

from typing import TYPE_CHECKING

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


a_     =			{
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'RoFormerForCausalLM',
			    'RoFormerForMaskedLM',
			    'RoFormerForMultipleChoice',
			    'RoFormerForQuestionAnswering',
			    'RoFormerForSequenceClassification',
			    'RoFormerForTokenClassification',
			    'RoFormerLayer',
			    'RoFormerModel',
			    'RoFormerPreTrainedModel',
			    'load_tf_weights_in_roformer',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFRoFormerForCausalLM',
			    'TFRoFormerForMaskedLM',
			    'TFRoFormerForMultipleChoice',
			    'TFRoFormerForQuestionAnswering',
			    'TFRoFormerForSequenceClassification',
			    'TFRoFormerForTokenClassification',
			    'TFRoFormerLayer',
			    'TFRoFormerModel',
			    'TFRoFormerPreTrainedModel',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaxRoFormerForMaskedLM',
			    'FlaxRoFormerForMultipleChoice',
			    'FlaxRoFormerForQuestionAnswering',
			    'FlaxRoFormerForSequenceClassification',
			    'FlaxRoFormerForTokenClassification',
			    'FlaxRoFormerModel',
			    'FlaxRoFormerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


else:
			import sys

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




"""simple docstring"""

import sys

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




def __lowercase			(							_a    ):
    snake_case_ :     List[Any]				=	1
    for digit in s:
        product *= int(_a    )
    return product




def __lowercase			(							_a = N    ):
    snake_case_ :     Optional[int]				=	-sys.maxsize - 1
    snake_case_ :     str				=	n[:13]
    snake_case_ :     List[Any]				=	13
    while cur_index < len(_a    ) - 13:
        if int(n[cur_index]    ) >= int(substr[0]    ):
            snake_case_ :     int				=	substr[1:] + n[cur_index]
            cur_index += 1
        else:
            snake_case_ :     Optional[Any]				=	max(_a		,			str_eval(_a    )    )
            snake_case_ :     Any				=	n[cur_index : cur_index + 13]
            cur_index += 13
    return largest_product


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





