code: 
  '''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   a_  (_a    ):
         __lowerCAmelCase    :				Union[str, Any]    =      c_float
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           return sizeof(self.value  )
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int  ,      _lowerCamelCase						:       int = 0		)    ->						int:
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
      _lowerCAmelCase       :  Optional[int]   				=					fixed_dimension
  
    dimension -= num_token_to_add
    return dimension
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       int  ,      _lowerCamelCase						:       ParameterFormat		)    ->						int:
    return num_parameters * dtype.size
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       str		)    ->						Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
    try:
      return AutoProcessor.from_pretrained(_lowerCamelCase		)
    except (ValueError, OSError, KeyError):
      _lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					None, None
      try:
        _lowerCAmelCase       :  str   				=					AutoTokenizer.from_pretrained(_lowerCamelCase		)
      except (OSError, KeyError):
        pass
      try:
        _lowerCAmelCase       :  List[str]   				=					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
  
  
  
style_context: 
  '''simple docstring'''
  
  import os
  
  
  def 	_UpperCAmelCase				(  )    ->						int:
    _lowerCAmelCase       :  Optional[int]   				=					os.path.dirname(os.path.realpath(_lowerCamelCase		)		)
    _lowerCAmelCase       :  Optional[Any]   				=					os.path.join(_lowerCamelCase  ,      """triangle.txt"""		)
  
    with open(_lowerCamelCase		) as in_file:
      _lowerCAmelCase       :  List[Any]   				=					[[int(_lowerCamelCase		) for i in line.split()] for line in in_file]
  
    while len(_lowerCamelCase		) != 1:
      _lowerCAmelCase       :  Any   				=					triangle.pop()
      _lowerCAmelCase       :  List[str]   				=					triangle[-1]
      for j in range(len(_lowerCamelCase		) - 1		):
        curr_row[j] += max(last_row[j]  ,      last_row[j + 1]		)
    return triangle[0][0]
  
  
  if __name__ == "__main__":
       print(solution())
  
  
  