



"""simple docstring"""







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
     from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore







class  UpperCamelCase__  (      __lowerCAmelCase    ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE    							=	c_float



      @property
      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[Any]			):
        return sizeof(self.value			)




def 							UpperCamelCase_       (       lowerCAmelCase__ :							Optional[Any]       ,  lowerCAmelCase__ :							Union[str, Any]       ,  lowerCAmelCase__ :							List[str] = 0		)		->  int:

  """simple docstring"""




  if dimension <= 0:
    lowerCAmelCase_  :							List[Any]								=					fixed_dimension

  dimension -= num_token_to_add
  return dimension

def 							UpperCamelCase_       (       lowerCAmelCase__ :							List[Any]       ,  lowerCAmelCase__ :							Optional[int]		)		->  int:

  """simple docstring"""




  return num_parameters * dtype.size

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Optional[int]		)		->  Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:

  """simple docstring"""




  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

  try:
    return AutoProcessor.from_pretrained(lowerCAmelCase__		)
  except (ValueError, OSError, KeyError):
    lowerCAmelCase_		,lowerCAmelCase_  :							Dict								=					None, None
    try:
      lowerCAmelCase_  :							Optional[int]								=					AutoTokenizer.from_pretrained(lowerCAmelCase__		)
    except (OSError, KeyError):
      pass
    try:
      lowerCAmelCase_  :							int								=					AutoFeatureExtractor.from_pretrained(lowerCAmelCase__		)
    except (OSError, KeyError):
      pass

    if tokenizer is not None and feature_extractor is not None:
      raise ValueError(
          f"Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."		)
    elif tokenizer is None and feature_extractor is None:
      return None
    elif tokenizer is not None:
      return tokenizer
    else:
      return feature_extractor











'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_vit import ViTImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						List[str]					,   *__UpperCamelCase						:						Tuple					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									warnings.warn(
									    """The class ViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use ViTImageProcessor instead."""					,   __UpperCamelCase					,   )
									super().__init__(*__UpperCamelCase					,   **__UpperCamelCase       )