



'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

						for letter in word:
												if letter not in letter_nums:
																		_UpperCamelCase      :	int				       =     str(UpperCAmelCase_							)
																		next_num += 1
												word_pattern.append(letter_nums[letter]							)
						return ".".join(UpperCAmelCase_							)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

					snake_case_				:			dict						 =						{}
					for word in word_list:
										snake_case_				:			Optional[int]						 =						get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_				:			Any						 =						[word]

					with open('word_patterns.txt', 'w') as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_				:			List[str]						 =						round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






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

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


