




"""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


_lowercase						   =     "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class    __lowercase							(      snake_case_						):


				'''simple docstring'''


				_lowerCAmelCase						      ='''google/flan-t5-base'''
				_lowerCAmelCase						      =(
				    '''This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the '''
				    '''text where to find the answer, and `question`, which is the question, and returns the answer to the question.'''
				)
				_lowerCAmelCase						      ='''text_qa'''
				_lowerCAmelCase						      =AutoTokenizer
				_lowerCAmelCase						      =AutoModelForSeqaSeqLM

				_lowerCAmelCase						      =['''text''', '''text''']
				_lowerCAmelCase						      =['''text''']

				def    _lowerCamelCase       ( self :  Optional[Any]   ,SCREAMING_SNAKE_CASE_ :  str   ,SCREAMING_SNAKE_CASE_ :  str   ):
										snake_case__							:       List[Any]				      =       QA_PROMPT.format(text=_snake_case   ,question=_snake_case   )
										return self.pre_processor(_snake_case   ,return_tensors='''pt'''   )

				def    _lowerCamelCase       ( self :  Dict   ,SCREAMING_SNAKE_CASE_ :  Union[str, Any]   ):
										snake_case__							:       List[str]				      =       self.model.generate(**_snake_case   )

										snake_case__      ,						snake_case__							:       str				      =       inputs['''input_ids'''].shape
										snake_case__							:       List[str]				      =       output_ids.shape[0]

										return output_ids.reshape(_snake_case   ,out_b // in_b   ,*output_ids.shape[1:]   )[0][0]





				def    _lowerCamelCase       ( self :  Union[str, Any]   ,SCREAMING_SNAKE_CASE_ :  Dict   ):
										return self.pre_processor.decode(_snake_case   ,skip_special_tokens=_snake_case   ,clean_up_tokenization_spaces=_snake_case   )








from .stack import Stack


def   A		(__A  :       str			)  -> bool:







     """simple docstring"""


     UpperCAmelCase_		=				Stack()
     UpperCAmelCase_		=				{'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__A			)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    snake_case_					:					Dict       						=			["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        snake_case_					:					Optional[int]       						=			"" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")






