



'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowerCamelCase	:       Union[str, Any]      	=	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       int      	=	["DebertaTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       int      	=	[
       "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
       "DebertaForMaskedLM",
       "DebertaForQuestionAnswering",
       "DebertaForSequenceClassification",
       "DebertaForTokenClassification",
       "DebertaModel",
       "DebertaPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase	:       Tuple      	=	[
       "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFDebertaForMaskedLM",
       "TFDebertaForQuestionAnswering",
       "TFDebertaForSequenceClassification",
       "TFDebertaForTokenClassification",
       "TFDebertaModel",
       "TFDebertaPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

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







def      A      (      _SCREAMING_SNAKE_CASE    )	-> list:
       if n_term == "":
              return []
       lowerCamelCase   :      list			 =					[]
       for temp in range(int(_SCREAMING_SNAKE_CASE    )    ):
              series.append(f'''1/{temp + 1}''' if series else "1"    )
       return series


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Dict  =   input('Enter the last number (nth term) of the Harmonic Series')
       print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
       print(harmonic_series(nth_term))






