






"""simple docstring"""



from ... import PretrainedConfig


A_    =							{
    '''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				__SCREAMING_SNAKE_CASE		):
       snake_case_     =							NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
       snake_case_     =							'nezha'


       def __init__(			self      :						int			,    snake_case      :						Union[str, Any]=2_1128			,    snake_case      :						int=768			,    snake_case      :						Any=12			,    snake_case      :						List[str]=12			,    snake_case      :						str=3072			,    snake_case      :						int="gelu"			,    snake_case      :						int=0.1			,    snake_case      :						str=0.1			,    snake_case      :						Tuple=512			,    snake_case      :						List[Any]=64			,    snake_case      :						Dict=2			,    snake_case      :						List[Any]=0.02			,    snake_case      :						Optional[Any]=1e-12			,    snake_case      :						List[Any]=0.1			,    snake_case      :						Union[str, Any]=0			,    snake_case      :						Any=2			,    snake_case      :						Union[str, Any]=3			,    snake_case      :						int=True			,    **snake_case      :						int			,    ):
             '''simple docstring'''





             super().__init__(pad_token_id=_a			,    bos_token_id=_a			,    eos_token_id=_a			,    **_a							)

             A__						:  Tuple		  =  vocab_size
             A__						:  int		  =  hidden_size
             A__						:  Any		  =  num_hidden_layers
             A__						:  List[Any]		  =  num_attention_heads
             A__						:  Tuple		  =  hidden_act
             A__						:  List[Any]		  =  intermediate_size
             A__						:  List[str]		  =  hidden_dropout_prob
             A__						:  Tuple		  =  attention_probs_dropout_prob
             A__						:  Dict		  =  max_position_embeddings
             A__						:  Optional[Any]		  =  max_relative_position
             A__						:  List[Any]		  =  type_vocab_size
             A__						:  int		  =  initializer_range
             A__						:  Tuple		  =  layer_norm_eps
             A__						:  Dict		  =  classifier_dropout
             A__						:  int		  =  use_cache














"""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')






