code: 
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						2_4
       __SCREAMING_SNAKE_CASE    				=						2_1
       __SCREAMING_SNAKE_CASE    				=						1_8
       __SCREAMING_SNAKE_CASE    				=						1_5
       __SCREAMING_SNAKE_CASE    				=						1_2
       __SCREAMING_SNAKE_CASE    				=						9
       __SCREAMING_SNAKE_CASE    				=						6
       __SCREAMING_SNAKE_CASE    				=						3
       __SCREAMING_SNAKE_CASE    				=						2
       __SCREAMING_SNAKE_CASE    				=						1
       __SCREAMING_SNAKE_CASE    				=						-1
       __SCREAMING_SNAKE_CASE    				=						-2
       __SCREAMING_SNAKE_CASE    				=						-3
       __SCREAMING_SNAKE_CASE    				=						-6
       __SCREAMING_SNAKE_CASE    				=						-9
       __SCREAMING_SNAKE_CASE    				=						-1_2
       __SCREAMING_SNAKE_CASE    				=						-1_5
       __SCREAMING_SNAKE_CASE    				=						-1_8
       __SCREAMING_SNAKE_CASE    				=						-2_1
       __SCREAMING_SNAKE_CASE    				=						-2_4
  
  
  class 		_SCREAMING_SNAKE_CASE    (	A    ):
       __SCREAMING_SNAKE_CASE    				=						8
       __SCREAMING_SNAKE_CASE    				=						7
       __SCREAMING_SNAKE_CASE    				=						6
       __SCREAMING_SNAKE_CASE    				=						5
       __SCREAMING_SNAKE_CASE    				=						4
       __SCREAMING_SNAKE_CASE    				=						3
       __SCREAMING_SNAKE_CASE    				=						2
       __SCREAMING_SNAKE_CASE    				=						1
  
  
  
  
  def       a__						(		snake_case__				:       float	,      snake_case__				:       str | SIUnit	,      snake_case__				:       str | SIUnit	,      ):
    if isinstance(snake_case__	,      snake_case__    ):
      _UpperCAmelCase		:       List[str]       =    SIUnit[known_prefix.lower()]
    if isinstance(snake_case__	,      snake_case__    ):
      _UpperCAmelCase		:       Optional[Any]       =    SIUnit[unknown_prefix.lower()]
    _UpperCAmelCase		:       float       =    known_amount * (
        10 ** (known_prefix.value - unknown_prefix.value)
    )
    return unknown_amount
  
  
  
  
  def       a__						(		snake_case__				:       float	,      snake_case__				:       str | BinaryUnit	,      snake_case__				:       str | BinaryUnit	,      ):
    if isinstance(snake_case__	,      snake_case__    ):
      _UpperCAmelCase		:       Optional[Any]       =    BinaryUnit[known_prefix.lower()]
    if isinstance(snake_case__	,      snake_case__    ):
      _UpperCAmelCase		:       List[Any]       =    BinaryUnit[unknown_prefix.lower()]
    _UpperCAmelCase		:       float       =    known_amount * (
        2 ** ((known_prefix.value - unknown_prefix.value) * 10)
    )
    return unknown_amount
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__				:    List[str]         =    {
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    int         =    [
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    Dict         =    [
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )
  
  else:
   import sys
  
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  