
'''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					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=c_float
 @property
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   return sizeof(self.value							)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	int = 0			)					->							int:





  """simple docstring"""

  if dimension <= 0:
    __A		:  List[str]       				=		fixed_dimension

  dimension -= num_token_to_add
  return dimension







def 							A_  (    __SCREAMING_SNAKE_CASE    :	int      ,  __SCREAMING_SNAKE_CASE    :	ParameterFormat			)					->							int:





  """simple docstring"""

  return num_parameters * dtype.size







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:





  """simple docstring"""

  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

  try:
    return AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE			)
  except (ValueError, OSError, KeyError):
    __A				,       __A		:  Optional[Any]       				=		None, None
    try:
      __A		:  Any       				=		AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
    except (OSError, KeyError):
      pass
    try:
      __A		:  Union[str, Any]       				=		AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
    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





'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



