import re
def 		snake_case__  (		lowercase				):
   def docstring_decorator(lowercase				):
      lowerCAmelCase_:       Any         =  fn.__doc__
      lowerCAmelCase_:       Optional[Any]         =  func_doc.split("\n"				)
      lowerCAmelCase_:       Optional[Any]         =  0
      while i < len(lowercase				) and re.search(r"^\s*Examples?:\s*$"       ,							lines[i]				) is None:
         i += 1
      if i < len(lowercase				):
         lowerCAmelCase_:       str         =  example_docstring
         lowerCAmelCase_:       Tuple         =  "\n".join(lowercase				)
      else:
         raise ValueError(
             F'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
             F'''current docstring is:\n{func_doc}'''				)
      lowerCAmelCase_:       Dict         =  func_doc
      return fn

   return docstring_decorator
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 				_lowercase						(    UpperCAmelCase__ ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						c_float




    @property
    def     _a						(						self   ):
       return sizeof(self.value   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = 0				):
   # < 0 is possible if using a dynamic axis
   if dimension <= 0:
      lowerCAmelCase_:       int         =  fixed_dimension

   dimension -= num_token_to_add
   return dimension
def 		snake_case__  (		lowercase       ,							lowercase				):
   return num_parameters * dtype.size







def 		snake_case__  (		lowercase				):
   from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

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