

'''simple docstring'''


def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_		:      str       	=					[0] * len(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					[]
	UpperCAmelCase_		:      List[str]       	=					[]
	UpperCAmelCase_		:      Dict       	=					0

	for values in graph.values():
		for i in values:
			indegree[i] += 1

	for i in range(len(UpperCamelCase  )  ):
		if indegree[i] == 0:
			queue.append(UpperCamelCase  )

	while queue:
		UpperCAmelCase_		:      Optional[Any]       	=					queue.pop(0  )
		cnt += 1
		topo.append(UpperCamelCase  )
		for x in graph[vertex]:
			indegree[x] -= 1
			if indegree[x] == 0:
				queue.append(UpperCamelCase  )

	if cnt != len(UpperCamelCase  ):
		print('Cycle exists'  )
	else:
		print(UpperCamelCase  )


# Adjacency List of Graph
lowerCAmelCase__										= {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)









'''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)






