


'''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      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							List[Any]=10_24 ,       UpperCAmelCase_       :							List[Any]=10_24 ,       UpperCAmelCase_       :							List[str]=False ,       **UpperCAmelCase_       :							str				)	->						Dict:
     __lowerCamelCase  :      str														=						AutoTokenizer.from_pretrained(UpperCAmelCase_				)
     __lowerCamelCase  :      Union[str, Any]														=						SeqaSeqDataset(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       type_path='train' ,       **UpperCAmelCase_				)
     __lowerCamelCase  :      List[str]														=						tok.pad_token_id

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

     __lowerCamelCase  :      int														=						get_lens(UpperCAmelCase_				)
     __lowerCamelCase  :      Any														=						SeqaSeqDataset(UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       UpperCAmelCase_ ,       type_path='val' ,       **UpperCAmelCase_				)
     __lowerCamelCase  :      Union[str, Any]														=						get_lens(UpperCAmelCase_				)
     pickle_save(UpperCAmelCase_ ,       train_ds.len_file				)
     pickle_save(UpperCAmelCase_ ,       val_ds.len_file				)


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









"""simple docstring"""
import cmath
import math




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> complex:
      UpperCAmelCase__			:							str							=    math.radians(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    math.radians(lowerCAmelCase    )

      # Convert voltage and current to rectangular form
      UpperCAmelCase__			:							Union[str, Any]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )
      UpperCAmelCase__			:							List[str]							=    cmath.rect(lowerCAmelCase			,	lowerCAmelCase    )

      # Calculate apparent power
      return voltage_rect * current_rect


if __name__ == "__main__":
    import doctest

    doctest.testmod()

