'''simple docstring'''

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
     import torch





@require_torch
@require_optimum
@slow
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"hf-internal-testing/tiny-random-t5"
   lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase  )

   lowerCamelCase_  				=						tokenizer("This is me"    ,						return_tensors="pt"  )

   lowerCamelCase_  				=						model.to_bettertransformer()

   self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

   lowerCamelCase_  				=						model.generate(**UpperCamelCase  )

   lowerCamelCase_  				=						model.reverse_bettertransformer()

   self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

   with tempfile.TemporaryDirectory() as tmpdirname:
    model.save_pretrained(UpperCamelCase  )

    lowerCamelCase_  				=						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase  )

    self.assertFalse(
        any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()  )  )

    lowerCamelCase_  				=						model_reloaded.generate(**UpperCamelCase  )
    self.assertTrue(torch.allclose(UpperCamelCase    ,						UpperCamelCase  )  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"hf-internal-testing/tiny-random-t5"
   lowerCamelCase_  				=						AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase  )

   lowerCamelCase_  				=						model.to_bettertransformer()

   with tempfile.TemporaryDirectory() as tmpdirname:
    with self.assertRaises(UpperCamelCase  ):
     model.save_pretrained(UpperCamelCase  )

    lowerCamelCase_  				=						model.reverse_bettertransformer()
    model.save_pretrained(UpperCamelCase  )







'''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 __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

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

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


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



