






from __future__ import annotations

lowercase    				=   [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowercase    				=   [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				[]
							snake_case_           =				len(a_)

							for i in range(a_):
														snake_case_           =				-1
														for j in range(i + 1				,	a_):
																					if arr[i] < arr[j]:
																												snake_case_           =				arr[j]
																												break
														result.append(a_)
							return result





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				[]
							for i, outer in enumerate(a_):
														snake_case_           =				-1
														for inner in arr[i + 1 :]:
																					if outer < inner:
																												snake_case_           =				inner
																												break
														result.append(a_)
							return result





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				len(a_)
							snake_case_           =				[]
							snake_case_           =				[-1] * arr_size

							for index in reversed(range(a_)):
														if stack:
																					while stack[-1] <= arr[index]:
																												stack.pop()
																												if not stack:
																																			break
														if stack:
																					snake_case_           =				stack[-1]
														stack.append(arr[index])
							return result


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

	testmod()
	print(next_greatest_element_slow(arr))
	print(next_greatest_element_fast(arr))
	print(next_greatest_element(arr))

	lowercase    				=   (
	    "from __main__ import arr, next_greatest_element_slow, "
	    "next_greatest_element_fast, next_greatest_element"
	)
	print(
	    "next_greatest_element_slow():",
	    timeit("next_greatest_element_slow(arr)", setup=setup),
	)
	print(
	    "next_greatest_element_fast():",
	    timeit("next_greatest_element_fast(arr)", setup=setup),
	)
	print(
	    "     next_greatest_element():",
	    timeit("next_greatest_element(arr)", setup=setup),
	)













from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

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

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).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
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





