
"""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     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__			)	->Optional[Any]:
			if isinstance(__magic_name__ ,      (list, tuple)			):
						return honor_type(__magic_name__ ,      (move_to_device(__magic_name__ ,      __magic_name__			) for t in state)			)
			elif isinstance(__magic_name__ ,      __magic_name__			):
						return type(__magic_name__			)({k: move_to_device(__magic_name__ ,      __magic_name__			) for k, v in state.items()}			)
			elif isinstance(__magic_name__ ,      torch.Tensor			):
						return state.to(__magic_name__			)
			return state







class 				__a    (   torch.optim.Optimizer					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=True     ,						_lowerCamelCase=None					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					optimizer
										__lowercase						      =					scaler
										__lowercase						      =					AcceleratorState()
										__lowercase						      =					GradientState()
										__lowercase						      =					device_placement
										__lowercase						      =					False
										__lowercase						      =					None

										# Handle device placement
										if device_placement:
													__lowercase						      =					self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																xm.send_cpu_data_to_device(_lowerCamelCase     ,						self.accelerator_state.device					)
													else:
																__lowercase						      =					move_to_device(_lowerCamelCase     ,						self.accelerator_state.device					)
													self.optimizer.load_state_dict(_lowerCamelCase					)
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										return self.optimizer.state
							@state.setter
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					state
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										return self.optimizer.param_groups
							@param_groups.setter
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					param_groups
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										return self.optimizer.defaults
							@defaults.setter
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										__lowercase						      =					defaults
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Dict:




										'''simple docstring'''
										self.optimizer.add_param_group(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Tuple:




										'''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      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										return self.optimizer.state_dict()
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase=None					) ->       str:




										'''simple docstring'''
										if self.gradient_state.sync_gradients:
													__lowercase						      =					"set_to_none" in inspect.signature(self.optimizer.zero_grad					).parameters
													if accept_arg:
																if set_to_none is None:
																			__lowercase						      =					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					) ->       List[Any]:




										'''simple docstring'''
										if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																__lowercase						      =					{"closure": closure} if closure is not None else {}
																xm.optimizer_step(self.optimizer     ,						optimizer_args=_lowerCamelCase					)
													elif self.scaler is not None:
																__lowercase						      =					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
																			__lowercase						      =					self.scaler.get_scale()
																			__lowercase						      =					True
																self.scaler.step(self.optimizer     ,						_lowerCamelCase					)
																self.scaler.update()
																__lowercase						      =					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.
																			__lowercase						      =					scale_after < self._last_scale
																__lowercase						      =					scale_after

													else:
																self.optimizer.step(_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       Union[str, Any]:




										'''simple docstring'''
										for param_group in self.optimizer.param_groups:
													__lowercase						      =					[parameters_map.get(_lowerCamelCase     ,						_lowerCamelCase					) for p in param_group["params"]]
							@property
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''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      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										return self._is_overflow
							def __getstate__(    self					) ->       Any:




										'''simple docstring'''
										return self.__dict__.copy()





							def __setstate__(    self     ,						_lowerCamelCase					) ->       str:




										'''simple docstring'''
										self.__dict__.update(_lowerCamelCase					)







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





