






"""simple docstring"""



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 						lowercase   (    *__snake_case    :			List[str]     ):
		lowercase_			:			List[str]								=       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     ):
				lowercase_			:			Union[str, Any]								=       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
						__A		:	Tuple      =		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
						__A		:	int      =		[
						    'pip:./',
						    'transformers',
						    'datasets',
						    'evaluate',
						    'tqdm',
						    'scipy',
						    'scikit-learn',
						    'tensorboard',
						    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
						]
						__A		:	Dict      =		rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

						# Define train args/config, run train function
						__A		:	int      =		argparse.Namespace(cpu=False, mixed_precision='''fp16''')
						__A		:	int      =		{'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'])











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



