



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 					a(   A :					Union[str, Any]				,  A :					Optional[int] )	-> 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 			_lowercase							(					torch.optim.Optimizer			):





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=True	, lowerCamelCase_=None	):
						"""simple docstring"""

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

						# Handle device placement
						if device_placement:
											a        =     self.optimizer.state_dict()
											if self.accelerator_state.distributed_type == DistributedType.TPU:
																xm.send_cpu_data_to_device(lowerCamelCase_	, self.accelerator_state.device	)
											else:
																a        =     move_to_device(lowerCamelCase_	, self.accelerator_state.device	)
											self.optimizer.load_state_dict(lowerCamelCase_	)




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self.optimizer.state




	@state.setter
	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     state




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self.optimizer.param_groups




	@param_groups.setter
	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     param_groups




	@property
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self.optimizer.defaults




	@defaults.setter
	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     defaults




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						self.optimizer.add_param_group(lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""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   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self.optimizer.state_dict()




	def   UpperCamelCase_ (self	, lowerCamelCase_=None	):
						"""simple docstring"""

						if self.gradient_state.sync_gradients:
											a        =     "set_to_none" in inspect.signature(self.optimizer.zero_grad	).parameters
											if accept_arg:
																if set_to_none is None:
																					a        =     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   UpperCamelCase_ (self	, lowerCamelCase_=None	):
						"""simple docstring"""

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

											else:
																self.optimizer.step(lowerCamelCase_	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						for param_group in self.optimizer.param_groups:
											a        =     [parameters_map.get(lowerCamelCase_	, lowerCamelCase_	) for p in param_group["params"]]




	@property
	def   UpperCamelCase_ (self	):
						"""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   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self._is_overflow




	def __getstate__(self	):
						"""simple docstring"""

						return self.__dict__.copy()




	def __setstate__(self	, lowerCamelCase_	):
						"""simple docstring"""

						self.__dict__.update(lowerCamelCase_	)





import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""
	__A					=							(EulerAncestralDiscreteScheduler,)
	__A					=							10




	def   UpperCamelCase_ (self	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     {
						    "num_train_timesteps": 1100,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						}

						config.update(**lowerCamelCase_	)
						return config




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	, [0.0002, 0.002, 0.02]	):
											self.check_over_configs(beta_start=lowerCamelCase_	, beta_end=lowerCamelCase_	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config(prediction_type="v_prediction"	)
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	)

						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma
						a        =     sample.to(lowerCamelCase_	)

						for i, t in enumerate(scheduler.timesteps	):
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.scheduler_classes[0]
						a        =     self.get_scheduler_config()
						a        =     scheduler_class(**lowerCamelCase_	)

						scheduler.set_timesteps(self.num_inference_steps	, device=lowerCamelCase_	)
						a        =     torch.manual_seed(0	)

						a        =     self.dummy_model()
						a        =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						a        =     sample.to(lowerCamelCase_	)

						for t in scheduler.timesteps:
											a        =     scheduler.scale_model_input(lowerCamelCase_	, lowerCamelCase_	)

											a        =     model(lowerCamelCase_	, lowerCamelCase_	)

											a        =     scheduler.step(lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, generator=lowerCamelCase_	)
											a        =     output.prev_sample

						a        =     torch.sum(torch.abs(lowerCamelCase_	)	)
						a        =     torch.mean(torch.abs(lowerCamelCase_	)	)

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