


from pathlib import Path

import fire
from tqdm import tqdm



def     __lowerCAmelCase						(							snake_case	:    Union[str, Any]="ro"						,      snake_case	:    List[str]="en"						,      snake_case	:    str="wmt16"						,      snake_case	:    Tuple=None )       ->							None:
   try:
      import datasets
   except (ModuleNotFoundError, ImportError):
      raise ImportError("""run pip install datasets""" )
   __lowerCamelCase:				Optional[Any]     =	f'{src_lang}-{tgt_lang}'
   print(f'Converting {dataset}-{pair}' )
   __lowerCamelCase:				Any     =	datasets.load_dataset(snake_case						,      snake_case )
   if save_dir is None:
      __lowerCamelCase:				Dict     =	f'{dataset}-{pair}'
   __lowerCamelCase:				Union[str, Any]     =	Path(snake_case )
   save_dir.mkdir(exist_ok=snake_case )

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






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
   _A		:				str									=       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.''',
   )

   _A		:				Optional[Any]									=       parser.parse_args()

   _A		:				str									=       UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

   _A		:				Optional[Any]									=       CLIPImageProcessor()
   _A		:				List[Any]									=       CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

   _A		:				str									=       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)


