


# Algorithm for the pigeonhole sorting




def    __lowerCamelCase   (      A__						: int					)     ->    Tuple:
   lowerCamelCase_						:   Union[str, Any]		= min(A__					)  # min() finds the minimum value
   lowerCamelCase_						:   Optional[Any]		= max(A__					)  # max() finds the maximum value

   lowerCamelCase_						:   int		= max_val - min_val + 1  # size is difference of max and min values plus one

   # list of pigeonholes of size equal to the variable size
   lowerCamelCase_						:   Any		= [0] * size

   # Populate the pigeonholes.
   for x in a:
      assert isinstance(A__						,						A__					), "integers only please"
      holes[x - min_val] += 1

   # Putting the elements back into the array in an order.
   lowerCamelCase_						:   List[Any]		= 0
   for count in range(A__					):
      while holes[count] > 0:
         holes[count] -= 1
         lowerCamelCase_						:   Optional[int]		= count + min_val
         i += 1


def    __lowerCamelCase   (      )     ->    int:
   lowerCamelCase_						:   List[Any]		= [8, 3, 2, 7, 4, 6, 8]
   pigeonhole_sort(A__					)
   print("""Sorted order is:"""						,						""" """.join(A__					)					)


if __name__ == "__main__":
      main()









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





