code: 
  
  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__				(							*_UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      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				):
          lowerCAmelCase				:			Optional[int]    			=      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   :     Optional[int]      =			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   :     Optional[int]      =			[
       'pip:./',
       'transformers',
       'datasets',
       'evaluate',
       'tqdm',
       'scipy',
       'scikit-learn',
       'tensorboard',
       'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
   ]
   __A   :     Optional[Any]      =			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')
  
   # Define train args/config, run train function
   __A   :     List[str]      =			argparse.Namespace(cpu=False, mixed_precision='''fp16''')
   __A   :     Dict      =			{'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'])
  
  
  
  
  
  
style_context: 
  
  from __future__ import annotations
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[str]    			=      {1}
      lowerCAmelCase				:			int    			=      [1]
      for _ in range(2,      depth + 1				):
          lowerCAmelCase				:			str    			=      [*previous_coefficients, 0]
          lowerCAmelCase				:			Optional[int]    			=      [0, *previous_coefficients]
          lowerCAmelCase				:			Tuple    			=      []
          for x, y in zip(_UpperCAmelCase,      _UpperCAmelCase				):
              coefficients.add(x + y				)
              previous_coefficients.append(x + y				)
      return coefficients
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			str    			=      set()
      for number in unique_coefficients:
          lowerCAmelCase				:			Dict    			=      2
          lowerCAmelCase				:			Tuple    			=      number
          while divisor**2 <= copy_number:
              lowerCAmelCase				:			List[str]    			=      0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(_UpperCAmelCase				)
                  break
              divisor += 1
  
      return unique_coefficients.difference(_UpperCAmelCase				)
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = 51				)					-> int:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			int    			=      get_pascal_triangle_unique_coefficients(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      get_squarefrees(_UpperCAmelCase				)
      return sum(_UpperCAmelCase				)
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  