

from __future__ import annotations

from collections import Counter
from random import random






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       List[Any]		)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				{}
			def 							__UpperCamelCase       (	self							:       List[Any]					,					a							:       str		)							->				None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				{}
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       str					,					a							:       str					,					a							:       float		)							->				None:

				"""simple docstring"""

				if nodea not in self.connections:
					self.add_node(a		)
				if nodea not in self.connections:
					self.add_node(a		)
				SCREAMING_SNAKE_CASE      :					Any    =				probability
			def 							__UpperCamelCase       (	self							:       Any		)							->				list[str]:

				"""simple docstring"""

				return list(self.connections		)






			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       str		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				0
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				random()

				for dest in self.connections[node]:
					current_probability += self.connections[node][dest]
					if current_probability > random_value:
						return dest
				return ""

def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					str    =				MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_a							,	_a							,	_a)

	SCREAMING_SNAKE_CASE      :					str    =				Counter(graph.get_nodes())
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				start

	for _ in range(_a):
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				graph.transition(_a)
		visited[node] += 1

	return visited


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)

