

'''simple docstring'''




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


_lowerCAmelCase         =						logging.get_logger(__name__)

_lowerCAmelCase         =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase         =						{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase         =						{
    "gpt-neox-20b": 2048,
}






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:Any      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:Any      	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE					:Union[str, Any]      	=					["""input_ids""", """attention_mask"""]



 def __init__(   self		:   Optional[Any]			,				a__		:   int=None			,				a__		:   Optional[int]=None			,				a__		:   Optional[Any]=None			,				a__		:   Tuple="<|endoftext|>"			,				a__		:   Optional[Any]="<|endoftext|>"			,				a__		:   Any="<|endoftext|>"			,				a__		:   Optional[int]=False			,				**a__		:   Any			,				):
      super().__init__(
          a__			,				a__			,				tokenizer_file=a__			,				unk_token=a__			,				bos_token=a__			,				eos_token=a__			,				add_prefix_space=a__			,				**a__			,				)

      __magic_name__						       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
      if pre_tok_state.get('''add_prefix_space'''			,				a__      ) != add_prefix_space:
           __magic_name__						       =    getattr(a__			,				pre_tok_state.pop('''type'''      )      )
           __magic_name__						       =    add_prefix_space
           __magic_name__						       =    pre_tok_class(**a__      )

      __magic_name__						       =    add_prefix_space



 def 			snake_case__  (   self		:   str			,				a__		:   str			,				a__		:   Optional[str] = None      ):
      __magic_name__						       =    self._tokenizer.model.save(a__			,				name=a__      )
      return tuple(a__      )



 def 			snake_case__  (   self		:   int			,				a__		:   "Conversation"      ):
      __magic_name__						       =    []
      for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(a__			,				add_special_tokens=a__      ) + [self.eos_token_id]      )

      if len(a__      ) > self.model_max_length:
           __magic_name__						       =    input_ids[-self.model_max_length :]
      return input_ids









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     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
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    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]			):
               __magic_name__						       =    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)






