




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    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
A_      			=       logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-classification/requirements.txt''')

A_      			=       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A_      			=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def       UpperCAmelCase__ (snake_case__							:      str					):
  """simple docstring"""

  with open(snake_case_	,     """rb"""					) as f:
    _snake_case							:	List[Any]			=						Image.open(snake_case_					)
    return im.convert("""RGB"""					)






@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={
         "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
     } ,    )
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
 lowercase__				      =      field(default=UpperCamelCase_ ,    metadata={"help": "A folder containing the training data."}				)
 lowercase__				      =      field(default=UpperCamelCase_ ,    metadata={"help": "A folder containing the validation data."}				)
 lowercase__				      =      field(
     default=0.15 ,    metadata={"help": "Percent to split off of train for validation."}				)
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     } ,    )
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
             "value if set."
         )
     } ,    )


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
     raise ValueError(
         """You must specify either a dataset name from the hub or a train and/or validation directory."""	)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="google/vit-base-patch16-224-in21k" ,    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ,    )
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(UpperCamelCase_				)} ,    )
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}				)
 lowercase__				      =      field(
     default="main" ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,    )
 lowercase__				      =      field(default=UpperCamelCase_ ,    metadata={"help": "Name or path of preprocessor config."}				)
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={
         "help": (
             "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
             "with private models)."
         )
     } ,    )
 lowercase__				      =      field(
     default=UpperCamelCase_ ,    metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} ,    )







def       UpperCAmelCase__ (snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	List[str]			=						torch.stack([example["""pixel_values"""] for example in examples]					)
  _snake_case							:	List[Any]			=						torch.tensor([example["""labels"""] for example in examples]					)
  return {"pixel_values": pixel_values, "labels": labels}


def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Tuple			=						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.
    _snake_case							:	Optional[int]			=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
  else:
    _snake_case							:	Optional[int]			=						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_image_classification"""	,     snake_case_	,     snake_case_					)

  # 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()

  _snake_case							:	Tuple			=						training_args.get_process_log_level()
  logger.setLevel(snake_case_					)
  transformers.utils.logging.set_verbosity(snake_case_					)
  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.fpaa}"					)
  logger.info(F"Training/evaluation parameters {training_args}"					)

  # Detecting last checkpoint.
  _snake_case							:	int			=						None
  if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
    _snake_case							:	int			=						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."""					)

       # Set seed before initializing model.
  set_seed(training_args.seed					)

  # Initialize our dataset and prepare it for the 'image-classification' task.
  if data_args.dataset_name is not None:
    _snake_case							:	List[str]			=						load_dataset(
        data_args.dataset_name	,     data_args.dataset_config_name	,     cache_dir=model_args.cache_dir	,     task="""image-classification"""	,     use_auth_token=True if model_args.use_auth_token else None	,     )
  else:
    _snake_case							:	Any			=						{}
    if data_args.train_dir is not None:
      _snake_case							:	str			=						os.path.join(data_args.train_dir	,     """**"""					)
    if data_args.validation_dir is not None:
      _snake_case							:	Tuple			=						os.path.join(data_args.validation_dir	,     """**"""					)
    _snake_case							:	int			=						load_dataset(
        """imagefolder"""	,     data_files=snake_case_	,     cache_dir=model_args.cache_dir	,     task="""image-classification"""	,     )

  # If we don't have a validation split, split off a percentage of train as validation.
  _snake_case							:	Tuple			=						None if '''validation''' in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split	,     snake_case_					) and data_args.train_val_split > 0.0:
    _snake_case							:	Optional[int]			=						dataset['''train'''].train_test_split(data_args.train_val_split					)
    _snake_case							:	List[Any]			=						split['''train''']
    _snake_case							:	List[Any]			=						split['''test''']

  # Prepare label mappings.
  # We'll include these in the model's config to get human readable labels in the Inference API.
  _snake_case							:	int			=						dataset['''train'''].features['''labels'''].names
  _snake_case							:	List[str]			=						{}, {}
  for i, label in enumerate(snake_case_					):
    _snake_case							:	Optional[Any]			=						str(snake_case_					)
    _snake_case							:	Optional[int]			=						label

