


'''simple docstring'''






from __future__ import annotations







def     UpperCamelCase_      (   A__ :       int | float | str							,   A__ :       int | float | str ):
  '''simple docstring'''





  if nth_term == "":
    return [""]
  lowerCAmelCase_      :				str      =       int(A__ )
  lowerCAmelCase_      :				Tuple      =       int(A__ )
  lowerCAmelCase_      :				list[str]      =       []
  for temp in range(int(A__ ) ):
    series.append(f'1 / {pow(temp + 1							,   int(A__ ) )}' if series else """1""" )
  return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __A :  str    							=				int(input("Enter the last number (nth term) of the P-Series"))
  __A :  Tuple    							=				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))







'''simple docstring'''






from typing import TYPE_CHECKING

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


__A :  List[Any]    							=				{
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

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



