


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    A_   ( torch.optim.Optimizer     ):







	"""simple docstring"""





	def __init__(			self					: Tuple       ,    _a					: Any       ,    _a					: Dict=True       ,    _a					: int=None				)    ->			Optional[Any]:
						UpperCAmelCase						:				List[str]		  =			optimizer
						UpperCAmelCase						:				Union[str, Any]		  =			scaler
						UpperCAmelCase						:				List[str]		  =			AcceleratorState()
						UpperCAmelCase						:				Union[str, Any]		  =			GradientState()
						UpperCAmelCase						:				Union[str, Any]		  =			device_placement
						UpperCAmelCase						:				str		  =			False
						UpperCAmelCase						:				Optional[int]		  =			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(_a       ,    self.accelerator_state.device				)
											else:
																UpperCAmelCase						:				Dict		  =			move_to_device(_a       ,    self.accelerator_state.device				)
											self.optimizer.load_state_dict(_a				)
	@property
	def   _UpperCamelCase    (			self					: Union[str, Any]				)    ->			int:
						return self.optimizer.state
	@state.setter
	def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: Optional[Any]				)    ->			Tuple:
						UpperCAmelCase						:				str		  =			state
	@property
	def   _UpperCamelCase    (			self					: int				)    ->			Union[str, Any]:
						return self.optimizer.param_groups
	@param_groups.setter
	def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: Dict				)    ->			List[str]:
						UpperCAmelCase						:				Optional[Any]		  =			param_groups
	@property
	def   _UpperCamelCase    (			self					: Tuple				)    ->			str:
						return self.optimizer.defaults
	@defaults.setter
	def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			Optional[Any]:
						UpperCAmelCase						:				Tuple		  =			defaults
	def   _UpperCamelCase    (			self					: Tuple       ,    _a					: List[str]				)    ->			int:
						self.optimizer.add_param_group(_a				)
	def   _UpperCamelCase    (			self					: int       ,    _a					: str				)    ->			List[Any]:







						if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
											xm.send_cpu_data_to_device(_a       ,    self.accelerator_state.device				)
						self.optimizer.load_state_dict(_a				)
	def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Any:
						return self.optimizer.state_dict()
	def   _UpperCamelCase    (			self					: Dict       ,    _a					: Dict=None				)    ->			Optional[int]:







						if self.gradient_state.sync_gradients:
											UpperCAmelCase						:				Dict		  =			"set_to_none" in inspect.signature(self.optimizer.zero_grad				).parameters
											if accept_arg:
																if set_to_none is None:
																					UpperCAmelCase						:				Tuple		  =			False
																self.optimizer.zero_grad(set_to_none=_a				)
											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					: Union[str, Any]       ,    _a					: Optional[int]=None				)    ->			int:







						if self.gradient_state.sync_gradients:
											if self.accelerator_state.distributed_type == DistributedType.TPU:
																UpperCAmelCase						:				Dict		  =			{"closure": closure} if closure is not None else {}
																xm.optimizer_step(self.optimizer       ,    optimizer_args=_a				)
											elif self.scaler is not None:
																UpperCAmelCase						:				Optional[Any]		  =			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						:				Optional[int]		  =			self.scaler.get_scale()
																					UpperCAmelCase						:				int		  =			True
																self.scaler.step(self.optimizer       ,    _a				)
																self.scaler.update()
																UpperCAmelCase						:				Union[str, 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.
																					UpperCAmelCase						:				str		  =			scale_after < self._last_scale
																UpperCAmelCase						:				Tuple		  =			scale_after

											else:
																self.optimizer.step(_a				)
	def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: Dict				)    ->			List[str]:







						for param_group in self.optimizer.param_groups:
											UpperCAmelCase						:				Union[str, Any]		  =			[parameters_map.get(_a       ,    _a				) for p in param_group["params"]]
	@property
	def   _UpperCamelCase    (			self					: Dict				)    ->			List[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."       ,    _a       ,    )
						return self._is_overflow
	@property
	def   _UpperCamelCase    (			self					: Optional[int]				)    ->			Union[str, Any]:
						return self._is_overflow
	def __getstate__(			self					: Optional[Any]				)    ->			Tuple:
						return self.__dict__.copy()

	def __setstate__(			self					: Optional[int]       ,    _a					: int				)    ->			Tuple:
						self.__dict__.update(_a				)







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class    A_   :







 """simple docstring"""





 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None
 @classmethod
 def   _UpperCamelCase    (			cls					: Union[str, Any]				)    ->			int:
      return cls()




@dataclass
class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: ScoreSdeVeSchedulerState
 _UpperCamelCase				: jnp.ndarray
 _UpperCamelCase				: Optional[jnp.ndarray]      =					None




class    A_   ( a_      ,						a_     ):







