




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)












import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(lowerCAmelCase__     )    ,				"""Tatoeba directory does not exist."""     )
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @cached_property
    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      str			 =					tempfile.mkdtemp()
           return TatoebaConverter(save_dir=UpperCamelCase__     )







    @slow
    def     _lowercase     (    self     )     ->      List[Any]:
           self.resolver.convert_models(["heb-eng"]     )







    @slow
    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.resolver.write_model_card("opus-mt-he-en"     ,  dry_run=UpperCamelCase__     )
           assert mmeta["long_pair"] == "heb-eng"






