




from typing import TYPE_CHECKING

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


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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=					[
         '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

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






def 							_A       (						lowerCAmelCase_					: int = 1000      ):
     """simple docstring"""

     return sum(2 * a * ((a - 1) // 2) for a in range(3       ,    n + 1      )      )


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