
"""simple docstring"""




def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		Optional[int]    )    ->	List[str]:

    if not root or root not in binary_tree_mirror_dictionary:
        return
    a__    = binary_tree_mirror_dictionary[root][:2]
    a__    = [right_child, left_child]
    binary_tree_mirror_dict(UpperCamelCase       ,		UpperCamelCase    )
    binary_tree_mirror_dict(UpperCamelCase       ,		UpperCamelCase    )



def 						__magic_name__		(				UpperCamelCase    :		Optional[Any]       ,		UpperCamelCase    :		List[str] = 1    )    ->	dict:

    if not binary_tree:
        raise ValueError('binary tree cannot be empty'    )
    if root not in binary_tree:
        a__    = f'root {root} is not present in the binary_tree'
        raise ValueError(UpperCamelCase    )
    a__    = dict(UpperCamelCase    )
    binary_tree_mirror_dict(UpperCamelCase       ,		UpperCamelCase    )
    return binary_tree_mirror_dictionary


if __name__ == "__main__":
    a		:					int    	=					{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
    print(F'''Binary tree: {binary_tree}''')
    a		:					Optional[int]    	=					binary_tree_mirror(binary_tree, 5)
    print(F'''Binary tree mirror: {binary_tree_mirror_dictionary}''')




"""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


a		:					str    	=					False







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



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



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



     def  lowercase__     (       self      )		->    Optional[int]:







         """simple docstring"""



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

         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=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=2    ,							output_type='numpy'    ,							).images

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

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

         a__    = generator.manual_seed(0      )
         a__    = pipe(
             prompt='first prompt'    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							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  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



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

         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=__SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							text_to_image_strength=0.75    ,							generator=__SCREAMING_SNAKE_CASE    ,							guidance_scale=7.5    ,							num_inference_steps=5_0    ,							output_type='numpy'    ,							).images

         a__    = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 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


