


import numpy as np

def   UpperCamelCase			(      snake_case__):
						return 1 / (1 + np.exp(-vector))

def   UpperCamelCase			(      snake_case__):
						return vector * sigmoid(snake_case__)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




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					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






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

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

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_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			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

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

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







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






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

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

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

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