"""simple docstring"""






from typing import TYPE_CHECKING

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


__lowerCAmelCase       :			Optional[Any]   	={
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			Optional[int]   	=["""ConditionalDetrFeatureExtractor"""]
       __lowerCAmelCase       :			List[str]   	=["""ConditionalDetrImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			List[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

       __lowerCAmelCase       :			List[Any]   	=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




"""simple docstring"""






import math


def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       bool:
      '''simple docstring'''


      lowercase								=      math.loga(math.sqrt(4 * positive_integer + 1						) / 2 + 1 / 2						)

      return exponent == int(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:float = 1 / 1_2_3_4_5						)					->       int:
      '''simple docstring'''


      lowercase								=      0
      lowercase								=      0

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


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



