






'''simple docstring'''





from pathlib import Path

import fire
from tqdm import tqdm






def     _snake_case    (__lowercase="ro"    ,      __lowercase="en"    ,      __lowercase="wmt16"    ,      __lowercase=None):






   try:
      import datasets
   except (ModuleNotFoundError, ImportError):
      raise ImportError('run pip install datasets')
   UpperCamelCase_									=     f"""{src_lang}-{tgt_lang}"""
   print(f"""Converting {dataset}-{pair}""")
   UpperCamelCase_									=     datasets.load_dataset(_snake_case    ,      _snake_case)
   if save_dir is None:
      UpperCamelCase_									=     f"""{dataset}-{pair}"""
   UpperCamelCase_									=     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
      UpperCamelCase_									=     'val' if split == 'validation' else split
      UpperCamelCase_									=     save_dir.joinpath(f"""{fn}.source""")
      UpperCamelCase_									=     save_dir.joinpath(f"""{fn}.target""")
      UpperCamelCase_									=     src_path.open('w+')
      UpperCamelCase_									=     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_									=     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()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     ImageTextPipelineOutput,
     UniDiffuserPipeline,
 )
else:
 from .modeling_text_decoder import UniDiffuserTextDecoder
 from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
 from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline





