






"""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 							__magic_name__     (		unittest.TestCase   ):





					def 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       "hf-internal-testing/tiny-random-t5"
												__snake_case     			=       AutoTokenizer.from_pretrained(snake_case_    )
												__snake_case     			=       AutoModelForSeqaSeqLM.from_pretrained(snake_case_    )

												__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(**snake_case_    )

												__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(snake_case_    )

																			__snake_case     			=       AutoModelForSeqaSeqLM.from_pretrained(snake_case_    )

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

																			__snake_case     			=       model_reloaded.generate(**snake_case_    )
																			self.assertTrue(torch.allclose(snake_case_							,						snake_case_    )    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]    ):
												__snake_case     			=       "hf-internal-testing/tiny-random-t5"
												__snake_case     			=       AutoModelForSeqaSeqLM.from_pretrained(snake_case_    )

												__snake_case     			=       model.to_bettertransformer()

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

																			__snake_case     			=       model.reverse_bettertransformer()
																			model.save_pretrained(snake_case_    )


"""simple docstring"""







from collections.abc import Sequence
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = None )  ->     int:







							"""simple docstring"""


							if nums is None or not nums:
														raise ValueError("Input sequence should not be empty" )

							__snake_case     			=       nums[0]
							for i in range(1							,    len(SCREAMING_SNAKE_CASE ) ):
														__snake_case     			=       nums[i]
														__snake_case     			=       max(SCREAMING_SNAKE_CASE							,    ans + num							,    SCREAMING_SNAKE_CASE )

							return ans


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Try on a sample input from the user
		_SCREAMING_SNAKE_CASE	       =      int(input("""Enter number of elements : """).strip())
		_SCREAMING_SNAKE_CASE	       =      list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
		print(max_subsequence_sum(array))
