code: 
  
  """simple docstring"""
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  _A						=     1_80
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   List[Any]:
        UpperCAmelCase__ :       str						=						k_size // 2
        UpperCAmelCase__				,		UpperCAmelCase__ :       int						=						np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
        UpperCAmelCase__ :       Tuple						=						(
            1
            / (2 * np.pi * sigma)
            * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
        )
        return g
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   str:
        UpperCAmelCase__ :       Optional[int]						=						np.zeros(lowerCAmelCase						)
  
        for row in range(1 ,  image_shape[0] - 1						):
              for col in range(1 ,  image_shape[1] - 1						):
                    UpperCAmelCase__ :       Tuple						=						gradient_direction[row, col]
  
                    if (
                        0 <= direction < PI / 8
                        or 15 * PI / 8 <= direction <= 2 * PI
                        or 7 * PI / 8 <= direction <= 9 * PI / 8
                    ):
                          UpperCAmelCase__ :       List[Any]						=						sobel_grad[row, col - 1]
                          UpperCAmelCase__ :       Tuple						=						sobel_grad[row, col + 1]
                          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                UpperCAmelCase__ :       Tuple						=						sobel_grad[row, col]
  
                    elif (
                        PI / 8 <= direction < 3 * PI / 8
                        or 9 * PI / 8 <= direction < 11 * PI / 8
                    ):
                          UpperCAmelCase__ :       Union[str, Any]						=						sobel_grad[row + 1, col - 1]
                          UpperCAmelCase__ :       int						=						sobel_grad[row - 1, col + 1]
                          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                UpperCAmelCase__ :       Optional[Any]						=						sobel_grad[row, col]
  
                    elif (
                        3 * PI / 8 <= direction < 5 * PI / 8
                        or 11 * PI / 8 <= direction < 13 * PI / 8
                    ):
                          UpperCAmelCase__ :       List[Any]						=						sobel_grad[row - 1, col]
                          UpperCAmelCase__ :       Dict						=						sobel_grad[row + 1, col]
                          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                UpperCAmelCase__ :       Any						=						sobel_grad[row, col]
  
                    elif (
                        5 * PI / 8 <= direction < 7 * PI / 8
                        or 13 * PI / 8 <= direction < 15 * PI / 8
                    ):
                          UpperCAmelCase__ :       Union[str, Any]						=						sobel_grad[row - 1, col - 1]
                          UpperCAmelCase__ :       Optional[int]						=						sobel_grad[row + 1, col + 1]
                          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                UpperCAmelCase__ :       Optional[Any]						=						sobel_grad[row, col]
  
        return destination
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   List[str]:
  
  
  
  
  
  
  
        for row in range(1 ,  image_shape[0] - 1						):
              for col in range(1 ,  image_shape[1] - 1						):
                    if destination[row, col] >= threshold_high:
                          UpperCAmelCase__ :       Tuple						=						strong
                    elif destination[row, col] <= threshold_low:
                          UpperCAmelCase__ :       Any						=						0
                    else:
                          UpperCAmelCase__ :       Any						=						weak
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
  
  
  
  
  
  
  
        for row in range(1 ,  image_shape[0]						):
              for col in range(1 ,  image_shape[1]						):
                    if destination[row, col] == weak:
                          if 2_55 in (
                              destination[row, col + 1],
                              destination[row, col - 1],
                              destination[row - 1, col],
                              destination[row + 1, col],
                              destination[row - 1, col - 1],
                              destination[row + 1, col - 1],
                              destination[row - 1, col + 1],
                              destination[row + 1, col + 1],
                          ):
                                UpperCAmelCase__ :       Optional[int]						=						strong
                          else:
                                UpperCAmelCase__ :       int						=						0
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=15 ,  lowerCAmelCase=30 ,  lowerCAmelCase=1_28 ,  lowerCAmelCase=2_55						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       List[str]						=						img_convolve(lowerCAmelCase ,  gen_gaussian_kernel(9 ,  sigma=1.4						)						)
        # get the gradient and degree by sobel_filter
        UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						sobel_filter(lowerCAmelCase						)
        UpperCAmelCase__ :       str						=						PI + np.radadeg(lowerCAmelCase						)
  
        UpperCAmelCase__ :       Optional[Any]						=						suppress_non_maximum(image.shape ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        detect_high_low_threshold(
            image.shape ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        track_edge(image.shape ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      _A						=     cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      _A						=     canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  