

'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase					 =	False


class 	lowerCAmelCase  (   unittest.TestCase ):
       pass






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







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            __lowercase      		=VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,   torch_dtype=torch.floataa   )
            pipe.to(__lowercase   )
            pipe.set_progress_bar_config(disable=__lowercase   )

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

            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pipe.dual_guided(
                prompt='first prompt'    ,   image=__lowercase    ,   text_to_image_strength=0.7_5    ,   generator=__lowercase    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'    ,   ).images

            with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(__lowercase   )
                 __lowercase      		=VersatileDiffusionPipeline.from_pretrained(__lowercase    ,   torch_dtype=torch.floataa   )
            pipe.to(__lowercase   )
            pipe.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		=generator.manual_seed(0   )
            __lowercase      		=pipe.dual_guided(
                prompt='first prompt'    ,   image=__lowercase    ,   text_to_image_strength=0.7_5    ,   generator=__lowercase    ,   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       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,   torch_dtype=torch.floataa   )
            pipe.to(__lowercase   )
            pipe.set_progress_bar_config(disable=__lowercase   )

            __lowercase      		='cyberpunk 2077'
            __lowercase      		=load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pipe.dual_guided(
                prompt=__lowercase    ,   image=__lowercase    ,   text_to_image_strength=0.7_5    ,   generator=__lowercase    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type='numpy'    ,   ).images

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]   )

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

            __lowercase      		='A painting of a squirrel eating a burger '
            __lowercase      		=torch.manual_seed(0   )
            __lowercase      		=pipe.text_to_image(
                prompt=__lowercase    ,   generator=__lowercase    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type='numpy'   ).images

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]   )

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

            __lowercase      		=pipe.image_variation(__lowercase    ,   generator=__lowercase    ,   output_type='numpy'   ).images

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

            assert image.shape == (1, 512, 512, 3)
            __lowercase      		=np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]   )

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





'''simple docstring'''

from __future__ import annotations

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	float,       lowercase__    :	float			):






     '''simple docstring'''





     if (voltage, current, resistance).count(0			) != 1:
          raise ValueError('One and only one argument must be 0'			)
     if resistance < 0:
          raise ValueError('Resistance cannot be negative'			)
     if voltage == 0:
          return {"voltage": float(current * resistance			)}
     elif current == 0:
          return {"current": voltage / resistance}
     elif resistance == 0:
          return {"resistance": voltage / current}
     else:
          raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


