
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		bool:
			lowerCAmelCase__      :      List[Any]         =		len(__lowerCAmelCase     ) + 1
			lowerCAmelCase__      :      Union[str, Any]         =		len(__lowerCAmelCase     ) + 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.
			lowerCAmelCase__      :      Any         =		[[0 for i in range(__lowerCAmelCase     )] for j in range(__lowerCAmelCase     )]

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

			# since pattern of zero length will never match with string of non-zero length
			for i in range(1       ,							__lowerCAmelCase     ):
						lowerCAmelCase__      :      int         =		0

			# since string of zero length will match with pattern where there
			# is at least one * alternatively
			for j in range(1       ,							__lowerCAmelCase     ):
						lowerCAmelCase__      :      Union[str, Any]         =		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       ,							__lowerCAmelCase     ):
						for j in range(1       ,							__lowerCAmelCase     ):
									if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
												lowerCAmelCase__      :      List[Any]         =		dp[i - 1][j - 1]

									elif pattern[j - 1] == "*":
												if dp[i][j - 2] == 1:
															lowerCAmelCase__      :      List[Any]         =		1
												elif pattern[j - 2] in (input_string[i - 1], "."):
															lowerCAmelCase__      :      str         =		dp[i - 1][j]
												else:
															lowerCAmelCase__      :      Dict         =		0
									else:
												lowerCAmelCase__      :      Optional[Any]         =		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__          =		'''aab'''
					lowerCamelCase__          =		'''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 VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







@slow
@require_torch_gpu
class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

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