






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      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Dict						=				'hf-internal-testing/tiny-random-t5'
							UpperCAmelCase_   :				Tuple						=				AutoTokenizer.from_pretrained(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase       )

							UpperCAmelCase_   :				int						=				tokenizer('This is me'    ,		return_tensors='pt'       )

							UpperCAmelCase_   :				Union[str, Any]						=				model.to_bettertransformer()

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

							UpperCAmelCase_   :				Tuple						=				model.generate(**_UpperCamelCase       )

							UpperCAmelCase_   :				List[Any]						=				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       )

												UpperCAmelCase_   :				List[Any]						=				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase       )

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

												UpperCAmelCase_   :				Any						=				model_reloaded.generate(**_UpperCamelCase       )
												self.assertTrue(torch.allclose(_UpperCamelCase    ,		_UpperCamelCase       )       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				List[Any]						=				'hf-internal-testing/tiny-random-t5'
							UpperCAmelCase_   :				Any						=				AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase       )

							UpperCAmelCase_   :				str						=				model.to_bettertransformer()

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

												UpperCAmelCase_   :				Optional[Any]						=				model.reverse_bettertransformer()
												model.save_pretrained(_UpperCamelCase       )














from __future__ import annotations




def 					lowercase__				(					__snake_case   :							list[int]					,	__snake_case   :							int  ):


					'''simple docstring'''


					if len(__snake_case  ) < k or k < 0:
										raise ValueError('Invalid Input'  )
					UpperCAmelCase_   :				int						=						sum(array[:k]  )
					for i in range(len(__snake_case  ) - k  ):
										UpperCAmelCase_   :				List[Any]						=						current_sum - array[i] + array[i + k]
										UpperCAmelCase_   :				List[Any]						=						max(__snake_case					,	__snake_case  )
					return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		__UpperCAmelCase     							=						[randint(-1000, 1000) for i in range(100)]
		__UpperCAmelCase     							=						randint(0, 110)
		print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')






