


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}""")





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
   from transformers import TvltImageProcessor

if is_speech_available():
   from transformers import TvltFeatureExtractor

from transformers import TvltProcessor
@require_torch
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''ZinengTang/tvlt-base'''
       snake_case__						= tempfile.mkdtemp()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:List[Any]   ):
       return TvltImageProcessor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		**_a:Tuple   ):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )
       processor.save_pretrained(self.tmpdirname   )
       snake_case__						= TvltProcessor.from_pretrained(self.tmpdirname   )

       self.assertIsInstance(processor.feature_extractor     ,		_a   )
       self.assertIsInstance(processor.image_processor     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )

       snake_case__						= feature_extractor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(audio=_a     ,		return_tensors='''np'''   )

       for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= image_processor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(images=_a     ,		return_tensors='''np'''   )

       for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )
       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= processor(audio=_a     ,		images=_a   )

       self.assertListEqual(list(inputs.keys()   )     ,		['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']   )

       # test if it raises when no input is passed
       with pytest.raises(_a   ):
         processor()







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       self.assertListEqual(
           processor.model_input_names     ,		image_processor.model_input_names + feature_extractor.model_input_names     ,		msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,		)

