code: 
  
  
  
  
  
  # 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
  
  
  lowerCAmelCase_   =   "Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"
  
  class 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							"""google/flan-t5-base"""
        _SCREAMING_SNAKE_CASE					=							(
            """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					=							"""text_qa"""
        _SCREAMING_SNAKE_CASE					=							AutoTokenizer
        _SCREAMING_SNAKE_CASE					=							AutoModelForSeqaSeqLM
  
        _SCREAMING_SNAKE_CASE					=							["""text""", """text"""]
        _SCREAMING_SNAKE_CASE					=							["""text"""]
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						QA_PROMPT.format(text=lowercase_     ,  question=lowercase_					)
               return self.pre_processor(lowercase_     ,  return_tensors='''pt'''					)
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						self.model.generate(**lowercase_					)
  
               _snake_case				,    _snake_case      :							Optional[int]     				=						inputs['''input_ids'''].shape
               _snake_case      :							Dict     				=						output_ids.shape[0]
  
               return output_ids.reshape(lowercase_     ,  out_b // in_b     ,  *output_ids.shape[1:]					)[0][0]
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               return self.pre_processor.decode(lowercase_     ,  skip_special_tokens=lowercase_     ,  clean_up_tokenization_spaces=lowercase_					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]
  
         # skip get_neighbors_pixel if center is null
         if center is None:
                return 0
  
         # Starting from the top right, assigning value to pixels clockwise
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   cva.imread(
         "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
     )
  
     # Create a numpy array as the same height and width of read image
     lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))
  
     # Iterating through the image and calculating the
     # local binary pattern value for each pixel.
     for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
           lowerCAmelCase_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  