






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   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  'hf-internal-testing/tiny-random-t5'
           UpperCamelCase					       =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  tokenizer('This is me'   ,						return_tensors='pt' )

           UpperCamelCase					       =  model.to_bettertransformer()

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

           UpperCamelCase					       =  model.generate(**_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  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(_SCREAMING_SNAKE_CASE )

                 UpperCamelCase					       =  AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE )

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

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  'hf-internal-testing/tiny-random-t5'
           UpperCamelCase					       =  AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  model.to_bettertransformer()

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

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__   :  Tuple        =							{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Optional[Any]        =							['''PLBartTokenizer''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Tuple        =							[
           '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PLBartForCausalLM''',
           '''PLBartForConditionalGeneration''',
           '''PLBartForSequenceClassification''',
           '''PLBartModel''',
           '''PLBartPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_plbart import PLBartTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_plbart import (
                  PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PLBartForCausalLM,
                  PLBartForConditionalGeneration,
                  PLBartForSequenceClassification,
                  PLBartModel,
                  PLBartPreTrainedModel,
              )


else:
       import sys

       __magic_name__   :  Optional[int]        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



