


'''simple docstring'''



from pathlib import Path

import fire
from tqdm import tqdm



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int="ro"      ,    UpperCAmelCase_    :				List[Any]="en"      ,    UpperCAmelCase_    :				Union[str, Any]="wmt16"      ,    UpperCAmelCase_    :				int=None							)     ->		None:
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('run pip install datasets'							)
     __lowerCamelCase   :	Dict								=		F'{src_lang}-{tgt_lang}'
     print(F'Converting {dataset}-{pair}'							)
     __lowerCamelCase   :	str								=		datasets.load_dataset(UpperCAmelCase_      ,    UpperCAmelCase_							)
     if save_dir is None:
          __lowerCamelCase   :	Union[str, Any]								=		F'{dataset}-{pair}'
     __lowerCamelCase   :	List[str]								=		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
          __lowerCamelCase   :	Optional[int]								=		'val' if split == 'validation' else split
          __lowerCamelCase   :	Optional[Any]								=		save_dir.joinpath(F'{fn}.source'							)
          __lowerCamelCase   :	List[Any]								=		save_dir.joinpath(F'{fn}.target'							)
          __lowerCamelCase   :	Optional[Any]								=		src_path.open('w+'							)
          __lowerCamelCase   :	Optional[int]								=		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   :	Dict								=		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_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






