



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def 						_lowerCAmelCase    (					*__lowerCamelCase :  Optional[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.cuda.device_count()
 print(F"""Device count: {num_processes}"""  )
 with patch_environment(
     world_size=__lowerCamelCase		,						master_addr="127.0.01"		,						master_port="29500"		,						mixed_precision=args[1].mixed_precision  ):
  __SCREAMING_SNAKE_CASE							:     Tuple							=						PrepareForLaunch(__lowerCamelCase		,						distributed_type="MULTI_GPU"  )
  torch.multiprocessing.start_processes(__lowerCamelCase		,						args=__lowerCamelCase		,						nprocs=__lowerCamelCase		,						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'])











def 						_lowerCAmelCase    (					__lowerCamelCase :  int  ):



 """simple docstring"""





 if not isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						(
      "operation can not be conducted on a object of type "
      F"""{type(__lowerCamelCase  ).__name__}"""
  )
  raise TypeError(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						""
 for _ in range(0		,						32  ):
  bit_string += str(number % 2  )
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						number >> 1
 return bit_string






def 						_lowerCAmelCase    (					__lowerCamelCase :  int  ):



 """simple docstring"""





 if number < 0:
  raise ValueError("the value of input must be positive"  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  raise TypeError("Input value must be a 'int' type"  )
 elif isinstance(__lowerCamelCase		,						__lowerCamelCase  ):
  raise TypeError("'<' not supported between instances of 'str' and 'int'"  )
 __SCREAMING_SNAKE_CASE							:     Any							=						0
 # iterator over [1 to 32],since we are dealing with 32 bit integer
 for _ in range(1		,						33  ):
  # left shift the bits by unity
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						result << 1
  # get the end bit
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						number % 2
  # right shift the bits by unity
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						number >> 1
  # add that bit to our ans
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						result | end_bit
 return get_reverse_bit_string(__lowerCamelCase  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






