






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_    =							logging.get_logger(__name__)

A_    =							{
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'xlm-roberta'


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						str=3_0522			,    snake_case      :						Union[str, Any]=768			,    snake_case      :						Dict=12			,    snake_case      :						Union[str, Any]=12			,    snake_case      :						Any=3072			,    snake_case      :						List[str]="gelu"			,    snake_case      :						Optional[Any]=0.1			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						Tuple=512			,    snake_case      :						Any=2			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						Optional[int]=1e-12			,    snake_case      :						Optional[Any]=1			,    snake_case      :						Union[str, Any]=0			,    snake_case      :						List[Any]=2			,    snake_case      :						List[Any]="absolute"			,    snake_case      :						Optional[int]=True			,    snake_case      :						Optional[Any]=None			,    **snake_case      :						List[str]			,    ):
													'''simple docstring'''





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

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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							@property
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													if self.task == "multiple-choice":
																			A__						:  List[Any]		  =  {0: """batch""", 1: """choice""", 2: """sequence"""}
													else:
																			A__						:  Optional[int]		  =  {0: """batch""", 1: """sequence"""}
													return OrderedDict(
													    [
													        ("""input_ids""", dynamic_axis),
													        ("""attention_mask""", dynamic_axis),
													    ]							)














"""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')






