






'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase		:		int								=			{
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		Optional[Any]								=			['SegformerFeatureExtractor']
						_UpperCamelCase		:		Optional[Any]								=			['SegformerImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		Optional[int]								=			[
						    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'SegformerDecodeHead',
						    'SegformerForImageClassification',
						    'SegformerForSemanticSegmentation',
						    'SegformerLayer',
						    'SegformerModel',
						    'SegformerPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase		:		int								=			[
						    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFSegformerDecodeHead',
						    'TFSegformerForImageClassification',
						    'TFSegformerForSemanticSegmentation',
						    'TFSegformerModel',
						    'TFSegformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_segformer import SegformerFeatureExtractor
												from .image_processing_segformer import SegformerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_segformer import (
												    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SegformerDecodeHead,
												    SegformerForImageClassification,
												    SegformerForSemanticSegmentation,
												    SegformerLayer,
												    SegformerModel,
												    SegformerPreTrainedModel,
												)
						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_segformer import (
												    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSegformerDecodeHead,
												    TFSegformerForImageClassification,
												    TFSegformerForSemanticSegmentation,
												    TFSegformerModel,
												    TFSegformerPreTrainedModel,
												)

else:
						import sys

						_UpperCamelCase		:		Optional[Any]								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''





def __UpperCAmelCase (			)  -> Optional[Any]:
			UpperCAmelCase_     :   Union[str, Any]       =   1

			while True:
						if (
						    sorted(str(A						)						)
						    == sorted(str(2 * i						)						)
						    == sorted(str(3 * i						)						)
						    == sorted(str(4 * i						)						)
						    == sorted(str(5 * i						)						)
						    == sorted(str(6 * i						)						)
						):
									return i

						i += 1


if __name__ == "__main__":
						print(solution())


