


"""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
__snake_case		=       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''')

__snake_case		=       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__snake_case		=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def       A_   (   _lowerCAmelCase     :					str       ):






     """simple docstring"""

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


@dataclass
class     __lowerCamelCase  :

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


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          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     __lowerCamelCase  :

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







def       A_   (   _lowerCAmelCase     :					List[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       A_   (   ):






     """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''',     _lowerCAmelCase,     _lowerCAmelCase       )

     # 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(_lowerCAmelCase       )
     transformers.utils.logging.set_verbosity(_lowerCAmelCase       )
     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=_lowerCAmelCase,     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,     _lowerCAmelCase       ) 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(_lowerCAmelCase       ):
          _a    	=				str(_lowerCAmelCase       )
          _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(_lowerCAmelCase     :					Optional[int]       ):
          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(_lowerCAmelCase       ),     labelaid=_lowerCAmelCase,     idalabel=_lowerCAmelCase,     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=_lowerCAmelCase,     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(_lowerCAmelCase       ),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]       )
     _a    	=				Compose(
         [
             Resize(_lowerCAmelCase       ),
             CenterCrop(_lowerCAmelCase       ),
             ToTensor(),
             normalize,
         ]       )

     def train_transforms(_lowerCAmelCase     :					str       ):
          _a    	=				[
              _train_transforms(pil_img.convert('''RGB'''       )       ) for pil_img in example_batch['''image''']
          ]
          return example_batch

     def val_transforms(_lowerCAmelCase     :					Any       ):
          _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(_lowerCAmelCase       )

     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(_lowerCAmelCase       )

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

     # 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=_lowerCAmelCase       )
          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''',     _lowerCAmelCase       )
          trainer.save_metrics('''eval''',     _lowerCAmelCase       )

     # 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(**_lowerCAmelCase       )
     else:
          trainer.create_model_card(**_lowerCAmelCase       )


if __name__ == "__main__":
   main()



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case		=       {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ViTMAEForPreTraining''',
       '''ViTMAELayer''',
       '''ViTMAEModel''',
       '''ViTMAEPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''TFViTMAEForPreTraining''',
       '''TFViTMAEModel''',
       '''TFViTMAEPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

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