



'''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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"hf-internal-testing/tiny-random-t5"
       _snake_case     		=				AutoTokenizer.from_pretrained(__UpperCamelCase	)
       _snake_case     		=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase	)

       _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(**__UpperCamelCase	)

       _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(__UpperCamelCase	)

         _snake_case     		=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase	)

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

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"hf-internal-testing/tiny-random-t5"
       _snake_case     		=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase	)

       _snake_case     		=				model.to_bettertransformer()

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

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









'''simple docstring'''



from __future__ import annotations

import numpy as np




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  return np.maximum(0					,	SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
 print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


