


def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							assert x is not None
							assert y is not None

							a__      =len(__a     )
							a__      =len(__a     )

							# declaring the array for storing the dp values
							a__      =[[0] * (n + 1) for _ in range(m + 1     )]  # noqa: E741

							for i in range(1    ,     m + 1     ):
														for j in range(1    ,     n + 1     ):
																					a__      =1 if x[i - 1] == y[j - 1] else 0

																					a__      =max(l[i - 1][j]    ,     l[i][j - 1]    ,     l[i - 1][j - 1] + match     )

							a__      =''
							a__    ,     a__      =m, n
							while i > 0 and j > 0:
														a__      =1 if x[i - 1] == y[j - 1] else 0

														if l[i][j] == l[i - 1][j - 1] + match:
																					if match == 1:
																												a__      =x[i - 1] + seq
																					i -= 1
																					j -= 1
														elif l[i][j] == l[i - 1][j]:
																					i -= 1
														else:
																					j -= 1

							return l[m][n], seq


if __name__ == "__main__":
	_lowerCAmelCase:						List[Any]   				=       'AGGTAB'
	_lowerCAmelCase:						Optional[int]   				=       'GXTXAYB'
	_lowerCAmelCase:						Optional[int]   				=       4
	_lowerCAmelCase:						List[Any]   				=       'GTAB'

	_lowerCAmelCase				,						_lowerCAmelCase:						Tuple   				=       longest_common_subsequence(a, b)
	print('len =', ln, ', sub-sequence =', subseq)
	import doctest

	doctest.testmod()








import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							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							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

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

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

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

         assert image.shape == (1, 32, 32, 3)
         _A     =							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      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=20				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

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





