code: 
  """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	 =							'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		int						  =	"""google/flan-t5-base"""
    SCREAMING_SNAKE_CASE__    :		Union[str, 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."""
    )
    SCREAMING_SNAKE_CASE__    :		Any						  =	"""text_qa"""
    SCREAMING_SNAKE_CASE__    :		Union[str, Any]						  =	AutoTokenizer
    SCREAMING_SNAKE_CASE__    :		Dict						  =	AutoModelForSeqaSeqLM
  
    SCREAMING_SNAKE_CASE__    :		List[Any]						  =	["""text""", """text"""]
    SCREAMING_SNAKE_CASE__    :		str						  =	["""text"""]
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					QA_PROMPT.format(text=lowercase_       ,     question=lowercase_						)
       return self.pre_processor(lowercase_       ,     return_tensors="pt"						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					self.model.generate(**lowercase_						)
  
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Any      =					inputs["input_ids"].shape
       UpperCAmelCase_      :      List[Any]      =					output_ids.shape[0]
  
       return output_ids.reshape(lowercase_       ,     out_b // in_b       ,     *output_ids.shape[1:]						)[0][0]
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.pre_processor.decode(lowercase_       ,     skip_special_tokens=lowercase_       ,     clean_up_tokenization_spaces=lowercase_						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from .hash_table import HashTable
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
       super().__init__(*lowercase_       ,     **lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_       ,     lowercase_=None						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					1
       UpperCAmelCase_      :      List[str]      =					self.hash_function(key + i * i						)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          i += 1
          UpperCAmelCase_      :      List[Any]      =					(
              self.hash_function(key + i * i						)
              if not self.balanced_factor() >= self.lim_charge
              else None
          )
  
          if new_key is None:
             break
  
       return new_key
  
  
  