


'''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


A__:   Any    =	'''Here is a text containing a lot of information: \'\'\'{text}\'\'\'.

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







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  List[str]     	=							"google/flan-t5-base"
      __UpperCamelCase	:  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."
      )
      __UpperCamelCase	:  int     	=							"text_qa"
      __UpperCamelCase	:  Dict     	=							AutoTokenizer
      __UpperCamelCase	:  Dict     	=							AutoModelForSeqaSeqLM

      __UpperCamelCase	:  Dict     	=							["text", "text"]
      __UpperCamelCase	:  Optional[int]     	=							["text"]






      def __UpperCAmelCase	(			self      :Dict					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :str     )	-> Tuple:




             '''simple docstring'''
             _a   :			str					 =QA_PROMPT.format(text=SCREAMING_SNAKE_CASE					,						question=SCREAMING_SNAKE_CASE     )
             return self.pre_processor(SCREAMING_SNAKE_CASE					,						return_tensors="""pt"""     )






      def __UpperCAmelCase	(			self      :List[str]					,						SCREAMING_SNAKE_CASE      :int     )	-> Optional[Any]:




             '''simple docstring'''
             _a   :			List[Any]					 =self.model.generate(**SCREAMING_SNAKE_CASE     )

             _a ,   _a   :			Dict					 =inputs["""input_ids"""].shape
             _a   :			int					 =output_ids.shape[0]

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






      def __UpperCAmelCase	(			self      :Dict					,						SCREAMING_SNAKE_CASE      :str     )	-> int:




             '''simple docstring'''
             return self.pre_processor.decode(SCREAMING_SNAKE_CASE					,						skip_special_tokens=SCREAMING_SNAKE_CASE					,						clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE     )






'''simple docstring'''







def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Union[str, Any]  )  ->      int:
       _a   :			List[str]					 =[701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(_UpperCAmelCase   ,len(_UpperCAmelCase  )  ):
                     _a   :			Tuple					 =collection[i]
                     _a   :			Optional[Any]					 =i
                     while j >= gap and collection[j - gap] > insert_value:
                            _a   :			Union[str, Any]					 =collection[j - gap]
                            j -= gap
                     if j != i:
                            _a   :			Union[str, Any]					 =insert_value
       return collection


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

   testmod()
   A__:   int    =	input('''Enter numbers separated by a comma:\n''').strip()
   A__:   Optional[Any]    =	[int(item) for item in user_input.split(''',''')]
   print(shell_sort(unsorted))


