

from pathlib import Path

import fire
from tqdm import tqdm





def      A__					(			__A  :						Any="ro"     ,				__A  :						int="en"     ,				__A  :						Tuple="wmt16"     ,				__A  :						Dict=None			)					->None:
      try:
            import datasets
      except (ModuleNotFoundError, ImportError):
            raise ImportError('''run pip install datasets'''			)
      __A    =F'''{src_lang}-{tgt_lang}'''
      print(F'''Converting {dataset}-{pair}'''			)
      __A    =datasets.load_dataset(__A     ,				__A			)
      if save_dir is None:
            __A    =F'''{dataset}-{pair}'''
      __A    =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
            __A    ='''val''' if split == '''validation''' else split
            __A    =save_dir.joinpath(F'''{fn}.source'''			)
            __A    =save_dir.joinpath(F'''{fn}.target'''			)
            __A    =src_path.open('''w+'''			)
            __A    =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]			):
                  __A    =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)





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
else:
   from .modeling_text_unet import UNetFlatConditionModel
   from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
   from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
   from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
   from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline


