



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   __lowerCamelCase         =			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.''',
   )

   __lowerCamelCase         =			parser.parse_args()

   __lowerCamelCase         =			UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

   __lowerCamelCase         =			CLIPImageProcessor()
   __lowerCamelCase         =			CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   __lowerCamelCase         =			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)





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass


@dataclass
class   snake_case_ :




       """simple docstring"""






       _lowerCamelCase			    =				42
       _lowerCamelCase			    =				42


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	list[list[Edge]]				      =		[[] for _ in range(lowercase)]
             UpperCAmelCase_  :	str				      =		size



       def __getitem__(     self     ,lowercase):

             """simple docstring"""





             return iter(self._graph[vertex])



       @property
       def  A_   (     self):

             """simple docstring"""





             return self._size



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             if weight not in (0, 1):
                   raise ValueError("Edge weight must be either 0 or 1.")

             if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError("Vertex indexes must be in [0; size).")

             self._graph[from_vertex].append(Edge(lowercase     ,lowercase))



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		deque([start_vertex])
             UpperCAmelCase_  :	list[int | None]				      =		[None] * self.size
             UpperCAmelCase_  :	Dict				      =		0

             while queue:
                   UpperCAmelCase_  :	List[Any]				      =		queue.popleft()
                   UpperCAmelCase_  :	Any				      =		distances[current_vertex]
                   if current_distance is None:
                         continue

                   for edge in self[current_vertex]:
                         UpperCAmelCase_  :	Tuple				      =		current_distance + edge.weight
                         UpperCAmelCase_  :	Optional[Any]				      =		distances[edge.destination_vertex]
                         if (
                             isinstance(lowercase     ,lowercase)
                             and new_distance >= dest_vertex_distance
                         ):
                               continue
                         UpperCAmelCase_  :	Tuple				      =		new_distance
                         if edge.weight == 0:
                               queue.appendleft(edge.destination_vertex)
                         else:
                               queue.append(edge.destination_vertex)

             if distances[finish_vertex] is None:
                   raise ValueError("No path from start_vertex to finish_vertex.")

             return distances[finish_vertex]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
