


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        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  """hf-internal-testing/tiny-random-t5"""
					lowerCamelCase    		=  AutoTokenizer.from_pretrained(A	)
					lowerCamelCase    		=  AutoModelForSeqaSeqLM.from_pretrained(A	)

					lowerCamelCase    		=  tokenizer("""This is me"""		,      return_tensors="""pt"""	)

					lowerCamelCase    		=  model.to_bettertransformer()

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

					lowerCamelCase    		=  model.generate(**A	)

					lowerCamelCase    		=  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	)

								lowerCamelCase    		=  AutoModelForSeqaSeqLM.from_pretrained(A	)

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

								lowerCamelCase    		=  model_reloaded.generate(**A	)
								self.assertTrue(torch.allclose(A		,      A	)	)







		def 					__A   ( self	)  -> Optional[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  """hf-internal-testing/tiny-random-t5"""
					lowerCamelCase    		=  AutoModelForSeqaSeqLM.from_pretrained(A	)

					lowerCamelCase    		=  model.to_bettertransformer()

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

								lowerCamelCase    		=  model.reverse_bettertransformer()
								model.save_pretrained(A	)





import random
from typing import Any






def 	__lowerCamelCase						(							lowerCamelCase__		:						list      ):





			'''simple docstring'''




			for _ in range(len(lowerCamelCase__      )      ):
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase    		=  random.randint(0  ,							len(lowerCamelCase__      ) - 1      )
						lowerCamelCase		,		lowerCamelCase    		=  data[b], data[a]
			return data


if __name__ == "__main__":
		UpperCAmelCase   :    str     =		[0, 1, 2, 3, 4, 5, 6, 7]
		UpperCAmelCase   :    Union[str, Any]     =		["python", "says", "hello", "!"]
		print("Fisher-Yates Shuffle:")
		print("List", integers, strings)
		print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))

