






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_rembert import RemBertTokenizer
else:
    lowerCAmelCase			:							Dict     =None

lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)
lowerCAmelCase			:							Optional[int]     ={"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase			:							Optional[int]     ={
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase			:							List[str]     ={
    "google/rembert": 256,
}

lowerCAmelCase			:							Dict     ="▁"




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       RemBertTokenizer



      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Union[str, Any]=None    ,  _UpperCamelCase     :				str=None    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				List[Any]=True    ,  _UpperCamelCase     :				Optional[Any]=False    ,  _UpperCamelCase     :				Optional[int]="[CLS]"    ,  _UpperCamelCase     :				Optional[int]="[SEP]"    ,  _UpperCamelCase     :				List[str]="<unk>"    ,  _UpperCamelCase     :				Optional[int]="[SEP]"    ,  _UpperCamelCase     :				List[Any]="<pad>"    ,  _UpperCamelCase     :				str="[CLS]"    ,  _UpperCamelCase     :				Dict="[MASK]"    ,  **_UpperCamelCase     :				int    ,  )				->int:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else mask_token

          super().__init__(
              _UpperCamelCase    ,  tokenizer_file=_UpperCamelCase    ,  do_lower_case=_UpperCamelCase    ,  remove_space=_UpperCamelCase    ,  keep_accents=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  unk_token=_UpperCamelCase    ,  sep_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  cls_token=_UpperCamelCase    ,  mask_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          _lowerCamelCase     :		Tuple 						=						do_lower_case
          _lowerCamelCase     :		Tuple 						=						remove_space
          _lowerCamelCase     :		Union[str, Any] 						=						keep_accents
          _lowerCamelCase     :		Union[str, Any] 						=						vocab_file
          _lowerCamelCase     :		Tuple 						=						False if not self.vocab_file else True



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						[self.sep_token_id]
          _lowerCamelCase     :		Any 						=						[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None    ,  _UpperCamelCase     :				bool = False)				->List[int]:


          """simple docstring"""


          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      """You should not supply a second sequence if the provided sequence of """
                      """ids is already formatted with special tokens for the model.""")
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(_UpperCamelCase)) + [1] + ([0] * len(_UpperCamelCase)) + [1]
          return [1] + ([0] * len(_UpperCamelCase)) + [1]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						[self.sep_token_id]
          _lowerCamelCase     :		int 						=						[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
          return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		List[str] 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          if os.path.abspath(self.vocab_file) != os.path.abspath(_UpperCamelCase):
              copyfile(self.vocab_file    ,  _UpperCamelCase)

          return (out_vocab_file,)





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        "repo_name",
        "path",
        "copies",
        "size",
        "content",
        "license",
        "hash",
        "line_mean",
        "line_max",
        "alpha_frac",
        "autogenerated",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




