




import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
       UpperCAmelCase_									=	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.',
       )

       UpperCAmelCase_									=	parser.parse_args()

       UpperCAmelCase_									=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

       UpperCAmelCase_									=	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)





'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					for split in ds.keys():
										print(f'''Splitting {split} with {ds[split].num_rows} records'''   )

										# to save to val.source, val.target like summary datasets
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= tgt_path.open('''w+'''   )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]   ):
															UpperCamelCase__   :Union[str, Any]												= x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''   )
															tgt_fp.write(ex[tgt_lang] + '''\n'''   )

					print(f'''Saved {dataset} dataset to {save_dir}'''   )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)