






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

import fire
from tqdm import tqdm




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase="ro"						,		_UpperCamelCase="en"						,		_UpperCamelCase="wmt16"						,		_UpperCamelCase=None							):


   """simple docstring"""




   try:
      import datasets
   except (ModuleNotFoundError, ImportError):
      raise ImportError("run pip install datasets"							)
   lowercase_	:    Dict           =     F"""{src_lang}-{tgt_lang}"""
   print(F"""Converting {dataset}-{pair}"""							)
   lowercase_	:    int           =     datasets.load_dataset(_UpperCamelCase						,		_UpperCamelCase							)
   if save_dir is None:
      lowercase_	:    Optional[int]           =     F"""{dataset}-{pair}"""
   lowercase_	:    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
      lowercase_	:    Any           =     "val" if split == "validation" else split
      lowercase_	:    Tuple           =     save_dir.joinpath(F"""{fn}.source"""							)
      lowercase_	:    Dict           =     save_dir.joinpath(F"""{fn}.target"""							)
      lowercase_	:    int           =     src_path.open("w+"							)
      lowercase_	:    List[str]           =     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]							):
         lowercase_	:    int           =     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'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__ =			{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
     'EncodecModel',
     'EncodecPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




