


'''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   (     A ,    A       )						->    Union[str, Any]:
							if isinstance(A ,    (list, tuple)       ):
														return honor_type(A ,    (move_to_device(A ,    A       ) for t in state)       )
							elif isinstance(A ,    A       ):
														return type(A       )({k: move_to_device(A ,    A       ) for k, v in state.items()}       )
							elif isinstance(A ,    torch.Tensor       ):
														return state.to(A       )
							return state






class 		a__				(							torch.optim.Optimizer  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=True					,    lowerCamelCase_=None   )       ->							Optional[Any]:
														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(lowerCamelCase_					,    self.accelerator_state.device   )
																					else:
																												lowerCAmelCase__          =	move_to_device(lowerCamelCase_					,    self.accelerator_state.device   )
																					self.optimizer.load_state_dict(lowerCamelCase_   )







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Tuple:
														return self.optimizer.state







							@state.setter
							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Tuple:
														lowerCAmelCase__          =	state







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														return self.optimizer.param_groups







							@param_groups.setter
							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							int:
														lowerCAmelCase__          =	param_groups







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														return self.optimizer.defaults







							@defaults.setter
							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Optional[int]:
														lowerCAmelCase__          =	defaults







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Optional[Any]:
														self.optimizer.add_param_group(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Optional[int]:
														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   __SCREAMING_SNAKE_CASE							(		self   )       ->							List[Any]:
														return self.optimizer.state_dict()







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_=None   )       ->							List[str]:
														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=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   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_=None   )       ->							Dict:
														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=lowerCamelCase_   )
																					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					,    lowerCamelCase_   )
																												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(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Tuple:
														for param_group in self.optimizer.param_groups:
																					lowerCAmelCase__          =	[parameters_map.get(lowerCamelCase_					,    lowerCamelCase_   ) for p in param_group['''params''']]







							@property
							def   __SCREAMING_SNAKE_CASE							(		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.'''					,    lowerCamelCase_					,    )
														return self._is_overflow







							@property
							def   __SCREAMING_SNAKE_CASE							(		self   )       ->							Any:
														return self._is_overflow







							def __getstate__(		self   )       ->							Optional[Any]:
														return self.__dict__.copy()







							def __setstate__(		self					,    lowerCamelCase_   )       ->							List[Any]:
														self.__dict__.update(lowerCamelCase_   )



'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


__UpperCAmelCase    				=							logging.get_logger(__name__)




class 		a__				(							a__  ):





							'''simple docstring'''






							def __init__(		self					,    *lowerCamelCase_					,    **lowerCamelCase_   )       ->							None:
														warnings.warn(
														    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
														    ''' Please use MobileNetV2ImageProcessor instead.'''					,    lowerCamelCase_					,    )
														super().__init__(*lowerCamelCase_					,    **lowerCamelCase_   )