


import argparse
import json

from tqdm import tqdm





def     _lowerCamelCase		(				):






							"""simple docstring"""



							a_							: Union[str, Any]								=						argparse.ArgumentParser()

							# Required parameters
							parser.add_argument(
							    """--src_path"""						,						type=__a						,						default="""biencoder-nq-dev.json"""						,						help="""Path to raw DPR training data"""						,						)
							parser.add_argument(
							    """--evaluation_set"""						,						type=__a						,						help="""where to store parsed evaluation_set file"""						,						)
							parser.add_argument(
							    """--gold_data_path"""						,						type=__a						,						help="""where to store parsed gold_data_path file"""						,						)
							a_							: Any								=						parser.parse_args()

							with open(args.src_path						,						"""r"""   ) as src_file, open(args.evaluation_set						,						"""w"""   ) as eval_file, open(
							    args.gold_data_path						,						"""w"""   ) as gold_file:
														a_							: List[Any]								=						json.load(__a   )
														for dpr_record in tqdm(__a   ):
																					a_							: Optional[int]								=						dpr_record['''question''']
																					a_							: Union[str, Any]								=						[context['''title'''] for context in dpr_record['''positive_ctxs''']]
																					eval_file.write(question + """\n"""   )
																					gold_file.write("""\t""".join(__a   ) + """\n"""   )


if __name__ == "__main__":
	main()






'''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)