"""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 SCREAMING_SNAKE_CASE  (     unittest.TestCase						):







 """simple docstring"""


 def 							_lowerCAmelCase					( self		:   List[str]					)					->      Union[str, 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		:   int					)					->      Optional[int]:




    '''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					)




"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A     :			Optional[int]  						=			{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			[
       """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MgpstrModel""",
       """MgpstrPreTrainedModel""",
       """MgpstrForSceneTextRecognition""",
   ]

if TYPE_CHECKING:
   from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
   from .processing_mgp_str import MgpstrProcessor
   from .tokenization_mgp_str import MgpstrTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mgp_str import (
          MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
          MgpstrForSceneTextRecognition,
          MgpstrModel,
          MgpstrPreTrainedModel,
      )
else:
   import sys

   _A     :			Tuple  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


