

from collections.abc import Sequence
from queue import Queue



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       A__=None			):
						A__							:    Dict        =		start
						A__							:    Optional[Any]        =		end
						A__							:    Optional[Any]        =		val
						A__							:    int        =		(start + end) // 2
						A__							:    str        =		left
						A__							:    Optional[Any]        =		right






					def __repr__( self			):
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__			):
						A__							:    List[str]        =		collection
						A__							:    Optional[Any]        =		function
						if self.collection:
							A__							:    str        =		self._build_tree(0    ,       len(__snake_case			) - 1			)






					def   __A			( self    ,       A__    ,       A__			):
						self._update_tree(self.root    ,       __snake_case    ,       __snake_case			)






					def   __A			( self    ,       A__    ,       A__			):
						return self._query_range(self.root    ,       __snake_case    ,       __snake_case			)






					def   __A			( self    ,       A__    ,       A__			):
						if start == end:
							return SegmentTreeNode(__snake_case    ,       __snake_case    ,       self.collection[start]			)
						A__							:    Tuple        =		(start + end) // 2
						A__							:    str        =		self._build_tree(__snake_case    ,       __snake_case			)
						A__							:    Optional[Any]        =		self._build_tree(mid + 1    ,       __snake_case			)
						return SegmentTreeNode(__snake_case    ,       __snake_case    ,       self.fn(left.val    ,       right.val			)    ,       __snake_case    ,       __snake_case			)






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						if node.start == i and node.end == i:
							A__							:    int        =		val
							return
						if i <= node.mid:
							self._update_tree(node.left    ,       __snake_case    ,       __snake_case			)
						else:
							self._update_tree(node.right    ,       __snake_case    ,       __snake_case			)
						A__							:    Union[str, Any]        =		self.fn(node.left.val    ,       node.right.val			)






					def   __A			( self    ,       A__    ,       A__    ,       A__			):
						if node.start == i and node.end == j:
							return node.val

						if i <= node.mid:
							if j <= node.mid:
								# range in left child tree
								return self._query_range(node.left    ,       __snake_case    ,       __snake_case			)
							else:
								# range in left child tree and right child tree
								return self.fn(
								    self._query_range(node.left    ,       __snake_case    ,       node.mid			)    ,       self._query_range(node.right    ,       node.mid + 1    ,       __snake_case			)    ,       )
						else:
							# range in right child tree
							return self._query_range(node.right    ,       __snake_case    ,       __snake_case			)






					def   __A			( self			):
						if self.root is not None:
							A__							:    str        =		Queue()
							queue.put(self.root			)
							while not queue.empty():
								A__							:    List[str]        =		queue.get()
								yield node

								if node.left is not None:
									queue.put(node.left			)

								if node.right is not None:
									queue.put(node.right			)


if __name__ == "__main__":
	import operator

	for fn in [operator.add, max, min]:
		print('*' * 50)
		A_       : int         =			SegmentTree([2, 1, 5, 3, 4], fn)
		for node in arr.traverse():
			print(node)
		print()

		arr.update(1, 5)
		for node in arr.traverse():
			print(node)
		print()

		print(arr.query_range(3, 4))  # 7
		print(arr.query_range(2, 2))  # 5
		print(arr.query_range(1, 3))  # 13
		print()







'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							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 = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




