





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






class 					_a		( torch.optim.Optimizer							):






				def __init__(					self						:       Tuple,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       List[Any]=True,					lowerCAmelCase__						:       Optional[int]=None				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       optimizer
										_UpperCamelCase     :   Optional[Any]      =       scaler
										_UpperCamelCase     :   Tuple      =       AcceleratorState()
										_UpperCamelCase     :   Dict      =       GradientState()
										_UpperCamelCase     :   Tuple      =       device_placement
										_UpperCamelCase     :   Dict      =       False
										_UpperCamelCase     :   Tuple      =       None

										# Handle device placement
										if device_placement:
																_UpperCamelCase     :   Optional[Any]      =       self.optimizer.state_dict()
																if self.accelerator_state.distributed_type == DistributedType.TPU:
																						xm.send_cpu_data_to_device(lowerCAmelCase__,					self.accelerator_state.device				)
																else:
																						_UpperCamelCase     :   Dict      =       move_to_device(lowerCAmelCase__,					self.accelerator_state.device				)
																self.optimizer.load_state_dict(lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       int				)      -> List[Any]:




										'''simple docstring'''



										return self.optimizer.state






				@state.setter
				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Tuple				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       state






				@property
				def 							snake_case      (					self						:       Optional[int]				)      -> List[str]:




										'''simple docstring'''



										return self.optimizer.param_groups






				@param_groups.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Optional[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       param_groups






				@property
				def 							snake_case      (					self						:       Any				)      -> Any:




										'''simple docstring'''



										return self.optimizer.defaults






				@defaults.setter
				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Tuple				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       defaults






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Any				)      -> Optional[Any]:




										'''simple docstring'''



										self.optimizer.add_param_group(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       Optional[Any]				)      -> Optional[Any]:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										return self.optimizer.state_dict()






				def 							snake_case      (					self						:       Optional[int],					lowerCAmelCase__						:       List[str]=None				)      -> Any:




										'''simple docstring'''



										if self.gradient_state.sync_gradients:
																_UpperCamelCase     :   Optional[Any]      =       '''set_to_none''' in inspect.signature(self.optimizer.zero_grad				).parameters
																if accept_arg:
																						if set_to_none is None:
																												_UpperCamelCase     :   List[Any]      =       False
																						self.optimizer.zero_grad(set_to_none=lowerCAmelCase__				)
																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 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       str=None				)      -> Dict:




										'''simple docstring'''



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

																else:
																						self.optimizer.step(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Optional[int]				)      -> List[str]:




										'''simple docstring'''



										for param_group in self.optimizer.param_groups:
																_UpperCamelCase     :   Optional[int]      =       [parameters_map.get(lowerCAmelCase__,					lowerCAmelCase__				) for p in param_group['''params''']]






				@property
				def 							snake_case      (					self						:       Tuple				)      -> str:




										'''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.''',					lowerCAmelCase__,					)
										return self._is_overflow






				@property
				def 							snake_case      (					self						:       int				)      -> int:




										'''simple docstring'''



										return self._is_overflow






				def __getstate__(					self						:       int				)      -> Tuple:




										'''simple docstring'''



										return self.__dict__.copy()






				def __setstate__(					self						:       Any,					lowerCAmelCase__						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



										self.__dict__.update(lowerCAmelCase__				)









"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


