




"""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
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 		_snake_case	(   _snake_case				:   str				)       ->   List[Any]:






      '''simple docstring'''


      with open(_snake_case							,    'rb'				) as f:
            _A     =  Image.open(_snake_case				)
            return im.convert('RGB'				)





@dataclass
class 							lowercase_			:



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

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






def 		_snake_case	(   _snake_case				:   str				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  torch.stack([example['pixel_values'] for example in examples]				)
      _A     =  torch.tensor([example['labels'] for example in examples]				)
      return {"pixel_values": pixel_values, "labels": labels}






def 		_snake_case	(   )       ->   str:






      '''simple docstring'''


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

      _A     =  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.
      _A     =  None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
            _A     =  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:
            _A     =  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:
            _A     =  {}
            if data_args.train_dir is not None:
                  _A     =  os.path.join(data_args.train_dir							,    '**'				)
            if data_args.validation_dir is not None:
                  _A     =  os.path.join(data_args.validation_dir							,    '**'				)
            _A     =  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.
      _A     =  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:
            _A     =  dataset['train'].train_test_split(data_args.train_val_split				)
            _A     =  split['train']
            _A     =  split['test']

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

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

      _A     =  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							,    )
      _A     =  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							,    )
      _A     =  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:
            _A     =  image_processor.size['shortest_edge']
      else:
            _A     =  (image_processor.size['height'], image_processor.size['width'])
      _A     =  Normalize(mean=image_processor.image_mean							,    std=image_processor.image_std				)
      _A     =  Compose(
          [
              RandomResizedCrop(_snake_case				),
              RandomHorizontalFlip(),
              ToTensor(),
              normalize,
          ]				)
      _A     =  Compose(
          [
              Resize(_snake_case				),
              CenterCrop(_snake_case				),
              ToTensor(),
              normalize,
          ]				)

      def train_transforms(_snake_case				:   Optional[int]				):
            _A     =  [
                _train_transforms(pil_img.convert('RGB'				)				) for pil_img in example_batch['image']
            ]
            return example_batch

      def val_transforms(_snake_case				:   List[str]				):
            _A     =  [_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:
                  _A     =  (
                      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:
                  _A     =  (
                      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
      _A     =  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:
            _A     =  None
            if training_args.resume_from_checkpoint is not None:
                  _A     =  training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  _A     =  last_checkpoint
            _A     =  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:
            _A     =  trainer.evaluate()
            trainer.log_metrics('eval'							,    _snake_case				)
            trainer.save_metrics('eval'							,    _snake_case				)

      # Write model card and (optionally) push to hub
      _A     =  {
          '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 typing import TYPE_CHECKING

from ..utils import _LazyModule


a							      =     {
    '''config''': [
        '''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
        '''OnnxConfig''',
        '''OnnxConfigWithPast''',
        '''OnnxSeq2SeqConfigWithPast''',
        '''PatchingSpec''',
    ],
    '''convert''': ['''export''', '''validate_model_outputs'''],
    '''features''': ['''FeaturesManager'''],
    '''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       a							      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

