



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase__					  =  logging.get_logger(__name__)

lowerCAmelCase__					  =  {'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase__					  =  {
    '''vocab_file''': {
        '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
        '''YituTech/conv-bert-medium-small''': (
            '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
        ),
        '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
    }
}

lowerCAmelCase__					  =  {
    '''YituTech/conv-bert-base''': 5_1_2,
    '''YituTech/conv-bert-medium-small''': 5_1_2,
    '''YituTech/conv-bert-small''': 5_1_2,
}


lowerCAmelCase__					  =  {
    '''YituTech/conv-bert-base''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
    '''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		VOCAB_FILES_NAMES
					lowercase_								=		PRETRAINED_VOCAB_FILES_MAP
					lowercase_								=		PRETRAINED_INIT_CONFIGURATION
					lowercase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_								=		ConvBertTokenizer







					def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       SCREAMING_SNAKE_CASE :     Dict=None				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=True				,       SCREAMING_SNAKE_CASE :     Tuple="[UNK]"				,       SCREAMING_SNAKE_CASE :     Optional[Any]="[SEP]"				,       SCREAMING_SNAKE_CASE :     str="[PAD]"				,       SCREAMING_SNAKE_CASE :     List[str]="[CLS]"				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="[MASK]"				,       SCREAMING_SNAKE_CASE :     str=True				,       SCREAMING_SNAKE_CASE :     int=None				,       **SCREAMING_SNAKE_CASE :     Union[str, Any]				,       ):
									super().__init__(
									    SCREAMING_SNAKE_CASE				,       tokenizer_file=SCREAMING_SNAKE_CASE				,       do_lower_case=SCREAMING_SNAKE_CASE				,       unk_token=SCREAMING_SNAKE_CASE				,       sep_token=SCREAMING_SNAKE_CASE				,       pad_token=SCREAMING_SNAKE_CASE				,       cls_token=SCREAMING_SNAKE_CASE				,       mask_token=SCREAMING_SNAKE_CASE				,       tokenize_chinese_chars=SCREAMING_SNAKE_CASE				,       strip_accents=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE				,       )

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

									lowercase__					:   Optional[int]													=					do_lower_case







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Any=None					):
									lowercase__					:   Any													=					[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 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None					):
									lowercase__					:   Optional[Any]													=					[self.sep_token_id]
									lowercase__					:   Union[str, Any]													=					[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 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[str] = None					):
									lowercase__					:   List[str]													=					self._tokenizer.model.save(SCREAMING_SNAKE_CASE				,       name=SCREAMING_SNAKE_CASE					)
									return tuple(SCREAMING_SNAKE_CASE					)









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


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

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  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__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




