





from typing import TYPE_CHECKING

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


UpperCAmelCase__  :	List[Any]     ={
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =['''MPNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	str     =[
							    '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MPNetForMaskedLM''',
							    '''MPNetForMultipleChoice''',
							    '''MPNetForQuestionAnswering''',
							    '''MPNetForSequenceClassification''',
							    '''MPNetForTokenClassification''',
							    '''MPNetLayer''',
							    '''MPNetModel''',
							    '''MPNetPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  :	Union[str, Any]     =[
							    '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFMPNetEmbeddings''',
							    '''TFMPNetForMaskedLM''',
							    '''TFMPNetForMultipleChoice''',
							    '''TFMPNetForQuestionAnswering''',
							    '''TFMPNetForSequenceClassification''',
							    '''TFMPNetForTokenClassification''',
							    '''TFMPNetMainLayer''',
							    '''TFMPNetModel''',
							    '''TFMPNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
							from .tokenization_mpnet import MPNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mpnet_fast import MPNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mpnet import (
														    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MPNetForMaskedLM,
														    MPNetForMultipleChoice,
														    MPNetForQuestionAnswering,
														    MPNetForSequenceClassification,
														    MPNetForTokenClassification,
														    MPNetLayer,
														    MPNetModel,
														    MPNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mpnet import (
														    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFMPNetEmbeddings,
														    TFMPNetForMaskedLM,
														    TFMPNetForMultipleChoice,
														    TFMPNetForQuestionAnswering,
														    TFMPNetForSequenceClassification,
														    TFMPNetForTokenClassification,
														    TFMPNetMainLayer,
														    TFMPNetModel,
														    TFMPNetPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__  :	str     =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












def    _lowercase    (  _UpperCAmelCase = 99_80_01 )  ->     int:

				# fetches the next number
				for number in range(n - 1	,					99_99	,					-1 ):
								lowerCamelCase		       =str(_UpperCAmelCase )

								# checks whether 'str_number' is a palindrome.
								if str_number == str_number[::-1]:
												lowerCamelCase		       =9_99

												# if 'number' is a product of two 3-digit numbers
												# then number is the answer otherwise fetch next number.
												while divisor != 99:
																if (number % divisor == 0) and (len(str(number // divisor ) ) == 3.0):
																				return number
																divisor -= 1
				raise ValueError("""That number is larger than our acceptable range.""" )


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





