

'''simple docstring'''


from pathlib import Path

import fire
from tqdm import tqdm




def 	_a       (				__lowerCAmelCase		:      str="ro"   ,							__lowerCAmelCase		:      Tuple="en"   ,							__lowerCAmelCase		:      Tuple="wmt16"   ,							__lowerCAmelCase		:      Any=None     ):

			"""simple docstring"""

			try:
						import datasets
			except (ModuleNotFoundError, ImportError):
						raise ImportError('''run pip install datasets'''     )
			snake_case__						:       Union[str, Any]       						=  F"""{src_lang}-{tgt_lang}"""
			print(F"""Converting {dataset}-{pair}"""     )
			snake_case__						:       str       						=  datasets.load_dataset(_UpperCamelCase   ,							_UpperCamelCase     )
			if save_dir is None:
						snake_case__						:       List[Any]       						=  F"""{dataset}-{pair}"""
			snake_case__						:       Optional[Any]       						=  Path(_UpperCamelCase     )
			save_dir.mkdir(exist_ok=_UpperCamelCase     )

			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
						snake_case__						:       List[Any]       						=  '''val''' if split == '''validation''' else split
						snake_case__						:       Optional[Any]       						=  save_dir.joinpath(F"""{fn}.source"""     )
						snake_case__						:       Optional[int]       						=  save_dir.joinpath(F"""{fn}.target"""     )
						snake_case__						:       List[str]       						=  src_path.open('''w+'''     )
						snake_case__						:       Union[str, Any]       						=  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]     ):
									snake_case__						:       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)






'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       Dict										=						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__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						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)