 """simple docstring"""





 @property
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			List[str]:
      return True
 @register_to_config
 def __init__(			self					: List[str]       ,    _a					: int = 2_0_0_0       ,    _a					: float = 0.1_5       ,    _a					: float = 0.0_1       ,    _a					: float = 1_3_4_8.0       ,    _a					: float = 1E-5       ,    _a					: int = 1       ,    )    ->			List[Any]:
      pass
 def   _UpperCamelCase    (			self					: int				)    ->			List[Any]:
      UpperCAmelCase						:				str		  =			ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _a       ,    self.config.num_train_timesteps       ,    self.config.sigma_min       ,    self.config.sigma_max       ,    self.config.sampling_eps       ,    )
 def   _UpperCamelCase    (			self					: Optional[int]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: Tuple = ()       ,    _a					: float = None				)    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				Tuple		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps

      UpperCAmelCase						:				Dict		  =			jnp.linspace(1       ,    _a       ,    _a				)
      return state.replace(timesteps=_a				)
 def   _UpperCamelCase    (			self					: Union[str, Any]       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: int       ,    _a					: float = None       ,    _a					: float = None       ,    _a					: float = None       ,    )    ->			ScoreSdeVeSchedulerState:
      UpperCAmelCase						:				str		  =			sigma_min if sigma_min is not None else self.config.sigma_min
      UpperCAmelCase						:				Any		  =			sigma_max if sigma_max is not None else self.config.sigma_max
      UpperCAmelCase						:				Any		  =			sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
           UpperCAmelCase						:				List[Any]		  =			self.set_timesteps(_a       ,    _a       ,    _a				)

      UpperCAmelCase						:				List[Any]		  =			jnp.exp(jnp.linspace(jnp.log(_a				)       ,    jnp.log(_a				)       ,    _a				)				)
      UpperCAmelCase						:				List[str]		  =			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

      return state.replace(discrete_sigmas=_a       ,    sigmas=_a				)
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: Optional[int]       ,    _a					: Optional[int]       ,    _a					: Union[str, Any]				)    ->			List[str]:
      return jnp.where(timesteps == 0       ,    jnp.zeros_like(_a				)       ,    state.discrete_sigmas[timesteps - 1]				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: int       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      UpperCAmelCase						:				Union[str, Any]		  =			timestep * jnp.ones(
          sample.shape[0]       ,    )
      UpperCAmelCase						:				int		  =			(timestep * (len(state.timesteps				) - 1)).long()

      UpperCAmelCase						:				List[str]		  =			state.discrete_sigmas[timesteps]
      UpperCAmelCase						:				Optional[int]		  =			self.get_adjacent_sigma(_a       ,    _a       ,    _a				)
      UpperCAmelCase						:				Tuple		  =			jnp.zeros_like(_a				)
      UpperCAmelCase						:				List[Any]		  =			(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      UpperCAmelCase						:				Union[str, Any]		  =			diffusion.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				Tuple		  =			drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      UpperCAmelCase						:				List[str]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)
      UpperCAmelCase						:				Any		  =			sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      UpperCAmelCase						:				str		  =			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
           return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    prev_sample_mean=_a       ,    state=_a				)
 def   _UpperCamelCase    (			self					: Any       ,    _a					: ScoreSdeVeSchedulerState       ,    _a					: jnp.ndarray       ,    _a					: jnp.ndarray       ,    _a					: random.KeyArray       ,    _a					: bool = True       ,    )    ->			Union[FlaxSdeVeOutput, Tuple]:
      if state.timesteps is None:
           raise ValueError(
               "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      UpperCAmelCase						:				Optional[int]		  =			random.split(_a       ,    num=1				)
      UpperCAmelCase						:				str		  =			random.normal(key=_a       ,    shape=sample.shape				)

      # compute step size from the model_output, the noise, and the snr
      UpperCAmelCase						:				str		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				Optional[int]		  =			jnp.linalg.norm(_a				)
      UpperCAmelCase						:				int		  =			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      UpperCAmelCase						:				List[Any]		  =			step_size * jnp.ones(sample.shape[0]				)

      # compute corrected sample: model_output term and noise term
      UpperCAmelCase						:				Optional[Any]		  =			step_size.flatten()
      UpperCAmelCase						:				Union[str, Any]		  =			broadcast_to_shape_from_left(_a       ,    sample.shape				)
      UpperCAmelCase						:				int		  =			sample + step_size * model_output
      UpperCAmelCase						:				int		  =			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
           return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_a       ,    state=_a				)

 def __len__(			self					: Any				)    ->			Optional[int]:
      return self.config.num_train_timesteps



