code: 
  
  
  
  
  
  '''simple docstring'''
  
  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       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     snake_case_(	self				,	A				,	A )	->     str:
  								_SCREAMING_SNAKE_CASE             =    QA_PROMPT.format(text=__UpperCamelCase				,	question=__UpperCamelCase )
  								return self.pre_processor(__UpperCamelCase				,	return_tensors="""pt""" )
  
  
  				def     snake_case_(	self				,	A )	->     Tuple:
  								_SCREAMING_SNAKE_CASE             =    self.model.generate(**__UpperCamelCase )
  
  								_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    inputs["""input_ids"""].shape
  								_SCREAMING_SNAKE_CASE             =    output_ids.shape[0]
  
  								return output_ids.reshape(__UpperCamelCase				,	out_b // in_b				,	*output_ids.shape[1:] )[0][0]
  
  
  
  
  
  				def     snake_case_(	self				,	A )	->     List[Any]:
  								return self.pre_processor.decode(__UpperCamelCase				,	skip_special_tokens=__UpperCamelCase				,	clean_up_tokenization_spaces=__UpperCamelCase )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      _SCREAMING_SNAKE_CASE             =    word.upper()
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    {}
      _SCREAMING_SNAKE_CASE             =    []
  
      for letter in word:
          if letter not in letter_nums:
              _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
              next_num += 1
          word_pattern.append(letter_nums[letter]					)
      return ".".join(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        import pprint
        import time
  
        lowercase_			       =				time.time()
        with open("""dictionary.txt""") as in_file:
              lowercase_			       =				in_file.read().splitlines()
  
        lowercase_			       =				{}
        for word in word_list:
              lowercase_			       =				get_word_pattern(word)
              if pattern in all_patterns:
                    all_patterns[pattern].append(word)
              else:
                    lowercase_			       =				[word]
  
        with open("""word_patterns.txt""", """w""") as out_file:
              out_file.write(pprint.pformat(all_patterns))
  
        lowercase_			       =				round(time.time() - start_time, 2)
        print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
        # Done!  9,581 word patterns found in 0.58 seconds.
  
  
  
  