


'''simple docstring'''







from __future__ import annotations

a__    :   Dict								=			[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
class    lowercase_	:


    def __init__(	self    ,   a       ):
          UpperCamelCase__      =							-1
          UpperCamelCase__      =							-1
          UpperCamelCase__      =							[-1] * length
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None


    def __repr__(	self       ):
          return f'''Node(min_value={self.minn} max_value={self.maxx})'''







def _UpperCamelCase			(	__A       )				->						Node | None:


      '''simple docstring'''



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

      UpperCamelCase__      =							[]
      UpperCamelCase__      =							[]

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







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						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
      UpperCamelCase__      =							(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			(	__A ,     __A ,     __A ,     __A       )				->						int:


      '''simple docstring'''



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







def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						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]
      UpperCamelCase__      =							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			(	__A ,     __A ,     __A ,     __A ,     __A       )				->						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
      UpperCamelCase__      =							range_counting(
          node.left ,     (node.map_left[start - 1] if start else 0) ,     node.map_left[end] - 1 ,     __lowerCamelCase ,     __lowerCamelCase ,     )
      UpperCamelCase__      =							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

