"""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 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[str])					->			List[Any]:
					__snake_case    :							Tuple   	=				'hf-internal-testing/tiny-random-t5'
					__snake_case    :							Optional[Any]   	=				AutoTokenizer.from_pretrained(_A)
					__snake_case    :							Optional[int]   	=				AutoModelForSeqaSeqLM.from_pretrained(_A)

					__snake_case    :							Union[str, Any]   	=				tokenizer('This is me'			,						return_tensors='pt')

					__snake_case    :							int   	=				model.to_bettertransformer()

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

					__snake_case    :							Dict   	=				model.generate(**_A)

					__snake_case    :							Tuple   	=				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)

						__snake_case    :							List[str]   	=				AutoModelForSeqaSeqLM.from_pretrained(_A)

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

						__snake_case    :							List[Any]   	=				model_reloaded.generate(**_A)
						self.assertTrue(torch.allclose(_A			,						_A))






				def       _lowercase  (self       :   Dict)					->			str:
					__snake_case    :							str   	=				'hf-internal-testing/tiny-random-t5'
					__snake_case    :							List[str]   	=				AutoModelForSeqaSeqLM.from_pretrained(_A)

					__snake_case    :							Optional[int]   	=				model.to_bettertransformer()

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

						__snake_case    :							List[str]   	=				model.reverse_bettertransformer()
						model.save_pretrained(_A)






"""simple docstring"""







from typing import TYPE_CHECKING

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


_a  :  Any=				{"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Union[str, Any]=				["SpeechEncoderDecoderModel"]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Tuple=				["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

	_a  :  str=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





