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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase=10_24						,    __lowerCAmelCase=10_24						,    __lowerCAmelCase=False						,    **__lowerCAmelCase  )->     Tuple:






      '''simple docstring'''





      UpperCAmelCase			:       Dict			     =AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE  )
      UpperCAmelCase			:       Tuple			     =SeqaSeqDataset(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    type_path='''train'''						,    **_SCREAMING_SNAKE_CASE  )
      UpperCAmelCase			:       Union[str, Any]			     =tok.pad_token_id

      def get_lens(__lowerCAmelCase  ):
            UpperCAmelCase			:       Any			     =tqdm(
                DataLoader(_SCREAMING_SNAKE_CASE						,    batch_size=5_12						,    num_workers=8						,    shuffle=_SCREAMING_SNAKE_CASE						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
            UpperCAmelCase			:       Optional[int]			     =[]
            for batch in dl:
                  UpperCAmelCase			:       List[Any]			     =batch["input_ids"].ne(_SCREAMING_SNAKE_CASE  ).sum(1  ).tolist()
                  UpperCAmelCase			:       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

      UpperCAmelCase			:       List[Any]			     =get_lens(_SCREAMING_SNAKE_CASE  )
      UpperCAmelCase			:       List[str]			     =SeqaSeqDataset(_SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    _SCREAMING_SNAKE_CASE						,    type_path='''val'''						,    **_SCREAMING_SNAKE_CASE  )
      UpperCAmelCase			:       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"






