





"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       bool:






       """simple docstring"""




       _UpperCAmelCase     =     len(SCREAMING_SNAKE_CASE							) + 1
       _UpperCAmelCase     =     len(SCREAMING_SNAKE_CASE							) + 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.
       _UpperCAmelCase     =     [[0 for i in range(SCREAMING_SNAKE_CASE							)] for j in range(SCREAMING_SNAKE_CASE							)]

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

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

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

                     elif pattern[j - 1] == "*":
                            if dp[i][j - 2] == 1:
                                   _UpperCAmelCase     =     1
                            elif pattern[j - 2] in (input_string[i - 1], "."):
                                   _UpperCAmelCase     =     dp[i - 1][j]
                            else:
                                   _UpperCAmelCase     =     0
                     else:
                            _UpperCAmelCase     =     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}''')













"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
           """and returns a summary of the text."""
       )
       lowerCAmelCase__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






