


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__,			A__							):
				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"""




											SCREAMING_SNAKE_CASE_		:    List[Any] 		=				optimizer
											SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				scaler
											SCREAMING_SNAKE_CASE_		:    Any 		=				AcceleratorState()
											SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				GradientState()
											SCREAMING_SNAKE_CASE_		:    Tuple 		=				device_placement
											SCREAMING_SNAKE_CASE_		:    Dict 		=				False
											SCREAMING_SNAKE_CASE_		:    Tuple 		=				None

											# Handle device placement
											if device_placement:
															SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(lowerCAmelCase__ ,	self.accelerator_state.device					)
															else:
																			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				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"""




											SCREAMING_SNAKE_CASE_		:    List[str] 		=				state






							@property
							def 				UpperCamelCase__    (			self					):





											"""simple docstring"""




											return self.optimizer.param_groups






							@param_groups.setter
							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





											"""simple docstring"""




											SCREAMING_SNAKE_CASE_		:    int 		=				param_groups






							@property
							def 				UpperCamelCase__    (			self					):





											"""simple docstring"""




											return self.optimizer.defaults






							@defaults.setter
							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





											"""simple docstring"""




											SCREAMING_SNAKE_CASE_		:    str 		=				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:
															SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				'set_to_none' in inspect.signature(self.optimizer.zero_grad					).parameters
															if accept_arg:
																			if set_to_none is None:
																							SCREAMING_SNAKE_CASE_		:    Any 		=				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:
																			SCREAMING_SNAKE_CASE_		:    Tuple 		=				{'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer ,	optimizer_args=lowerCAmelCase__					)
															elif self.scaler is not None:
																			SCREAMING_SNAKE_CASE_		:    List[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
																							SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.scaler.get_scale()
																							SCREAMING_SNAKE_CASE_		:    List[Any] 		=				True
																			self.scaler.step(self.optimizer ,	lowerCAmelCase__					)
																			self.scaler.update()
																			SCREAMING_SNAKE_CASE_		:    Any 		=				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.
																							SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				scale_after < self._last_scale
																			SCREAMING_SNAKE_CASE_		:    Dict 		=				scale_after

															else:
																			self.optimizer.step(lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





											"""simple docstring"""




											for param_group in self.optimizer.param_groups:
															SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				[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__					)









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

										self.register_modules(unet=lowerCAmelCase__ ,	scheduler=lowerCAmelCase__					)






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





