





from copy import deepcopy

class 		UpperCamelCase__ :



     '''simple docstring'''

     def __init__(							self						:    Tuple						,lowerCamelCase__						:    list[int] | None = None						,lowerCamelCase__						:    int | None = None       )     ->		None:




           '''simple docstring'''


           if arr is None and size is not None:
                 SCREAMING_SNAKE_CASE		=						size
                 SCREAMING_SNAKE_CASE		=						[0] * size
           elif arr is not None:
                 self.init(lowerCamelCase__       )
           else:
                 raise ValueError("""Either arr or size must be specified"""       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    list[int]       )     ->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						deepcopy(lowerCamelCase__       )
           for i in range(1						,self.size       ):
                 SCREAMING_SNAKE_CASE		=						self.next_(lowerCamelCase__       )
                 if j < self.size:
                       self.tree[j] += self.tree[i]






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		list[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.tree[:]
           for i in range(self.size - 1						,0						,-1       ):
                 SCREAMING_SNAKE_CASE		=						self.next_(lowerCamelCase__       )
                 if j < self.size:
                       arr[j] -= arr[i]
           return arr






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


           return index + (index & (-index))






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


           return index - (index & (-index))






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


           if index == 0:
                 self.tree[0] += value
                 return
           while index < self.size:
                 self.tree[index] += value
                 SCREAMING_SNAKE_CASE		=						self.next_(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		None:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


           if right == 0:
                 return 0
           SCREAMING_SNAKE_CASE		=						self.tree[0]
           right -= 1  # make right inclusive
           while right > 0:
                 result += self.tree[right]
                 SCREAMING_SNAKE_CASE		=						self.prev(lowerCamelCase__       )
           return result






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    int						,lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


           return self.prefix(lowerCamelCase__       ) - self.prefix(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]						,lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


           return self.query(lowerCamelCase__						,index + 1       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,lowerCamelCase__						:    int       )     ->		int:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						1  # Largest power of 2 <= size
           while j * 2 < self.size:
                 j *= 2

           SCREAMING_SNAKE_CASE		=						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 KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						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 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""						,return_dict=lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE		=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0, 1.0, 0.0, 0.0, 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 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=20						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

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


