"""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      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    c_float







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return sizeof(self.value	)



def       A__    (	lowercase_     :	int      , lowercase_     :	int      , lowercase_     :	int = 0						)  ->							int:
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      __lowerCamelCase    		=					fixed_dimension

   dimension -= num_token_to_add
   return dimension



def       A__    (	lowercase_     :	int      , lowercase_     :	ParameterFormat						)  ->							int:
   return num_parameters * dtype.size



def       A__    (	lowercase_     :	str						)  ->							Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

   try:
      return AutoProcessor.from_pretrained(lowercase_						)
   except (ValueError, OSError, KeyError):
      __lowerCamelCase		,__lowerCamelCase    		=					None, None
      try:
         __lowerCamelCase    		=					AutoTokenizer.from_pretrained(lowercase_						)
      except (OSError, KeyError):
         pass
      try:
         __lowerCamelCase    		=					AutoFeatureExtractor.from_pretrained(lowercase_						)
      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










def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   list     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=int((lower_bound + upper_bound) // 2							)
     if array[r] == value:
          return r
     if lower_bound >= upper_bound:
          return -1
     if array[r] < value:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	r + 1     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)
     else:
          return binary_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	r - 1     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   list							):
     __UpperCamelCase       				=0
     if matrix[index][0] == value:
          return [index, 0]
     while index < len(SCREAMING_SNAKE_CASE__							) and matrix[index][0] < value:
          __UpperCamelCase       				=binary_search(matrix[index]     ,	0     ,	len(matrix[index]							) - 1     ,	SCREAMING_SNAKE_CASE__							)
          if r != -1:
               return [index, r]
          index += 1
     return [-1, -1]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





