




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowerCamelCase							:						Optional[int]					       =  False







class     A(		unittest.TestCase  ):



 '''simple docstring'''


 pass







@nightly
@require_torch_gpu
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    str    )  ->      Tuple:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def 							a__					(							self   :    str    )  ->      str:

       """simple docstring"""






       lowerCamelCase_							=       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'		,				torch_dtype=torch.floataa    )
       pipe.to(A_    )
       pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )

       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       pipe.dual_guided(
           prompt='first prompt'		,				image=A_		,				text_to_image_strength=0.75		,				generator=A_		,				guidance_scale=7.5		,				num_inference_steps=2		,				output_type='numpy'		,				).images

       with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A_    )
             lowerCamelCase_							=       VersatileDiffusionPipeline.from_pretrained(A_		,				torch_dtype=torch.floataa    )
       pipe.to(A_    )
       pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       generator.manual_seed(0    )
       lowerCamelCase_							=       pipe.dual_guided(
           prompt='first prompt'		,				image=A_		,				text_to_image_strength=0.75		,				generator=A_		,				guidance_scale=7.5		,				num_inference_steps=2		,				output_type='numpy'		,				).images

       assert np.abs(image - new_image    ).sum() < 1E-5, "Models don't have the same forward pass"





 def 							a__					(							self   :    str    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'		,				torch_dtype=torch.floataa    )
       pipe.to(A_    )
       pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       'cyberpunk 2077'
       lowerCamelCase_							=       load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       pipe.dual_guided(
           prompt=A_		,				image=A_		,				text_to_image_strength=0.75		,				generator=A_		,				guidance_scale=7.5		,				num_inference_steps=50		,				output_type='numpy'		,				).images

       lowerCamelCase_							=       image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_							=       np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]    )

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

       lowerCamelCase_							=       'A painting of a squirrel eating a burger '
       lowerCamelCase_							=       torch.manual_seed(0    )
       lowerCamelCase_							=       pipe.text_to_image(
           prompt=A_		,				generator=A_		,				guidance_scale=7.5		,				num_inference_steps=50		,				output_type='numpy'    ).images

       lowerCamelCase_							=       image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_							=       np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]    )

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

       lowerCamelCase_							=       pipe.image_variation(A_		,				generator=A_		,				output_type='numpy'    ).images

       lowerCamelCase_							=       image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_							=       np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]    )

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






from collections.abc import Sequence


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Sequence[int] | None = None						)		->				int:
 '''simple docstring'''


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

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

 return ans


if __name__ == "__main__":
      import doctest

      doctest.testmod()

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

