





"""simple docstring"""






import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
	import jax.numpy as jnp







class       snake_case__  ( lowerCAmelCase_     ):







			def __get__(			self			: List[str]			,  lowercase			: List[Any]			,  lowercase			: Dict=None  ):





								'''simple docstring'''



								if obj is None:
													return self
								if self.fget is None:
													raise AttributeError("unreadable attribute"  )
								UpperCAmelCase			:   Optional[int]      =				"__cached_" + self.fget.__name__
								UpperCAmelCase			:   Any      =				getattr(lowercase			,  lowercase			,  lowercase  )
								if cached is None:
													UpperCAmelCase			:   Tuple      =				self.fget(lowercase  )
													setattr(lowercase			,  lowercase			,  lowercase  )
								return cached



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					UpperCAmelCase			:   List[str]      =				val.lower()
					if val in {"y", "yes", "t", "true", "on", "1"}:
										return 1
					if val in {"n", "no", "f", "false", "off", "0"}:
										return 0
					raise ValueError(F"""invalid truth value {val!r}"""	)



def   lowercase_	(    _lowercase       :		Dict	):




					'''simple docstring'''






					if is_torch_fx_proxy(_lowercase	):
										return True
					if is_torch_available():
										import torch

										if isinstance(_lowercase							,      torch.Tensor	):
															return True
					if is_tf_available():
										import tensorflow as tf

										if isinstance(_lowercase							,      tf.Tensor	):
															return True

					if is_flax_available():
										import jax.numpy as jnp
										from jax.core import Tracer

										if isinstance(_lowercase							,      (jnp.ndarray, Tracer)	):
															return True

					return isinstance(_lowercase							,      np.ndarray	)



def   lowercase_	(    _lowercase       :		Dict	):




					'''simple docstring'''






					return isinstance(_lowercase							,      np.ndarray	)



def   lowercase_	(    _lowercase       :		Optional[int]	):




					'''simple docstring'''






					return _is_numpy(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					import torch

					return isinstance(_lowercase							,      torch.Tensor	)



def   lowercase_	(    _lowercase       :		Optional[int]	):




					'''simple docstring'''






					return False if not is_torch_available() else _is_torch(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					import torch

					return isinstance(_lowercase							,      torch.device	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					return False if not is_torch_available() else _is_torch_device(_lowercase	)



def   lowercase_	(    _lowercase       :		List[Any]	):




					'''simple docstring'''






					import torch

					if isinstance(_lowercase							,      _lowercase	):
										if hasattr(_lowercase							,      _lowercase	):
															UpperCAmelCase			:   List[Any]      =				getattr(_lowercase							,      _lowercase	)
										else:
															return False
					return isinstance(_lowercase							,      torch.dtype	)



def   lowercase_	(    _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					return False if not is_torch_available() else _is_torch_dtype(_lowercase	)



def   lowercase_	(    _lowercase       :		Dict	):




					'''simple docstring'''






					import tensorflow as tf

					return isinstance(_lowercase							,      tf.Tensor	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					return False if not is_tf_available() else _is_tensorflow(_lowercase	)



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					import tensorflow as tf

					# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
					if hasattr(_lowercase							,      "is_symbolic_tensor"	):
										return tf.is_symbolic_tensor(_lowercase	)
					return type(_lowercase	) == tf.Tensor



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowercase	)



def   lowercase_	(    _lowercase       :		Any	):




					'''simple docstring'''






					import jax.numpy as jnp  # noqa: F811

					return isinstance(_lowercase							,      jnp.ndarray	)



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					return False if not is_flax_available() else _is_jax(_lowercase	)



def   lowercase_	(    _lowercase       :		Any	):




