


'''simple docstring'''

from typing import TYPE_CHECKING

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


a_  :				str						= {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_  :				str						= ["""ConditionalDetrFeatureExtractor"""]
     a_  :				Any						= ["""ConditionalDetrImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_  :				Union[str, Any]						= [
         """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_  :				Dict						= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







'''simple docstring'''

import math





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  math.loga(math.sqrt(4 * positive_integer + 1		) / 2 + 1 / 2		)

 return exponent == int(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						float = 1 / 12345		):
 lowerCamelCase_      =  0
 lowerCamelCase_      =  0

 lowerCamelCase_      =  3
 while True:
  lowerCamelCase_      =  (integer**2 - 1) / 4
  # if candidate is an integer, then there is a partition for k
  if partition_candidate == int(UpperCAmelCase_		):
   lowerCamelCase_      =  int(UpperCAmelCase_		)
   total_partitions += 1
   if check_partition_perfect(UpperCAmelCase_		):
    perfect_partitions += 1
  if perfect_partitions > 0:
   if perfect_partitions / total_partitions < max_proportion:
    return int(UpperCAmelCase_		)
  integer += 1


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



