


'''simple docstring'''

class     __SCREAMING_SNAKE_CASE       :






     def __init__(      self		)							->     Any:
          _a            =		[0]
          _a            =		0






     def 			a_       (      self  ,				__UpperCamelCase		)							->     None:
          _a            =		self.__heap[i]
          while i // 2 > 0:
               if self.__heap[i] > self.__heap[i // 2]:
                    _a            =		self.__heap[i // 2]
                    _a            =		temporary
               i //= 2






     def 			a_       (      self  ,				__UpperCamelCase		)							->     None:
          self.__heap.append(__UpperCamelCase		)
          self.__size += 1
          self.__swap_up(self.__size		)






     def 			a_       (      self  ,				__UpperCamelCase		)							->     None:
          while self.__size >= 2 * i:
               if 2 * i + 1 > self.__size:
                    _a            =		2 * i
               else:
                    if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                         _a            =		2 * i
                    else:
                         _a            =		2 * i + 1
               _a            =		self.__heap[i]
               if self.__heap[i] < self.__heap[bigger_child]:
                    _a            =		self.__heap[bigger_child]
                    _a            =		temporary
               _a            =		bigger_child






     def 			a_       (      self		)							->     int:
          _a            =		self.__heap[1]
          _a            =		self.__heap[self.__size]
          self.__size -= 1
          self.__heap.pop()
          self.__swap_down(1		)
          return max_value






     @property
     def 			a_       (      self		)							->     List[str]:
          return self.__heap[1:]






     def __len__(      self		)							->     Optional[int]:
          return self.__size


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # create an instance of BinaryHeap
   lowercase__					  =		BinaryHeap()
   binary_heap.insert(6)
   binary_heap.insert(10)
   binary_heap.insert(15)
   binary_heap.insert(12)
   # pop root(max-values because it is max heap)
   print(binary_heap.pop())  # 15
   print(binary_heap.pop())  # 12
   # get the list and size after operations
   print(binary_heap.get_list)
   print(len(binary_heap))





'''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

