




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment
def 		a_       (   *__lowerCAmelCase							):
  lowerCAmelCase__								=			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							):
    lowerCAmelCase__								=			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
      __magic_name__     :     List[Any]  							=					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
      __magic_name__     :     str  							=					[
          """pip:./""",
          """transformers""",
          """datasets""",
          """evaluate""",
          """tqdm""",
          """scipy""",
          """scikit-learn""",
          """tensorboard""",
          """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
      ]
      __magic_name__     :     Optional[Any]  							=					rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

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







from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		int					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			data
       lowerCAmelCase__								=			None



     def __str__(	self  :		int	):



       """simple docstring"""
       return F"""{self.data}"""





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			None
     def __iter__(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.front
       while node:
         yield node.data
         lowerCAmelCase__								=			node.next
     def __len__(	self  :		int	):



       """simple docstring"""
       return len(tuple(iter(self	)	)	)
     def __str__(	self  :		Optional[int]	):



       """simple docstring"""
       return " <- ".join(str(__lowerCamelCase	) for item in self	)
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       return len(self	) == 0
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			Node(__lowerCamelCase	)
       if self.is_empty():
         lowerCAmelCase__								=			lowerCAmelCase__								=			node
       else:
         assert isinstance(self.rear					,	__lowerCamelCase	)
         lowerCAmelCase__								=			node
         lowerCAmelCase__								=			node
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       if self.is_empty():
         raise IndexError('''dequeue from empty queue'''	)
       assert isinstance(self.front					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self.front
       lowerCAmelCase__								=			self.front.next
       if self.front is None:
         lowerCAmelCase__								=			None
       return node.data



     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			lowerCAmelCase__								=			None


if __name__ == "__main__":
      from doctest import testmod

      testmod()

