

'''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     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
								snake_case__						:       Union[str, Any]       						=  sd_pipe.to(snake_case_  )
								sd_pipe.set_progress_bar_config(disable=snake_case_  )

								sd_pipe.set_scheduler('''sample_euler'''  )

								snake_case__						:       Any       						=  '''A painting of a squirrel eating a burger'''
								snake_case__						:       Union[str, Any]       						=  torch.manual_seed(0  )
								snake_case__						:       Union[str, Any]       						=  sd_pipe([prompt]			,			generator=snake_case_			,			guidance_scale=9.0			,			num_inference_steps=2_0			,			output_type='''np'''  )

								snake_case__						:       Optional[Any]       						=  output.images

								snake_case__						:       Dict       						=  image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								snake_case__						:       Optional[Any]       						=  np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]  )

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







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
								snake_case__						:       List[Any]       						=  sd_pipe.to(snake_case_  )
								sd_pipe.set_progress_bar_config(disable=snake_case_  )

								sd_pipe.set_scheduler('''sample_euler'''  )

								snake_case__						:       Union[str, Any]       						=  '''A painting of a squirrel eating a burger'''
								snake_case__						:       Any       						=  torch.manual_seed(0  )
								snake_case__						:       Dict       						=  sd_pipe([prompt]			,			generator=snake_case_			,			guidance_scale=9.0			,			num_inference_steps=2_0			,			output_type='''np'''  )

								snake_case__						:       Optional[int]       						=  output.images

								snake_case__						:       Union[str, Any]       						=  image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								snake_case__						:       Optional[int]       						=  np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]  )

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







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
								snake_case__						:       int       						=  sd_pipe.to(snake_case_  )
								sd_pipe.set_progress_bar_config(disable=snake_case_  )

								sd_pipe.set_scheduler('''sample_dpmpp_2m'''  )

								snake_case__						:       Any       						=  '''A painting of a squirrel eating a burger'''
								snake_case__						:       List[Any]       						=  torch.manual_seed(0  )
								snake_case__						:       Dict       						=  sd_pipe(
								    [prompt]			,			generator=snake_case_			,			guidance_scale=7.5			,			num_inference_steps=1_5			,			output_type='''np'''			,			use_karras_sigmas=snake_case_			,			)

								snake_case__						:       Optional[int]       						=  output.images

								snake_case__						:       List[str]       						=  image[0, -3:, -3:, -1]

								assert image.shape == (1, 5_1_2, 5_1_2, 3)
								snake_case__						:       str       						=  np.array(
								    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]  )

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






'''simple docstring'''


from bisect import bisect
from itertools import accumulate




def 	_a       (				__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      Tuple   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Any       						=  sorted(zip(__lowerCAmelCase   ,							__lowerCAmelCase     )   ,							key=lambda __lowerCAmelCase     : x[0] / x[1]   ,							reverse=__lowerCAmelCase     )
		snake_case__						,       snake_case__						:       Union[str, Any]       						=  [i[0] for i in r], [i[1] for i in r]
		snake_case__						:       Dict       						=  list(accumulate(__lowerCAmelCase     )     )
		snake_case__						:       Optional[int]       						=  bisect(__lowerCAmelCase   ,							__lowerCAmelCase     )
		return (
		    0
		    if k == 0
		    else sum(vl[:k]     ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
		    if k != n
		    else sum(vl[:k]     )
		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



