


def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					len(A__				) + 1
			SCREAMING_SNAKE_CASE_		:     List[str]					=					len(A__				) + 1

			# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
			# length i of input_string matches with prefix string of length j of
			# given pattern.
			# "dp" stands for dynamic programming.
			SCREAMING_SNAKE_CASE_		:     Any					=					[[0 for i in range(A__				)] for j in range(A__				)]

			# since string of zero length match pattern of zero length
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					1

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1,      A__				):
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1,      A__				):
						SCREAMING_SNAKE_CASE_		:     List[str]					=					dp[0][j - 2] if pattern[j - 1] == '*' else 0

			# now using bottom-up approach to find for all remaining lengths
			for i in range(1,      A__				):
						for j in range(1,      A__				):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												SCREAMING_SNAKE_CASE_		:     Any					=					dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															SCREAMING_SNAKE_CASE_		:     List[str]					=					1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					dp[i - 1][j]
												else:
															SCREAMING_SNAKE_CASE_		:     List[str]					=					0
									else:
												SCREAMING_SNAKE_CASE_		:     int					=					0

			return bool(dp[-1][-1]				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				lowerCAmelCase__			:      List[Any]								='aab'
				lowerCAmelCase__			:      Dict								='c*a*b'

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F"""{input_string} matches the given pattern {pattern}""")
				else:
								print(F"""{input_string} does not match with the given pattern {pattern}""")








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 _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							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 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							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 _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							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