



'''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					:       Tuple									=     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[str]									=     list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
A					:       List[Any]									=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 						_a       (    lowerCamelCase_				):



   with open(UpperCAmelCase__	,		'''rb'''				) as f:
      snake_case       :				Optional[int]		    =Image.open(UpperCAmelCase__				)
      return im.convert('''RGB'''				)





@dataclass
class 				lowerCAmelCase_						:
    __UpperCAmelCase				=			field(
        default=UpperCAmelCase_    ,     metadata={
            'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
        }    ,     )
    __UpperCAmelCase				=			field(
        default=UpperCAmelCase_    ,     metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}		)
    __UpperCAmelCase				=			field(default=UpperCAmelCase_    ,     metadata={'help': 'A folder containing the training data.'}		)
    __UpperCAmelCase				=			field(default=UpperCAmelCase_    ,     metadata={'help': 'A folder containing the validation data.'}		)
    __UpperCAmelCase				=			field(
        default=0.1_5    ,     metadata={'help': 'Percent to split off of train for validation.'}		)
    __UpperCAmelCase				=			field(
        default=UpperCAmelCase_    ,     metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of training examples to this '
                'value if set.'
            )
        }    ,     )
    __UpperCAmelCase				=			field(
        default=UpperCAmelCase_    ,     metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                'value if set.'
            )
        }    ,     )





    def 	__snake_case  (							self							:							List[Any]      ):



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




def 						_a       (    lowerCamelCase_				):
   snake_case       :				Optional[Any]		    =torch.stack([example['''pixel_values'''] for example in examples]				)
   snake_case       :				Union[str, Any]		    =torch.tensor([example['''labels'''] for example in examples]				)
   return {"pixel_values": pixel_values, "labels": labels}
def 						_a       (    ):
   snake_case       :				Dict		    =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.
      snake_case    ,							snake_case    ,							snake_case       :				Union[str, Any]		    =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
   else:
      snake_case    ,							snake_case    ,							snake_case       :				List[Any]		    =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()

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

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

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

   snake_case       :				List[Any]		    =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	,		)
   snake_case       :				str		    =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	,		)
   snake_case       :				List[str]		    =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:
      snake_case       :				Union[str, Any]		    =image_processor.size['''shortest_edge''']
   else:
      snake_case       :				str		    =(image_processor.size['''height'''], image_processor.size['''width'''])
   snake_case       :				List[Any]		    =Normalize(mean=image_processor.image_mean	,		std=image_processor.image_std				)
   snake_case       :				str		    =Compose(
       [
           RandomResizedCrop(UpperCAmelCase__				),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ]				)
   snake_case       :				Tuple		    =Compose(
       [
           Resize(UpperCAmelCase__				),
           CenterCrop(UpperCAmelCase__				),
           ToTensor(),
           normalize,
       ]				)

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

   def val_transforms(lowerCamelCase_				):
      snake_case       :				List[Any]		    =[_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:
         snake_case       :				str		    =(
             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:
         snake_case       :				str		    =(
             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
   snake_case       :				List[str]		    =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:
      snake_case       :				Optional[int]		    =None
      if training_args.resume_from_checkpoint is not None:
         snake_case       :				Union[str, Any]		    =training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         snake_case       :				List[Any]		    =last_checkpoint
      snake_case       :				Optional[int]		    =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:
      snake_case       :				Dict		    =trainer.evaluate()
      trainer.log_metrics('''eval'''	,		UpperCAmelCase__				)
      trainer.save_metrics('''eval'''	,		UpperCAmelCase__				)

   # Write model card and (optionally) push to hub
   snake_case       :				str		    ={
       '''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()










from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
     import tensorflow as tf


a_    :  Any						  =		logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      [
         'no_inference',
         'no_cuda',
         'no_tpu',
         'no_speed',
         'no_memory',
         'no_env_print',
         'no_multi_process',
     ]







     def __init__(self					,      **__a     ):






           '''simple docstring'''



           for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                       lowerCamelCase             =		deprecated_arg[3:]
                       lowerCamelCase             =		not kwargs.pop(__a     )
                       logger.warning(
                           F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                           F""" {positive_arg}={kwargs[positive_arg]}"""     )
           lowerCamelCase             =		kwargs.pop("tpu_name"					,      self.tpu_name     )
           lowerCamelCase             =		kwargs.pop("device_idx"					,      self.device_idx     )
           lowerCamelCase             =		kwargs.pop("eager_mode"					,      self.eager_mode     )
           lowerCamelCase             =		kwargs.pop("use_xla"					,      self.use_xla     )
           super().__init__(**__a     )

     _A      =      field(
         default=UpperCAmelCase_							,     metadata={'help': 'Name of TPU'}							,     )
     _A      =      field(
         default=0							,     metadata={'help': 'CPU / GPU device index. Defaults to 0.'}							,     )
     _A      =      field(default=UpperCAmelCase_							,     metadata={'help': 'Benchmark models in eager model.'})
     _A      =      field(
         default=UpperCAmelCase_							,     metadata={
             'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
         }							,     )







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           lowerCamelCase             =		None
           if self.tpu:
                 try:
                       if self.tpu_name:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
                       else:
                             lowerCamelCase             =		tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                       lowerCamelCase             =		None
           return tpu







     @cached_property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.is_tpu:
                 tf.config.experimental_connect_to_cluster(self._setup_tpu     )
                 tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

                 lowerCamelCase             =		tf.distribute.TPUStrategy(self._setup_tpu     )
           else:
                 # currently no multi gpu is allowed
                 if self.is_gpu:
                       # TODO: Currently only single GPU is supported
                       tf.config.set_visible_devices(self.gpu_list[self.device_idx]					,      "GPU"     )
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""     )
                 else:
                       tf.config.set_visible_devices([]					,      "GPU"     )  # disable GPU
                       lowerCamelCase             =		tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""     )

           return strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_tpu is not None







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return self._setup_strategy







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           return tf.config.list_physical_devices("GPU"     )







     @property
     def     _a   (self     ):






           '''simple docstring'''



           requires_backends(self					,      ["tf"]     )
           if self.cuda:
                 return len(self.gpu_list     )
           return 0







     @property
     def     _a   (self     ):






           '''simple docstring'''



           return self.n_gpu > 0