






'''simple docstring'''







from typing import TYPE_CHECKING

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


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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''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:
			_A	:			str         =[
			    '''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

			_A	:			Tuple         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''







def  SCREAMING_SNAKE_CASE_       (UpperCamelCase = 1000      )      ->       int:
							lowerCamelCase__					:	str         =       -1
							lowerCamelCase__					:	Dict         =       0
							for a in range(1				,			n // 3      ):
														# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
														lowerCamelCase__					:	Dict         =       (n * n - 2 * a * n) // (2 * n - 2 * a)
														lowerCamelCase__					:	Any         =       n - a - b
														if c * c == (a * a + b * b):
																					lowerCamelCase__					:	Dict         =       a * b * c
																					if candidate >= product:
																												lowerCamelCase__					:	Union[str, Any]         =       candidate
							return product


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

