'''simple docstring'''





import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
 import jax.numpy as jnp






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __get__(       self    ,							lowercase__    ,							lowercase__=None )							-> Any:
         # See docs.python.org/3/howto/descriptor.html#properties
         if obj is None:
              return self
         if self.fget is None:
              raise AttributeError('unreadable attribute' )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       '__cached_' + self.fget.__name__
         SCREAMING_SNAKE_CASE					:			Tuple    	=       getattr(lowercase__    ,							lowercase__    ,							lowercase__ )
         if cached is None:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.fget(lowercase__ )
              setattr(lowercase__    ,							lowercase__    ,							lowercase__ )
         return cached






def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       val.lower()
     if val in {"y", "yes", "t", "true", "on", "1"}:
          return 1
     if val in {"n", "no", "f", "false", "off", "0"}:
          return 0
     raise ValueError(f"""invalid truth value {val!r}"""		)




def     __lowerCAmelCase						(       a_		)      ->	Dict:





     '''simple docstring'''





     if is_torch_fx_proxy(a_		):
          return True
     if is_torch_available():
          import torch

          if isinstance(a_       ,      torch.Tensor		):
               return True
     if is_tf_available():
          import tensorflow as tf

          if isinstance(a_       ,      tf.Tensor		):
               return True

     if is_flax_available():
          import jax.numpy as jnp
          from jax.core import Tracer

          if isinstance(a_       ,      (jnp.ndarray, Tracer)		):
               return True

     return isinstance(a_       ,      np.ndarray		)




def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     return isinstance(a_       ,      np.ndarray		)




def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     return _is_numpy(a_		)




def     __lowerCAmelCase						(       a_		)      ->	Dict:





     '''simple docstring'''





     import torch

     return isinstance(a_       ,      torch.Tensor		)




def     __lowerCAmelCase						(       a_		)      ->	Optional[int]:





     '''simple docstring'''





     return False if not is_torch_available() else _is_torch(a_		)




def     __lowerCAmelCase						(       a_		)      ->	int:





     '''simple docstring'''





     import torch

     return isinstance(a_       ,      torch.device		)




def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     return False if not is_torch_available() else _is_torch_device(a_		)




def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     import torch

     if isinstance(a_       ,      a_		):
          if hasattr(a_       ,      a_		):
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       getattr(a_       ,      a_		)
          else:
               return False
     return isinstance(a_       ,      torch.dtype		)




def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     return False if not is_torch_available() else _is_torch_dtype(a_		)




def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     import tensorflow as tf

     return isinstance(a_       ,      tf.Tensor		)




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





     '''simple docstring'''





     return False if not is_tf_available() else _is_tensorflow(a_		)




def     __lowerCAmelCase						(       a_		)      ->	Optional[Any]:





     '''simple docstring'''





     import tensorflow as tf

     # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
     if hasattr(a_       ,      'is_symbolic_tensor'		):
          return tf.is_symbolic_tensor(a_		)
     return type(a_		) == tf.Tensor




def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     return False if not is_tf_available() else _is_tf_symbolic_tensor(a_		)




def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     import jax.numpy as jnp  # noqa: F811

     return isinstance(a_       ,      jnp.ndarray		)




def     __lowerCAmelCase						(       a_		)      ->	Optional[int]:





     '''simple docstring'''





     return False if not is_flax_available() else _is_jax(a_		)




def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     if isinstance(a_       ,      (dict, UserDict)		):
          return {k: to_py_obj(a_		) for k, v in obj.items()}
     elif isinstance(a_       ,      (list, tuple)		):
          return [to_py_obj(a_		) for o in obj]
     elif is_tf_tensor(a_		):
          return obj.numpy().tolist()
     elif is_torch_tensor(a_		):
          return obj.detach().cpu().tolist()
     elif is_jax_tensor(a_		):
          return np.asarray(a_		).tolist()
     elif isinstance(a_       ,      (np.ndarray, np.number)		):  # tolist also works on 0d np arrays
          return obj.tolist()
     else:
          return obj




def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      (dict, UserDict)		):
          return {k: to_numpy(a_		) for k, v in obj.items()}
     elif isinstance(a_       ,      (list, tuple)		):
          return np.array(a_		)
     elif is_tf_tensor(a_		):
          return obj.numpy()
     elif is_torch_tensor(a_		):
          return obj.detach().cpu().numpy()
     elif is_jax_tensor(a_		):
          return np.asarray(a_		)
     else:
          return obj