					'''simple docstring'''






					if isinstance(_lowercase							,      (dict, UserDict)	):
										return {k: to_py_obj(_lowercase	) for k, v in obj.items()}
					elif isinstance(_lowercase							,      (list, tuple)	):
										return [to_py_obj(_lowercase	) for o in obj]
					elif is_tf_tensor(_lowercase	):
										return obj.numpy().tolist()
					elif is_torch_tensor(_lowercase	):
										return obj.detach().cpu().tolist()
					elif is_jax_tensor(_lowercase	):
										return np.asarray(_lowercase	).tolist()
					elif isinstance(_lowercase							,      (np.ndarray, np.number)	):  # tolist also works on 0d np arrays
										return obj.tolist()
					else:
										return obj



def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					if isinstance(_lowercase							,      (dict, UserDict)	):
										return {k: to_numpy(_lowercase	) for k, v in obj.items()}
					elif isinstance(_lowercase							,      (list, tuple)	):
										return np.array(_lowercase	)
					elif is_tf_tensor(_lowercase	):
										return obj.numpy()
					elif is_torch_tensor(_lowercase	):
										return obj.detach().cpu().numpy()
					elif is_jax_tensor(_lowercase	):
										return np.asarray(_lowercase	)
					else:
										return obj







class       snake_case__  ( lowerCAmelCase_     ):







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				fields(self  )

								# Safety and consistency checks
								if not len(lowercase  ):
													raise ValueError(f"""{self.__class__.__name__} has no fields."""  )
								if not all(field.default is None for field in class_fields[1:]  ):
													raise ValueError(f"""{self.__class__.__name__} should not have more than one required field."""  )

								UpperCAmelCase			:   Optional[int]      =				getattr(self			,  class_fields[0].name  )
								UpperCAmelCase			:   Union[str, Any]      =				all(getattr(self			,  field.name  ) is None for field in class_fields[1:]  )

								if other_fields_are_none and not is_tensor(lowercase  ):
													if isinstance(lowercase			,  lowercase  ):
																		UpperCAmelCase			:   Tuple      =				first_field.items()
																		UpperCAmelCase			:   Union[str, Any]      =				True
													else:
																		try:
																							UpperCAmelCase			:   Optional[int]      =				iter(lowercase  )
																							UpperCAmelCase			:   Optional[Any]      =				True
																		except TypeError:
																							UpperCAmelCase			:   Tuple      =				False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
													if first_field_iterator:
																		for idx, element in enumerate(lowercase  ):
																							if (
																							    not isinstance(lowercase			,  (list, tuple)  )
																							    or not len(lowercase  ) == 2
																							    or not isinstance(element[0]			,  lowercase  )
																							):
																												if idx == 0:
																																	# If we do not have an iterator of key/values, set it as attribute
																																	UpperCAmelCase			:   List[Any]      =				first_field
																												else:
																																	# If we have a mixed iterator, raise an error
																																	raise ValueError(
																																	    f"""Cannot set key/value for {element}. It needs to be a tuple (key, value)."""  )
																												break
																							setattr(self			,  element[0]			,  element[1]  )
																							if element[1] is not None:
																												UpperCAmelCase			:   Dict      =				element[1]
													elif first_field is not None:
																		UpperCAmelCase			:   str      =				first_field
								else:
													for field in class_fields:
																		UpperCAmelCase			:   Tuple      =				getattr(self			,  field.name  )
																		if v is not None:
																							UpperCAmelCase			:   List[Any]      =				v







			def __delitem__(			self			: Union[str, Any]			,  *lowercase			: str			,  **lowercase			: Dict  ):





								'''simple docstring'''



								raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."""  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  *lowercase			: Any			,  **lowercase			: List[Any]  ):





								'''simple docstring'''



								raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance."""  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  *lowercase			: List[Any]			,  **lowercase			: Any  ):





								'''simple docstring'''



								raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance."""  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  *lowercase			: Dict			,  **lowercase			: List[str]  ):





								'''simple docstring'''



								raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance."""  )







			def __getitem__(			self			: str			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								if isinstance(lowercase			,  lowercase  ):
													UpperCAmelCase			:   Optional[int]      =				dict(self.items()  )
													return inner_dict[k]
								else:
													return self.to_tuple()[k]







			def __setattr__(			self			: Union[str, Any]			,  lowercase			: Tuple			,  lowercase			: int  ):





								'''simple docstring'''



								if name in self.keys() and value is not None:
													# Don't call self.__setitem__ to avoid recursion errors
													super().__setitem__(lowercase			,  lowercase  )
								super().__setattr__(lowercase			,  lowercase  )







			def __setitem__(			self			: Union[str, Any]			,  lowercase			: int			,  lowercase			: Tuple  ):





