
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def       _lowercase					(      *__SCREAMING_SNAKE_CASE )	->     List[str]:
			UpperCamelCase__    :		int     =				torch.cuda.device_count()
			print(F"""Device count: {num_processes}""" )
			with patch_environment(
			    world_size=__A				,    master_addr='127.0.01'				,    master_port='29500'				,    mixed_precision=args[1].mixed_precision ):
						UpperCamelCase__    :		Optional[int]     =				PrepareForLaunch(__A				,    distributed_type='MULTI_GPU' )
						torch.multiprocessing.start_processes(__A				,    args=__A				,    nprocs=__A				,    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
							UpperCAmelCase__				:  Optional[int]       					=				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
							UpperCAmelCase__				:  Optional[int]       					=				[
							    'pip:./',
							    'transformers',
							    'datasets',
							    'evaluate',
							    'tqdm',
							    'scipy',
							    'scikit-learn',
							    'tensorboard',
							    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
							]
							UpperCAmelCase__				:  Optional[Any]       					=				rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

							# Define train args/config, run train function
							UpperCAmelCase__				:  List[str]       					=				argparse.Namespace(cpu=False, mixed_precision='''fp16''')
							UpperCAmelCase__				:  Dict       					=				{'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'])





def       _lowercase					(      __SCREAMING_SNAKE_CASE = "mytext.txt"				,    __SCREAMING_SNAKE_CASE = False )	->     None:
			import socket

			UpperCamelCase__    :		Tuple     =				1_2312  # Reserve a port for your service.
			UpperCamelCase__    :		Optional[int]     =				socket.socket()  # Create a socket object
			UpperCamelCase__    :		Optional[int]     =				socket.gethostname()  # Get local machine name
			sock.bind((host, port) )  # Bind to the port
			sock.listen(5 )  # Now wait for client connection.

			print('Server listening....' )

			while True:
						UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				sock.accept()  # Establish connection with client.
						print(F"""Got connection from {addr}""" )
						UpperCamelCase__    :		Union[str, Any]     =				conn.recv(1024 )
						print(F"""Server received: {data = }""" )

						with open(__SCREAMING_SNAKE_CASE				,    'rb' ) as in_file:
									UpperCamelCase__    :		Tuple     =				in_file.read(1024 )
									while data:
												conn.send(__SCREAMING_SNAKE_CASE )
												print(F"""Sent {data!r}""" )
												UpperCamelCase__    :		str     =				in_file.read(1024 )

						print('Done sending' )
						conn.close()
						if testing:  # Allow the test to complete
									break

			sock.shutdown(1 )
			sock.close()


if __name__ == "__main__":
							send_file()



