
"""simple docstring"""

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Optional[int]	)	-> Dict:






    """simple docstring"""




    UpperCAmelCase       =   ConvBertConfig.from_json_file(lowerCAmelCase	)
    UpperCAmelCase       =   ConvBertModel(lowerCAmelCase	)

    UpperCAmelCase       =   load_tf_weights_in_convbert(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)
    model.save_pretrained(lowerCAmelCase	)

    UpperCAmelCase       =   TFConvBertModel.from_pretrained(lowerCAmelCase	,			from_pt=lowerCAmelCase	)
    tf_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  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(
       '''--convbert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained ConvBERT 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.'''
   )
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)





"""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

SCREAMING_SNAKE_CASE_         =  180




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[Any]	)	-> Any:






    """simple docstring"""




    UpperCAmelCase       =   k_size // 2
    UpperCAmelCase     ,							UpperCAmelCase       =   np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    UpperCAmelCase       =   (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(lowerCAmelCase	) + np.square(lowerCAmelCase	)) / (2 * np.square(lowerCAmelCase	))	)
    )
    return g




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   np.zeros(lowerCAmelCase	)

    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            UpperCAmelCase       =   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       =   sobel_grad[row, col - 1]
                UpperCAmelCase       =   sobel_grad[row, col + 1]
                if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                PI / 8 <= direction < 3 * PI / 8
                or 9 * PI / 8 <= direction < 11 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row + 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row - 1, col + 1]
                if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                3 * PI / 8 <= direction < 5 * PI / 8
                or 11 * PI / 8 <= direction < 13 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col]
                UpperCAmelCase       =   sobel_grad[row + 1, col]
                if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                5 * PI / 8 <= direction < 7 * PI / 8
                or 13 * PI / 8 <= direction < 15 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row + 1, col + 1]
                if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                    UpperCAmelCase       =   sobel_grad[row, col]

    return destination




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   List[str]	)	-> Any:






    """simple docstring"""




    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       =   strong
            elif destination[row, col] <= threshold_low:
                UpperCAmelCase       =   0
            else:
                UpperCAmelCase       =   weak




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> List[str]:






    """simple docstring"""




    for row in range(1	,			image_shape[0]	):
        for col in range(1	,			image_shape[1]	):
            if destination[row, col] == weak:
                if 255 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       =   strong
                else:
                    UpperCAmelCase       =   0




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   int=15	,			lowerCAmelCase			:   Dict=30	,			lowerCAmelCase			:   str=128	,			lowerCAmelCase			:   Optional[Any]=255	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   img_convolve(lowerCAmelCase	,			gen_gaussian_kernel(9	,			sigma=1.4	)	)
    # get the gradient and degree by sobel_filter
    UpperCAmelCase     ,							UpperCAmelCase       =   sobel_filter(lowerCAmelCase	)
    UpperCAmelCase       =   PI + np.radadeg(lowerCAmelCase	)

    UpperCAmelCase       =   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
   SCREAMING_SNAKE_CASE_         =  cva.imread(r'''../image_data/lena.jpg''', 0)
   # canny edge detection
   SCREAMING_SNAKE_CASE_         =  canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)



