
from typing import TYPE_CHECKING

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


__a         =   {
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   ['ConditionalDetrFeatureExtractor']
						__a         =   ['ConditionalDetrImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a         =   [
						    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ConditionalDetrForObjectDetection',
						    'ConditionalDetrForSegmentation',
						    'ConditionalDetrModel',
						    'ConditionalDetrPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_conditional_detr import (
						    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ConditionalDetrConfig,
						    ConditionalDetrOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
												from .image_processing_conditional_detr import ConditionalDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_conditional_detr import (
												    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ConditionalDetrForObjectDetection,
												    ConditionalDetrForSegmentation,
												    ConditionalDetrModel,
												    ConditionalDetrPreTrainedModel,
												)

else:
						import sys

						__a         =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				if not isinstance(snake_case__		,  snake_case__   ):
								raise ValueError('''Input must be an integer'''   )
				if input_num <= 0:
								raise ValueError('''Input must be positive'''   )
				return sum(
				    divisor for divisor in range(1		,  input_num // 2 + 1   ) if input_num % divisor == 0   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




