

from typing import TYPE_CHECKING

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


lowerCamelCase     =      {
    '''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:
       lowerCamelCase     =      ['''DebertaV2TokenizerFast''']

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

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



import math
from decimal import Decimal, getcontext




def     UpperCAmelCase__       (    _A   :				int = 4_00_00_00							):





  '''simple docstring'''







  try:
    a__						=int(_A							)
  except (TypeError, ValueError):
    raise TypeError('''Parameter n must be int or castable to int.'''							)
  if n <= 0:
    raise ValueError('''Parameter n must be greater than or equal to one.'''							)
  a__						=1_00
  a__						=(Decimal(5							) ** Decimal(0.5							) + 1) / Decimal(2							)

  a__						=(math.floor(math.log(n * (phi + 2)			,   _A							) - 1							) // 3) * 3 + 2
  a__						=Decimal(round(phi ** Decimal(index + 1							)							)							) / (phi + 2)
  a__						=num // 2
  return int(_A							)


if __name__ == "__main__":
       print(f"""{solution() = }""")
