





from __future__ import annotations

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



@dataclass
class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=			42
 lowercase_		=			42



class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   List[Any]       , UpperCAmelCase_				:   int)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      list[list[Edge]]			  =[[] for _ in range(UpperCAmelCase_)]
   lowerCamelCase__:      List[Any]			  =size

 def __getitem__(self				:   List[str]       , UpperCAmelCase_				:   int)       ->Iterator[Edge]:



   '''simple docstring'''



   return iter(self._graph[vertex])

 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->List[Any]:



   '''simple docstring'''



   return self._size

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int]       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   int)       ->Optional[int]:



   '''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(UpperCAmelCase_       , UpperCAmelCase_))







 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict       , UpperCAmelCase_				:   int       , UpperCAmelCase_				:   int)       ->int | None:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =deque([start_vertex])
   lowerCamelCase__:      list[int | None]			  =[None] * self.size
   lowerCamelCase__:      List[Any]			  =0

   while queue:
     lowerCamelCase__:      int			  =queue.popleft()
     lowerCamelCase__:      Dict			  =distances[current_vertex]
     if current_distance is None:
       continue

     for edge in self[current_vertex]:
       lowerCamelCase__:      Optional[Any]			  =current_distance + edge.weight
       lowerCamelCase__:      Tuple			  =distances[edge.destination_vertex]
       if (
           isinstance(UpperCAmelCase_       , UpperCAmelCase_)
           and new_distance >= dest_vertex_distance
       ):
         continue
       lowerCamelCase__:      str			  =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()




'''simple docstring'''






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)

