


'''simple docstring'''
def      a (     __a					,   __a = False  )    ->      str:



					'''simple docstring'''






					if not isinstance(__a					,   __a  ):
										UpperCamelCase__  :Optional[int]				   =					f'''Expected string as input, found {type(__a  )}'''
										raise ValueError(__a  )
					if not isinstance(__a					,   __a  ):
										UpperCamelCase__  :Any				   =					f'''Expected boolean as use_pascal parameter, found {type(__a  )}'''
										raise ValueError(__a  )

					UpperCamelCase__  :List[Any]				   =					input_str.split('''_'''  )

					UpperCamelCase__  :Tuple				   =					0 if use_pascal else 1

					UpperCamelCase__  :List[str]				   =					words[start_index:]

					UpperCamelCase__  :List[Any]				   =					[word[0].upper() + word[1:] for word in words_to_capitalize]

					UpperCamelCase__  :List[str]				   =					'''''' if use_pascal else words[0]

					return "".join([initial_word, *capitalized_words]  )


if __name__ == "__main__":
		from doctest import testmod

		testmod()



'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		__snake_case				 =	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				 =	parser.parse_args()

		__snake_case				 =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

		__snake_case				 =	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)