


"""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      (						*UpperCAmelCase     :							List[str]				):


					'''simple docstring'''






					UpperCamelCase__		:    str         =torch.cuda.device_count()
					print(F'''Device count: {num_processes}'''				)
					with patch_environment(
					    world_size=UpperCAmelCase      ,							master_addr='''127.0.01'''      ,							master_port='''29500'''      ,							mixed_precision=args[1].mixed_precision				):
										UpperCamelCase__		:    Any         =PrepareForLaunch(UpperCAmelCase      ,							distributed_type='''MULTI_GPU'''				)
										torch.multiprocessing.start_processes(UpperCAmelCase      ,							args=UpperCAmelCase      ,							nprocs=UpperCAmelCase      ,							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
				_SCREAMING_SNAKE_CASE       :  Tuple        = 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
				_SCREAMING_SNAKE_CASE       :  List[str]        = [
				    """pip:./""",
				    """transformers""",
				    """datasets""",
				    """evaluate""",
				    """tqdm""",
				    """scipy""",
				    """scikit-learn""",
				    """tensorboard""",
				    """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
				]
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

				# Define train args/config, run train function
				_SCREAMING_SNAKE_CASE       :  int        = argparse.Namespace(cpu=False, mixed_precision="""fp16""")
				_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""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"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										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

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, 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}}}''')


