



from typing import TYPE_CHECKING

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


a__      =		{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__      =		["""DebertaV2TokenizerFast"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__      =		[
     """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaV2ForMaskedLM""",
     """TFDebertaV2ForQuestionAnswering""",
     """TFDebertaV2ForSequenceClassification""",
     """TFDebertaV2ForTokenClassification""",
     """TFDebertaV2Model""",
     """TFDebertaV2PreTrainedModel""",
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__      =		[
     """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaV2ForMaskedLM""",
     """DebertaV2ForMultipleChoice""",
     """DebertaV2ForQuestionAnswering""",
     """DebertaV2ForSequenceClassification""",
     """DebertaV2ForTokenClassification""",
     """DebertaV2Model""",
     """DebertaV2PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

 a__      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""recursive_lucas_number accepts only integer arguments."""			)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1			) + recursive_lucas_number(
     n_th_number - 2			)



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		int			)					->       int:
 if not isinstance(SCREAMING_SNAKE_CASE__			,    SCREAMING_SNAKE_CASE__			):
  raise TypeError("""dynamic_lucas_number accepts only integer arguments."""			)
 _snake_case		,      _snake_case      :					Any					=   2, 1
 for _ in range(SCREAMING_SNAKE_CASE__			):
  _snake_case		,      _snake_case      :					Union[str, Any]					=   b, a + b
 return a


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  a__      =		int(input("""Enter the number of terms in lucas series:\n""").strip())
  print("""Using recursive function to calculate lucas series:""")
  print(""" """.join(str(recursive_lucas_number(i)) for i in range(n)))
  print("""\nUsing dynamic function to calculate lucas series:""")
  print(""" """.join(str(dynamic_lucas_number(i)) for i in range(n)))
