






'''simple docstring'''






import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 							_SCREAMING_SNAKE_CASE	(					*A	:							Union[str, Any]	)     ->			int:







   """simple docstring"""
   __snake_case					:   List[Any]  				=			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	):
      __snake_case					:   Union[str, Any]  				=			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
      __A     						= 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
      __A     						= [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      __A     						= rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      __A     						= argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      __A     						= {'''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'])







'''simple docstring'''






def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(a + 1	,		4_00	):
         for c in range(b + 1	,		5_00	):
            if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
               return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			int:







   """simple docstring"""

   for a in range(3_00	):
      for b in range(4_00	):
         __snake_case					:   Optional[Any]  				=			10_00 - a - b
         if a < b < c and (a**2) + (b**2) == (c**2):
            return a * b * c

   return -1




def 							_SCREAMING_SNAKE_CASE	(					)     ->			None:







   """simple docstring"""
   import timeit

   print(
       timeit.timeit('solution()'	,		setup='from __main__ import solution'	,		number=10_00	)	)
   print(
       timeit.timeit(
           'solution_fast()'	,		setup='from __main__ import solution_fast'	,		number=10_00	)	)


if __name__ == "__main__":
      print(f'''{solution() = }''')