
"""simple docstring"""


from math import sqrt






def        lowerCAmelCase_       (		snake_case_		:      int      )       ->list[int]:
			lowerCamelCase__     :					Any		=[1]
			for i in range(2							,   int(sqrt(snake_case_      )      ) + 1							,   1      ):
						if number % i == 0:
									values.append(snake_case_      )
									if int(number // i      ) != i:
												values.append(int(number // i      )      )
			return sorted(snake_case_      )






def        lowerCAmelCase_       (		snake_case_		:      int      )       ->bool:
			return sum(factors(snake_case_      )      ) > n






def        lowerCAmelCase_       (		snake_case_		:      int      )       ->bool:
			lowerCamelCase__     :					List[str]		=factors(snake_case_      )
			lowerCamelCase__     :					Optional[Any]		=len(snake_case_      )
			lowerCamelCase__     :					Tuple		=[[0 for i in range(number + 1      )] for j in range(r + 1      )]
			for i in range(r + 1      ):
						lowerCamelCase__     :					Dict		=True

			for i in range(1							,   number + 1      ):
						lowerCamelCase__     :					List[Any]		=False

			for i in range(1							,   r + 1      ):
						for j in range(1							,   number + 1      ):
									if j < values[i - 1]:
												lowerCamelCase__     :					Optional[int]		=subset[i - 1][j]
									else:
												lowerCamelCase__     :					List[str]		=subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

			return subset[r][number] != 0






def        lowerCAmelCase_       (		snake_case_		:      int      )       ->bool:
			return abundant(snake_case_      ) and not semi_perfect(snake_case_      )


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)
							for number in (69, 70, 71):
														print(f"""{number} is {"" if weird(number) else "not "}weird.""")

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""MobileNetV1FeatureExtractor"""]
							lowerCAmelCase								=						["""MobileNetV1ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MobileNetV1ForImageClassification""",
							    """MobileNetV1Model""",
							    """MobileNetV1PreTrainedModel""",
							    """load_tf_weights_in_mobilenet_v1""",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)