
"""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 lowercase_  (       _snake_case       ,_snake_case						):






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



class      lowerCAmelCase_						(torch.optim.Optimizer  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=True       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[str]									=       optimizer
											SCREAMING_SNAKE_CASE__						:	str									=       scaler
											SCREAMING_SNAKE_CASE__						:	Any									=       AcceleratorState()
											SCREAMING_SNAKE_CASE__						:	Dict									=       GradientState()
											SCREAMING_SNAKE_CASE__						:	Any									=       device_placement
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       False
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       None

											# Handle device placement
											if device_placement:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       self.optimizer.state_dict()
																		if self.accelerator_state.distributed_type == DistributedType.TPU:
																									xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE__       ,	self.accelerator_state.device   )
																		else:
																									SCREAMING_SNAKE_CASE__						:	List[str]									=       move_to_device(SCREAMING_SNAKE_CASE__       ,	self.accelerator_state.device   )
																		self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE__   )



				@property
				def      __magic_name__   (self   ) ->		int:



											"""simple docstring"""




											return self.optimizer.state



				@state.setter
				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Tuple									=       state



				@property
				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											return self.optimizer.param_groups



				@param_groups.setter
				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       param_groups



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											return self.optimizer.defaults



				@defaults.setter
				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       defaults



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		int:



											"""simple docstring"""




											self.optimizer.add_param_group(SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[Any]:



											"""simple docstring"""




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



				def      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




											return self.optimizer.state_dict()



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Optional[Any]:



											"""simple docstring"""




											if self.gradient_state.sync_gradients:
																		SCREAMING_SNAKE_CASE__						:	str									=       """set_to_none""" in inspect.signature(self.optimizer.zero_grad   ).parameters
																		if accept_arg:
																									if set_to_none is None:
																																SCREAMING_SNAKE_CASE__						:	Any									=       False
																									self.optimizer.zero_grad(set_to_none=SCREAMING_SNAKE_CASE__   )
																		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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Dict:



											"""simple docstring"""




											if self.gradient_state.sync_gradients:
																		if self.accelerator_state.distributed_type == DistributedType.TPU:
																									SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {"""closure""": closure} if closure is not None else {}
																									xm.optimizer_step(self.optimizer       ,	optimizer_args=SCREAMING_SNAKE_CASE__   )
																		elif self.scaler is not None:
																									SCREAMING_SNAKE_CASE__						:	Dict									=       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
																																SCREAMING_SNAKE_CASE__						:	List[str]									=       self.scaler.get_scale()
																																SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       True
																									self.scaler.step(self.optimizer       ,	SCREAMING_SNAKE_CASE__   )
																									self.scaler.update()
																									SCREAMING_SNAKE_CASE__						:	Tuple									=       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.
																																SCREAMING_SNAKE_CASE__						:	str									=       scale_after < self._last_scale
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       scale_after

																		else:
																									self.optimizer.step(SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




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



				@property
				def      __magic_name__   (self   ) ->		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."""       ,	SCREAMING_SNAKE_CASE__       ,	)
											return self._is_overflow



				@property
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											return self._is_overflow



				def __getstate__(self   ) ->		Tuple:



											"""simple docstring"""




											return self.__dict__.copy()



				def __setstate__(self       ,	SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




											self.__dict__.update(SCREAMING_SNAKE_CASE__   )




"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def lowercase_  (       *_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.cuda.device_count()
							print(f'''Device count: {num_processes}'''						)
							with patch_environment(
							    world_size=_snake_case       ,master_addr="""127.0.01"""       ,master_port="""29500"""       ,mixed_precision=args[1].mixed_precision						):
														SCREAMING_SNAKE_CASE__						:	int									=       PrepareForLaunch(_snake_case       ,distributed_type="""MULTI_GPU"""						)
														torch.multiprocessing.start_processes(_snake_case       ,args=_snake_case       ,nprocs=_snake_case       ,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__				:   str   							=			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__				:   List[str]   							=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					UpperCAmelCase__				:   Dict   							=			argparse.Namespace(cpu=False, mixed_precision='fp16')
					UpperCAmelCase__				:   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'])




