


'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	List[str]		=     '''dpr'''





     def __init__( self       :      List[Any]    ,      UpperCAmelCase__       :      Tuple=3_0_5_2_2    ,      UpperCAmelCase__       :      Tuple=7_6_8    ,      UpperCAmelCase__       :      str=1_2    ,      UpperCAmelCase__       :      str=1_2    ,      UpperCAmelCase__       :      Tuple=3_0_7_2    ,      UpperCAmelCase__       :      Any="gelu"    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Union[str, Any]=0.1    ,      UpperCAmelCase__       :      Optional[int]=5_1_2    ,      UpperCAmelCase__       :      Any=2    ,      UpperCAmelCase__       :      Union[str, Any]=0.02    ,      UpperCAmelCase__       :      Dict=1E-12    ,      UpperCAmelCase__       :      Tuple=0    ,      UpperCAmelCase__       :      Union[str, Any]="absolute"    ,      UpperCAmelCase__       :      int = 0    ,      **UpperCAmelCase__       :      Union[str, Any]    ,      )				->  List[str]:
        super().__init__(pad_token_id=UpperCAmelCase__    ,      **UpperCAmelCase__    )

        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			layer_norm_eps
        lowerCAmelCase     =			projection_dim
        lowerCAmelCase     =			position_embedding_type









'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


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

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

__snake_case    						=time.time()
__snake_case    						=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''')

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




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





