






"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       Dict  				=		{
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Any  				=		[
						    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''NezhaForNextSentencePrediction''',
						    '''NezhaForMaskedLM''',
						    '''NezhaForPreTraining''',
						    '''NezhaForMultipleChoice''',
						    '''NezhaForQuestionAnswering''',
						    '''NezhaForSequenceClassification''',
						    '''NezhaForTokenClassification''',
						    '''NezhaModel''',
						    '''NezhaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						__A		:       List[Any]  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





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  A							(  self	:    Optional[int]	):


				'''simple docstring'''


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

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

				_snake_case		=			model.to_bettertransformer()

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

				_snake_case		=			model.generate(**lowercase	)

				_snake_case		=			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(lowercase	)

							_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

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

							_snake_case		=			model_reloaded.generate(**lowercase	)
							self.assertTrue(torch.allclose(lowercase			,  lowercase	)	)





	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			'hf-internal-testing/tiny-random-t5'
				_snake_case		=			AutoModelForSeqaSeqLM.from_pretrained(lowercase	)

				_snake_case		=			model.to_bettertransformer()

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

							_snake_case		=			model.reverse_bettertransformer()
							model.save_pretrained(lowercase	)