								'''simple docstring'''



								super().__setitem__(lowercase			,  lowercase  )
								# Don't call self.__setattr__ to avoid recursion errors
								super().__setattr__(lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								return tuple(self[k] for k in self.keys()  )







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):







			@classmethod
			def 			__lowerCAmelCase				(			cls			: List[Any]			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								raise ValueError(
								    f"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()  )}"""  )







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''longest'''
			SCREAMING_SNAKE_CASE__     				=						'''max_length'''
			SCREAMING_SNAKE_CASE__     				=						'''do_not_pad'''







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''pt'''
			SCREAMING_SNAKE_CASE__     				=						'''tf'''
			SCREAMING_SNAKE_CASE__     				=						'''np'''
			SCREAMING_SNAKE_CASE__     				=						'''jax'''







class       snake_case__  :







			def __init__(			self			: Tuple			,  lowercase			: List[ContextManager]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				context_managers
								UpperCAmelCase			:   List[Any]      =				ExitStack()







			def __enter__(			self			: Optional[int]  ):





								'''simple docstring'''



								for context_manager in self.context_managers:
													self.stack.enter_context(lowercase  )







			def __exit__(			self			: Union[str, Any]			,  *lowercase			: List[str]			,  **lowercase			: Optional[Any]  ):





								'''simple docstring'''



								self.stack.__exit__(*lowercase			,  **lowercase  )



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				infer_framework(_lowercase	)
					if framework == "tf":
										UpperCAmelCase			:   List[Any]      =				inspect.signature(model_class.call	)  # TensorFlow models
					elif framework == "pt":
										UpperCAmelCase			:   int      =				inspect.signature(model_class.forward	)  # PyTorch models
					else:
										UpperCAmelCase			:   List[str]      =				inspect.signature(model_class.__call__	)  # Flax models

					for p in signature.parameters:
										if p == "return_loss" and signature.parameters[p].default is True:
															return True

					return False



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				model_class.__name__
					UpperCAmelCase			:   int      =				infer_framework(_lowercase	)
					if framework == "tf":
										UpperCAmelCase			:   Optional[Any]      =				inspect.signature(model_class.call	)  # TensorFlow models
					elif framework == "pt":
										UpperCAmelCase			:   Tuple      =				inspect.signature(model_class.forward	)  # PyTorch models
					else:
										UpperCAmelCase			:   Optional[Any]      =				inspect.signature(model_class.__call__	)  # Flax models

					if "QuestionAnswering" in model_name:
										return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
					else:
										return [p for p in signature.parameters if "label" in p]



def   lowercase_	(    _lowercase       :		MutableMapping							,      _lowercase       :		str = ""							,      _lowercase       :		str = "."	):




					'''simple docstring'''






					def _flatten_dict(_lowercase       :		Union[str, Any]							,      _lowercase       :		str=""							,      _lowercase       :		Optional[Any]="."	):
										for k, v in d.items():
															UpperCAmelCase			:   Any      =				str(_lowercase	) + delimiter + str(_lowercase	) if parent_key else k
															if v and isinstance(_lowercase							,      _lowercase	):
																				yield from flatten_dict(_lowercase							,      _lowercase							,      delimiter=_lowercase	).items()
															else:
																				yield key, v

					return dict(_flatten_dict(_lowercase							,      _lowercase							,      _lowercase	)	)



@contextmanager
def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		bool = False	):




					'''simple docstring'''






					if use_temp_dir:
										with tempfile.TemporaryDirectory() as tmp_dir:
															yield tmp_dir
					else:
										yield working_dir



def   lowercase_	(    _lowercase       :		Dict							,      _lowercase       :		Optional[Any]=None	):




					'''simple docstring'''






					if is_numpy_array(_lowercase	):
										return np.transpose(_lowercase							,      axes=_lowercase	)
					elif is_torch_tensor(_lowercase	):
										return array.T if axes is None else array.permute(*_lowercase	)
					elif is_tf_tensor(_lowercase	):
										import tensorflow as tf

										return tf.transpose(_lowercase							,      perm=_lowercase	)
					elif is_jax_tensor(_lowercase	):
										return jnp.transpose(_lowercase							,      axes=_lowercase	)
					else:
										raise ValueError(F"""Type not supported for transpose: {type(_lowercase	)}."""	)



