




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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1024 ,_SCREAMING_SNAKE_CASE=1024 ,_SCREAMING_SNAKE_CASE=False ,**_SCREAMING_SNAKE_CASE    )	-> Tuple:
       lowerCamelCase   :      Dict			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					SeqaSeqDataset(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,type_path="train" ,**_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					tok.pad_token_id

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

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


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










'''simple docstring'''

a_  :				str						= """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

a_  :				Any						= [{"""type""": """code""", """content""": INSTALL_CONTENT}]
a_  :				int						= {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}



