



"""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


__SCREAMING_SNAKE_CASE     :  List[Any]										=					'''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

Can you answer this question about the text: \'{question}\''''


class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					lowercase__      :				Optional[Any]        =       'google/flan-t5-base'
					lowercase__      :				Optional[Any]        =       (
					    '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.'
					)
					lowercase__      :				Optional[Any]        =       'text_qa'
					lowercase__      :				List[Any]        =       AutoTokenizer
					lowercase__      :				List[str]        =       AutoModelForSeqaSeqLM

					lowercase__      :				Optional[int]        =       ['text', 'text']
					lowercase__      :				Any        =       ['text']

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = QA_PROMPT.format(text=lowerCamelCase__ ,				question=lowerCamelCase__      )
									return self.pre_processor(lowerCamelCase__ ,				return_tensors='''pt'''      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.model.generate(**lowerCamelCase__      )

									_lowerCamelCase     ,   _lowerCamelCase					     = inputs['''input_ids'''].shape
									_lowerCamelCase					     = output_ids.shape[0]

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





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									return self.pre_processor.decode(lowerCamelCase__ ,				skip_special_tokens=lowerCamelCase__ ,				clean_up_tokenization_spaces=lowerCamelCase__      )





"""simple docstring"""
from .stack import Stack



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				bool:
				_lowerCamelCase					     = Stack()
				_lowerCamelCase					     = {'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
				for bracket in parentheses:
								if bracket in bracket_pairs:
												stack.push(lowercase_							)
								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()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					['''((()))''', '''((())''', '''(()))''']
			print('''Balanced parentheses demonstration:\n''')
			for example in examples:
						__SCREAMING_SNAKE_CASE     :  Any										=					'''''' if balanced_parentheses(example) else '''not '''
						print(F"""{example} is {not_str}balanced""")
