



'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCAmelCase__						:       List[str]										=						False




class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						pass





@slow
@require_torch_gpu
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''  )
								pipe.to(snake_case_  )
								pipe.set_progress_bar_config(disable=snake_case_  )

								snake_case__						:       int       						=  load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
								snake_case__						:       Optional[int]       						=  torch.manual_seed(0  )
								snake_case__						:       Tuple       						=  pipe(
								    image=snake_case_			,			generator=snake_case_			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type='''numpy'''			,			).images

								snake_case__						:       Union[str, Any]       						=  image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								snake_case__						:       Optional[Any]       						=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-2






'''simple docstring'''


from collections.abc import Sequence




def 	_a       (				__lowerCAmelCase		:      Sequence[int] | None = None     ):

		"""simple docstring"""

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

		snake_case__						:       Optional[Any]       						=  nums[0]
		for i in range(1   ,							len(__lowerCAmelCase     )     ):
				snake_case__						:       Tuple       						=  nums[i]
				snake_case__						:       Optional[int]       						=  max(__lowerCAmelCase   ,							ans + num   ,							__lowerCAmelCase     )

		return ans


if __name__ == "__main__":
					import doctest

					doctest.testmod()

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



