

"""simple docstring"""

import cva
import numpy as np





def 				__UpperCamelCase   (    a__: List[Any] ,	a__: Union[str, Any] ,	a__: Union[str, Any] ,	a__: Dict			)				->			int:




     """simple docstring"""



     try:
          return int(image[x_coordinate][y_coordinate] >= center			)
     except (IndexError, TypeError):
          return 0





def 				__UpperCamelCase   (    a__: Tuple ,	a__: Tuple ,	a__: Dict			)				->			int:




     """simple docstring"""



     __lowercase												=			image[x_coordinate][y_coordinate]
     __lowercase												=			[1, 2, 4, 8, 16, 32, 64, 1_28]

     # skip get_neighbors_pixel if center is null
     if center is None:
          return 0

     # Starting from the top right, assigning value to pixels clockwise
     __lowercase												=			[
         get_neighbors_pixel(__snake_case ,	x_coordinate - 1 ,	y_coordinate + 1 ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	__snake_case ,	y_coordinate + 1 ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	x_coordinate - 1 ,	__snake_case ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	x_coordinate + 1 ,	y_coordinate + 1 ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	x_coordinate + 1 ,	__snake_case ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	x_coordinate + 1 ,	y_coordinate - 1 ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	__snake_case ,	y_coordinate - 1 ,	__snake_case			),
         get_neighbors_pixel(__snake_case ,	x_coordinate - 1 ,	y_coordinate - 1 ,	__snake_case			),
     ]

     # Converting the binary value to decimal.
     return sum(
         binary_value * power for binary_value, power in zip(__snake_case ,	__snake_case			)			)


if __name__ == "__main__":
       # Reading the image and converting it to grayscale.
       a__ :	Tuple						= cva.imread(
           """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
       )

       # Create a numpy array as the same height and width of read image
       a__ :	Any						= np.zeros((image.shape[0], image.shape[1]))

       # Iterating through the image and calculating the
       # local binary pattern value for each pixel.
       for i in range(0, image.shape[0]):
              for j in range(0, image.shape[1]):
                     a__ :	Union[str, Any]						= local_binary_value(image, i, j)

       cva.imshow("""local binary pattern""", lbp_image)
       cva.waitKey(0)
       cva.destroyAllWindows()





"""simple docstring"""

import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a__ :	List[str]						= logging.getLogger(__name__)





def 				__UpperCamelCase   (    )				->			Optional[Any]:




					"""simple docstring"""



					__lowercase												=			ArgumentParser()
					__lowercase						, __lowercase												=			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(('''-''', '''--''')			):
															parser.add_argument(arg.split('''='''			)[0]			)

					return parser.parse_args()





def 				__UpperCamelCase   (    )				->			int:




					"""simple docstring"""



					__lowercase												=			parse_args()
					__lowercase												=			88_88
					__lowercase												=			int(os.environ['''SM_NUM_GPUS''']			)
					__lowercase												=			json.loads(os.environ['''SM_HOSTS''']			)
					__lowercase												=			len(a__			)
					__lowercase												=			os.environ['''SM_CURRENT_HOST''']
					__lowercase												=			hosts.index(a__			)
					__lowercase												=			'''INFO'''

					if num_nodes > 1:
										__lowercase												=			f'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					else:
										__lowercase												=			f'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]			)}'''
					try:
										subprocess.run(a__ ,	shell=a__			)
					except Exception as e:
										logger.info(a__			)


if __name__ == "__main__":
							main()


