


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     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       List[Any]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
         __UpperCamelCase    							=sd_pipe.to(_snake_case    )
         sd_pipe.set_progress_bar_config(disable=_snake_case    )

         sd_pipe.set_scheduler('sample_euler'    )

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

         __UpperCamelCase    							=output.images

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

         assert image.shape == (1, 512, 512, 3)
         __UpperCamelCase    							=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 _a					(			self    )  ->       str:
         __UpperCamelCase    							=StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
         __UpperCamelCase    							=sd_pipe.to(_snake_case    )
         sd_pipe.set_progress_bar_config(disable=_snake_case    )

         sd_pipe.set_scheduler('sample_euler'    )

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

         __UpperCamelCase    							=output.images

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

         assert image.shape == (1, 512, 512, 3)
         __UpperCamelCase    							=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 _a					(			self    )  ->       int:
         __UpperCamelCase    							=StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
         __UpperCamelCase    							=sd_pipe.to(_snake_case    )
         sd_pipe.set_progress_bar_config(disable=_snake_case    )

         sd_pipe.set_scheduler('sample_dpmpp_2m'    )

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

         __UpperCamelCase    							=output.images

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

         assert image.shape == (1, 512, 512, 3)
         __UpperCamelCase    							=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      __UpperCAmelCase      (    __lowerCamelCase = 50	)							->      int:
		lowercase__   :    int				=			[[0] * 3 for _ in range(length + 1	)]

		for row_length in range(length + 1	):
				for tile_length in range(2						,     5	):
						for tile_start in range(row_length - tile_length + 1	):
								different_colour_ways_number[row_length][tile_length - 2] += (
								    different_colour_ways_number[row_length - tile_start - tile_length][
								        tile_length - 2
								    ]
								    + 1
								)

		return sum(different_colour_ways_number[length]	)


if __name__ == "__main__":
							print(F'''{solution() = }''')
