
"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCamelCase     :    List[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:
      UpperCamelCase     :    List[str]       =			["DebertaTokenizerFast"]

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

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase     :    Optional[int]       =			[
          "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

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





"""simple docstring"""

from math import factorial

UpperCamelCase     :    List[Any]       =			{str(d): factorial(d) for d in range(1_0)}






def 							A						(    snake_case				:int		)				->     int:
 return sum(DIGIT_FACTORIAL[d] for d in str(snake_case		)		)






def 							A						(    )				->     int:
 __UpperCamelCase      =	7 * factorial(9		) + 1
 return sum(i for i in range(3    , snake_case		) if sum_of_digit_factorial(snake_case		) == i		)


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