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Any    	=       fields(self )

         # Safety and consistency checks
         if not len(lowercase__ ):
              raise ValueError(F"""{self.__class__.__name__} has no fields.""" )
         if not all(field.default is None for field in class_fields[1:] ):
              raise ValueError(F"""{self.__class__.__name__} should not have more than one required field.""" )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       getattr(self    ,							class_fields[0].name )
         SCREAMING_SNAKE_CASE					:			Dict    	=       all(getattr(self    ,							field.name ) is None for field in class_fields[1:] )

         if other_fields_are_none and not is_tensor(lowercase__ ):
              if isinstance(lowercase__    ,							lowercase__ ):
                   SCREAMING_SNAKE_CASE					:			int    	=       first_field.items()
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True
              else:
                   try:
                        SCREAMING_SNAKE_CASE					:			Any    	=       iter(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       True
                   except TypeError:
                        SCREAMING_SNAKE_CASE					:			int    	=       False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
              if first_field_iterator:
                   for idx, element in enumerate(lowercase__ ):
                        if (
                            not isinstance(lowercase__    ,							(list, tuple) )
                            or not len(lowercase__ ) == 2
                            or not isinstance(element[0]    ,							lowercase__ )
                        ):
                             if idx == 0:
                                  # If we do not have an iterator of key/values, set it as attribute
                                  SCREAMING_SNAKE_CASE					:			Tuple    	=       first_field
                             else:
                                  # If we have a mixed iterator, raise an error
                                  raise ValueError(
                                      F"""Cannot set key/value for {element}. It needs to be a tuple (key, value).""" )
                             break
                        setattr(self    ,							element[0]    ,							element[1] )
                        if element[1] is not None:
                             SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       element[1]
              elif first_field is not None:
                   SCREAMING_SNAKE_CASE					:			str    	=       first_field
         else:
              for field in class_fields:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       getattr(self    ,							field.name )
                   if v is not None:
                        SCREAMING_SNAKE_CASE					:			List[str]    	=       v






    def __delitem__(       self    ,							*lowercase__    ,							**lowercase__ )							-> Dict:
         raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.""" )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> str:
         raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance.""" )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> Optional[int]:
         raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance.""" )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> int:
         raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance.""" )






    def __getitem__(       self    ,							lowercase__ )							-> Optional[Any]:
         if isinstance(lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			List[str]    	=       dict(self.items() )
              return inner_dict[k]
         else:
              return self.to_tuple()[k]






    def __setattr__(       self    ,							lowercase__    ,							lowercase__ )							-> Optional[int]:
         if name in self.keys() and value is not None:
              # Don't call self.__setitem__ to avoid recursion errors
              super().__setitem__(lowercase__    ,							lowercase__ )
         super().__setattr__(lowercase__    ,							lowercase__ )






    def __setitem__(       self    ,							lowercase__    ,							lowercase__ )							-> Dict:
         # Will raise a KeyException if needed
         super().__setitem__(lowercase__    ,							lowercase__ )
         # Don't call self.__setattr__ to avoid recursion errors
         super().__setattr__(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple[Any]:
         return tuple(self[k] for k in self.keys() )






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    @classmethod
    def       _UpperCamelCase    (       cls    ,							lowercase__ )							-> Optional[int]:
         raise ValueError(
             F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}""" )






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						"longest"
    snake_case__							:   Optional[Any]							=						"max_length"
    snake_case__							:   Tuple							=						"do_not_pad"






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						"pt"
    snake_case__							:   Optional[int]							=						"tf"
    snake_case__							:   Optional[Any]							=						"np"
    snake_case__							:   Dict							=						"jax"






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       context_managers
         SCREAMING_SNAKE_CASE					:			Dict    	=       ExitStack()






    def __enter__(       self )							-> str:
         for context_manager in self.context_managers:
              self.stack.enter_context(lowercase__ )






    def __exit__(       self    ,							*lowercase__    ,							**lowercase__ )							-> List[str]:
         self.stack.__exit__(*lowercase__    ,							**lowercase__ )






def     __lowerCAmelCase						(       a_		)      ->	Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       infer_framework(a_		)
     if framework == "tf":
          SCREAMING_SNAKE_CASE					:			int    	=       inspect.signature(model_class.call		)  # TensorFlow models
     elif framework == "pt":
          SCREAMING_SNAKE_CASE					:			int    	=       inspect.signature(model_class.forward		)  # PyTorch models
     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       inspect.signature(model_class.__call__		)  # Flax models

