
'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	list							)       ->	bool:
 if not isinstance(snake_case_   ,     snake_case_							):
  raise ValueError('Input series is not valid, valid series - [1, 2/3, 2]'							)
 if len(snake_case_							) == 0:
  raise ValueError('Input list must be a non empty list'							)
 if len(snake_case_							) == 1 and series[0] != 0:
  return True
 SCREAMING_SNAKE_CASE  :  str								=		[]
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		len(snake_case_							)
 for i in range(0   ,     snake_case_							):
  if series[i] == 0:
   raise ValueError('Input series cannot have 0 as an element'							)
  rec_series.append(1 / series[i]							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		rec_series[1] - rec_series[0]
 for index in range(2   ,     snake_case_							):
  if rec_series[index] - rec_series[index - 1] != common_diff:
   return False
 return True



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	list							)       ->	float:
 if not isinstance(snake_case_   ,     snake_case_							):
  raise ValueError('Input series is not valid, valid series - [2, 4, 6]'							)
 if len(snake_case_							) == 0:
  raise ValueError('Input list must be a non empty list'							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		0
 for val in series:
  answer += 1 / val
 return len(snake_case_							) / answer


if __name__ == "__main__":
   import doctest

   doctest.testmod()




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase												=   {'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase												=   [
       'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWDForCTC',
       'SEWDForSequenceClassification',
       'SEWDModel',
       'SEWDPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

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


