code: 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
style_context: 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