


'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case          =  {
    '''configuration_mobilebert''': [
        '''MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileBertConfig''',
        '''MobileBertOnnxConfig''',
    ],
    '''tokenization_mobilebert''': ['''MobileBertTokenizer'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case          =  [
		    '''MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MobileBertForMaskedLM''',
		    '''MobileBertForMultipleChoice''',
		    '''MobileBertForNextSentencePrediction''',
		    '''MobileBertForPreTraining''',
		    '''MobileBertForQuestionAnswering''',
		    '''MobileBertForSequenceClassification''',
		    '''MobileBertForTokenClassification''',
		    '''MobileBertLayer''',
		    '''MobileBertModel''',
		    '''MobileBertPreTrainedModel''',
		    '''load_tf_weights_in_mobilebert''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case          =  [
		    '''TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFMobileBertForMaskedLM''',
		    '''TFMobileBertForMultipleChoice''',
		    '''TFMobileBertForNextSentencePrediction''',
		    '''TFMobileBertForPreTraining''',
		    '''TFMobileBertForQuestionAnswering''',
		    '''TFMobileBertForSequenceClassification''',
		    '''TFMobileBertForTokenClassification''',
		    '''TFMobileBertMainLayer''',
		    '''TFMobileBertModel''',
		    '''TFMobileBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mobilebert import (
		    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileBertConfig,
		    MobileBertOnnxConfig,
		)
		from .tokenization_mobilebert import MobileBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mobilebert_fast import MobileBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilebert import (
				    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileBertForMaskedLM,
				    MobileBertForMultipleChoice,
				    MobileBertForNextSentencePrediction,
				    MobileBertForPreTraining,
				    MobileBertForQuestionAnswering,
				    MobileBertForSequenceClassification,
				    MobileBertForTokenClassification,
				    MobileBertLayer,
				    MobileBertModel,
				    MobileBertPreTrainedModel,
				    load_tf_weights_in_mobilebert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilebert import (
				    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileBertForMaskedLM,
				    TFMobileBertForMultipleChoice,
				    TFMobileBertForNextSentencePrediction,
				    TFMobileBertForPreTraining,
				    TFMobileBertForQuestionAnswering,
				    TFMobileBertForSequenceClassification,
				    TFMobileBertForTokenClassification,
				    TFMobileBertMainLayer,
				    TFMobileBertModel,
				    TFMobileBertPreTrainedModel,
				)

else:
		import sys

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



'''simple docstring'''
import sys

__snake_case							   =  (
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)


def     a   (    __a     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				1
					for digit in s:
										product *= int(__a     )
					return product


def     a   (    __a = N     )						->			int:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				-sys.maxsize - 1
					UpperCamelCase__     :Dict							      =				n[:13]
					UpperCamelCase__     :Dict							      =				13
					while cur_index < len(__a     ) - 13:
										if int(n[cur_index]     ) >= int(substr[0]     ):
															UpperCamelCase__     :Dict							      =				substr[1:] + n[cur_index]
															cur_index += 1
										else:
															UpperCamelCase__     :List[str]							      =				max(__a				,      str_eval(__a     )     )
															UpperCamelCase__     :Union[str, Any]							      =				n[cur_index : cur_index + 13]
															cur_index += 13
					return largest_product


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