


'''simple docstring'''

from __future__ import annotations

lowercase__					  =		[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self  ,				__UpperCamelCase		)							->     None:
          _a            =		-1
          _a            =		-1
          _a            =		[-1] * length
          _a            =		None
          _a            =		None






     def __repr__(      self		)							->     str:
          return f"Node(min_value={self.minn} max_value={self.maxx})"







def __UpperCamelCase (      __lowerCamelCase		:			list[int]					)   ->  Node | None:







     '''simple docstring'''






     _a            =		Node(len(__lowerCamelCase					)					)
     _a      ,      _a            =		min(__lowerCamelCase					), max(__lowerCamelCase					)
     # Leaf node case where the node contains only one unique value
     if root.minn == root.maxx:
          return root
     _a            =		(root.minn + root.maxx) // 2

     _a            =		[]
     _a            =		[]

     for index, num in enumerate(__lowerCamelCase					):
          if num <= pivot:
               left_arr.append(__lowerCamelCase					)
          else:
               right_arr.append(__lowerCamelCase					)
          _a            =		len(__lowerCamelCase					)
     _a            =		build_tree(__lowerCamelCase					)
     _a            =		build_tree(__lowerCamelCase					)
     return root






def __UpperCamelCase (      __lowerCamelCase		:			Node | None   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     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
     _a            =		(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   ,	__lowerCamelCase   ,	node.map_left[index] - 1					)
     else:
          # go to the right subtree and map index to the right subtree
          return rank_till_index(node.right   ,	__lowerCamelCase   ,	index - node.map_left[index]					)






def __UpperCamelCase (      __lowerCamelCase		:			Node | None   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     if start > end:
          return 0
     _a            =		rank_till_index(__lowerCamelCase   ,	__lowerCamelCase   ,	__lowerCamelCase					)
     _a            =		rank_till_index(__lowerCamelCase   ,	__lowerCamelCase   ,	start - 1					)
     return rank_till_end - rank_before_start






def __UpperCamelCase (      __lowerCamelCase		:			Node | None   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     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]
     _a            =		node.map_left[end] - (
         node.map_left[start - 1] if start else 0
     )
     if num_elements_in_left_tree > index:
          return quantile(
              node.left   ,	__lowerCamelCase   ,	(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 __UpperCamelCase (      __lowerCamelCase		:			Node | None   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int   ,	__lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     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
     _a            =		range_counting(
         node.left   ,	(node.map_left[start - 1] if start else 0)   ,	node.map_left[end] - 1   ,	__lowerCamelCase   ,	__lowerCamelCase   ,	)
     _a            =		range_counting(
         node.right   ,	start - (node.map_left[start - 1] if start else 0)   ,	end - node.map_left[end]   ,	__lowerCamelCase   ,	__lowerCamelCase   ,	)
     return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__					  =		False





@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Dict:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase		)

          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		generator.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def 			a_       (      self		)							->     int:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		"cyberpunk 2077"
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt=__UpperCamelCase  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

          _a            =		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

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

