


def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"     )
  if len(SCREAMING_SNAKE_CASE_     ) == 0:
    raise ValueError("Input list must be a non empty list"     )
  if len(SCREAMING_SNAKE_CASE_     ) == 1 and series[0] != 0:
    return True
  lowerCamelCase			: int   =    []
  lowerCamelCase			: List[str]   =    len(SCREAMING_SNAKE_CASE_     )
  for i in range(0				,						SCREAMING_SNAKE_CASE_     ):
    if series[i] == 0:
      raise ValueError("Input series cannot have 0 as an element"     )
    rec_series.append(1 / series[i]     )
  lowerCamelCase			: str   =    rec_series[1] - rec_series[0]
  for index in range(2				,						SCREAMING_SNAKE_CASE_     ):
    if rec_series[index] - rec_series[index - 1] != common_diff:
      return False
  return True






def 		lowercase_(  SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  if not isinstance(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):
    raise ValueError("Input series is not valid, valid series - [2, 4, 6]"     )
  if len(SCREAMING_SNAKE_CASE_     ) == 0:
    raise ValueError("Input list must be a non empty list"     )
  lowerCamelCase			: Any   =    0
  for val in series:
    answer += 1 / val
  return len(SCREAMING_SNAKE_CASE_     ) / answer


if __name__ == "__main__":
   import doctest

   doctest.testmod()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case					=					{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''HubertForCTC''',
       '''HubertForSequenceClassification''',
       '''HubertModel''',
       '''HubertPreTrainedModel''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFHubertForCTC''',
       '''TFHubertModel''',
       '''TFHubertPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