  # Load the accuracy metric from the datasets package
  _snake_case							:	List[Any]			=						evaluate.load("""accuracy"""					)

  # 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.
  def compute_metrics(snake_case__							:      Tuple					):
    return metric.compute(predictions=np.argmax(p.predictions	,     axis=1					)	,     references=p.label_ids					)

  _snake_case							:	int			=						AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path	,     num_labels=len(snake_case_					)	,     labelaid=snake_case_	,     idalabel=snake_case_	,     finetuning_task="""image-classification"""	,     cache_dir=model_args.cache_dir	,     revision=model_args.model_revision	,     use_auth_token=True if model_args.use_auth_token else None	,     )
  _snake_case							:	int			=						AutoModelForImageClassification.from_pretrained(
      model_args.model_name_or_path	,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path					)	,     config=snake_case_	,     cache_dir=model_args.cache_dir	,     revision=model_args.model_revision	,     use_auth_token=True if model_args.use_auth_token else None	,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,     )
  _snake_case							:	Optional[int]			=						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	,     use_auth_token=True if model_args.use_auth_token else None	,     )

  # Define torchvision transforms to be applied to each image.
  if "shortest_edge" in image_processor.size:
    _snake_case							:	Optional[Any]			=						image_processor.size['''shortest_edge''']
  else:
    _snake_case							:	Optional[Any]			=						(image_processor.size['''height'''], image_processor.size['''width'''])
  _snake_case							:	int			=						Normalize(mean=image_processor.image_mean	,     std=image_processor.image_std					)
  _snake_case							:	Tuple			=						Compose(
      [
          RandomResizedCrop(snake_case_					),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ]					)
  _snake_case							:	Union[str, Any]			=						Compose(
      [
          Resize(snake_case_					),
          CenterCrop(snake_case_					),
          ToTensor(),
          normalize,
      ]					)

  def train_transforms(snake_case__							:      Any					):
    _snake_case							:	Any			=						[
        _train_transforms(pil_img.convert("""RGB"""					)					) for pil_img in example_batch['''image''']
    ]
    return example_batch

  def val_transforms(snake_case__							:      int					):
    _snake_case							:	int			=						[_val_transforms(pil_img.convert("""RGB"""					)					) for pil_img in example_batch['''image''']]
    return example_batch

  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:
      _snake_case							:	Optional[Any]			=						(
          dataset['''train'''].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
      )
    # Set the training transforms
    dataset["train"].set_transform(snake_case_					)

  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:
      _snake_case							:	List[str]			=						(
          dataset['''validation'''].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
      )
    # Set the validation transforms
    dataset["validation"].set_transform(snake_case_					)

  # Initalize our trainer
  _snake_case							:	Optional[Any]			=						Trainer(
      model=snake_case_	,     args=snake_case_	,     train_dataset=dataset["""train"""] if training_args.do_train else None	,     eval_dataset=dataset["""validation"""] if training_args.do_eval else None	,     compute_metrics=snake_case_	,     tokenizer=snake_case_	,     data_collator=snake_case_	,     )

  # Training
  if training_args.do_train:
    _snake_case							:	List[str]			=						None
    if training_args.resume_from_checkpoint is not None:
      _snake_case							:	str			=						training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _snake_case							:	List[Any]			=						last_checkpoint
    _snake_case							:	str			=						trainer.train(resume_from_checkpoint=snake_case_					)
    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:
    _snake_case							:	Optional[int]			=						trainer.evaluate()
    trainer.log_metrics("""eval"""	,     snake_case_					)
    trainer.save_metrics("""eval"""	,     snake_case_					)

  # Write model card and (optionally) push to hub
  _snake_case							:	List[Any]			=						{
      '''finetuned_from''': model_args.model_name_or_path,
      '''tasks''': '''image-classification''',
      '''dataset''': data_args.dataset_name,
      '''tags''': ['''image-classification''', '''vision'''],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**snake_case_					)
  else:
    trainer.create_model_card(**snake_case_					)


if __name__ == "__main__":
     main()








"""simple docstring"""


from ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


