

'''simple docstring'''



from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


UpperCAmelCase				: Optional[Any]         =  """Here is a text containing a lot of information: '''{text}'''.

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







class 		lowerCAmelCase__     (   lowerCAmelCase_      ):



 """simple docstring"""



 lowerCAmelCase__						      =   """google/flan-t5-base"""
 lowerCAmelCase__						      =   (
     """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."""
 )
 lowerCAmelCase__						      =   """text_qa"""
 lowerCAmelCase__						      =   AutoTokenizer
 lowerCAmelCase__						      =   AutoModelForSeqaSeqLM

 lowerCAmelCase__						      =   ["""text""", """text"""]
 lowerCAmelCase__						      =   ["""text"""]
 def    UpperCAmelCase__	(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   QA_PROMPT.format(text=__SCREAMING_SNAKE_CASE      ,							question=__SCREAMING_SNAKE_CASE    )
      return self.pre_processor(__SCREAMING_SNAKE_CASE      ,							return_tensors="""pt"""    )
 def    UpperCAmelCase__	(	self      : int      ,							__SCREAMING_SNAKE_CASE      : Tuple    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model.generate(**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   inputs["""input_ids"""].shape
      __SCREAMING_SNAKE_CASE						     =   output_ids.shape[0]

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


 def    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Tuple:


      """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'''



from __future__ import annotations

from collections.abc import Callable







def      a__ (						a__   ,					a__   ,					a__   ,					a__ = 1_00   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   x_start
     __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
     __SCREAMING_SNAKE_CASE						     =   0.0

     for _ in range(a__		):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          __SCREAMING_SNAKE_CASE						     =   (x_end - x_start) / steps + xa
          __SCREAMING_SNAKE_CASE						     =   fnc(a__		)
          area += abs(fxa + fxa		) * (xa - xa) / 2

          # Increment step
          __SCREAMING_SNAKE_CASE						     =   xa
          __SCREAMING_SNAKE_CASE						     =   fxa
     return area


if __name__ == "__main__":







  def      a__ (						a__		):




       """simple docstring"""


       return x**3

  print('f(x) = x^3')
  print('The area between the curve, x = -10, x = 10 and the x axis is:')
  UpperCAmelCase				: Optional[Any]         =  1_0
  while i <= 1_0_0_0_0_0:
    UpperCAmelCase				: Union[str, Any]         =  trapezoidal_area(f, -5, 5, i)
    print(f"""with {i} steps: {area}""")
    i *= 1_0






