





'''simple docstring'''





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   k_size // 2
				_snake_case :			List[Any]  						=   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				_snake_case :			int  						=   1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase ) + square(__lowerCAmelCase )) / (2 * square(__lowerCAmelCase )) )
				return g

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   image.shape[0], image.shape[1]
				# dst image height and width
				_snake_case :			int  						=   height - k_size + 1
				_snake_case :			Optional[Any]  						=   width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				_snake_case :			Union[str, Any]  						=   zeros((dst_height * dst_width, k_size * k_size) )
				_snake_case :			int  						=   0
				for i, j in product(range(__lowerCAmelCase )	,			range(__lowerCAmelCase ) ):
								_snake_case :			Optional[Any]  						=   ravel(image[i : i + k_size, j : j + k_size] )
								_snake_case :			List[str]  						=   window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				_snake_case :			List[str]  						=   gen_gaussian_kernel(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   ravel(__lowerCAmelCase )

				# reshape and get the dst image
				_snake_case :			Any  						=   dot(__lowerCAmelCase	,			__lowerCAmelCase ).reshape(__lowerCAmelCase	,			__lowerCAmelCase ).astype(__lowerCAmelCase )

				return dst


if __name__ == "__main__":
		# read original image
		lowercase_		:				List[Any]				=						imread(r'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowercase_		:				Tuple				=						cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowercase_		:				int				=						gaussian_filter(gray, 3, sigma=1)
		lowercase_		:				Union[str, Any]				=						gaussian_filter(gray, 5, sigma=0.8)

		# show result images
		imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
		imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
		waitKey()













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}''' )
				torch.save(model.state_dict()	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						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(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT 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.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






