'''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__					(					_lowercase    :				Tuple   ,    _lowercase    :				int   ,    _lowercase    :				Optional[Any]=1_0_2_4   ,    _lowercase    :				Optional[int]=1_0_2_4   ,    _lowercase    :				List[str]=False   ,    **_lowercase    :				List[Any]     )							->	List[Any]:
  __UpperCAmelCase:					Any		= AutoTokenizer.from_pretrained(_lowercase     )
  __UpperCAmelCase:					Dict		= SeqaSeqDataset(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    type_path="""train"""   ,    **_lowercase     )
  __UpperCAmelCase:					str		= tok.pad_token_id

  def get_lens(_lowercase    :				str     ):
    __UpperCAmelCase:					Optional[Any]		= tqdm(
        DataLoader(_lowercase   ,    batch_size=5_1_2   ,    num_workers=8   ,    shuffle=_lowercase   ,    collate_fn=ds.collate_fn     )   ,    desc=str(ds.len_file     )   ,    )
    __UpperCAmelCase:					List[Any]		= []
    for batch in dl:
      __UpperCAmelCase:					int		= batch["""input_ids"""].ne(_lowercase     ).sum(1     ).tolist()
      __UpperCAmelCase:					Union[str, Any]		= batch["""labels"""].ne(_lowercase     ).sum(1     ).tolist()
      if consider_target:
        for src, tgt in zip(_lowercase   ,    _lowercase     ):
          max_lens.append(max(_lowercase   ,    _lowercase     )     )
      else:
        max_lens.extend(_lowercase     )
    return max_lens

  __UpperCAmelCase:					List[str]		= get_lens(_lowercase     )
  __UpperCAmelCase:					Any		= SeqaSeqDataset(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase   ,    type_path="""val"""   ,    **_lowercase     )
  __UpperCAmelCase:					List[Any]		= get_lens(_lowercase     )
  pickle_save(_lowercase   ,    train_ds.len_file     )
  pickle_save(_lowercase   ,    val_ds.len_file     )


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






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				list[int]     )							->	None:
  __UpperCAmelCase:					Dict		= len(_lowercase     )
  print("""The following activities are selected:"""     )

  # The first activity is always selected
  __UpperCAmelCase:					Dict		= 0
  print(_lowercase   ,    end=""","""     )

  # Consider rest of the activities
  for j in range(_lowercase     ):
    # If this activity has start time greater than
    # or equal to the finish time of previously
    # selected activity, then select it
    if start[j] >= finish[i]:
      print(_lowercase   ,    end=""","""     )
      __UpperCAmelCase:					Optional[int]		= j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    SCREAMING_SNAKE_CASE_	   =			[1, 3, 0, 5, 8, 5]
    SCREAMING_SNAKE_CASE_	   =			[2, 4, 6, 7, 9, 9]
    print_max_activities(start, finish)