

"""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)








"""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)

