


from typing import TYPE_CHECKING

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


snake_case				:    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:
     snake_case				:    Any       				=     ['''DebertaTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Optional[Any]       				=     [
         '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DebertaForMaskedLM''',
         '''DebertaForQuestionAnswering''',
         '''DebertaForSequenceClassification''',
         '''DebertaForTokenClassification''',
         '''DebertaModel''',
         '''DebertaPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case				:    Any       				=     [
         '''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

     snake_case				:    int       				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       list:


     '''simple docstring'''



     if n_term == "":
          return []
     _UpperCAmelCase     							=   []
     for temp in range(int(__lowercase       )       ):
          series.append(f'1/{temp + 1}' if series else "1"       )
     return series


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :str       =				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))




