"""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       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   """hf-internal-testing/tiny-random-t5"""
							lowerCAmelCase__        =   AutoTokenizer.from_pretrained(snake_case__     )
							lowerCAmelCase__        =   AutoModelForSeqaSeqLM.from_pretrained(snake_case__     )

							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(**snake_case__     )

							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(snake_case__     )

									lowerCAmelCase__        =   AutoModelForSeqaSeqLM.from_pretrained(snake_case__     )

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

									lowerCAmelCase__        =   model_reloaded.generate(**snake_case__     )
									self.assertTrue(torch.allclose(snake_case__  ,						snake_case__     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   """hf-internal-testing/tiny-random-t5"""
							lowerCAmelCase__        =   AutoModelForSeqaSeqLM.from_pretrained(snake_case__     )

							lowerCAmelCase__        =   model.to_bettertransformer()

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

									lowerCAmelCase__        =   model.reverse_bettertransformer()
									model.save_pretrained(snake_case__     )



"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__ = 50     ):





		"""simple docstring"""




		lowerCAmelCase__        =   [[0] * 3 for _ in range(length + 1     )]

		for row_length in range(length + 1     ):
				for tile_length in range(2			,  5     ):
						for tile_start in range(row_length - tile_length + 1     ):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]     )


if __name__ == "__main__":
			print(F"{solution() = }")




