code: 
  
  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     UpperCAmelCase_		(      SCREAMING_SNAKE_CASE__				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Any     =c_float
        @property
        def 					UpperCAmelCase		(       self				)       ->   Any:
            return sizeof(self.value				)
  
  
  
  
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			int = 0 ):
  
  
  
  
  
  
  
      if dimension <= 0:
          UpperCamelCase :Tuple										=     fixed_dimension
  
      dimension -= num_token_to_add
      return dimension
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			ParameterFormat ):
      return num_parameters * dtype.size
  
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			str ):
      from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
      try:
          return AutoProcessor.from_pretrained(_UpperCamelCase )
      except (ValueError, OSError, KeyError):
          UpperCamelCase							,     UpperCamelCase :Any										=     None, None
          try:
              UpperCamelCase :Union[str, Any]										=     AutoTokenizer.from_pretrained(_UpperCamelCase )
          except (OSError, KeyError):
              pass
          try:
              UpperCamelCase :str										=     AutoFeatureExtractor.from_pretrained(_UpperCamelCase )
          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: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :int										=     {1}
      UpperCamelCase :Optional[int]										=     [1]
      for _ in range(2  ,						depth + 1 ):
          UpperCamelCase :Union[str, Any]										=     [*previous_coefficients, 0]
          UpperCamelCase :List[str]										=     [0, *previous_coefficients]
          UpperCamelCase :List[str]										=     []
          for x, y in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
              coefficients.add(x + y )
              previous_coefficients.append(x + y )
      return coefficients
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			set[int] ):
      UpperCamelCase :Union[str, Any]										=     set()
      for number in unique_coefficients:
          UpperCamelCase :Optional[int]										=     2
          UpperCamelCase :List[Any]										=     number
          while divisor**2 <= copy_number:
              UpperCamelCase :List[Any]										=     0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(SCREAMING_SNAKE_CASE__ )
                  break
              divisor += 1
  
      return unique_coefficients.difference(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int = 51 ):
      UpperCamelCase :Any										=     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :str										=     get_squarefrees(SCREAMING_SNAKE_CASE__ )
      return sum(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')
  
  
  
  
  
  