


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      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def  _lowerCamelCase (						self)			->				Tuple:
            _A      :       Optional[int]				    = "hf-internal-testing/tiny-random-t5"
            _A      :       str				    = AutoTokenizer.from_pretrained(__lowerCamelCase)
            _A      :       Optional[int]				    = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase)

            _A      :       str				    = tokenizer("This is me" ,						return_tensors="pt")

            _A      :       str				    = model.to_bettertransformer()

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

            _A      :       Union[str, Any]				    = model.generate(**__lowerCamelCase)

            _A      :       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(__lowerCamelCase)

                   _A      :       int				    = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase)

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

                   _A      :       Optional[int]				    = model_reloaded.generate(**__lowerCamelCase)
                   self.assertTrue(torch.allclose(__lowerCamelCase ,						__lowerCamelCase))


     def  _lowerCamelCase (						self)			->				Optional[Any]:
            _A      :       int				    = "hf-internal-testing/tiny-random-t5"
            _A      :       Any				    = AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase)

            _A      :       Tuple				    = model.to_bettertransformer()

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

                   _A      :       int				    = model.reverse_bettertransformer()
                   model.save_pretrained(__lowerCamelCase)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowerCAmelCase__      		= {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['SpeechEncoderDecoderModel']

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__      		= ['FlaxSpeechEncoderDecoderModel']

if TYPE_CHECKING:
    from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
    import sys

    lowerCAmelCase__      		= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
