"""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  (    _lowerCamelCase		: Any    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: List[str]=1024    ,_lowerCamelCase		: Union[str, Any]=1024    ,_lowerCamelCase		: str=False    ,**_lowerCamelCase		: List[Any]			) ->   str:
  _lowerCAmelCase		: Optional[Any]						    =  AutoTokenizer.from_pretrained(_lowerCamelCase			)
  _lowerCAmelCase		: List[Any]						    =  SeqaSeqDataset(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,type_path="""train"""    ,**_lowerCamelCase			)
  _lowerCAmelCase		: List[Any]						    =  tok.pad_token_id

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

  _lowerCAmelCase		: Optional[Any]						    =  get_lens(_lowerCamelCase			)
  _lowerCAmelCase		: int						    =  SeqaSeqDataset(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase    ,type_path="""val"""    ,**_lowerCamelCase			)
  _lowerCAmelCase		: int						    =  get_lens(_lowerCamelCase			)
  pickle_save(_lowerCamelCase    ,train_ds.len_file			)
  pickle_save(_lowerCamelCase    ,val_ds.len_file			)


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





"""simple docstring"""




_a   :	Optional[Any] 					=     '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a   :	List[str] 					=     [{'type': 'code', 'content': INSTALL_CONTENT}]
_a   :	str 					=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




