
'''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	) ->      List[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     )				->    Tuple:
				_UpperCamelCase       : List[str]    =      optimizer
				_UpperCamelCase       : Any    =      scaler
				_UpperCamelCase       : Optional[Any]    =      AcceleratorState()
				_UpperCamelCase       : int    =      GradientState()
				_UpperCamelCase       : Dict    =      device_placement
				_UpperCamelCase       : str    =      False
				_UpperCamelCase       : List[Any]    =      None

				# Handle device placement
				if device_placement:
						_UpperCamelCase       : Any    =      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:
								_UpperCamelCase       : Any    =      move_to_device(SCREAMING_SNAKE_CASE_  ,					self.accelerator_state.device     )
						self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )
		@property
		def _lowercase  (    self     )				->    Tuple:
				return self.optimizer.state
		@state.setter
		def _lowercase  (    self  ,					_snake_case     )				->    Tuple:
				_UpperCamelCase       : Optional[int]    =      state
		@property
		def _lowercase  (    self     )				->    Any:
				return self.optimizer.param_groups
		@param_groups.setter
		def _lowercase  (    self  ,					_snake_case     )				->    Optional[int]:
				_UpperCamelCase       : Tuple    =      param_groups
		@property
		def _lowercase  (    self     )				->    List[str]:
				return self.optimizer.defaults
		@defaults.setter
		def _lowercase  (    self  ,					_snake_case     )				->    Tuple:
				_UpperCamelCase       : Optional[int]    =      defaults
		def _lowercase  (    self  ,					_snake_case     )				->    Dict:
				self.optimizer.add_param_group(SCREAMING_SNAKE_CASE_     )
		def _lowercase  (    self  ,					_snake_case     )				->    Tuple:
				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 _lowercase  (    self     )				->    Any:
				return self.optimizer.state_dict()
		def _lowercase  (    self  ,					_snake_case=None     )				->    Any:
				if self.gradient_state.sync_gradients:
						_UpperCamelCase       : 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=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 _lowercase  (    self  ,					_snake_case=None     )				->    int:
				if self.gradient_state.sync_gradients:
						if self.accelerator_state.distributed_type == DistributedType.TPU:
								_UpperCamelCase       : int    =      {'''closure''': closure} if closure is not None else {}
								xm.optimizer_step(self.optimizer  ,					optimizer_args=SCREAMING_SNAKE_CASE_     )
						elif self.scaler is not None:
								_UpperCamelCase       : str    =      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       : Union[str, Any]    =      self.scaler.get_scale()
										_UpperCamelCase       : Any    =      True
								self.scaler.step(self.optimizer  ,					SCREAMING_SNAKE_CASE_     )
								self.scaler.update()
								_UpperCamelCase       : int    =      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       : List[str]    =      scale_after

						else:
								self.optimizer.step(SCREAMING_SNAKE_CASE_     )
		def _lowercase  (    self  ,					_snake_case     )				->    Dict:
				for param_group in self.optimizer.param_groups:
						_UpperCamelCase       : Optional[Any]    =      [parameters_map.get(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_     ) for p in param_group['''params''']]
		@property
		def _lowercase  (    self     )				->    Optional[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.'''  ,					SCREAMING_SNAKE_CASE_  ,					)
				return self._is_overflow
		@property
		def _lowercase  (    self     )				->    Any:
				return self._is_overflow
		def __getstate__(    self     )				->    str:
				return self.__dict__.copy()




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







'''simple docstring'''

from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any

class 			UpperCAmelCase				:





		"""simple docstring"""





		A__  :   List[str]          =    ('_front', '_back', '_len')

		@dataclass
		class 			UpperCAmelCase				:





				"""simple docstring"""





				A__  :   Any          =    None
				A__  :   Deque._Node | None          =    None
				A__  :   Deque._Node | None          =    None







		class 			UpperCAmelCase				:





				"""simple docstring"""





