

"""simple docstring"""



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





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


if __name__ == "__main__":
 import doctest

 doctest.testmod()









"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






