






'''simple docstring'''







from typing import TYPE_CHECKING

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


_A	:			Any         ={
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecAudioForAudioFrameClassification''',
			    '''Data2VecAudioForCTC''',
			    '''Data2VecAudioForSequenceClassification''',
			    '''Data2VecAudioForXVector''',
			    '''Data2VecAudioModel''',
			    '''Data2VecAudioPreTrainedModel''',
			]
			_A	:			Optional[Any]         =[
			    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecTextForCausalLM''',
			    '''Data2VecTextForMaskedLM''',
			    '''Data2VecTextForMultipleChoice''',
			    '''Data2VecTextForQuestionAnswering''',
			    '''Data2VecTextForSequenceClassification''',
			    '''Data2VecTextForTokenClassification''',
			    '''Data2VecTextModel''',
			    '''Data2VecTextPreTrainedModel''',
			]
			_A	:			Optional[int]         =[
			    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Data2VecVisionForImageClassification''',
			    '''Data2VecVisionForMaskedImageModeling''',
			    '''Data2VecVisionForSemanticSegmentation''',
			    '''Data2VecVisionModel''',
			    '''Data2VecVisionPreTrainedModel''',
			]

if is_tf_available():
			_A	:			List[Any]         =[
			    '''TFData2VecVisionForImageClassification''',
			    '''TFData2VecVisionForSemanticSegmentation''',
			    '''TFData2VecVisionModel''',
			    '''TFData2VecVisionPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
			from .configuration_dataavec_text import (
			    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecTextConfig,
			    DataaVecTextOnnxConfig,
			)
			from .configuration_dataavec_vision import (
			    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DataaVecVisionConfig,
			    DataaVecVisionOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dataavec_audio import (
						    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecAudioForAudioFrameClassification,
						    DataaVecAudioForCTC,
						    DataaVecAudioForSequenceClassification,
						    DataaVecAudioForXVector,
						    DataaVecAudioModel,
						    DataaVecAudioPreTrainedModel,
						)
						from .modeling_dataavec_text import (
						    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecTextForCausalLM,
						    DataaVecTextForMaskedLM,
						    DataaVecTextForMultipleChoice,
						    DataaVecTextForQuestionAnswering,
						    DataaVecTextForSequenceClassification,
						    DataaVecTextForTokenClassification,
						    DataaVecTextModel,
						    DataaVecTextPreTrainedModel,
						)
						from .modeling_dataavec_vision import (
						    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForMaskedImageModeling,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						    DataaVecVisionPreTrainedModel,
						)
			if is_tf_available():
						from .modeling_tf_dataavec_vision import (
						    TFDataaVecVisionForImageClassification,
						    TFDataaVecVisionForSemanticSegmentation,
						    TFDataaVecVisionModel,
						    TFDataaVecVisionPreTrainedModel,
						)

else:
			import sys

			_A	:			Optional[int]         =_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() = }')

