code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __snake_case             =	{
      '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
      '''tokenization_splinter''': ['''SplinterTokenizer'''],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __snake_case             =	['''SplinterTokenizerFast''']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      __snake_case             =	[
          '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''SplinterForQuestionAnswering''',
          '''SplinterForPreTraining''',
          '''SplinterLayer''',
          '''SplinterModel''',
          '''SplinterPreTrainedModel''',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
      from .tokenization_splinter import SplinterTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_splinter_fast import SplinterTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_splinter import (
              SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SplinterForPreTraining,
              SplinterForQuestionAnswering,
              SplinterLayer,
              SplinterModel,
              SplinterPreTrainedModel,
          )
  
  
  else:
      import sys
  
      __snake_case             =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
style_context: 
  
  
  def 		_A		(		_lowercase   )				->   bool:
  
  
       """simple docstring"""
  
  
       if not isinstance(_lowercase				,     _lowercase   ):
            raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'   )
       if len(_lowercase   ) == 0:
            raise ValueError('Input list must be a non empty list'   )
       if len(_lowercase   ) == 1 and series[0] != 0:
            return True
       __UpperCamelCase   = []
       __UpperCamelCase   = len(_lowercase   )
       for i in range(0				,     _lowercase   ):
            if series[i] == 0:
                 raise ValueError('Input series cannot have 0 as an element'   )
            rec_series.append(1 / series[i]   )
       __UpperCamelCase   = rec_series[1] - rec_series[0]
       for index in range(2				,     _lowercase   ):
            if rec_series[index] - rec_series[index - 1] != common_diff:
                 return False
       return True
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   float:
  
  
       """simple docstring"""
  
  
       if not isinstance(_lowercase				,     _lowercase   ):
            raise ValueError('Input series is not valid, valid series - [2, 4, 6]'   )
       if len(_lowercase   ) == 0:
            raise ValueError('Input list must be a non empty list'   )
       __UpperCamelCase   = 0
       for val in series:
            answer += 1 / val
       return len(_lowercase   ) / answer
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
  