code: 
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __lowerCamelCase  				=					{
      """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
      """tokenization_flaubert""": ["""FlaubertTokenizer"""],
  }
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __lowerCamelCase  				=					[
          """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:
      __lowerCamelCase  				=					[
          """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
  
      __lowerCamelCase  				=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
style_context: 
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case = 1_000							)   -> str:
    if (not isinstance(__snake_case						,				__snake_case							)) or (digit_position <= 0):
      raise ValueError('''Digit position must be a positive integer'''							)
    elif (not isinstance(__snake_case						,				__snake_case							)) or (precision < 0):
      raise ValueError('''Precision must be a nonnegative integer'''							)
  
    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    _A           =    (
        4 * _subsum(__snake_case						,				1						,				__snake_case							)
        - 2 * _subsum(__snake_case						,				4						,				__snake_case							)
        - _subsum(__snake_case						,				5						,				__snake_case							)
        - _subsum(__snake_case						,				6						,				__snake_case							)
    )
  
    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16							)							)[2:]
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> float:
    _A           =    0.0
    for sum_index in range(digit_pos_to_extract + precision							):
      _A           =    8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
        # if the exponential term is an integer and we mod it by the denominator
        # before dividing, only the integer part of the sum will change;
        # the fractional part will not
        _A           =    pow(
            16						,				digit_pos_to_extract - 1 - sum_index						,				__snake_case							)
      else:
        _A           =    pow(16						,				digit_pos_to_extract - 1 - sum_index							)
      total += exponential_term / denominator
    return total
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()