

from bisect import bisect
from itertools import accumulate





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[Any]			)					->List[Any]:
      __A    =sorted(zip(__A     ,				__A			)     ,				key=lambda __A			: x[0] / x[1]     ,				reverse=__A			)
      __A    ,		__A    =[i[0] for i in r], [i[1] for i in r]
      __A    =list(accumulate(__A			)			)
      __A    =bisect(__A     ,				__A			)
      return (
          0
          if k == 0
          else sum(vl[:k]			) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
          if k != n
          else sum(vl[:k]			)
      )


if __name__ == "__main__":
   import doctest

   doctest.testmod()





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
else:
   from .modeling_text_unet import UNetFlatConditionModel
   from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
   from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
   from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
   from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline


