Running tokenizer on train dataset",
          )
  
          # Transform images on the fly as doing it on the whole dataset takes too much time.
          train_dataset.set_transform(transform_images)
  
      if training_args.do_eval:
          if "validation" not in dataset:
              raise ValueError("--do_eval requires a train validation")
          eval_dataset = dataset["validation"]
          if data_args.max_eval_samples is not None:
              max_eval_samples = min(
                  len(eval_dataset), data_args.max_eval_samples
              )
              eval_dataset = eval_dataset.select(
                  range(max_eval_samples)
              )
  
          eval_dataset = eval_dataset.filter(
              filter_corrupt_images,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
          )
          eval_dataset = eval_dataset.map(
              function=tokenize_captions,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              remove_columns=[
                  col for col in column_names if col != image_column
              ],
              load_from_cache_file=not data_args.overwrite_cache,
              desc="Running tokenizer on validation dataset",
          )
  
          # Transform images on the fly as doing it on the whole dataset takes too much time.
          eval_dataset.set_transform(transform_images)
  
      if training_args.do_predict:
          if "test" not in dataset:
              raise ValueError("--do_predict requires a test dataset")
          test_dataset = dataset["test"]
          if data_args.max_eval_samples is not None:
              max_eval_samples = min(
                  len(test_dataset), data_args.max_eval_samples
              )
              test_dataset = test_dataset.select(
                  range(max_eval_samples)
              )
  
          test_dataset = test_dataset.filter(
              filter_corrupt_images,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
          )
          test_dataset = test_dataset.map(
              function=tokenize_captions,
              batched=True,
              num_proc=data_args.preprocessing_num_workers,
              remove_columns=[
                  col for col in column_names if col != image_column
              ],
              load_from_cache_file=not data_args.overwrite_cache,
              desc="Running tokenizer on test dataset",
          )
  
          # Transform images on the fly as doing it on the whole dataset takes too much time.
          test_dataset.set_transform(transform_images)
  
      # 8. Initalize our trainer
      trainer = Trainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset
          if training_args.do_train
          else None,
          eval_dataset=eval_dataset if training_args.do_eval else None,
          data_collator=collate_fn,
      )
  
      # 9. 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()
  
      # 10. Evaluation
      if training_args.do_eval:
          metrics = trainer.evaluate()
          trainer.log_metrics("eval", metrics)
          trainer.save_metrics("eval", metrics)
  
      # 11. Write Training Stats and push to hub.
      kwargs = {
          "finetuned_from": model_args.model_name_or_path,
          "tasks": "contrastive-image-text-modeling",
      }
      if data_args.dataset_name is not None:
          kwargs["dataset_tags"] = data_args.dataset_name
          if data_args.dataset_config_name is not None:
              kwargs["dataset_args"] = data_args.dataset_config_name
              kwargs[
                  "dataset"
              ] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
          else:
              kwargs["dataset"] = data_args.dataset_name
  
      if training_args.push_to_hub:
          trainer.push_to_hub(**kwargs)
      else:
          trainer.create_model_card(**kwargs)
  
imports: import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import Optional
  import torch
  from datasets import load_dataset
  from PIL import Image
  from torchvision.io import ImageReadMode, read_image
  from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
  from torchvision.transforms.functional import InterpolationMode
  import transformers
  from transformers import AutoFeatureExtractor, AutoModel, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, set_seed
  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: examples, examples, valid_images