


from __future__ import annotations

lowerCAmelCase__ :			Optional[Any]         =[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				-1
										SCREAMING_SNAKE_CASE_		:    int 		=				-1
										SCREAMING_SNAKE_CASE_		:    list[int] 		=				[-1] * length
										SCREAMING_SNAKE_CASE_		:    Node | None 		=				None
										SCREAMING_SNAKE_CASE_		:    Node | None 		=				None






							def __repr__(			self					):





										"""simple docstring"""




										return F'''Node(min_value={self.minn} max_value={self.maxx})'''







def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		:    Any 		=				Node(len(A__							)							)
			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    int 		=				min(A__							), max(A__							)
			# Leaf node case where the node contains only one unique value
			if root.minn == root.maxx:
						return root
			SCREAMING_SNAKE_CASE_		:    Dict 		=				(root.minn + root.maxx) // 2

			SCREAMING_SNAKE_CASE_		:    list[int] 		=				[]
			SCREAMING_SNAKE_CASE_		:    list[int] 		=				[]

			for index, num in enumerate(A__							):
						if num <= pivot:
									left_arr.append(A__							)
						else:
									right_arr.append(A__							)
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				len(A__							)
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				build_tree(A__							)
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				build_tree(A__							)
			return root






def 	a__ (							A__,			A__,			A__							):
			if index < 0 or node is None:
						return 0
			# Leaf node cases
			if node.minn == node.maxx:
						return index + 1 if node.minn == num else 0
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				(node.minn + node.maxx) // 2
			if num <= pivot:
						# go the left subtree and map index to the left subtree
						return rank_till_index(node.left,			A__,			node.map_left[index] - 1							)
			else:
						# go to the right subtree and map index to the right subtree
						return rank_till_index(node.right,			A__,			index - node.map_left[index]							)






def 	a__ (							A__,			A__,			A__,			A__							):
			if start > end:
						return 0
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				rank_till_index(A__,			A__,			A__							)
			SCREAMING_SNAKE_CASE_		:    int 		=				rank_till_index(A__,			A__,			start - 1							)
			return rank_till_end - rank_before_start






def 	a__ (							A__,			A__,			A__,			A__							):
			if index > (end - start) or start > end or node is None:
						return -1
			# Leaf node case
			if node.minn == node.maxx:
						return node.minn
			# Number of elements in the left subtree in interval [start, end]
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				node.map_left[end] - (
			    node.map_left[start - 1] if start else 0
			)
			if num_elements_in_left_tree > index:
						return quantile(
						    node.left,			A__,			(node.map_left[start - 1] if start else 0),			node.map_left[end] - 1,			)
			else:
						return quantile(
						    node.right,			index - num_elements_in_left_tree,			start - (node.map_left[start - 1] if start else 0),			end - node.map_left[end],			)






def 	a__ (							A__,			A__,			A__,			A__,			A__							):
			if (
			    start > end
			    or node is None
			    or start_num > end_num
			    or node.minn > end_num
			    or node.maxx < start_num
			):
						return 0
			if start_num <= node.minn and node.maxx <= end_num:
						return end - start + 1
			SCREAMING_SNAKE_CASE_		:    str 		=				range_counting(
			    node.left,			(node.map_left[start - 1] if start else 0),			node.map_left[end] - 1,			A__,			A__,			)
			SCREAMING_SNAKE_CASE_		:    Any 		=				range_counting(
			    node.right,			start - (node.map_left[start - 1] if start else 0),			end - node.map_left[end],			A__,			A__,			)
			return left + right


if __name__ == "__main__":
				import doctest

				doctest.testmod()









import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput







@dataclass
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42







class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				CustomOutput(images=np.random.rand(1 ,	3 ,	4 ,	4					)					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	np.ndarray					)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs['images'] ,	np.ndarray					)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0] ,	np.ndarray					)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				CustomOutput(images=[PIL.Image.new('RGB' ,	(4, 4)					)]					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	lowerCAmelCase__					)
										assert isinstance(outputs.images[0] ,	PIL.Image.Image					)
										assert isinstance(outputs['images'] ,	lowerCAmelCase__					)
										assert isinstance(outputs['images'][0] ,	PIL.Image.Image					)
										assert isinstance(outputs[0] ,	lowerCAmelCase__					)
										assert isinstance(outputs[0][0] ,	PIL.Image.Image					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				CustomOutput({'images': np.random.rand(1 ,	3 ,	4 ,	4					)}					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	np.ndarray					)
										assert outputs.images.shape == (1, 3, 4, 4)
										assert isinstance(outputs['images'] ,	np.ndarray					)
										assert outputs["images"].shape == (1, 3, 4, 4)
										assert isinstance(outputs[0] ,	np.ndarray					)
										assert outputs[0].shape == (1, 3, 4, 4)

										# test with a non-tensor attribute
										SCREAMING_SNAKE_CASE_		:    Any 		=				CustomOutput({'images': [PIL.Image.new('RGB' ,	(4, 4)					)]}					)

										# check every way of getting the attribute
										assert isinstance(outputs.images ,	lowerCAmelCase__					)
										assert isinstance(outputs.images[0] ,	PIL.Image.Image					)
										assert isinstance(outputs['images'] ,	lowerCAmelCase__					)
										assert isinstance(outputs['images'][0] ,	PIL.Image.Image					)
										assert isinstance(outputs[0] ,	lowerCAmelCase__					)
										assert isinstance(outputs[0][0] ,	PIL.Image.Image					)





