
'''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 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      str:







		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 			UpperCAmelCase				(					torch.optim.Optimizer ):





		"""simple docstring"""





		def __init__(    self  ,					_snake_case  ,					_snake_case=True  ,					_snake_case=None     )				->    str:
				_UpperCamelCase       : Dict    =      optimizer
				_UpperCamelCase       : str    =      scaler
				_UpperCamelCase       : str    =      AcceleratorState()
				_UpperCamelCase       : int    =      GradientState()
				_UpperCamelCase       : List[Any]    =      device_placement
				_UpperCamelCase       : Optional[Any]    =      False
				_UpperCamelCase       : Optional[Any]    =      None

				# Handle device placement
				if device_placement:
						_UpperCamelCase       : str    =      self.optimizer.state_dict()
						if self.accelerator_state.distributed_type == DistributedType.TPU:
								xm.send_cpu_data_to_device(_snake_case  ,					self.accelerator_state.device     )
						else:
								_UpperCamelCase       : Union[str, Any]    =      move_to_device(_snake_case  ,					self.accelerator_state.device     )
						self.optimizer.load_state_dict(_snake_case     )
		@property
		def _lowercase  (    self     )				->    Tuple:
				return self.optimizer.state
		@state.setter
		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
				_UpperCamelCase       : List[str]    =      state
		@property
		def _lowercase  (    self     )				->    List[Any]:
				return self.optimizer.param_groups
		@param_groups.setter
		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
				_UpperCamelCase       : int    =      param_groups
		@property
		def _lowercase  (    self     )				->    Dict:
				return self.optimizer.defaults
		@defaults.setter
		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
				_UpperCamelCase       : int    =      defaults
		def _lowercase  (    self  ,					_snake_case     )				->    List[Any]:
				self.optimizer.add_param_group(_snake_case     )
		def _lowercase  (    self  ,					_snake_case     )				->    Optional[int]:







				if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
						xm.send_cpu_data_to_device(_snake_case  ,					self.accelerator_state.device     )
				self.optimizer.load_state_dict(_snake_case     )
		def _lowercase  (    self     )				->    Union[str, Any]:
				return self.optimizer.state_dict()
		def _lowercase  (    self  ,					_snake_case=None     )				->    Dict:







				if self.gradient_state.sync_gradients:
						_UpperCamelCase       : Union[str, Any]    =      '''set_to_none''' in inspect.signature(self.optimizer.zero_grad     ).parameters
						if accept_arg:
								if set_to_none is None:
										_UpperCamelCase       : Optional[Any]    =      False
								self.optimizer.zero_grad(set_to_none=_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 _lowercase  (    self  ,					_snake_case=None     )				->    Any:







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

						else:
								self.optimizer.step(_snake_case     )
		def _lowercase  (    self  ,					_snake_case     )				->    Optional[Any]:







				for param_group in self.optimizer.param_groups:
						_UpperCamelCase       : Union[str, Any]    =      [parameters_map.get(_snake_case  ,					_snake_case     ) for p in param_group['''params''']]
		@property
		def _lowercase  (    self     )				->    Union[str, Any]:
				warnings.warn(
				    '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
				    '''`optimizer.step_was_skipped` instead.'''  ,					_snake_case  ,					)
				return self._is_overflow
		@property
		def _lowercase  (    self     )				->    int:
				return self._is_overflow
		def __getstate__(    self     )				->    int:
				return self.__dict__.copy()




		def __setstate__(    self  ,					_snake_case     )				->    Dict:
				self.__dict__.update(_snake_case     )





'''simple docstring'''

_UpperCAmelCase   :      Optional[int] 						=  """
# Transformers installation
! pip install transformers datasets evaluate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

_UpperCAmelCase   :      Union[str, Any] 						=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
_UpperCAmelCase   :      Union[str, Any] 						=  {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}





