





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

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




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   """google/flan-t5-base"""
      UpperCamelCase__       						=   (
          """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__       						=   """text_qa"""
      UpperCamelCase__       						=   AutoTokenizer
      UpperCamelCase__       						=   AutoModelForSeqaSeqLM

      UpperCamelCase__       						=   ["""text""", """text"""]
      UpperCamelCase__       						=   ["""text"""]






      def 	UpperCAmelCase_   (						self:	int					,  __lowerCamelCase:	str					,  __lowerCamelCase:	str			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      QA_PROMPT.format(text=__lowerCamelCase					,  question=__lowerCamelCase			)
          return self.pre_processor(__lowerCamelCase					,  return_tensors="pt"			)






      def 	UpperCAmelCase_   (						self:	List[Any]					,  __lowerCamelCase:	Optional[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self.model.generate(**__lowerCamelCase			)

          UpperCamelCase__:					Dict												=      inputs["input_ids"].shape
          UpperCamelCase__:					Optional[int]												=      output_ids.shape[0]

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






      def 	UpperCAmelCase_   (						self:	Tuple					,  __lowerCamelCase:	Optional[Any]			):
          '''simple docstring'''


          return self.pre_processor.decode(__lowerCamelCase					,  skip_special_tokens=__lowerCamelCase					,  clean_up_tokenization_spaces=__lowerCamelCase			)








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

