
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 		lowerCAmelCase_     (     *_SCREAMING_SNAKE_CASE  )   ->      Optional[int]:

							"""simple docstring"""






							_UpperCAmelCase  :			Union[str, 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  ):
														_UpperCAmelCase  :			List[str]	       =		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': 4_2, 'batch_size': 1_6}
				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'])






from __future__ import annotations

from string import ascii_letters




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE = None  )   ->      str:

							"""simple docstring"""






							_UpperCAmelCase  :			List[Any]	       =		alphabet or ascii_letters

							# The final result string
							_UpperCAmelCase  :			Optional[Any]	       =		""

							for character in input_string:
														if character not in alpha:
																					# Append without encryption if character is not in the alphabet
																					result += character
														else:
																					# Get the index of the new key and make sure it isn't too large
																					_UpperCAmelCase  :			Any	       =		(alpha.index(_SCREAMING_SNAKE_CASE  ) + key) % len(_SCREAMING_SNAKE_CASE  )

																					# Append the encoded character to the alphabet
																					result += alpha[new_key]

							return result




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE = None  )   ->      str:

							"""simple docstring"""






							key *= -1

							return encrypt(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )




def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE = None  )   ->      dict[int, str]:

							"""simple docstring"""






							_UpperCAmelCase  :			Any	       =		alphabet or ascii_letters

							# To store data on all the combinations
							_UpperCAmelCase  :			Dict	       =		{}

							# Cycle through each combination
							for key in range(1      ,	len(_SCREAMING_SNAKE_CASE  ) + 1  ):
														# Decrypt the message and store the result in the data
														_UpperCAmelCase  :			Union[str, Any]	       =		decrypt(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )

							return brute_force_data


if __name__ == "__main__":
				while True:
								print(f'''\n{'-' * 1_0}\n Menu\n{'-' * 1_0}''')
								print(*['1.Encrypt', '2.Decrypt', '3.BruteForce', '4.Quit'], sep='\n')

								# get user input
								__lowerCamelCase								=					input('\nWhat would you like to do?: ').strip() or '4'

								# run functions based on what the user chose
								if choice not in ("1", "2", "3", "4"):
												print('Invalid choice, please enter a valid choice')
								elif choice == "1":
												__lowerCamelCase								=					input('Please enter the string to be encrypted: ')
												__lowerCamelCase								=					int(input('Please enter off-set: ').strip())

												print(encrypt(input_string, key))
								elif choice == "2":
												__lowerCamelCase								=					input('Please enter the string to be decrypted: ')
												__lowerCamelCase								=					int(input('Please enter off-set: ').strip())

												print(decrypt(input_string, key))
								elif choice == "3":
												__lowerCamelCase								=					input('Please enter the string to be decrypted: ')
												__lowerCamelCase								=					brute_force(input_string)

												for key, value in brute_force_data.items():
																print(f'''Key: {key} | Message: {value}''')

								elif choice == "4":
												print('Goodbye.')
												break




