






"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





@slow
@require_torch_gpu
class       snake_case_       (    unittest.TestCase       ):


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
					A__							 =       sd_pipe.to(__a						)
					sd_pipe.set_progress_bar_config(disable=__a						)

					sd_pipe.set_scheduler('sample_euler'						)

					A__							 =       'A painting of a squirrel eating a burger'
					A__							 =       torch.manual_seed(0						)
					A__							 =       sd_pipe([prompt]  ,					generator=__a  ,					guidance_scale=9.0  ,					num_inference_steps=20  ,					output_type='np'						)

					A__							 =       output.images

					A__							 =       image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					A__							 =       np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]						)

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
					A__							 =       sd_pipe.to(__a						)
					sd_pipe.set_progress_bar_config(disable=__a						)

					sd_pipe.set_scheduler('sample_euler'						)

					A__							 =       'A painting of a squirrel eating a burger'
					A__							 =       torch.manual_seed(0						)
					A__							 =       sd_pipe([prompt]  ,					generator=__a  ,					guidance_scale=9.0  ,					num_inference_steps=20  ,					output_type='np'						)

					A__							 =       output.images

					A__							 =       image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					A__							 =       np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]						)

					assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-1






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
					A__							 =       sd_pipe.to(__a						)
					sd_pipe.set_progress_bar_config(disable=__a						)

					sd_pipe.set_scheduler('sample_dpmpp_2m'						)

					A__							 =       'A painting of a squirrel eating a burger'
					A__							 =       torch.manual_seed(0						)
					A__							 =       sd_pipe(
					    [prompt]  ,					generator=__a  ,					guidance_scale=7.5  ,					num_inference_steps=15  ,					output_type='np'  ,					use_karras_sigmas=__a  ,					)

					A__							 =       output.images

					A__							 =       image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					A__							 =       np.array(
					    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]						)

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










"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				return 1 if input_a == input_a else 0



def  __lowerCamelCase					(					):
				assert xnor_gate(0       ,0				) == 1
				assert xnor_gate(0       ,1				) == 0
				assert xnor_gate(1       ,0				) == 0
				assert xnor_gate(1       ,1				) == 1


if __name__ == "__main__":
					print(xnor_gate(0, 0))
					print(xnor_gate(0, 1))
					print(xnor_gate(1, 0))
					print(xnor_gate(1, 1))


