


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def      a_				(   *SCREAMING_SNAKE_CASE__ :				Dict					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =torch.cuda.device_count()
       print(F'''Device count: {num_processes}'''					)
       with patch_environment(
           world_size=SCREAMING_SNAKE_CASE__		,      master_addr='127.0.01'		,      master_port='29500'		,      mixed_precision=args[1].mixed_precision					):
              _lowerCamelCase	:		int          =PrepareForLaunch(SCREAMING_SNAKE_CASE__		,      distributed_type='MULTI_GPU'					)
              torch.multiprocessing.start_processes(SCREAMING_SNAKE_CASE__		,      args=SCREAMING_SNAKE_CASE__		,      nprocs=SCREAMING_SNAKE_CASE__		,      start_method='spawn'					)


if __name__ == "__main__":
 # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
 # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

 # on-demand GPU
 # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
 lowerCamelCase						 =	rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
 gpu.up_if_not()

 # on-prem GPU
 # gpu = rh.cluster(
 #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
 #       )

 # Set up remote function
 lowerCamelCase						 =	[
     'pip:./',
     'transformers',
     'datasets',
     'evaluate',
     'tqdm',
     'scipy',
     'scikit-learn',
     'tensorboard',
     'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
 ]
 lowerCamelCase						 =	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

 # Define train args/config, run train function
 lowerCamelCase						 =	argparse.Namespace(cpu=False, mixed_precision='fp16')
 lowerCamelCase						 =	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
 launch_train_gpu(config, train_args, stream_logs=True)

 # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
 # gpu.install_packages(reqs)
 # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


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

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


