





from pathlib import Path

import fire
from tqdm import tqdm




def    __UpperCAmelCase(      lowercase_="ro" ,    lowercase_="en" ,    lowercase_="wmt16" ,    lowercase_=None      ):
       try:
              import datasets
       except (ModuleNotFoundError, ImportError):
              raise ImportError('''run pip install datasets'''      )
       _lowerCamelCase							:     int	=		F"""{src_lang}-{tgt_lang}"""
       print(F"""Converting {dataset}-{pair}"""      )
       _lowerCamelCase							:     int	=		datasets.load_dataset(lowercase_ ,    lowercase_      )
       if save_dir is None:
              _lowerCamelCase							:     List[Any]	=		F"""{dataset}-{pair}"""
       _lowerCamelCase							:     Dict	=		Path(lowercase_      )
       save_dir.mkdir(exist_ok=lowercase_      )

       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							:     List[str]	=		'''val''' if split == '''validation''' else split
              _lowerCamelCase							:     List[str]	=		save_dir.joinpath(F"""{fn}.source"""      )
              _lowerCamelCase							:     int	=		save_dir.joinpath(F"""{fn}.target"""      )
              _lowerCamelCase							:     Any	=		src_path.open('''w+'''      )
              _lowerCamelCase							:     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]      ):
                     _lowerCamelCase							:     Any	=		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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

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