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					if is_numpy_array(_lowercase	):
										return np.reshape(_lowercase							,      _lowercase	)
					elif is_torch_tensor(_lowercase	):
										return array.reshape(*_lowercase	)
					elif is_tf_tensor(_lowercase	):
										import tensorflow as tf

										return tf.reshape(_lowercase							,      _lowercase	)
					elif is_jax_tensor(_lowercase	):
										return jnp.reshape(_lowercase							,      _lowercase	)
					else:
										raise ValueError(F"""Type not supported for reshape: {type(_lowercase	)}."""	)



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		Any=None	):




					'''simple docstring'''






					if is_numpy_array(_lowercase	):
										return np.squeeze(_lowercase							,      axis=_lowercase	)
					elif is_torch_tensor(_lowercase	):
										return array.squeeze() if axis is None else array.squeeze(dim=_lowercase	)
					elif is_tf_tensor(_lowercase	):
										import tensorflow as tf

										return tf.squeeze(_lowercase							,      axis=_lowercase	)
					elif is_jax_tensor(_lowercase	):
										return jnp.squeeze(_lowercase							,      axis=_lowercase	)
					else:
										raise ValueError(F"""Type not supported for squeeze: {type(_lowercase	)}."""	)



def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		Dict	):




					'''simple docstring'''






					if is_numpy_array(_lowercase	):
										return np.expand_dims(_lowercase							,      _lowercase	)
					elif is_torch_tensor(_lowercase	):
										return array.unsqueeze(dim=_lowercase	)
					elif is_tf_tensor(_lowercase	):
										import tensorflow as tf

										return tf.expand_dims(_lowercase							,      axis=_lowercase	)
					elif is_jax_tensor(_lowercase	):
										return jnp.expand_dims(_lowercase							,      axis=_lowercase	)
					else:
										raise ValueError(F"""Type not supported for expand_dims: {type(_lowercase	)}."""	)



def   lowercase_	(    _lowercase       :		Optional[int]	):




					'''simple docstring'''






					if is_numpy_array(_lowercase	):
										return np.size(_lowercase	)
					elif is_torch_tensor(_lowercase	):
										return array.numel()
					elif is_tf_tensor(_lowercase	):
										import tensorflow as tf

										return tf.size(_lowercase	)
					elif is_jax_tensor(_lowercase	):
										return array.size
					else:
										raise ValueError(F"""Type not supported for expand_dims: {type(_lowercase	)}."""	)



def   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		List[Any]	):




					'''simple docstring'''






					for key, value in auto_map.items():
										if isinstance(_lowercase							,      (tuple, list)	):
															UpperCAmelCase			:   Tuple      =				[F"""{repo_id}--{v}""" if (v is not None and "--" not in v) else v for v in value]
										elif value is not None and "--" not in value:
															UpperCAmelCase			:   Optional[int]      =				F"""{repo_id}--{value}"""

					return auto_map




def   lowercase_	(    _lowercase       :		List[Any]	):




					'''simple docstring'''






					for base_class in inspect.getmro(_lowercase	):
										UpperCAmelCase			:   Dict      =				base_class.__module__
										UpperCAmelCase			:   int      =				base_class.__name__
										if module.startswith("tensorflow"	) or module.startswith("keras"	) or name == "TFPreTrainedModel":
															return "tf"
										elif module.startswith("torch"	) or name == "PreTrainedModel":
															return "pt"
										elif module.startswith("flax"	) or module.startswith("jax"	) or name == "FlaxPreTrainedModel":
															return "flax"
					else:
										raise TypeError(F"""Could not infer framework from class {model_class}."""	)





"""simple docstring"""






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

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput







@dataclass
class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						42
			SCREAMING_SNAKE_CASE__     				=						42







class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						1







			@register_to_config
			def __init__(			self			: Optional[int]			,  lowercase			: int = 20_00			,  lowercase			: float = 0.1_5			,  lowercase			: float = 0.0_1			,  lowercase			: float = 1_3_4_8.0			,  lowercase			: float = 1E-5			,  lowercase			: int = 1			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_max

