





from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_SCREAMING_SNAKE_CASE   						=		TypeVar("""T""", bound=bool)
class SCREAMING_SNAKE_CASE_    (       Generic[T]       ):






						def __init__(   self   :   Optional[int]		, _A   :   T						)				->     None:







							"""simple docstring"""


							snake_case_		:      str           =						value
							snake_case_		:      str           =						None
							snake_case_		:      Union[str, Any]           =						None






						@property
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     T:







							"""simple docstring"""


							return self._value






						@staticmethod
						def UpperCAmelCase_					(   _A   :   SkewNode[T] | None		, _A   :   SkewNode[T] | None						)				->     SkewNode[T] | None:







							"""simple docstring"""


							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								snake_case_    ,snake_case_		:      str           =						roota, roota

							snake_case_		:      Dict           =						roota
							snake_case_		:      Union[str, Any]           =						roota.right
							snake_case_		:      Optional[Any]           =						roota.left
							snake_case_		:      Any           =						SkewNode.merge(UpperCamelCase__		, UpperCamelCase__						)

							return result
class SCREAMING_SNAKE_CASE_    (       Generic[T]       ):






						def __init__(   self   :   Optional[Any]		, _A   :   Iterable[T] | None = ()						)				->     None:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						None
							if data:
								for item in data:
									self.insert(UpperCamelCase__						)






						def __bool__(   self   :   Optional[Any]						)				->     bool:







							"""simple docstring"""


							return self._root is not None






						def __iter__(   self   :   Optional[Any]						)				->     Iterator[T]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						[]
							while self:
								result.append(self.pop()						)

							# Pushing items back to the heap not to clear it.
							for item in result:
								self.insert(UpperCamelCase__						)

							return iter(UpperCamelCase__						)






						def UpperCAmelCase_					(   self   :   int		, _A   :   T						)				->     None:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						SkewNode.merge(self._root		, SkewNode(UpperCamelCase__						)						)






						def UpperCAmelCase_					(   self   :   Dict						)				->     T | None:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						self.top()
							snake_case_		:      List[str]           =						(
							    SkewNode.merge(self._root.left		, self._root.right						) if self._root else None
							)

							return result






						def UpperCAmelCase_					(   self   :   Tuple						)				->     T:







							"""simple docstring"""


							if not self._root:
								raise IndexError('Can\'t get top element for the empty heap.'						)
							return self._root.value






						def UpperCAmelCase_					(   self   :   str						)				->     None:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						None


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 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[Any]:
													'''simple docstring'''



													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

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

													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=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__				)
																				__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained(UpperCamelCase__				)

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

													__UpperCamelCase							=generator.manual_seed(0				)
													__UpperCamelCase							=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   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''				)
													pipe.remove_unused_weights()
													pipe.to(UpperCamelCase__				)
													pipe.set_progress_bar_config(disable=UpperCamelCase__				)

													__UpperCamelCase							='''cyberpunk 2077'''
													__UpperCamelCase							=load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
													__UpperCamelCase							=torch.manual_seed(0				)
													__UpperCamelCase							=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

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

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase							=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



