


'''simple docstring'''




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :		Optional[int]		     =	{
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		List[str]		     =	['PerceiverFeatureExtractor']
				_lowerCAmelCase  :		Optional[Any]		     =	['PerceiverImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Optional[Any]		     =	[
				    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'PerceiverForImageClassificationConvProcessing',
				    'PerceiverForImageClassificationFourier',
				    'PerceiverForImageClassificationLearned',
				    'PerceiverForMaskedLM',
				    'PerceiverForMultimodalAutoencoding',
				    'PerceiverForOpticalFlow',
				    'PerceiverForSequenceClassification',
				    'PerceiverLayer',
				    'PerceiverModel',
				    'PerceiverPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
				from .tokenization_perceiver import PerceiverTokenizer

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_perceiver import PerceiverFeatureExtractor
								from .image_processing_perceiver import PerceiverImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_perceiver import (
								    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PerceiverForImageClassificationConvProcessing,
								    PerceiverForImageClassificationFourier,
								    PerceiverForImageClassificationLearned,
								    PerceiverForMaskedLM,
								    PerceiverForMultimodalAutoencoding,
								    PerceiverForOpticalFlow,
								    PerceiverForSequenceClassification,
								    PerceiverLayer,
								    PerceiverModel,
								    PerceiverPreTrainedModel,
								)

else:
				import sys

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



'''simple docstring'''




import math

def 			__UpperCamelCase    (    _A    :				int = 1_00				)    ->      int:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= sum(i * i for i in range(1					,   n + 1				)				)
	lowerCAmelCase					:    Optional[Any]      							= int(math.pow(sum(range(1					,   n + 1				)				)					,   2				)				)
	return square_of_sum - sum_of_squares


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