






from collections.abc import Iterable
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE   =  TypeVar("_T")






class 					SCREAMING_SNAKE_CASE_  (       Generic[_T] ):
       """simple docstring"""


       def __init__(				self	:Union[str, Any],      snake_case	:Iterable[_T] | None = None):



          """simple docstring"""

          _lowercase              =list(iterable or [])
          _lowercase              =[]





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



          """simple docstring"""

          return len(self._stacka) + len(self._stacka)





       def __repr__(				self	:str):



          """simple docstring"""

          return f'''Queue({tuple(self._stacka[::-1] + self._stacka)})'''





       def 				UpperCamelCase__						(				self	:Dict,      snake_case	:_T):



          """simple docstring"""

          self._stacka.append(snake_case)





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self._stacka.pop
          _lowercase              =self._stacka.append

          if not self._stacka:
             while self._stacka:
                stacka_append(stacka_pop())

          if not self._stacka:
             raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()










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()