     for p in signature.parameters:
          if p == "return_loss" and signature.parameters[p].default is True:
               return True

     return False




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





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       model_class.__name__
     SCREAMING_SNAKE_CASE					:			Tuple    	=       infer_framework(a_		)
     if framework == "tf":
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       inspect.signature(model_class.call		)  # TensorFlow models
     elif framework == "pt":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       inspect.signature(model_class.forward		)  # PyTorch models
     else:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       inspect.signature(model_class.__call__		)  # Flax models

     if "QuestionAnswering" in model_name:
          return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
     else:
          return [p for p in signature.parameters if "label" in p]




def     __lowerCAmelCase						(       a_       ,      a_ = ""       ,      a_ = "."		)      ->	Optional[int]:





     '''simple docstring'''





     def _flatten_dict(a_       ,      a_=""       ,      a_="."		):
          for k, v in d.items():
               SCREAMING_SNAKE_CASE					:			List[str]    	=       str(a_		) + delimiter + str(a_		) if parent_key else k
               if v and isinstance(a_       ,      a_		):
                    yield from flatten_dict(a_       ,      a_       ,      delimiter=a_		).items()
               else:
                    yield key, v

     return dict(_flatten_dict(a_       ,      a_       ,      a_		)		)




@contextmanager
def     __lowerCAmelCase						(       a_       ,      a_ = False		)      ->	Optional[Any]:





     '''simple docstring'''





     if use_temp_dir:
          with tempfile.TemporaryDirectory() as tmp_dir:
               yield tmp_dir
     else:
          yield working_dir




def     __lowerCAmelCase						(       a_       ,      a_=None		)      ->	Optional[int]:





     '''simple docstring'''





     if is_numpy_array(a_		):
          return np.transpose(a_       ,      axes=a_		)
     elif is_torch_tensor(a_		):
          return array.T if axes is None else array.permute(*a_		)
     elif is_tf_tensor(a_		):
          import tensorflow as tf

          return tf.transpose(a_       ,      perm=a_		)
     elif is_jax_tensor(a_		):
          return jnp.transpose(a_       ,      axes=a_		)
     else:
          raise ValueError(f"""Type not supported for transpose: {type(a_		)}."""		)




def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Dict:





     '''simple docstring'''





     if is_numpy_array(a_		):
          return np.reshape(a_       ,      a_		)
     elif is_torch_tensor(a_		):
          return array.reshape(*a_		)
     elif is_tf_tensor(a_		):
          import tensorflow as tf

          return tf.reshape(a_       ,      a_		)
     elif is_jax_tensor(a_		):
          return jnp.reshape(a_       ,      a_		)
     else:
          raise ValueError(f"""Type not supported for reshape: {type(a_		)}."""		)




def     __lowerCAmelCase						(       a_       ,      a_=None		)      ->	Optional[Any]:





     '''simple docstring'''





     if is_numpy_array(a_		):
          return np.squeeze(a_       ,      axis=a_		)
     elif is_torch_tensor(a_		):
          return array.squeeze() if axis is None else array.squeeze(dim=a_		)
     elif is_tf_tensor(a_		):
          import tensorflow as tf

          return tf.squeeze(a_       ,      axis=a_		)
     elif is_jax_tensor(a_		):
          return jnp.squeeze(a_       ,      axis=a_		)
     else:
          raise ValueError(f"""Type not supported for squeeze: {type(a_		)}."""		)




def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     if is_numpy_array(a_		):
          return np.expand_dims(a_       ,      a_		)
     elif is_torch_tensor(a_		):
          return array.unsqueeze(dim=a_		)
     elif is_tf_tensor(a_		):
          import tensorflow as tf

          return tf.expand_dims(a_       ,      axis=a_		)
     elif is_jax_tensor(a_		):
          return jnp.expand_dims(a_       ,      axis=a_		)
     else:
          raise ValueError(f"""Type not supported for expand_dims: {type(a_		)}."""		)




def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     if is_numpy_array(a_		):
          return np.size(a_		)
     elif is_torch_tensor(a_		):
          return array.numel()
     elif is_tf_tensor(a_		):
          import tensorflow as tf

          return tf.size(a_		)
     elif is_jax_tensor(a_		):
          return array.size
     else:
          raise ValueError(f"""Type not supported for expand_dims: {type(a_		)}."""		)




def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     for key, value in auto_map.items():
          if isinstance(a_       ,      (tuple, list)		):
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [f"""{repo_id}--{v}""" if (v is not None and '--' not in v) else v for v in value]
          elif value is not None and "--" not in value:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       f"""{repo_id}--{value}"""

     return auto_map




def     __lowerCAmelCase						(       a_		)      ->	Optional[int]:





