






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase            =	{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	['SqueezeBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase            =	[
				    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SqueezeBertForMaskedLM',
				    'SqueezeBertForMultipleChoice',
				    'SqueezeBertForQuestionAnswering',
				    'SqueezeBertForSequenceClassification',
				    'SqueezeBertForTokenClassification',
				    'SqueezeBertModel',
				    'SqueezeBertModule',
				    'SqueezeBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_squeezebert import (
				    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SqueezeBertConfig,
				    SqueezeBertOnnxConfig,
				)
				from .tokenization_squeezebert import SqueezeBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_squeezebert import (
								    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SqueezeBertForMaskedLM,
								    SqueezeBertForMultipleChoice,
								    SqueezeBertForQuestionAnswering,
								    SqueezeBertForSequenceClassification,
								    SqueezeBertForTokenClassification,
								    SqueezeBertModel,
								    SqueezeBertModule,
								    SqueezeBertPreTrainedModel,
								)

else:
				import sys

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








"""simple docstring"""







def      UpperCamelCase       ( _A )     ->       int:
				lowercase    :      Dict       = 0
				while num > 0:
								digit_sum += num % 10
								num //= 10
				return digit_sum


def      UpperCamelCase       ( _A = 100 )     ->       int:
				lowercase    :      Union[str, Any]       = 1
				lowercase    :      Tuple       = 2

				for i in range(2  ,				max_n + 1 ):
								lowercase    :      Any       = pre_numerator
								lowercase    :      Dict       = 2 * i // 3 if i % 3 == 0 else 1
								lowercase    :      Optional[Any]       = cur_numerator
								lowercase    :      str       = e_cont * pre_numerator + temp

				return sum_digits(_A )


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