

'''simple docstring'''
# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , snake_case_  , snake_case_   ):
   super().__init__()
   self.register_modules(unet=snake_case_  , scheduler=snake_case_   )







 @torch.no_grad()
 def __call__(       self  , snake_case_ = 1  , snake_case_ = None  , snake_case_ = 50  , snake_case_ = "pil"  , snake_case_ = True  , **snake_case_  , ):
   lowercase									=torch.randn(
       (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)  , generator=snake_case_  , )
   lowercase									=image.to(self.device   )

   # set step values
   self.scheduler.set_timesteps(snake_case_   )

   for t in self.progress_bar(self.scheduler.timesteps   ):
     # 1. predict noise model_output
     lowercase									=self.unet(snake_case_  , snake_case_   ).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     lowercase									=self.scheduler.step(snake_case_  , snake_case_  , snake_case_   ).prev_sample

   lowercase									=(image / 2 + 0.5).clamp(0  , 1   )
   lowercase									=image.cpu().permute(0  , 2  , 3  , 1   ).numpy()
   if output_type == "pil":
     lowercase									=self.numpy_to_pil(snake_case_   )

   if not return_dict:
     return (image,), "This is a local test"

   return ImagePipelineOutput(images=snake_case_   ), "This is a local test"

'''simple docstring'''


from collections.abc import Sequence





def    lowerCAmelCase       (		UpperCamelCase__					: Sequence[float]     ,       UpperCamelCase__					: bool = False							):


	"""simple docstring"""
	if not arr:
		return 0

	__UpperCAmelCase      					=      0 if allow_empty_subarrays else float('''-inf'''							)
	__UpperCAmelCase      					=      0.0
	for num in arr:
		__UpperCAmelCase      					=      max(0 if allow_empty_subarrays else num     ,       curr_sum + num							)
		__UpperCAmelCase      					=      max(UpperCamelCase__     ,       UpperCamelCase__							)

	return max_sum


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

					testmod()

					__lowerCAmelCase						:							int    							=						[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")
