


"""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
		a__				:							List[Any]						=     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''')
		a__			,     a__				:							Optional[Any]						=     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''')
				a__				:							List[str]						=     rh.cluster(
				    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
				)
		else:
				a__				:							Union[str, Any]						=     rh.cluster(
				    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
				)
		a__				:							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)




"""simple docstring"""







import math
import random




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_ = False   ):




					'''simple docstring'''


					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value   ))


# Initial Value
a__				:							Tuple						=     0.02




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				float(2 * (random.randint(1  ,						100   )) - 1   )

					for _ in range(lowerCAmelCase_   ):
										# Forward propagation
										__SCREAMING_SNAKE_CASE						   =				sigmoid_function(INITIAL_VALUE * weight   )
										# How much did we miss?
										__SCREAMING_SNAKE_CASE						   =				(expected / 100) - layer_a
										# Error delta
										__SCREAMING_SNAKE_CASE						   =				layer_1_error * sigmoid_function(lowerCAmelCase_  ,						lowerCAmelCase_   )
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 100


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a__				:							List[str]						=     int(input('''Expected value: '''))
		a__				:							str						=     int(input('''Number of propagations: '''))
		print(forward_propagation(expected, number_propagations))
