




'''simple docstring'''






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def      __lowerCAmelCase   (       *snake_case__		):
			__UpperCamelCase :     int										=  torch.cuda.device_count()
			print(F"Device count: {num_processes}"		)
			with patch_environment(
			    world_size=snake_case__   , master_addr="127.0.01"   , master_port="29500"   , mixed_precision=args[1].mixed_precision		):
						__UpperCamelCase :     List[Any]										=  PrepareForLaunch(snake_case__   , distributed_type="MULTI_GPU"		)
						torch.multiprocessing.start_processes(snake_case__   , args=snake_case__   , nprocs=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''': 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'''






def      __lowerCAmelCase   (       snake_case__		):
			__UpperCamelCase :     int										=  word.upper()
			__UpperCamelCase :     List[str]										=  0
			__UpperCamelCase :     int										=  {}
			__UpperCamelCase :     Tuple										=  []

			for letter in word:
						if letter not in letter_nums:
									__UpperCamelCase :     Tuple										=  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


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

				_lowerCAmelCase       =   time.time()
				with open('''dictionary.txt''') as in_file:
								_lowerCAmelCase       =   in_file.read().splitlines()

				_lowerCAmelCase       =   {}
				for word in word_list:
								_lowerCAmelCase       =   get_word_pattern(word)
								if pattern in all_patterns:
												all_patterns[pattern].append(word)
								else:
												_lowerCAmelCase       =   [word]

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

				_lowerCAmelCase       =   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.



