






"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_    =							logging.get_logger(__name__)

A_    =							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A_    =							{
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

A_    =							{
    '''junnyu/roformer_chinese_small''': 1536,
    '''junnyu/roformer_chinese_base''': 1536,
    '''junnyu/roformer_chinese_char_small''': 512,
    '''junnyu/roformer_chinese_char_base''': 512,
    '''junnyu/roformer_small_discriminator''': 128,
    '''junnyu/roformer_small_generator''': 128,
}


A_    =							{
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							PRETRAINED_INIT_CONFIGURATION
							snake_case_     =							RoFormerTokenizer


							def __init__(			self      :						str			,    snake_case      :						List[str]=None			,    snake_case      :						Optional[Any]=None			,    snake_case      :						List[str]=True			,    snake_case      :						Dict="[UNK]"			,    snake_case      :						Union[str, Any]="[SEP]"			,    snake_case      :						Any="[PAD]"			,    snake_case      :						Any="[CLS]"			,    snake_case      :						Optional[Any]="[MASK]"			,    snake_case      :						Any=True			,    snake_case      :						Dict=None			,    **snake_case      :						List[str]			,    ):
													'''simple docstring'''





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

													A__						:  Optional[Any]		  =  json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
													if (
													    pre_tok_state.get("""lowercase"""			,    snake_case							) != do_lower_case
													    or pre_tok_state.get("""strip_accents"""			,    snake_case							) != strip_accents
													):
																			A__						:  Tuple		  =  getattr(snake_case			,    pre_tok_state.pop("""type"""							)							)
																			A__						:  int		  =  do_lower_case
																			A__						:  Optional[Any]		  =  strip_accents
																			A__						:  List[Any]		  =  pre_tok_class(**snake_case							)

													A__						:  Tuple		  =  do_lower_case


							def __getstate__(			self      :						int							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.__dict__.copy()
													A__						:  Optional[Any]		  =  BertPreTokenizer()
													return state


							def __setstate__(			self      :						List[str]			,    snake_case      :						str							):
													'''simple docstring'''





													A__						:  Any		  =  d
													A__						:  int		  =  self.__dict__["""_tokenizer"""].get_vocab()
													A__						:  Tuple		  =  PreTokenizer.custom(JiebaPreTokenizer(snake_case							)							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						List[Any]=None							):
													'''simple docstring'''





													A__						:  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 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  int		  =  [self.sep_token_id]
													A__						:  List[str]		  =  [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 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													A__						:  Tuple		  =  self._tokenizer.model.save(snake_case			,    name=snake_case							)
													return tuple(snake_case							)


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Union[str, Any]			,    snake_case      :						Dict=None			,    snake_case      :						List[str]=None			,    snake_case      :						Dict=False			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													A__						:  Tuple		  =  BertPreTokenizer()
													return super().save_pretrained(snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case							)














"""simple docstring"""



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->str:
						A__						:  Tuple		  =  {}
						A__						:  Union[str, Any]		  =  tokenizer(example["""content"""],						truncation=UpperCAmelCase__      )["""input_ids"""]
						A__						:  Any		  =  len(example["""content"""]      ) / len(output["""input_ids"""]      )
						return output


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

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

A_    =							time.time()
A_    =							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')

A_    =							time.time()
ds.push_to_hub(args.tokenized_data_repo)

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






