






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like







def   snake_case		(				UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							Any=3 ):
				A									=   int(mask / 2 )
				# copy image size
				A									=   zeros_like(lowerCAmelCase__ )
				for i in range(lowerCAmelCase__,    gray_img.shape[0] - bd ):
								for j in range(lowerCAmelCase__,    gray_img.shape[1] - bd ):
												# get mask according with mask
												A									=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1] )
												# calculate mask median
												A									=   sort(lowerCAmelCase__ )[inta(divide((multiply(lowerCAmelCase__,    lowerCAmelCase__ )),    2 ) + 1 )]
												A									=   median
				return median_img


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

					# get values with two different mask size
					lowerCAmelCase_     =			median_filter(gray, 3)
					lowerCAmelCase_     =			median_filter(gray, 5)

					# show result images
					imshow('median filter with 3x3 mask', medianaxa)
					imshow('median filter with 5x5 mask', medianaxa)
					waitKey(0)







import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




