

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 ):



 """simple docstring"""







 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "hf-internal-testing/tiny-random-t5"
      _lowerCAmelCase   :					Dict        =    AutoTokenizer.from_pretrained(A_       )
      _lowerCAmelCase   :					List[str]        =    AutoModelForSeqaSeqLM.from_pretrained(A_       )

      _lowerCAmelCase   :					Optional[int]        =    tokenizer("This is me"     ,						return_tensors="pt"       )

      _lowerCAmelCase   :					Optional[Any]        =    model.to_bettertransformer()

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

      _lowerCAmelCase   :					Tuple        =    model.generate(**A_       )

      _lowerCAmelCase   :					Dict        =    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_       )

           _lowerCAmelCase   :					Optional[Any]        =    AutoModelForSeqaSeqLM.from_pretrained(A_       )

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

           _lowerCAmelCase   :					Optional[Any]        =    model_reloaded.generate(**A_       )
           self.assertTrue(torch.allclose(A_     ,						A_       )       )


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    "hf-internal-testing/tiny-random-t5"
      _lowerCAmelCase   :					List[Any]        =    AutoModelForSeqaSeqLM.from_pretrained(A_       )

      _lowerCAmelCase   :					Tuple        =    model.to_bettertransformer()

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

           _lowerCAmelCase   :					List[Any]        =    model.reverse_bettertransformer()
           model.save_pretrained(A_       )









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






