



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'])










from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 				UpperCamelCase_	(					__snake_case      :    Dict  )->   str:




						'''simple docstring'''
						if is_torch_available():
												import torch

												if isinstance(__snake_case						,      torch.Tensor  ):
																		return True

						return isinstance(__snake_case						,      np.ndarray  )




class      __snake_case				(   _UpperCAmelCase):
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      fields(self  )

											# Safety and consistency checks
											if not len(_lowerCAmelCase  ):
																	raise ValueError(F'''{self.__class__.__name__} has no fields.'''  )

											__magic_name__					=      getattr(self	,      class_fields[0].name  )
											__magic_name__					=      all(getattr(self	,      field.name  ) is None for field in class_fields[1:]  )

											if other_fields_are_none and isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	for key, value in first_field.items():
																							__magic_name__					=      value
											else:
																	for field in class_fields:
																							__magic_name__					=      getattr(self	,      field.name  )
																							if v is not None:
																													__magic_name__					=      v
					def __delitem__(						self       :						Dict	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						List[str]  )							->					List[str]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Optional[int]	,      *_lowerCAmelCase       :						Optional[Any]	,      **_lowerCAmelCase       :						Optional[Any]  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Union[str, Any]	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						Any  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Any	,      *_lowerCAmelCase       :						str	,      **_lowerCAmelCase       :						Optional[int]  )							->					List[Any]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''  )
					def __getitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Any  )							->					Tuple:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      dict(self.items()  )
																	return inner_dict[k]
											else:
																	return self.to_tuple()[k]
					def __setattr__(						self       :						str	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[int]  )							->					int:



											'''simple docstring'''





											if name in self.keys() and value is not None:
																	# Don't call self.__setitem__ to avoid recursion errors
																	super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )
					def __setitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						List[str]  )							->					Optional[int]:



											'''simple docstring'''





											super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											# Don't call self.__setattr__ to avoid recursion errors
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )







					def 	a			(						self       :						str  )							->					Tuple[Any]:



											'''simple docstring'''





											return tuple(self[k] for k in self.keys()  )

