




'''simple docstring'''





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase       =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__UpperCAmelCase       =		"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"
class 		SCREAMING_SNAKE_CASE			(     unittest.TestCase							):




				'''simple docstring'''





				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Tuple        =       tempfile.mkdtemp()
							os.makedirs(os.path.join(self.transformer_dir  ,						'models/bert/'				)				)
							snake_case:       str        =       self.transformer_dir
							shutil.copy(
							    os.path.join(SCREAMING_SNAKE_CASE__  ,						'src/transformers/models/bert/modeling_bert.py'				)  ,						os.path.join(self.transformer_dir  ,						'models/bert/modeling_bert.py'				)  ,						)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       str        =       'src/transformers'
							shutil.rmtree(self.transformer_dir				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__=None				):
							'''simple docstring'''







							snake_case:       List[str]        =       comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
							if overwrite_result is not None:
										snake_case:       Optional[Any]        =       comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
							snake_case:       Tuple        =       black.Mode(target_versions={black.TargetVersion.PYaa}  ,						line_length=1_19				)
							snake_case:       List[Any]        =       black.format_str(SCREAMING_SNAKE_CASE__  ,						mode=SCREAMING_SNAKE_CASE__				)
							snake_case:       int        =       os.path.join(self.transformer_dir  ,						'new_code.py'				)
							with open(SCREAMING_SNAKE_CASE__  ,						'w'  ,						newline='\n'				) as f:
										f.write(SCREAMING_SNAKE_CASE__				)
							if overwrite_result is None:
										self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__				)				) == 0				)
							else:
										check_copies.is_copy_consistent(f.name  ,						overwrite=SCREAMING_SNAKE_CASE__				)
										with open(SCREAMING_SNAKE_CASE__  ,						'r'				) as f:
													self.assertTrue(f.read()  ,						SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       List[str]        =       check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'				)
							self.assertEqual(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'  ,						'BertLMPredictionHead'  ,						REFERENCE_CODE + '\n'  ,						)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'  ,						'BertLMPredictionHead'  ,						SCREAMING_SNAKE_CASE__  ,						)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'  ,						'TestModelLMPredictionHead'  ,						re.sub('Bert'  ,						'TestModel'  ,						SCREAMING_SNAKE_CASE__				)  ,						)

							# Copy consistency with a really long name
							snake_case:       int        =       'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""  ,						F"""{long_class_name}LMPredictionHead"""  ,						re.sub('Bert'  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)  ,						)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'  ,						'TestModelLMPredictionHead'  ,						SCREAMING_SNAKE_CASE__  ,						overwrite_result=re.sub('Bert'  ,						'TestModel'  ,						SCREAMING_SNAKE_CASE__				)  ,						)







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							snake_case:       Optional[Any]        =       check_copies.LOCALIZED_READMES['README_zh-hans.md']

							snake_case:       Dict        =       (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
							    ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
							    ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
							    ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
							    ' Luong, Quoc V. Le, Christopher D. Manning.'
							)
							snake_case:       Optional[Any]        =       (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							snake_case:       Optional[int]        =       (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
							    ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
							    ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
							    ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
							    ' method has been applied to compress GPT2 into'
							    ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
							    ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
							    ' Multilingual BERT into'
							    ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
							    ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
							    ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
							    ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
							    ' Christopher D. Manning 发布。\n'
							)

							snake_case    ,						snake_case:       Any        =       check_copies.convert_to_localized_md(
							    SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						localized_readme['format_model_list']				)

							self.assertFalse(SCREAMING_SNAKE_CASE__				)
							self.assertEqual(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)

							snake_case    ,						snake_case:       List[str]        =       check_copies.convert_to_localized_md(
							    SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						localized_readme['format_model_list']				)

							# Check whether the number of models is equal to README.md after conversion.
							self.assertTrue(SCREAMING_SNAKE_CASE__				)

							snake_case:       str        =       (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
							    ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
							    ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
							    ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
							)
							snake_case:       int        =       (
							    '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
							    ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)
							snake_case:       Optional[int]        =       (
							    '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
							    ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
							    ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
							    ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
							)

							snake_case    ,						snake_case:       List[str]        =       check_copies.convert_to_localized_md(
							    SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						localized_readme['format_model_list']				)

							# Check if the model link is synchronized.
							self.assertEqual(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)







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





def        _snake_case   (_snake_case				:       str)					->	List[str]:
   with open(_snake_case      ,    'rb') as f:
      _lowercase              =Image.open(_snake_case)
      return im.convert('RGB')






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __lowerCAmelCase    :		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).'''
           }  ,						)
       __lowerCAmelCase    :		Optional[str]					=field(
           default=_a  ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
       __lowerCAmelCase    :		Optional[str]					=field(default=_a  ,						metadata={'''help''': '''A folder containing the training data.'''} )
       __lowerCAmelCase    :		Optional[str]					=field(default=_a  ,						metadata={'''help''': '''A folder containing the validation data.'''} )
       __lowerCAmelCase    :		Optional[float]					=field(
           default=0.15  ,						metadata={'''help''': '''Percent to split off of train for validation.'''} )
       __lowerCAmelCase    :		Optional[int]					=field(
           default=_a  ,						metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                   '''value if set.'''
               )
           }  ,						)
       __lowerCAmelCase    :		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	:List[str]):



          """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 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


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





def        _snake_case   (_snake_case				:       Any)					->	Dict:
   _lowercase              =torch.stack([example['pixel_values'] for example in examples])
   _lowercase              =torch.tensor([example['labels'] for example in examples])
   return {"pixel_values": pixel_values, "labels": labels}





def        _snake_case   ()					->	Any:
   # 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.

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

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

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

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

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

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

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

   # Write model card and (optionally) push to hub
   _lowercase              ={
       '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()


