




import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        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.
 a_										=       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
 a_										=       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]):
   a_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()












# 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 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	Union[str, Any]      =   "google/flan-t5-base"
      lowerCAmelCase__     :	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."
      )
      lowerCAmelCase__     :	List[Any]      =   "text_qa"
      lowerCAmelCase__     :	int      =   AutoTokenizer
      lowerCAmelCase__     :	Optional[Any]      =   AutoModelForSeqaSeqLM

      lowerCAmelCase__     :	Tuple      =   ["text", "text"]
      lowerCAmelCase__     :	Any      =   ["text"]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	str	,    snake_case   :	str			):
          __UpperCamelCase											=  QA_PROMPT.format(text=snake_case	,    question=snake_case			)
          return self.pre_processor(snake_case	,    return_tensors='''pt'''			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  self.model.generate(**snake_case			)

          __UpperCamelCase						, __UpperCamelCase											=  inputs['''input_ids'''].shape
          __UpperCamelCase											=  output_ids.shape[0]

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







      def  snake_case		(       self   :	Tuple	,    snake_case   :	Optional[Any]			):
          return self.pre_processor.decode(snake_case	,    skip_special_tokens=snake_case	,    clean_up_tokenization_spaces=snake_case			)






