

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        _lowercase  (					a_					:       Any       ,a_					:       Optional[int]					)					->						Optional[int]:




					'''simple docstring'''







					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 					__UpperCamelCase					(       torch.optim.Optimizer      ):







	def __init__( self:			Optional[Any]	,			__UpperCamelCase:			List[Any]	,			__UpperCamelCase:			str=True	,			__UpperCamelCase:			List[Any]=None   ):

						'''simple docstring'''





						__magic_name__				=     optimizer
						__magic_name__				=     scaler
						__magic_name__				=     AcceleratorState()
						__magic_name__				=     GradientState()
						__magic_name__				=     device_placement
						__magic_name__				=     False
						__magic_name__				=     None

						# Handle device placement
						if device_placement:
											__magic_name__				=     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:
																__magic_name__				=     move_to_device(_SCREAMING_SNAKE_CASE	,			self.accelerator_state.device   )
											self.optimizer.load_state_dict(_SCREAMING_SNAKE_CASE   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						return self.optimizer.state







	@state.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     state







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						return self.optimizer.param_groups







	@param_groups.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     param_groups







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						return self.optimizer.defaults







	@defaults.setter
	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__				=     defaults







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						self.optimizer.add_param_group(_SCREAMING_SNAKE_CASE   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			Optional[Any]   ):

						'''simple docstring'''





						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 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						return self.optimizer.state_dict()







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			Tuple=None   ):

						'''simple docstring'''





						if self.gradient_state.sync_gradients:
											__magic_name__				=     'set_to_none' in inspect.signature(self.optimizer.zero_grad   ).parameters
											if accept_arg:
																if set_to_none is None:
																					__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			Union[str, Any]=None   ):

						'''simple docstring'''





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

											else:
																self.optimizer.step(_SCREAMING_SNAKE_CASE   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			Union[str, Any]   ):

						'''simple docstring'''





						for param_group in self.optimizer.param_groups:
											__magic_name__				=     [parameters_map.get(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE   ) 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.'	,			_SCREAMING_SNAKE_CASE	,			)
						return self._is_overflow







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						return self._is_overflow







	def __getstate__( self:			Any   ):

						'''simple docstring'''





						return self.__dict__.copy()







	def __setstate__( self:			Tuple	,			__UpperCamelCase:			Tuple   ):

						'''simple docstring'''





						self.__dict__.update(_SCREAMING_SNAKE_CASE   )












"""simple docstring"""



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_		:				int=							10






						def 		lowerCAmelCase (       self     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,    [0.0002, 0.002, 0.02]):
																self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE     ,    beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 108.4439) < 1E-2
											assert abs(result_mean.item() - 0.1412) < 1E-3






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps     ,    device=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3