code: 
  
  
  
  
  
  '''simple docstring'''
  
  from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
  from numpy import array, dot, pad, ravel, uinta, zeros
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					Tuple					)		->Optional[int]:
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    image.shape
      _SCREAMING_SNAKE_CASE             =    cols - block_size[1] + 1
      _SCREAMING_SNAKE_CASE             =    rows - block_size[0] + 1
      _SCREAMING_SNAKE_CASE             =    zeros((dst_height * dst_width, block_size[1] * block_size[0])					)
      _SCREAMING_SNAKE_CASE             =    0
      for i in range(0      , __lowerCamelCase					):
          for j in range(0      , __lowerCamelCase					):
              _SCREAMING_SNAKE_CASE             =    ravel(image[i : i + block_size[0], j : j + block_size[1]]					)
              _SCREAMING_SNAKE_CASE             =    window
              row += 1
  
      return image_array
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Optional[int]					)		->Optional[Any]:
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    image.shape[0], image.shape[1]
      _SCREAMING_SNAKE_CASE             =    filter_kernel.shape[0]
      _SCREAMING_SNAKE_CASE             =    k_size // 2
      # Pads image with the edge values of array.
      _SCREAMING_SNAKE_CASE             =    pad(__lowerCamelCase      , __lowerCamelCase      , mode="""edge"""					)
  
      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      _SCREAMING_SNAKE_CASE             =    imacol(__lowerCamelCase      , (k_size, k_size)					)
  
      #  turn the kernel into shape(k*k, 1)
      _SCREAMING_SNAKE_CASE             =    ravel(__lowerCamelCase					)
      # reshape and get the dst image
      _SCREAMING_SNAKE_CASE             =    dot(__lowerCamelCase      , __lowerCamelCase					).reshape(__lowerCamelCase      , __lowerCamelCase					)
      return dst
  
  
  if __name__ == "__main__":
        # read original image
        lowercase_			       =				imread(R"""../image_data/lena.jpg""")
        # turn image in gray scale value
        lowercase_			       =				cvtColor(img, COLOR_BGR2GRAY)
        # Laplace operator
        lowercase_			       =				array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
        lowercase_			       =				img_convolve(gray, Laplace_kernel).astype(uinta)
        imshow("""Laplacian""", out)
        waitKey(0)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  # Copyright 2022 The HuggingFace 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.
  import os
  from typing import List
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					str					)		->Tuple:
      if name != "training_function" and name != "main":
          raise ValueError(F'Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''					)
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    [], False
      for line in lines:
          if not found_start and F'def {name}' in line:
              _SCREAMING_SNAKE_CASE             =    True
              good_lines.append(__lowerCamelCase					)
              continue
          if found_start:
              if name == "training_function" and "def main" in line:
                  return good_lines
              if name == "main" and "if __name__" in line:
                  return good_lines
              good_lines.append(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]					)		->List[str]:
      return [line for line in lines if not line.lstrip().startswith("""#"""					) and line != "\n"]
  
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					str      , __lowerCamelCase				:					str      , __lowerCamelCase				:					bool      , __lowerCamelCase				:					str = None					)		->List[str]:
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(os.path.abspath(os.path.join("""examples"""      , """nlp_example.py"""					)					)      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      with open(__lowerCamelCase      , """r"""					) as f:
          _SCREAMING_SNAKE_CASE             =    f.readlines()
      if secondary_filename is not None:
          with open(__lowerCamelCase      , """r"""					) as f:
              _SCREAMING_SNAKE_CASE             =    f.readlines()
  
      # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
      if parser_only:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """main"""					)					)
      else:
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          _SCREAMING_SNAKE_CASE             =    clean_lines(get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
          if secondary_filename is not None:
              _SCREAMING_SNAKE_CASE             =    clean_lines(
                  get_function_contents_by_name(__lowerCamelCase      , """training_function"""					)					)
  
      _SCREAMING_SNAKE_CASE             =    """train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""
  
      # Specific code in our script that differs from the full version, aka what is new
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      _SCREAMING_SNAKE_CASE             =    iter(__lowerCamelCase					)
      for i in range(len(__lowerCamelCase					) - 1					):
          if i not in passed_idxs:
              _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_feature_code.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
                  else:
                      # Skip over the `config['num_epochs'] = 2` statement
                      _SCREAMING_SNAKE_CASE             =    next(__lowerCamelCase					)
  
      # Extract out just the new parts from the full_file_training_func
      _SCREAMING_SNAKE_CASE             =    []
      _SCREAMING_SNAKE_CASE             =    []  # We keep track of the idxs just in case it's a repeated statement
      for i, line in enumerate(__lowerCamelCase					):
          if i not in passed_idxs:
              if (line not in full_file_func) and (line.lstrip() != _dl_line):
                  if "TESTING_MOCKED_DATALOADERS" not in line:
                      new_full_example_parts.append(__lowerCamelCase					)
                      passed_idxs.append(__lowerCamelCase					)
  
      # Finally, get the overall diff
      _SCREAMING_SNAKE_CASE             =    [line for line in new_feature_code if line not in new_full_example_parts]
      if secondary_filename is not None:
          _SCREAMING_SNAKE_CASE             =    [line for line in full_file_contents if line not in secondary_file_func]
          _SCREAMING_SNAKE_CASE             =    [line for line in diff_from_example if line not in diff_from_two]
  
      return diff_from_example
  
  
  
  