     '''simple docstring'''





     for base_class in inspect.getmro(a_		):
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       base_class.__module__
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       base_class.__name__
          if module.startswith('tensorflow'		) or module.startswith('keras'		) or name == "TFPreTrainedModel":
               return "tf"
          elif module.startswith('torch'		) or name == "PreTrainedModel":
               return "pt"
          elif module.startswith('flax'		) or module.startswith('jax'		) or name == "FlaxPreTrainedModel":
               return "flax"
     else:
          raise TypeError(f"""Could not infer framework from class {model_class}."""		)




'''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
_lowerCAmelCase  :Union[str, Any]  				=    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""")

_lowerCAmelCase  :List[str]  				=    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowerCAmelCase  :List[Any]  				=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     with open(a_       ,      'rb'		) as f:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       Image.open(a_		)
          return im.convert('RGB'		)






@dataclass
class 				UpperCAmelCase :




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self )							-> Optional[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 				UpperCAmelCase :




    '''simple docstring'''







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






def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





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


def     __lowerCAmelCase						(       )      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       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.
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       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'       ,      a_       ,      a_		)

     # 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()

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       training_args.get_process_log_level()
     logger.setLevel(a_		)
     transformers.utils.logging.set_verbosity(a_		)
     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.
     SCREAMING_SNAKE_CASE					:			List[str]    	=       None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          SCREAMING_SNAKE_CASE					:			Union[str, 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:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       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:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {}
          if data_args.train_dir is not None:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       os.path.join(data_args.train_dir       ,      '**'		)
          if data_args.validation_dir is not None:
               SCREAMING_SNAKE_CASE					:			int    	=       os.path.join(data_args.validation_dir       ,      '**'		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       load_dataset(
              'imagefolder'       ,      data_files=a_       ,      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.
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split       ,      a_		) and data_args.train_val_split > 0.0:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset['train'].train_test_split(data_args.train_val_split		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       split['train']
          SCREAMING_SNAKE_CASE					:			Tuple    	=       split['test']

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

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

     SCREAMING_SNAKE_CASE					:			Tuple    	=       AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path       ,      num_labels=len(a_		)       ,      labelaid=a_       ,      idalabel=a_       ,      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       ,      )
     SCREAMING_SNAKE_CASE					:			str    	=       AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path       ,      from_tf=bool('.ckpt' in model_args.model_name_or_path		)       ,      config=a_       ,      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       ,      )
     SCREAMING_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:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processor.size['shortest_edge']
     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (image_processor.size['height'], image_processor.size['width'])
     SCREAMING_SNAKE_CASE					:			Any    	=       Normalize(mean=image_processor.image_mean       ,      std=image_processor.image_std		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       Compose(
         [
             RandomResizedCrop(a_		),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       Compose(
         [
             Resize(a_		),
             CenterCrop(a_		),
             ToTensor(),
             normalize,
         ]		)

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

     def val_transforms(a_		):
          SCREAMING_SNAKE_CASE					:			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:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
                   dataset['train'].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
               )
          # Set the training transforms
          dataset["train"].set_transform(a_		)

     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:
               SCREAMING_SNAKE_CASE					:			int    	=       (
                   dataset['validation'].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(a_		)

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

     # Training
     if training_args.do_train:
          SCREAMING_SNAKE_CASE					:			int    	=       None
          if training_args.resume_from_checkpoint is not None:
               SCREAMING_SNAKE_CASE					:			Dict    	=       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               SCREAMING_SNAKE_CASE					:			Tuple    	=       last_checkpoint
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       trainer.train(resume_from_checkpoint=a_		)
          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:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       trainer.evaluate()
          trainer.log_metrics('eval'       ,      a_		)
          trainer.save_metrics('eval'       ,      a_		)

     # Write model card and (optionally) push to hub
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
         '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(**a_		)
     else:
          trainer.create_model_card(**a_		)


if __name__ == "__main__":
 main()