				A__  :   Any          =    ('_cur',)
				def __init__(    self  ,					_snake_case     )				->    None:
						_UpperCamelCase       : Any    =      cur
				def __iter__(    self     )				->    Deque._Iterator:
						return self




				def _lowercase  (    self     )				->    Any:
						if self._cur is None:
								# finished iterating
								raise StopIteration
						_UpperCamelCase       : List[Any]    =      self._cur.val
						_UpperCamelCase       : List[Any]    =      self._cur.next_node

						return val







		def __init__(    self  ,					_snake_case = None     )				->    None:
				_UpperCamelCase       : Any    =      None
				_UpperCamelCase       : Any    =      None
				_UpperCamelCase       : int    =      0

				if iterable is not None:
						# append every value to the deque
						for val in iterable:
								self.append(_snake_case     )
		def _lowercase  (    self  ,					_snake_case     )				->    None:
				_UpperCamelCase       : List[str]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
				if self.is_empty():
						# front = back
						_UpperCamelCase       : Union[str, Any]    =      node
						_UpperCamelCase       : Tuple    =      1
				else:
						# connect nodes
						_UpperCamelCase       : Union[str, Any]    =      node
						_UpperCamelCase       : int    =      self._back
						_UpperCamelCase       : List[Any]    =      node  # assign new back to the new node

						self._len += 1

						# make sure there were no errors
						assert not self.is_empty(), "Error on appending value."
		def _lowercase  (    self  ,					_snake_case     )				->    None:
				_UpperCamelCase       : Optional[Any]    =      self._Node(_snake_case  ,					_snake_case  ,					_snake_case     )
				if self.is_empty():
						# front = back
						_UpperCamelCase       : Union[str, Any]    =      node
						_UpperCamelCase       : Optional[int]    =      1
				else:
						# connect nodes
						_UpperCamelCase       : Tuple    =      self._front
						_UpperCamelCase       : Optional[int]    =      node
						_UpperCamelCase       : List[str]    =      node  # assign new front to the new node

						self._len += 1

						# make sure there were no errors
						assert not self.is_empty(), "Error on appending value."
		def _lowercase  (    self  ,					_snake_case     )				->    None:
				for val in iterable:
						self.append(_snake_case     )
		def _lowercase  (    self  ,					_snake_case     )				->    None:
				for val in iterable:
						self.appendleft(_snake_case     )
		def _lowercase  (    self     )				->    Any:
				assert not self.is_empty(), "Deque is empty."

				_UpperCamelCase       : int    =      self._back
				_UpperCamelCase       : List[Any]    =      self._back.prev_node  # set new back
				# drop the last node - python will deallocate memory automatically
				_UpperCamelCase       : Union[str, Any]    =      None

				self._len -= 1

				return topop.val
		def _lowercase  (    self     )				->    Any:
				assert not self.is_empty(), "Deque is empty."

				_UpperCamelCase       : List[Any]    =      self._front
				_UpperCamelCase       : str    =      self._front.next_node  # set new front and drop the first node
				_UpperCamelCase       : Union[str, Any]    =      None

				self._len -= 1

				return topop.val
		def _lowercase  (    self     )				->    bool:
				return self._front is None
		def __len__(    self     )				->    int:
				return self._len
		def __eq__(    self  ,					_snake_case     )				->    bool:

				if not isinstance(_snake_case  ,					_snake_case     ):
						return NotImplemented

				_UpperCamelCase       : Optional[int]    =      self._front
				_UpperCamelCase       : List[Any]    =      other._front

				# if the length of the dequeues are not the same, they are not equal
				if len(self     ) != len(_snake_case     ):
						return False

				while me is not None and oth is not None:
						# compare every value
						if me.val != oth.val:
								return False
						_UpperCamelCase       : str    =      me.next_node
						_UpperCamelCase       : List[str]    =      oth.next_node

				return True
		def __iter__(    self     )				->    Deque._Iterator:
				return Deque._Iterator(self._front     )




		def __repr__(    self     )				->    str:
				_UpperCamelCase       : str    =      []
				_UpperCamelCase       : Tuple    =      self._front
				while aux is not None:
						# append the values in a list to display
						values_list.append(aux.val     )
						_UpperCamelCase       : List[str]    =      aux.next_node

				return F'''[{', '.join(repr(_snake_case     ) for val in values_list     )}]'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()





