
from pathlib import Path

import fire
from tqdm import tqdm







def 						_snake_case(  SCREAMING_SNAKE_CASE__="ro"					,	SCREAMING_SNAKE_CASE__="en"					,	SCREAMING_SNAKE_CASE__="wmt16"					,	SCREAMING_SNAKE_CASE__=None				) ->    None:
       try:
              import datasets
       except (ModuleNotFoundError, ImportError):
              raise ImportError("""run pip install datasets"""				)
       lowercase							:     Tuple       	=      f"{src_lang}-{tgt_lang}"
       print(f"Converting {dataset}-{pair}"				)
       lowercase							:     List[str]       	=      datasets.load_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       if save_dir is None:
              lowercase							:     Tuple       	=      f"{dataset}-{pair}"
       lowercase							:     List[Any]       	=      Path(SCREAMING_SNAKE_CASE__				)
       save_dir.mkdir(exist_ok=SCREAMING_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
              lowercase							:     Dict       	=      """val""" if split == """validation""" else split
              lowercase							:     Any       	=      save_dir.joinpath(f"{fn}.source"				)
              lowercase							:     Union[str, Any]       	=      save_dir.joinpath(f"{fn}.target"				)
              lowercase							:     Any       	=      src_path.open("""w+"""				)
              lowercase							:     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							:     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)


from typing import TYPE_CHECKING

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


lowercase  :					str						    =							{
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Tuple						    =							["""FunnelTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					List[str]						    =							[
      """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FunnelBaseModel""",
      """FunnelForMaskedLM""",
      """FunnelForMultipleChoice""",
      """FunnelForPreTraining""",
      """FunnelForQuestionAnswering""",
      """FunnelForSequenceClassification""",
      """FunnelForTokenClassification""",
      """FunnelModel""",
      """FunnelPreTrainedModel""",
      """load_tf_weights_in_funnel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Optional[Any]						    =							[
      """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFFunnelBaseModel""",
      """TFFunnelForMaskedLM""",
      """TFFunnelForMultipleChoice""",
      """TFFunnelForPreTraining""",
      """TFFunnelForQuestionAnswering""",
      """TFFunnelForSequenceClassification""",
      """TFFunnelForTokenClassification""",
      """TFFunnelModel""",
      """TFFunnelPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
  from .tokenization_funnel import FunnelTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_funnel_fast import FunnelTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_funnel import (
        FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        FunnelBaseModel,
        FunnelForMaskedLM,
        FunnelForMultipleChoice,
        FunnelForPreTraining,
        FunnelForQuestionAnswering,
        FunnelForSequenceClassification,
        FunnelForTokenClassification,
        FunnelModel,
        FunnelPreTrainedModel,
        load_tf_weights_in_funnel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_funnel import (
        TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFunnelBaseModel,
        TFFunnelForMaskedLM,
        TFFunnelForMultipleChoice,
        TFFunnelForPreTraining,
        TFFunnelForQuestionAnswering,
        TFFunnelForSequenceClassification,
        TFFunnelForTokenClassification,
        TFFunnelModel,
        TFFunnelPreTrainedModel,
    )

else:
  import sys

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