
"""simple docstring"""




def 						__magic_name__		(				UpperCamelCase    :		list       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int    )    ->	int:
    a__    = int((lower_bound + upper_bound) // 2    )
    if array[r] == value:
        return r
    if lower_bound >= upper_bound:
        return -1
    if array[r] < value:
        return binary_search(UpperCamelCase       ,		r + 1       ,		UpperCamelCase       ,		UpperCamelCase    )
    else:
        return binary_search(UpperCamelCase       ,		UpperCamelCase       ,		r - 1       ,		UpperCamelCase    )



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		list    )    ->	list:
    a__    = 0
    if matrix[index][0] == value:
        return [index, 0]
    while index < len(UpperCamelCase    ) and matrix[index][0] < value:
        a__    = binary_search(matrix[index]       ,		0       ,		len(matrix[index]    ) - 1       ,		UpperCamelCase    )
        if r != -1:
            return [index, r]
        index += 1
    return [-1, -1]


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


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


