


from typing import TYPE_CHECKING

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


__lowerCAmelCase			=							{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''RobertaPreLayerNormForCausalLM''',
     '''RobertaPreLayerNormForMaskedLM''',
     '''RobertaPreLayerNormForMultipleChoice''',
     '''RobertaPreLayerNormForQuestionAnswering''',
     '''RobertaPreLayerNormForSequenceClassification''',
     '''RobertaPreLayerNormForTokenClassification''',
     '''RobertaPreLayerNormModel''',
     '''RobertaPreLayerNormPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFRobertaPreLayerNormForCausalLM''',
     '''TFRobertaPreLayerNormForMaskedLM''',
     '''TFRobertaPreLayerNormForMultipleChoice''',
     '''TFRobertaPreLayerNormForQuestionAnswering''',
     '''TFRobertaPreLayerNormForSequenceClassification''',
     '''TFRobertaPreLayerNormForTokenClassification''',
     '''TFRobertaPreLayerNormMainLayer''',
     '''TFRobertaPreLayerNormModel''',
     '''TFRobertaPreLayerNormPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase			=							[
     '''FlaxRobertaPreLayerNormForCausalLM''',
     '''FlaxRobertaPreLayerNormForMaskedLM''',
     '''FlaxRobertaPreLayerNormForMultipleChoice''',
     '''FlaxRobertaPreLayerNormForQuestionAnswering''',
     '''FlaxRobertaPreLayerNormForSequenceClassification''',
     '''FlaxRobertaPreLayerNormForTokenClassification''',
     '''FlaxRobertaPreLayerNormModel''',
     '''FlaxRobertaPreLayerNormPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_roberta_prelayernorm import (
     ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
     RobertaPreLayerNormConfig,
     RobertaPreLayerNormOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaPreLayerNormForCausalLM,
      RobertaPreLayerNormForMaskedLM,
      RobertaPreLayerNormForMultipleChoice,
      RobertaPreLayerNormForQuestionAnswering,
      RobertaPreLayerNormForSequenceClassification,
      RobertaPreLayerNormForTokenClassification,
      RobertaPreLayerNormModel,
      RobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta_prelayernorm import (
      TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaPreLayerNormForCausalLM,
      TFRobertaPreLayerNormForMaskedLM,
      TFRobertaPreLayerNormForMultipleChoice,
      TFRobertaPreLayerNormForQuestionAnswering,
      TFRobertaPreLayerNormForSequenceClassification,
      TFRobertaPreLayerNormForTokenClassification,
      TFRobertaPreLayerNormMainLayer,
      TFRobertaPreLayerNormModel,
      TFRobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
      FlaxRobertaPreLayerNormPreTrainedModel,
  )

else:
 import sys

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






from __future__ import annotations




def        snake_case_					(  snake_case      ,							snake_case					)  ->    list[str]:
       if nth_term == "":
              return [""]
       lowercase__:       Tuple        =       int(snake_case					)
       lowercase__:       int        =       int(snake_case					)
       lowercase__:       list[str]        =       []
       for temp in range(int(snake_case					)					):
              series.append(f'1 / {pow(temp + 1      ,							int(snake_case					)					)}' if series else '1'					)
       return series


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 __lowerCAmelCase			=							int(input('''Enter the last number (nth term) of the P-Series'''))
 __lowerCAmelCase			=							int(input('''Enter the power for  P-Series'''))
 print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
 print(p_series(nth_term, power))


