


'''simple docstring'''





def        a__     (					UpperCamelCase_						:      int = 100						):
   UpperCAmelCase__	:List[str]      =      0
   UpperCAmelCase__	:str      =      0
   for i in range(1,   n + 1						):
      sum_of_squares += i**2
      sum_of_ints += i
   return sum_of_ints**2 - sum_of_squares


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







'''simple docstring'''





from typing import TYPE_CHECKING

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


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



