






class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				name
								snake_case_           =				val




	def __str__(			self   )			->       str:
								return F'''{self.__class__.__name__}({self.name}, {self.val})'''






	def __lt__(			self			,     a   )			->       Any:
								return self.val < other.val






class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       List[str]:
								snake_case_           =				{}
								snake_case_           =				{}
								snake_case_           =				self.build_heap(a   )




	def __getitem__(			self			,     a   )			->       List[Any]:
								return self.get_value(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								return (idx - 1) // 2




	def 				_UpperCamelCase     (			self			,     a   )			->       Optional[int]:
								return idx * 2 + 1




	def 				_UpperCamelCase     (			self			,     a   )			->       Tuple:
								return idx * 2 + 2




	def 				_UpperCamelCase     (			self			,     a   )			->       List[Any]:
								return self.heap_dict[key]




	def 				_UpperCamelCase     (			self			,     a   )			->       int:
								snake_case_           =				len(a   ) - 1
								snake_case_           =				self.get_parent_idx(a   )

								for idx, i in enumerate(a   ):
															snake_case_           =				idx
															snake_case_           =				i.val

								for i in range(a			,     -1			,     -1   ):
															self.sift_down(a			,     a   )
								return array




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       int:
								while True:
															snake_case_           =				self.get_left_child_idx(a   )  # noqa: E741
															snake_case_           =				self.get_right_child_idx(a   )

															snake_case_           =				idx
															if l < len(a   ) and array[l] < array[idx]:
																						snake_case_           =				l
															if r < len(a   ) and array[r] < array[smallest]:
																						snake_case_           =				r

															if smallest != idx:
																						snake_case_     ,						snake_case_           =				array[smallest], array[idx]
																						(
																						    (
																						    snake_case_
																						)     ,						(
																						    snake_case_
																						)     ,						
																						)           =				(
																						    self.idx_of_element[array[smallest]],
																						    self.idx_of_element[array[idx]],
																						)
																						snake_case_           =				smallest
															else:
																						break




	def 				_UpperCamelCase     (			self			,     a   )			->       str:
								snake_case_           =				self.get_parent_idx(a   )
								while p >= 0 and self.heap[p] > self.heap[idx]:
															snake_case_     ,						snake_case_           =				self.heap[idx], self.heap[p]
															snake_case_     ,						snake_case_           =				(
															    self.idx_of_element[self.heap[idx]],
															    self.idx_of_element[self.heap[p]],
															)
															snake_case_           =				p
															snake_case_           =				self.get_parent_idx(a   )




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								return self.heap[0]




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_     ,						snake_case_           =				self.heap[-1], self.heap[0]
								snake_case_     ,						snake_case_           =				(
								    self.idx_of_element[self.heap[-1]],
								    self.idx_of_element[self.heap[0]],
								)

								snake_case_           =				self.heap.pop()
								del self.idx_of_element[x]
								self.sift_down(0			,     self.heap   )
								return x




	def 				_UpperCamelCase     (			self			,     a   )			->       Optional[Any]:
								self.heap.append(a   )
								snake_case_           =				len(self.heap   ) - 1
								snake_case_           =				node.val
								self.sift_up(len(self.heap   ) - 1   )




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								return len(self.heap   ) == 0






	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       List[str]:
								assert (
								    self.heap[self.idx_of_element[node]].val > new_value
								), "newValue must be less that current value"
								snake_case_           =				new_value
								snake_case_           =				new_value
								self.sift_up(self.idx_of_element[node]   )






lowercase    				=   Node("R", -1)
lowercase    				=   Node("B", 6)
lowercase    				=   Node("A", 3)
lowercase    				=   Node("X", 1)
lowercase    				=   Node("E", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowercase    				=   MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
	print(i)

print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
	print(i)

if __name__ == "__main__":
	import doctest

	doctest.testmod()













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   )





