
"""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() = }''')



