

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

import fire
from tqdm import tqdm


def        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]="ro"							, _UpperCamelCase						:     Optional[Any]="en"							, _UpperCamelCase						:     List[str]="wmt16"							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			None:
     """simple docstring"""




     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('run pip install datasets'				)
     _SCREAMING_SNAKE_CASE							    =f"{src_lang}-{tgt_lang}"
     print(f"Converting {dataset}-{pair}"				)
     _SCREAMING_SNAKE_CASE							    =datasets.load_dataset(_UpperCamelCase							, _UpperCamelCase				)
     if save_dir is None:
          _SCREAMING_SNAKE_CASE							    =f"{dataset}-{pair}"
     _SCREAMING_SNAKE_CASE							    =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
          _SCREAMING_SNAKE_CASE							    ='val' if split == 'validation' else split
          _SCREAMING_SNAKE_CASE							    =save_dir.joinpath(f"{fn}.source"				)
          _SCREAMING_SNAKE_CASE							    =save_dir.joinpath(f"{fn}.target"				)
          _SCREAMING_SNAKE_CASE							    =src_path.open('w+'				)
          _SCREAMING_SNAKE_CASE							    =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]				):
               _SCREAMING_SNAKE_CASE							    =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 Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 							A__    (       A__     ,   A__  ):





  @register_to_config
  def __init__(		self      :	Dict							,					_a      :	int = 768							,					)	->    Union[str, Any]:





       '''simple docstring'''






       super().__init__()

       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.zeros(1							,					_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(torch.ones(1							,					_a ) )





  def 						A  (		self      :	Tuple							,					_a      :	Optional[Union[str, torch.device]] = None							,					_a      :	Optional[torch.dtype] = None							,					)	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.mean.to(_a ).to(_a ) )
       _SCREAMING_SNAKE_CASE							    =nn.Parameter(self.std.to(_a ).to(_a ) )
       return self





  def 						A  (		self      :	Tuple							,					_a      :	str )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds - self.mean) * 1.0 / self.std
       return embeds





  def 						A  (		self      :	List[str]							,					_a      :	Optional[Any] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =(embeds * self.std) + self.mean
       return embeds
