






"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase_     :				str         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Dict         =				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase_     :				Any         =				{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

UpperCAmelCase_     :				Dict         =				{"""mobilebert-uncased""": 512}


UpperCAmelCase_     :				Tuple         =				{}




class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						VOCAB_FILES_NAMES
      __UpperCamelCase							=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase							=						PRETRAINED_INIT_CONFIGURATION
      __UpperCamelCase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase							=						MobileBertTokenizer
      def __init__(		self  :						List[str]		,			lowercase_  :						Optional[int]=None		,			lowercase_  :						Dict=None		,			lowercase_  :						Any=True		,			lowercase_  :						List[Any]="[UNK]"		,			lowercase_  :						List[Any]="[SEP]"		,			lowercase_  :						Tuple="[PAD]"		,			lowercase_  :						List[Any]="[CLS]"		,			lowercase_  :						Optional[int]="[MASK]"		,			lowercase_  :						Optional[Any]=True		,			lowercase_  :						Optional[Any]=None		,			**lowercase_  :						Optional[int]		,			):




          '''simple docstring'''


          super().__init__(
              lowercase_		,			tokenizer_file=lowercase_		,			do_lower_case=lowercase_		,			unk_token=lowercase_		,			sep_token=lowercase_		,			pad_token=lowercase_		,			cls_token=lowercase_		,			mask_token=lowercase_		,			tokenize_chinese_chars=lowercase_		,			strip_accents=lowercase_		,			**lowercase_		,			)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
          if (
              normalizer_state.get('''lowercase'''		,			lowercase_) != do_lower_case
              or normalizer_state.get('''strip_accents'''		,			lowercase_) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''		,			lowercase_) != tokenize_chinese_chars
          ):
              SCREAMING_SNAKE_CASE_   :						Dict							=	getattr(lowercase_		,			normalizer_state.pop('''type'''))
              SCREAMING_SNAKE_CASE_   :						int							=	do_lower_case
              SCREAMING_SNAKE_CASE_   :						int							=	strip_accents
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenize_chinese_chars
              SCREAMING_SNAKE_CASE_   :						str							=	normalizer_class(**lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	do_lower_case
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						int		,			lowercase_  :						str=None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						List[int]		,			lowercase_  :						Optional[List[int]] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_   :						Optional[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  :						Optional[int]		,			lowercase_  :						str		,			lowercase_  :						Optional[str] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	self._tokenizer.model.save(lowercase_		,			name=lowercase_)
          return tuple(lowercase_)










"""simple docstring"""






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


UpperCAmelCase_     :				Tuple         =				HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_     :				Tuple         =				parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase_     :				Tuple         =				multiprocessing.cpu_count()
UpperCAmelCase_     :				str         =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase_     :				Tuple         =				time.time()
UpperCAmelCase_     :				List[Any]         =				load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				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''')

UpperCAmelCase_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