								# setable values
								UpperCAmelCase			:   Any      =				None

								self.set_sigmas(lowercase			,  lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[int] = None  ):





								'''simple docstring'''



								return sample







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: Union[str, torch.device] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

								UpperCAmelCase			:   Tuple      =				torch.linspace(1			,  lowercase			,  lowercase			,  device=lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: int			,  lowercase			: float = None			,  lowercase			: float = None			,  lowercase			: float = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				sigma_min if sigma_min is not None else self.config.sigma_min
								UpperCAmelCase			:   List[str]      =				sigma_max if sigma_max is not None else self.config.sigma_max
								UpperCAmelCase			:   Union[str, Any]      =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
													self.set_timesteps(lowercase			,  lowercase  )

								UpperCAmelCase			:   List[Any]      =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								UpperCAmelCase			:   Any      =				torch.exp(torch.linspace(math.log(lowercase  )			,  math.log(lowercase  )			,  lowercase  )  )
								UpperCAmelCase			:   str      =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Dict			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								return torch.where(
								    timesteps == 0			,  torch.zeros_like(t.to(timesteps.device  )  )			,  self.discrete_sigmas[timesteps - 1].to(timesteps.device  )			,  )







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: int			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

								UpperCAmelCase			:   Any      =				timestep * torch.ones(
								    sample.shape[0]			,  device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
								UpperCAmelCase			:   Dict      =				(timestep * (len(self.timesteps  ) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								UpperCAmelCase			:   List[str]      =				timesteps.to(self.discrete_sigmas.device  )

								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas[timesteps].to(sample.device  )
								UpperCAmelCase			:   Tuple      =				self.get_adjacent_sigma(lowercase			,  lowercase  ).to(sample.device  )
								UpperCAmelCase			:   Optional[int]      =				torch.zeros_like(lowercase  )
								UpperCAmelCase			:   Union[str, 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			:   List[Any]      =				diffusion.flatten()
								while len(diffusion.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Optional[int]      =				diffusion.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								UpperCAmelCase			:   List[Any]      =				randn_tensor(
								    sample.shape			,  layout=sample.layout			,  generator=lowercase			,  device=sample.device			,  dtype=sample.dtype  )
								UpperCAmelCase			:   int      =				sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								UpperCAmelCase			:   Tuple      =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
													return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowercase			,  prev_sample_mean=lowercase  )







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: Optional[torch.Generator] = None			,  lowercase			: bool = True			,  ):





								'''simple docstring'''



								if self.timesteps is None:
													raise ValueError(
													    "`self.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			:   str      =				randn_tensor(sample.shape			,  layout=sample.layout			,  generator=lowercase  ).to(sample.device  )

								# compute step size from the model_output, the noise, and the snr
								UpperCAmelCase			:   Tuple      =				torch.norm(model_output.reshape(model_output.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   List[Any]      =				torch.norm(noise.reshape(noise.shape[0]			,  -1  )			,  dim=-1  ).mean()
								UpperCAmelCase			:   Tuple      =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								UpperCAmelCase			:   int      =				step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								UpperCAmelCase			:   int      =				step_size.flatten()
								while len(step_size.shape  ) < len(sample.shape  ):
													UpperCAmelCase			:   Union[str, Any]      =				step_size.unsqueeze(-1  )
								UpperCAmelCase			:   Optional[Any]      =				sample + step_size * model_output
								UpperCAmelCase			:   Union[str, Any]      =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
													return (prev_sample,)

								return SchedulerOutput(prev_sample=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  lowercase			: torch.FloatTensor			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				timesteps.to(original_samples.device  )
								UpperCAmelCase			:   Optional[Any]      =				self.discrete_sigmas.to(original_samples.device  )[timesteps]
								UpperCAmelCase			:   List[str]      =				(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowercase  ) * sigmas[:, None, None, None]
								)
								UpperCAmelCase			:   Optional[Any]      =				noise + original_samples
								return noisy_samples







			def __len__(			self			: List[str]  ):





								'''simple docstring'''



								return self.config.num_train_timesteps


