
import math as m




def 					lowerCamelCase__					(    a__	:	int						,   a__	:	Union[str, Any]						,   a__	:	List[str]=0.001  )		->						List[str]:
       return (f(a + h  ) - f(a - h  )) / (2 * h)




def 					lowerCamelCase__					(    a__	:	Any						,   a__	:	str=0						,   a__	:	int=100						,   a__	:	Optional[int]=0.0_001						,   a__	:	str=1e-6						,   a__	:	int=False  )		->						str:
       UpperCamelCase_											=							xa  # set the initial guess
       UpperCamelCase_											=							[a]
       UpperCamelCase_											=							abs(f(a__  )  )
       UpperCamelCase_											=							lambda a__  : calc_derivative(a__						,   a__						,   h=a__  )  # noqa: E731  Derivative of f(x)
       for _ in range(a__  ):
              if fa(a__  ) == 0:
                     raise ValueError("""No converging solution found"""  )
              UpperCamelCase_											=							a - f(a__  ) / fa(a__  )  # Calculate the next estimate
              if logsteps:
                     steps.append(a__  )
              if error < maxerror:
                     break
       else:
              raise ValueError("""Iteration limit reached, no converging solution found"""  )
       if logsteps:
              # If logstep is true, then log intermediate steps
              return a, error, steps
       return a, error


if __name__ == "__main__":
      from matplotlib import pyplot as plt

      _A							=       lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
      _A	,    _A	,    _A							=       newton_raphson(
          f, xa=10, maxiter=1_000, step=1E-6, logsteps=True
      )
      plt.plot([abs(f(x)) for x in steps])
      plt.xlabel('''step''')
      plt.ylabel('''error''')
      plt.show()
      print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							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  ):
              UpperCamelCase_											=							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''': 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'])



