
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	)

import numpy as np






def        a_		(       __lowercase   :	np.array ) ->  np.array:
			return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
			import doctest

			doctest.testmod()