

'''simple docstring'''




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
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         =						{"tokenizer_file": "tokenizer.json"}

_lowerCAmelCase         =						{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}






class   _SCREAMING_SNAKE_CASE   (   A__     ):
 __SCREAMING_SNAKE_CASE					:Dict      	=					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE					:int      	=					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE					:Any      	=					["input_ids", "attention_mask"]
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					None



 def __init__(   self		:   int			,				a__		:   List[str]=None			,				a__		:   Tuple=None			,				a__		:   Optional[int]=None			,				a__		:   List[str]="<unk>"			,				a__		:   Dict="<s>"			,				a__		:   Union[str, Any]="</s>"			,				a__		:   Optional[int]="<pad>"			,				a__		:   int=False			,				a__		:   Dict=False			,				**a__		:   str			,				):
      super().__init__(
          __A			,				__A			,				tokenizer_file=__A			,				unk_token=__A			,				bos_token=__A			,				eos_token=__A			,				pad_token=__A			,				add_prefix_space=__A			,				clean_up_tokenization_spaces=__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		:   Optional[Any]			,				*a__		:   Optional[int]			,				**a__		:   List[Any]      ):
      __magic_name__						       =kwargs.get('''is_split_into_words'''			,				__A      )
      if not (self.add_prefix_space or not is_split_into_words):
           raise Exception(
               F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
               ''' pretokenized inputs.'''      )

      return super()._batch_encode_plus(*__A			,				**__A      )



 def 			snake_case__  (   self		:   Optional[Any]			,				*a__		:   Tuple			,				**a__		:   Any      ):
      __magic_name__						       =kwargs.get('''is_split_into_words'''			,				__A      )

      if not (self.add_prefix_space or not is_split_into_words):
           raise Exception(
               F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
               ''' pretokenized inputs.'''      )

      return super()._encode_plus(*__A			,				**__A      )



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



 def 			snake_case__  (   self		:   str			,				a__		:   List[str]      ):
      __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)






