





"""simple docstring"""



import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save







def 		__lowercase     (   snake_case_  :  Optional[int]				,snake_case_  :  Dict				,snake_case_  :  Any=1024				,snake_case_  :  List[Any]=1024				,snake_case_  :  Optional[Any]=False				,**snake_case_  :  Tuple						)   ->Any:



    '''simple docstring'''







    __A    :						Any						 =     AutoTokenizer.from_pretrained(snake_case_						)
    __A    :						int						 =     SeqaSeqDataset(snake_case_				,snake_case_				,snake_case_				,snake_case_				,type_path='''train'''				,**snake_case_						)
    __A    :						str						 =     tok.pad_token_id

    def get_lens(snake_case_  :  int						):
        __A    :						Dict						 =     tqdm(
            DataLoader(snake_case_				,batch_size=512				,num_workers=8				,shuffle=snake_case_				,collate_fn=ds.collate_fn						)				,desc=str(ds.len_file						)				,)
        __A    :						int						 =     []
        for batch in dl:
            __A    :						Optional[int]						 =     batch['''input_ids'''].ne(snake_case_						).sum(1						).tolist()
            __A    :						List[Any]						 =     batch['''labels'''].ne(snake_case_						).sum(1						).tolist()
            if consider_target:
                for src, tgt in zip(snake_case_				,snake_case_						):
                    max_lens.append(max(snake_case_				,snake_case_						)						)
            else:
                max_lens.extend(snake_case_						)
        return max_lens

    __A    :						List[str]						 =     get_lens(snake_case_						)
    __A    :						Union[str, Any]						 =     SeqaSeqDataset(snake_case_				,snake_case_				,snake_case_				,snake_case_				,type_path='''val'''				,**snake_case_						)
    __A    :						Tuple						 =     get_lens(snake_case_						)
    pickle_save(snake_case_				,train_ds.len_file						)
    pickle_save(snake_case_				,val_ds.len_file						)


if __name__ == "__main__":
       fire.Fire(save_len_file)











"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




