






"""simple docstring"""


from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
lowercase_								=	TypeVar('T', bound='Enum')


@unique
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[Any]							=					80
   A_		:      Dict							=					70
   A_		:      Optional[int]							=					60
   A_		:      Any							=					50
   A_		:      Dict							=					40
   A_		:      str							=					30
   A_		:      Tuple							=					20
   A_		:      Tuple							=					10





@unique
class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					24
   A_		:      Any							=					21
   A_		:      Union[str, Any]							=					18
   A_		:      str							=					15
   A_		:      List[str]							=					12
   A_		:      str							=					9
   A_		:      List[str]							=					6
   A_		:      str							=					3
   A_		:      Any							=					2
   A_		:      Any							=					1
   A_		:      List[Any]							=					-1
   A_		:      str							=					-2
   A_		:      Tuple							=					-3
   A_		:      List[str]							=					-6
   A_		:      int							=					-9
   A_		:      List[Any]							=					-12
   A_		:      List[Any]							=					-15
   A_		:      Optional[int]							=					-18
   A_		:      Optional[Any]							=					-21
   A_		:      Optional[int]							=					-24


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	type[T]    ):

     '''simple docstring'''
     return {unit.name: unit.value for unit in cls if unit.value > 0}






   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	type[T]    ):

     '''simple docstring'''
     return {unit.name: unit.value for unit in cls if unit.value < 0}





def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
  for name_prefix, value_prefix in prefixes.items():
    __A		     =					value / (1_0**value_prefix)
    if numerical_part > 1:
      return f'{numerical_part!s} {name_prefix}'
  return str(__UpperCamelCase					)



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  for prefix in BinaryUnit:
    __A		     =					value / (2**prefix.value)
    if numerical_part > 1:
      return f'{numerical_part!s} {prefix.name}'
  return str(__UpperCamelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """simple docstring"""





   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

   if text is not None:
     __A		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.decode(*lowerCamelCase					, **lowerCamelCase							)






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



