


def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				bool:
  snake_case__						= len(__lowerCAmelCase				) + 1
  snake_case__						= 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.
  snake_case__						= [[0 for i in range(__lowerCAmelCase				)] for j in range(__lowerCAmelCase				)]

  # since string of zero length match pattern of zero length
  snake_case__						= 1

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

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

      elif pattern[j - 1] == "*":
        if dp[i][j - 2] == 1:
          snake_case__						= 1
        elif pattern[j - 2] in (input_string[i - 1], "."):
          snake_case__						= dp[i - 1][j]
        else:
          snake_case__						= 0
      else:
        snake_case__						= 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__							:						int							     =   """aab"""
   lowerCamelCase__							:						List[Any]							     =   """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}""")





"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileNetVaImageProcessor






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__=7					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=18					,				SCREAMING_SNAKE_CASE__=30					,				SCREAMING_SNAKE_CASE__=4_00					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				)      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	size if size is not None else {'''shortest_edge''': 20}
					_UpperCamelCase					:Union[str, Any]					     =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
					_UpperCamelCase					:str					     =	parent
					_UpperCamelCase					:str					     =	batch_size
					_UpperCamelCase					:str					     =	num_channels
					_UpperCamelCase					:Optional[Any]					     =	image_size
					_UpperCamelCase					:List[str]					     =	min_resolution
					_UpperCamelCase					:Tuple					     =	max_resolution
					_UpperCamelCase					:List[Any]					     =	do_resize
					_UpperCamelCase					:Tuple					     =	size
					_UpperCamelCase					:int					     =	do_center_crop
					_UpperCamelCase					:Optional[int]					     =	crop_size





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}






@require_torch
@require_vision
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							MobileNetVaImageProcessor if is_vision_available() else None
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	MobileNetVaImageProcessingTester(self   )
		@property
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					return self.image_processor_tester.prepare_image_processor_dict()
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''do_resize'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''size'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''do_center_crop'''   )   )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					,				'''crop_size'''   )   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.image_processing_class.from_dict(self.image_processor_dict   )
					self.assertEqual(image_processor.size					,				{'''shortest_edge''': 20}   )
					self.assertEqual(image_processor.crop_size					,				{'''height''': 18, '''width''': 18}   )

					_UpperCamelCase					:List[Any]					     =	self.image_processing_class.from_dict(self.image_processor_dict					,				size=42					,				crop_size=84   )
					self.assertEqual(image_processor.size					,				{'''shortest_edge''': 42}   )
					self.assertEqual(image_processor.crop_size					,				{'''height''': 84, '''width''': 84}   )
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					pass
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random PIL images
					_UpperCamelCase					:Union[str, Any]					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				Image.Image   )

					# Test not batched input
					_UpperCamelCase					:Any					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Any					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_UpperCamelCase					:Any					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__					,				numpify=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				np.ndarray   )

					# Test not batched input
					_UpperCamelCase					:Any					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:List[str]					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)





		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	self.image_processing_class(**self.image_processor_dict   )
					# create random PyTorch tensors
					_UpperCamelCase					:Union[str, Any]					     =	prepare_image_inputs(self.image_processor_tester					,				equal_resolution=SCREAMING_SNAKE_CASE__					,				torchify=SCREAMING_SNAKE_CASE__   )
					for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE__					,				torch.Tensor   )

					# Test not batched input
					_UpperCamelCase					:Union[str, Any]					     =	image_processing(image_inputs[0]					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

					# Test batched
					_UpperCamelCase					:Optional[int]					     =	image_processing(SCREAMING_SNAKE_CASE__					,				return_tensors='''pt'''   ).pixel_values
					self.assertEqual(
					    encoded_images.shape					,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )					,				)

