

import sys





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= len(lowercase__	)
	snake_case_      		= [[0 for x in range(lowercase__	)] for x in range(lowercase__	)]
	snake_case_      		= [[0 for x in range(lowercase__	)] for x in range(lowercase__	)]

	for chain_length in range(2			, lowercase__	):
		for a in range(1			, n - chain_length + 1	):
			snake_case_      		= a + chain_length - 1

			snake_case_      		= sys.maxsize
			for c in range(lowercase__			, lowercase__	):
				snake_case_      		= (
				    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
				)
				if cost < matrix[a][b]:
					snake_case_      		= cost
					snake_case_      		= c
	return matrix, sol





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	if i == j:
		print('A' + str(lowercase__	)			, end=' '	)
	else:
		print('('			, end=' '	)
		print_optiomal_solution(lowercase__			, lowercase__			, optimal_solution[i][j]	)
		print_optiomal_solution(lowercase__			, optimal_solution[i][j] + 1			, lowercase__	)
		print(')'			, end=' '	)





def a():





	'''simple docstring'''
	snake_case_      		= [30, 35, 15, 5, 10, 20, 25]
	snake_case_      		= len(lowercase__	)
	# Size of matrix created from above array will be
	# 30*35 35*15 15*5 5*10 10*20 20*25
	snake_case_		,       snake_case_      		= matrix_chain_order(lowercase__	)

	print('No. of Operation required: ' + str(matrix[1][n - 1]	)	)
	print_optiomal_solution(lowercase__			, 1			, n - 1	)


if __name__ == "__main__":
		main()



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

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







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

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

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

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