





# 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      :							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_					)













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()






