


'''simple docstring'''

import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
							# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
							# setup instructions, if using on-demand hardware

							# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
							# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
							# Throw an error if user passes both BYO and on-demand cluster args
							# Otherwise, use default values
							lowerCAmelCase      :List[str]    	=			argparse.ArgumentParser()
							parser.add_argument('''--user''', type=str, default='''ubuntu''')
							parser.add_argument('''--host''', type=str, default='''localhost''')
							parser.add_argument('''--key_path''', type=str, default=None)
							parser.add_argument('''--instance''', type=str, default='''V100:1''')
							parser.add_argument('''--provider''', type=str, default='''cheapest''')
							parser.add_argument('''--use_spot''', type=bool, default=False)
							parser.add_argument('''--example''', type=str, default='''pytorch/text-generation/run_generation.py''')
							lowerCAmelCase	,    lowerCAmelCase      :Tuple    	=			parser.parse_known_args()
							if args.host != "localhost":
														if args.instance != "V100:1" or args.provider != "cheapest":
																					raise ValueError('''Cannot specify both BYO and on-demand cluster args''')
														lowerCAmelCase      :int    	=			rh.cluster(
														    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
														)
							else:
														lowerCAmelCase      :List[Any]    	=			rh.cluster(
														    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
														)
							lowerCAmelCase      :Any    	=			args.example.rsplit('''/''', 1)[0]

							# Set up remote environment
							cluster.install_packages(['''pip:./'''])  # Installs transformers from local source
							# Note transformers is copied into the home directory on the remote machine, so we can install from there
							cluster.run([F'pip install -r transformers/examples/{example_dir}/requirements.txt'])
							cluster.run(['''pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'''])

							# Run example. You can bypass the CLI wrapper and paste your own code here.
							cluster.run([F'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

							# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
							# from my_script... import train
							# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
							# launch_train_gpu = rh.function(fn=train,
							#                                system=gpu,
							#                                reqs=reqs,
							#                                name='train_bert_glue')
							#
							# We can pass in arguments just like we would to a function:
							# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
							#                  stream_logs=True)




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						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	k_size // 2
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__UpperCAmelCase    :     Any													=	1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase						) + square(_UpperCAmelCase						)) / (2 * square(_UpperCAmelCase						))						)
		return g

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	image.shape[0], image.shape[1]
		# dst image height and width
		__UpperCAmelCase    :     str													=	height - k_size + 1
		__UpperCAmelCase    :     Optional[int]													=	width - k_size + 1

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

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


if __name__ == "__main__":
				# read original image
				__A   			=imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				__A   			=cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=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()





