


import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	__magic_name__	:   List[Any]					     =					{}
	__magic_name__	:   str					     =					tokenizer(example['content']		,		truncation=lowerCAmelCase		)['input_ids']
	__magic_name__	:   List[Any]					     =					len(example['content']		) / len(output['input_ids']		)
	return output


lowerCAmelCase				:List[str]        =      HfArgumentParser(PretokenizationArguments)
lowerCAmelCase				:int        =      parser.parse_args()
if args.num_workers is None:
							lowerCAmelCase				:Any        =      multiprocessing.cpu_count()
lowerCAmelCase				:int        =      AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

lowerCAmelCase				:Union[str, Any]        =      time.time()
lowerCAmelCase				:Optional[Any]        =      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				:str        =      time.time()
ds.push_to_hub(args.tokenized_data_repo)





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



'''simple docstring'''

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


lowerCAmelCase				:Any        =      logging.get_logger(__name__)

lowerCAmelCase				:int        =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    """lxmert"""
		A_     :      int				   =    {}







		def __init__( self		:						int  ,					_A		:						List[Any]=30522  ,					_A		:						int=768  ,					_A		:						Any=12  ,					_A		:						Optional[Any]=9500  ,					_A		:						Any=1600  ,					_A		:						Tuple=400  ,					_A		:						str=3072  ,					_A		:						Tuple="gelu"  ,					_A		:						int=0.1  ,					_A		:						List[Any]=0.1  ,					_A		:						Dict=512  ,					_A		:						Dict=2  ,					_A		:						Tuple=0.02  ,					_A		:						List[Any]=1E-12  ,					_A		:						Optional[Any]=9  ,					_A		:						List[Any]=5  ,					_A		:						str=5  ,					_A		:						int=2048  ,					_A		:						Tuple=4  ,					_A		:						Tuple=6.67  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						str=True  ,					_A		:						Dict=True  ,					_A		:						str=True  ,					_A		:						int=True  ,					_A		:						Tuple=True  ,					**_A		:						int  ,					)  ->			str:
			__magic_name__	:   List[Any]					     =					vocab_size
			__magic_name__	:   List[Any]					     =					hidden_size
			__magic_name__	:   Optional[int]					     =					num_attention_heads
			__magic_name__	:   Dict					     =					hidden_act
			__magic_name__	:   Optional[Any]					     =					intermediate_size
			__magic_name__	:   str					     =					hidden_dropout_prob
			__magic_name__	:   List[str]					     =					attention_probs_dropout_prob
			__magic_name__	:   Tuple					     =					max_position_embeddings
			__magic_name__	:   Dict					     =					type_vocab_size
			__magic_name__	:   str					     =					initializer_range
			__magic_name__	:   str					     =					layer_norm_eps
			__magic_name__	:   Union[str, Any]					     =					num_qa_labels
			__magic_name__	:   str					     =					num_object_labels
			__magic_name__	:   List[str]					     =					num_attr_labels
			__magic_name__	:   Tuple					     =					l_layers
			__magic_name__	:   List[Any]					     =					x_layers
			__magic_name__	:   Optional[int]					     =					r_layers
			__magic_name__	:   Dict					     =					visual_feat_dim
			__magic_name__	:   Optional[int]					     =					visual_pos_dim
			__magic_name__	:   Optional[int]					     =					visual_loss_normalizer
			__magic_name__	:   int					     =					task_matched
			__magic_name__	:   Dict					     =					task_mask_lm
			__magic_name__	:   List[Any]					     =					task_obj_predict
			__magic_name__	:   str					     =					task_qa
			__magic_name__	:   List[Any]					     =					visual_obj_loss
			__magic_name__	:   Dict					     =					visual_attr_loss
			__magic_name__	:   int					     =					visual_feat_loss
			__magic_name__	:   Tuple					     =					{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
			super().__init__(**_A      )