





from copy import deepcopy



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ = None ,							__magic_name__ = None   )					->       None:


							'''simple docstring'''



							if arr is None and size is not None:
											snake_case_						:   Union[str, Any]           =  size
											snake_case_						:   Dict           =  [0] * size
							elif arr is not None:
											self.init(__magic_name__   )
							else:
											raise ValueError('''Either arr or size must be specified'''   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  len(__magic_name__   )
							snake_case_						:   Optional[Any]           =  deepcopy(__magic_name__   )
							for i in range(1 ,							self.size   ):
											snake_case_						:   Any           =  self.next_(__magic_name__   )
											if j < self.size:
															self.tree[j] += self.tree[i]






			def       lowerCamelCase  (self   )					->       list[int]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.tree[:]
							for i in range(self.size - 1 ,							0 ,							-1   ):
											snake_case_						:   Optional[Any]           =  self.next_(__magic_name__   )
											if j < self.size:
															arr[j] -= arr[i]
							return arr






			@staticmethod
			def       lowerCamelCase  (__magic_name__   )					->       int:


							'''simple docstring'''



							return index + (index & (-index))






			@staticmethod
			def       lowerCamelCase  (__magic_name__   )					->       int:


							'''simple docstring'''



							return index - (index & (-index))






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if index == 0:
											self.tree[0] += value
											return
							while index < self.size:
											self.tree[index] += value
											snake_case_						:   Dict           =  self.next_(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							self.add(__magic_name__ ,							value - self.get(__magic_name__   )   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							if right == 0:
											return 0
							snake_case_						:   List[Any]           =  self.tree[0]
							right -= 1  # make right inclusive
							while right > 0:
											result += self.tree[right]
											snake_case_						:   Union[str, Any]           =  self.prev(__magic_name__   )
							return result






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return self.prefix(__magic_name__   ) - self.prefix(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return self.query(__magic_name__ ,							index + 1   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							value -= self.tree[0]
							if value < 0:
											return -1

							snake_case_						:   Optional[int]           =  1  # Largest power of 2 <= size
							while j * 2 < self.size:
											j *= 2

							snake_case_						:   Any           =  0

							while j > 0:
											if i + j < self.size and self.tree[i + j] <= value:
															value -= self.tree[i + j]
															i += j
											j //= 2
							return i


if __name__ == "__main__":
	import doctest

	doctest.testmod()







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  UNetaDModel(
							    block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=3 ,							out_channels=3 ,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') ,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') ,							)
							return model






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2




@slow
@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
