




'''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 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Union[str, Any]	,					__A    :			Optional[Any]=10_24	,					__A    :			List[str]=10_24	,					__A    :			Union[str, Any]=False	,					**__A    :			Tuple  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				AutoTokenizer.from_pretrained(__A  )
   _SCREAMING_SNAKE_CASE	=				SeqaSeqDataset(__A	,					__A	,					__A	,					__A	,					type_path="train"	,					**__A  )
   _SCREAMING_SNAKE_CASE	=				tok.pad_token_id

   def get_lens(__A    :			Tuple  ):
      _SCREAMING_SNAKE_CASE	=				tqdm(
          DataLoader(__A	,					batch_size=5_12	,					num_workers=8	,					shuffle=__A	,					collate_fn=ds.collate_fn  )	,					desc=str(ds.len_file  )	,					)
      _SCREAMING_SNAKE_CASE	=				[]
      for batch in dl:
         _SCREAMING_SNAKE_CASE	=				batch["input_ids"].ne(__A  ).sum(1  ).tolist()
         _SCREAMING_SNAKE_CASE	=				batch["labels"].ne(__A  ).sum(1  ).tolist()
         if consider_target:
            for src, tgt in zip(__A	,					__A  ):
               max_lens.append(max(__A	,					__A  )  )
         else:
            max_lens.extend(__A  )
      return max_lens

   _SCREAMING_SNAKE_CASE	=				get_lens(__A  )
   _SCREAMING_SNAKE_CASE	=				SeqaSeqDataset(__A	,					__A	,					__A	,					__A	,					type_path="val"	,					**__A  )
   _SCREAMING_SNAKE_CASE	=				get_lens(__A  )
   pickle_save(__A	,					train_ds.len_file  )
   pickle_save(__A	,					val_ds.len_file  )


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







'''simple docstring'''



from ...configuration_utils import PretrainedConfig







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''bert-generation'''



   def __init__(   self :     int  ,				__lowerCamelCase :     int=5_0_3_5_8  ,				__lowerCamelCase :     Union[str, Any]=1_0_2_4  ,				__lowerCamelCase :     str=2_4  ,				__lowerCamelCase :     int=1_6  ,				__lowerCamelCase :     Union[str, Any]=4_0_9_6  ,				__lowerCamelCase :     Optional[Any]="gelu"  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     str=0.1  ,				__lowerCamelCase :     Optional[Any]=5_1_2  ,				__lowerCamelCase :     List[Any]=0.0_2  ,				__lowerCamelCase :     Union[str, Any]=1e-12  ,				__lowerCamelCase :     List[Any]=0  ,				__lowerCamelCase :     List[Any]=2  ,				__lowerCamelCase :     Dict=1  ,				__lowerCamelCase :     Tuple="absolute"  ,				__lowerCamelCase :     Union[str, Any]=True  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      super().__init__(pad_token_id=__lowerCamelCase  ,				bos_token_id=__lowerCamelCase  ,				eos_token_id=__lowerCamelCase  ,				**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps
      _SCREAMING_SNAKE_CASE	=				position_embedding_type
      _SCREAMING_SNAKE_CASE	=				use_cache

