





"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment





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

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











class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Union[str, Any]      ):
		UpperCamelCase	:Union[str, Any]		=      [0, 1]





	def 		_A					(						self							:   str					,    __lowerCamelCase							:   int      ):
		if (difference := index - (len(self.sequence      ) - 2)) >= 1:
			for _ in range(__lowerCamelCase      ):
				self.sequence.append(self.sequence[-1] + self.sequence[-2]      )
		return self.sequence[:index]

def   SCREAMING_SNAKE_CASE_				(				)						->     None:






	"""simple docstring"""



	print(
	    """Fibonacci Series Using Dynamic Programming\n"""      ,     """Enter the index of the Fibonacci number you want to calculate """      ,     """in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,     sep=""""""      ,     )
	UpperCamelCase	:Any		=      Fibonacci()

	while True:
		UpperCamelCase	:str		=      input(""">> """   )
		if prompt in {"exit", "quit"}:
			break

		try:
			UpperCamelCase	:int		=      int(__magic_name__   )
		except ValueError:
			print("""Enter a number or 'exit'"""   )
			continue

		print(fibonacci.get(__magic_name__   )   )


if __name__ == "__main__":
	main()
