'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm

def _lowercase	(      lowerCamelCase__="ro"							,    lowerCamelCase__="en"							,    lowerCamelCase__="wmt16"							,    lowerCamelCase__=None						)					->       Optional[int]:





       """simple docstring"""


       try:
              import datasets
       except (ModuleNotFoundError, ImportError):
              raise ImportError("run pip install datasets"						)
       __UpperCAmelCase  :				Any       				=		f"""{src_lang}-{tgt_lang}"""
       print(f"""Converting {dataset}-{pair}"""						)
       __UpperCAmelCase  :				str       				=		datasets.load_dataset(_lowerCamelCase							,    _lowerCamelCase						)
       if save_dir is None:
              __UpperCAmelCase  :				str       				=		f"""{dataset}-{pair}"""
       __UpperCAmelCase  :				List[Any]       				=		Path(_lowerCamelCase						)
       save_dir.mkdir(exist_ok=_lowerCamelCase						)

       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  :				Union[str, Any]       				=		"val" if split == "validation" else split
              __UpperCAmelCase  :				Optional[int]       				=		save_dir.joinpath(f"""{fn}.source"""						)
              __UpperCAmelCase  :				Tuple       				=		save_dir.joinpath(f"""{fn}.target"""						)
              __UpperCAmelCase  :				Optional[Any]       				=		src_path.open("w+"						)
              __UpperCAmelCase  :				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]						):
                     __UpperCAmelCase  :				Optional[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)







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a   :	str 							=   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	str 							=   ["ViTFeatureExtractor"]
   _a   :	Dict 							=   ["ViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTForImageClassification",
       "ViTForMaskedImageModeling",
       "ViTModel",
       "ViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[str] 							=   [
       "TFViTForImageClassification",
       "TFViTModel",
       "TFViTPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   [
       "FlaxViTForImageClassification",
       "FlaxViTModel",
       "FlaxViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _a   :	Dict 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






