

'''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"""






def      lowercase__ (					lowerCAmelCase__   :			str			)    ->			list[int]:



			'''simple docstring'''
			a__  :	List[str]		 =			[0 for i in range(len(lowerCAmelCase__			)			)]

			# initialize interval's left pointer and right pointer
			a__ ,	a__  :	int		 =			0, 0

			for i in range(1							,			len(lowerCAmelCase__			)			):
						# case when current index is inside the interval
						if i <= right_pointer:
									a__  :	List[Any]		 =			min(right_pointer - i + 1							,			z_result[i - left_pointer]			)
									a__  :	List[str]		 =			min_edge

						while go_next(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									a__ ,	a__  :	str		 =			i, i + z_result[i] - 1

			return z_result






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			list[int]							,			lowerCAmelCase__   :			str			)    ->			bool:



			'''simple docstring'''
			return i + z_result[i] < len(lowerCAmelCase__			) and s[z_result[i]] == s[i + z_result[i]]






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	List[Any]		 =			0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			a__  :	List[str]		 =			z_function(pattern + input_str			)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(lowerCAmelCase__			):
									answer += 1

			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()