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   __lowerCamelCase (a_	):
         _lowercase       							=    c_float
  
  
  
  
  
  
  
         @property
         def 		snake_case_  ( self:   Tuple				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              return sizeof(self.value				)
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase = 0   )				->   Union[str, Any]:
  
  
       """simple docstring"""
  
  
       if dimension <= 0:
            __UpperCamelCase   = fixed_dimension
  
       dimension -= num_token_to_add
       return dimension
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       return num_parameters * dtype.size
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   Tuple:
  
  
       """simple docstring"""
  
  
       from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
       try:
            return AutoProcessor.from_pretrained(lowercase_   )
       except (ValueError, OSError, KeyError):
            __UpperCamelCase   = None, None
            try:
                 __UpperCamelCase   = AutoTokenizer.from_pretrained(lowercase_   )
            except (OSError, KeyError):
                 pass
            try:
                 __UpperCamelCase   = 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
  
  
  
style_context: 
  
  
  from math import cos, sin
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = float(_lowercase   )
       return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       return [[c, -s], [s, c]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[c * c, cs], [cs, s * s]]
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   list[list[float]]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
       __UpperCamelCase   = c * s
       return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]
  
  
  print(f"""    {scaling(5) = }""")
  print(f"""  {rotation(4_5) = }""")
  print(f"""{projection(4_5) = }""")
  
  
  print(f"""{reflection(4_5) = }""")
  
  
  