






"""simple docstring"""





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
_SCREAMING_SNAKE_CASE					:				List[str]					    =						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''')

_SCREAMING_SNAKE_CASE					:				Any					    =						list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				str:
  with open(UpperCAmelCase_		,				'rb' ) as f:
    lowerCamelCase_         =						Image.open(UpperCAmelCase_ )
    return im.convert('RGB' )






@dataclass
class      a						:
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(
        default=_UpperCAmelCase  ,      metadata={
            """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
        }  ,      )
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(
        default=_UpperCAmelCase  ,      metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(default=_UpperCAmelCase  ,      metadata={"""help""": """A folder containing the training data."""}		)
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(default=_UpperCAmelCase  ,      metadata={"""help""": """A folder containing the validation data."""}		)
    SCREAMING_SNAKE_CASE						:     Optional[float]						= field(
        default=0.15  ,      metadata={"""help""": """Percent to split off of train for validation."""}		)
    SCREAMING_SNAKE_CASE						:     Optional[int]						= field(
        default=_UpperCAmelCase  ,      metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }  ,      )
    SCREAMING_SNAKE_CASE						:     Optional[int]						= 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     :							Optional[Any]					)     ->		Tuple:
      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      a						:
    SCREAMING_SNAKE_CASE						:     str						= field(
        default="""google/vit-base-patch16-224-in21k"""  ,      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  ,      )
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(
        default=_UpperCAmelCase  ,      metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCAmelCase		)}  ,      )
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(
        default=_UpperCAmelCase  ,      metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
    SCREAMING_SNAKE_CASE						:     Optional[str]						= field(
        default=_UpperCAmelCase  ,      metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}		)
    SCREAMING_SNAKE_CASE						:     str						= field(
        default="""main"""  ,      metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}  ,      )
    SCREAMING_SNAKE_CASE						:     str						= field(default=_UpperCAmelCase  ,      metadata={"""help""": """Name or path of preprocessor config."""}		)
    SCREAMING_SNAKE_CASE						:     bool						= field(
        default=_UpperCAmelCase  ,      metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }  ,      )
    SCREAMING_SNAKE_CASE						:     bool						= field(
        default=_UpperCAmelCase  ,      metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}  ,      )







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				Dict:
  lowerCamelCase_         =						torch.stack([example['pixel_values'] for example in examples] )
  lowerCamelCase_         =						torch.tensor([example['labels'] for example in examples] )
  return {"pixel_values": pixel_values, "labels": labels}







def      lowerCamelCase__       (							)      ->				int:
  # 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.

  lowerCamelCase_         =						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.
    lowerCamelCase_         =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
  else:
    lowerCamelCase_         =						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'		,				UpperCAmelCase_		,				UpperCAmelCase_ )

  # 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()

  lowerCamelCase_         =						training_args.get_process_log_level()
  logger.setLevel(UpperCAmelCase_ )
  transformers.utils.logging.set_verbosity(UpperCAmelCase_ )
  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.
  lowerCamelCase_         =						None
  if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
    lowerCamelCase_         =						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:
    lowerCamelCase_         =						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:
    lowerCamelCase_         =						{}
    if data_args.train_dir is not None:
      lowerCamelCase_         =						os.path.join(data_args.train_dir		,				'**' )
    if data_args.validation_dir is not None:
      lowerCamelCase_         =						os.path.join(data_args.validation_dir		,				'**' )
    lowerCamelCase_         =						load_dataset(
        'imagefolder'		,				data_files=UpperCAmelCase_		,				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.
  lowerCamelCase_         =						None if 'validation' in dataset.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split		,				UpperCAmelCase_ ) and data_args.train_val_split > 0.0:
    lowerCamelCase_         =						dataset['train'].train_test_split(data_args.train_val_split )
    lowerCamelCase_         =						split['train']
    lowerCamelCase_         =						split['test']

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

  # Load the accuracy metric from the datasets package
  lowerCamelCase_         =						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(_lowerCamelCase       :		Any ):
    return metric.compute(predictions=np.argmax(p.predictions		,				axis=1 )		,				references=p.label_ids )

  lowerCamelCase_         =						AutoConfig.from_pretrained(
      model_args.config_name or model_args.model_name_or_path		,				num_labels=len(UpperCAmelCase_ )		,				labelaid=UpperCAmelCase_		,				idalabel=UpperCAmelCase_		,				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		,				)
  lowerCamelCase_         =						AutoModelForImageClassification.from_pretrained(
      model_args.model_name_or_path		,				from_tf=bool('.ckpt' in model_args.model_name_or_path )		,				config=UpperCAmelCase_		,				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		,				)
  lowerCamelCase_         =						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:
    lowerCamelCase_         =						image_processor.size['shortest_edge']
  else:
    lowerCamelCase_         =						(image_processor.size['height'], image_processor.size['width'])
  lowerCamelCase_         =						Normalize(mean=image_processor.image_mean		,				std=image_processor.image_std )
  lowerCamelCase_         =						Compose(
      [
          RandomResizedCrop(UpperCAmelCase_ ),
          RandomHorizontalFlip(),
          ToTensor(),
          normalize,
      ] )
  lowerCamelCase_         =						Compose(
      [
          Resize(UpperCAmelCase_ ),
          CenterCrop(UpperCAmelCase_ ),
          ToTensor(),
          normalize,
      ] )

  def train_transforms(_lowerCamelCase       :		List[Any] ):
    lowerCamelCase_         =						[
        _train_transforms(pil_img.convert('RGB' ) ) for pil_img in example_batch['image']
    ]
    return example_batch

  def val_transforms(_lowerCamelCase       :		int ):
    lowerCamelCase_         =						[_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:
      lowerCamelCase_         =						(
          dataset['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
      )
    # Set the training transforms
    dataset["train"].set_transform(UpperCAmelCase_ )

  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:
      lowerCamelCase_         =						(
          dataset['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
      )
    # Set the validation transforms
    dataset["validation"].set_transform(UpperCAmelCase_ )

  # Initalize our trainer
  lowerCamelCase_         =						Trainer(
      model=UpperCAmelCase_		,				args=UpperCAmelCase_		,				train_dataset=dataset['train'] if training_args.do_train else None		,				eval_dataset=dataset['validation'] if training_args.do_eval else None		,				compute_metrics=UpperCAmelCase_		,				tokenizer=UpperCAmelCase_		,				data_collator=UpperCAmelCase_		,				)

  # Training
  if training_args.do_train:
    lowerCamelCase_         =						None
    if training_args.resume_from_checkpoint is not None:
      lowerCamelCase_         =						training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      lowerCamelCase_         =						last_checkpoint
    lowerCamelCase_         =						trainer.train(resume_from_checkpoint=UpperCAmelCase_ )
    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:
    lowerCamelCase_         =						trainer.evaluate()
    trainer.log_metrics('eval'		,				UpperCAmelCase_ )
    trainer.save_metrics('eval'		,				UpperCAmelCase_ )

  # Write model card and (optionally) push to hub
  lowerCamelCase_         =						{
      '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(**UpperCAmelCase_ )
  else:
    trainer.create_model_card(**UpperCAmelCase_ )


if __name__ == "__main__":
  main()








'''simple docstring'''



from numpy import exp, pi, sqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				float = 0.0      ,    UpperCAmelCase_    :				float = 1.0							)     ->		int:
     return 1 / sqrt(2 * pi * sigma**2							) * exp(-((x - mu) ** 2) / (2 * sigma**2)							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






