



'''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     (      *UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							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      ):
						lowerCAmelCase__								=							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
		UpperCAmelCase__   : Optional[int]					=		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__   : int					=		[
		    'pip:./',
		    'transformers',
		    'datasets',
		    'evaluate',
		    'tqdm',
		    'scipy',
		    'scikit-learn',
		    'tensorboard',
		    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
		]
		UpperCAmelCase__   : Optional[Any]					=		rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

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







'''simple docstring'''

import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm







def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



			if isinstance(UpperCamelCase_						,	(list, tuple)      ):
						return honor_type(UpperCamelCase_						,	(move_to_device(UpperCamelCase_						,	UpperCamelCase_      ) for t in state)      )
			elif isinstance(UpperCamelCase_						,	UpperCamelCase_      ):
						return type(UpperCamelCase_      )({k: move_to_device(UpperCamelCase_						,	UpperCamelCase_      ) for k, v in state.items()}      )
			elif isinstance(UpperCamelCase_						,	torch.Tensor      ):
						return state.to(UpperCamelCase_      )
			return state






class    A     (	torch.optim.Optimizer    ):





	def __init__(					self			:       Union[str, Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       List[str]=True      ,							__magic_name__			:       Optional[int]=None							):


				"""simple docstring"""

				lowerCAmelCase__								=							optimizer
				lowerCAmelCase__								=							scaler
				lowerCAmelCase__								=							AcceleratorState()
				lowerCAmelCase__								=							GradientState()
				lowerCAmelCase__								=							device_placement
				lowerCAmelCase__								=							False
				lowerCAmelCase__								=							None

				# Handle device placement
				if device_placement:
							lowerCAmelCase__								=							self.optimizer.state_dict()
							if self.accelerator_state.distributed_type == DistributedType.TPU:
										xm.send_cpu_data_to_device(__magic_name__      ,							self.accelerator_state.device							)
							else:
										lowerCAmelCase__								=							move_to_device(__magic_name__      ,							self.accelerator_state.device							)
							self.optimizer.load_state_dict(__magic_name__							)





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				return self.optimizer.state





	@state.setter
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							state





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				return self.optimizer.param_groups





	@param_groups.setter
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__								=							param_groups





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[int]							):


				"""simple docstring"""

				return self.optimizer.defaults





	@defaults.setter
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							defaults





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

				self.optimizer.add_param_group(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       List[Any]							):


				"""simple docstring"""

				if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
							xm.send_cpu_data_to_device(__magic_name__      ,							self.accelerator_state.device							)
				self.optimizer.load_state_dict(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple							):


				"""simple docstring"""

				return self.optimizer.state_dict()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       List[Any]=None							):


				"""simple docstring"""

				if self.gradient_state.sync_gradients:
							lowerCAmelCase__								=							"set_to_none" in inspect.signature(self.optimizer.zero_grad							).parameters
							if accept_arg:
										if set_to_none is None:
													lowerCAmelCase__								=							False
										self.optimizer.zero_grad(set_to_none=__magic_name__							)
							else:
										if set_to_none is not None:
													raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."							)
										self.optimizer.zero_grad()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Dict      ,							__magic_name__			:       Dict=None							):


				"""simple docstring"""

				if self.gradient_state.sync_gradients:
							if self.accelerator_state.distributed_type == DistributedType.TPU:
										lowerCAmelCase__								=							{"closure": closure} if closure is not None else {}
										xm.optimizer_step(self.optimizer      ,							optimizer_args=__magic_name__							)
							elif self.scaler is not None:
										lowerCAmelCase__								=							False
										if self._last_scale is None:
													# `get_scale` is an async operation requiring full synchronization
													# on CPU and GPUs before finishing. As a result, we store away
													# the prior one to reduce the call overhead
													lowerCAmelCase__								=							self.scaler.get_scale()
													lowerCAmelCase__								=							True
										self.scaler.step(self.optimizer      ,							__magic_name__							)
										self.scaler.update()
										lowerCAmelCase__								=							self.scaler.get_scale()
										if not new_scale:
													# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
													lowerCAmelCase__								=							scale_after < self._last_scale
										lowerCAmelCase__								=							scale_after

							else:
										self.optimizer.step(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]      ,							__magic_name__			:       List[str]							):


				"""simple docstring"""

				for param_group in self.optimizer.param_groups:
							lowerCAmelCase__								=							[parameters_map.get(__magic_name__      ,							__magic_name__							) for p in param_group["params"]]





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]							):


				"""simple docstring"""

				warnings.warn(
				    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
				    "`optimizer.step_was_skipped` instead."      ,							__magic_name__      ,							)
				return self._is_overflow





	@property
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				return self._is_overflow





	def __getstate__(					self			:       int							):


				"""simple docstring"""

				return self.__dict__.copy()







	def __setstate__(					self			:       List[str]      ,							__magic_name__			:       Dict							):


				"""simple docstring"""

				self.__dict__.update(__magic_name__							)





