




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       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				"hf-internal-testing/tiny-random-t5"
          a     =				AutoTokenizer.from_pretrained(A			)
          a     =				AutoModelForSeqaSeqLM.from_pretrained(A			)

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

          a     =				model.to_bettertransformer()

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

          a     =				model.generate(**A			)

          a     =				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(A			)

               a     =				AutoModelForSeqaSeqLM.from_pretrained(A			)

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

               a     =				model_reloaded.generate(**A			)
               self.assertTrue(torch.allclose(A  ,		A			)			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				"hf-internal-testing/tiny-random-t5"
          a     =				AutoModelForSeqaSeqLM.from_pretrained(A			)

          a     =				model.to_bettertransformer()

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

               a     =				model.reverse_bettertransformer()
               model.save_pretrained(A			)







lowercase__				:      Optional[int]	   = [
    "DownloadConfig",
    "DownloadManager",
    "DownloadMode",
    "StreamingDownloadManager",
]

from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager

