
'''simple docstring'''





from __future__ import annotations

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

a__		: Optional[Any]  							=							TypeVar("T", bound=bool)


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:List[Any]						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      value
       __A													=      None
       __A													=      None






 @property
 def 						lowercase_    (      self	:Tuple      )					->      T:







       '''simple docstring'''



       return self._value






 @staticmethod
 def 						lowercase_    (      _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:
             __A						,   __A													=      roota, roota

       __A													=      roota
       __A													=      roota.right
       __A													=      roota.left
       __A													=      SkewNode.merge(_A						,					_A      )

       return result


class 			UpperCamelCase__	(     Generic[T]):






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







       '''simple docstring'''



       __A													=      None
       if data:
             for item in data:
                   self.insert(_A      )






 def __bool__(      self	:List[str]      )					->      bool:







       '''simple docstring'''



       return self._root is not None






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







       '''simple docstring'''



       __A													=      []
       while self:
             result.append(self.pop()      )

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

       return iter(_A      )






 def 						lowercase_    (      self	:Tuple						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      SkewNode.merge(self._root						,					SkewNode(_A      )      )






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







       '''simple docstring'''



       __A													=      self.top()
       __A													=      (
           SkewNode.merge(self._root.left						,					self._root.right      ) if self._root else None
       )

       return result






 def 						lowercase_    (      self	:int      )					->      T:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:int      )					->      None:







       '''simple docstring'''



       __A													=      None


if __name__ == "__main__":
 import doctest

 doctest.testmod()










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


a					:				Optional[int]       =      False

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			Union[str, Any]							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )

        snake_case :			str							=   torch.manual_seed(0       )
        snake_case :			Tuple							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=2						,    output_type='''numpy'''						,    ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__       )
         snake_case :			str							=   VersatileDiffusionDualGuidedPipeline.from_pretrained(a__       )

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

        snake_case :			Optional[Any]							=   generator.manual_seed(0       )
        snake_case :			List[str]							=   pipe(
            prompt='''first prompt'''						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    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       ):






        '''simple docstring'''






        snake_case :			Tuple							=   VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       )
        pipe.remove_unused_weights()
        pipe.to(a__       )
        pipe.set_progress_bar_config(disable=a__       )

        snake_case :			int							=   '''cyberpunk 2077'''
        snake_case :			Dict							=   load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''       )
        snake_case :			Optional[int]							=   torch.manual_seed(0       )
        snake_case :			Union[str, Any]							=   pipe(
            prompt=a__						,    image=a__						,    text_to_image_strength=0.75						,    generator=a__						,    guidance_scale=7.5						,    num_inference_steps=50						,    output_type='''numpy'''						,    ).images

        snake_case :			str							=   image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case :			int							=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]       )

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






