


'''simple docstring'''



def       UpperCAmelCase__ (     UpperCAmelCase_   :    list							)					->		bool:





					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       :     Optional[int]        =		[]
					__lowerCamelCase       :     int        =		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       :     Tuple        =		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       UpperCAmelCase__ (     UpperCAmelCase_   :    list							)					->		float:





					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       :     Dict        =		0
					for val in series:
										answer += 1 / val
					return len(SCREAMING_SNAKE_CASE_							) / answer


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """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

      A__		: Optional[int]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






