



'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


snake_case_				:			Tuple						 =						logging.get_logger(__name__)

snake_case_				:			Optional[int]						 =						{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case_				:			List[Any]						 =						{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case_				:			Tuple						 =						{
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

snake_case_				:			List[str]						 =						{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class 							lowercase__			(							lowercase     ):
	lowercase__						       =    VOCAB_FILES_NAMES
	lowercase__						       =    PRETRAINED_VOCAB_FILES_MAP
	lowercase__						       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__						       =    ["""input_ids""", """attention_mask""", """token_type_ids"""]
	lowercase__						       =    DebertaTokenizer





	def __init__(				self      :       Any				,lowerCamelCase__      :       Optional[Any]=None				,lowerCamelCase__      :       int=None				,lowerCamelCase__      :       int=None				,lowerCamelCase__      :       Tuple="replace"				,lowerCamelCase__      :       List[Any]="[CLS]"				,lowerCamelCase__      :       Optional[Any]="[SEP]"				,lowerCamelCase__      :       List[str]="[SEP]"				,lowerCamelCase__      :       Tuple="[CLS]"				,lowerCamelCase__      :       Union[str, Any]="[UNK]"				,lowerCamelCase__      :       List[Any]="[PAD]"				,lowerCamelCase__      :       Optional[Any]="[MASK]"				,lowerCamelCase__      :       Any=False				,**lowerCamelCase__      :       List[str]				,):







							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__				,lowerCamelCase__				,tokenizer_file=lowerCamelCase__				,errors=lowerCamelCase__				,bos_token=lowerCamelCase__				,eos_token=lowerCamelCase__				,unk_token=lowerCamelCase__				,sep_token=lowerCamelCase__				,cls_token=lowerCamelCase__				,pad_token=lowerCamelCase__				,mask_token=lowerCamelCase__				,add_prefix_space=lowerCamelCase__				,**lowerCamelCase__				,)
							_UpperCamelCase      :	Optional[int]				       =     kwargs.pop('add_bos_token'				,lowerCamelCase__				)

							_UpperCamelCase      :	List[str]				       =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
							if pre_tok_state.get('add_prefix_space'				,lowerCamelCase__				) != add_prefix_space:
													_UpperCamelCase      :	Tuple				       =     getattr(lowerCamelCase__				,pre_tok_state.pop('type'				)				)
													_UpperCamelCase      :	str				       =     add_prefix_space
													_UpperCamelCase      :	Optional[Any]				       =     pre_tok_class(**lowerCamelCase__				)

							_UpperCamelCase      :	Any				       =     add_prefix_space





	@property
	def 			UpperCamelCase_						(				self      :       Optional[int]				):







							'''simple docstring'''
							if self._mask_token is None:
													if self.verbose:
																			logger.error('Using mask_token, but it is not set yet.'				)
													return None
							return str(self._mask_token				)





	@mask_token.setter
	def 			UpperCamelCase_						(				self      :       Any				,lowerCamelCase__      :       List[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     AddedToken(lowerCamelCase__				,lstrip=lowerCamelCase__				,rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__				,lowerCamelCase__				) else value
							_UpperCamelCase      :	Tuple				       =     value





	def 			UpperCamelCase_						(				self      :       str				,lowerCamelCase__      :       List[int]				,lowerCamelCase__      :       Optional[List[int]] = None				):







							'''simple docstring'''
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_UpperCamelCase      :	Union[str, Any]				       =     [self.cls_token_id]
							_UpperCamelCase      :	List[str]				       =     [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       List[int]				,lowerCamelCase__      :       Optional[List[int]] = None				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     [self.sep_token_id]
							_UpperCamelCase      :	Optional[int]				       =     [self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep				) * [0]
							return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]





	def 			UpperCamelCase_						(				self      :       Any				,*lowerCamelCase__      :       List[Any]				,**lowerCamelCase__      :       str				):







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     kwargs.get('is_split_into_words'				,lowerCamelCase__				)
							assert self.add_prefix_space or not is_split_into_words, (
							    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
							    "to use it with pretokenized inputs."
							)

							return super()._batch_encode_plus(*lowerCamelCase__				,**lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Any				,*lowerCamelCase__      :       Union[str, Any]				,**lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[int]				       =     kwargs.get('is_split_into_words'				,lowerCamelCase__				)

							assert self.add_prefix_space or not is_split_into_words, (
							    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
							    "to use it with pretokenized inputs."
							)

							return super()._encode_plus(*lowerCamelCase__				,**lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       str				,lowerCamelCase__      :       str				,lowerCamelCase__      :       Optional[str] = None				):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     self._tokenizer.model.save(lowerCamelCase__				,name=lowerCamelCase__				)
							return tuple(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				,lowerCamelCase__      :       "Conversation"				):







							'''simple docstring'''
							_UpperCamelCase      :	Tuple				       =     []
							for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(lowerCamelCase__				,add_special_tokens=lowerCamelCase__				) + [self.eos_token_id]				)

							if len(lowerCamelCase__				) > self.model_max_length:
													_UpperCamelCase      :	List[Any]				       =     input_ids[-self.model_max_length :]
							return input_ids







'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=0.999						,						UpperCAmelCase_="cosine"						,						):
						if alpha_transform_type == "cosine":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(UpperCAmelCase_							):
																		return math.exp(t * -12.0							)

						else:
												raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

						_UpperCamelCase      :	Union[str, Any]				       =     []
						for i in range(UpperCAmelCase_							):
												_UpperCamelCase      :	Any				       =     i / num_diffusion_timesteps
												_UpperCamelCase      :	Any				       =     (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_							) / alpha_bar_fn(UpperCAmelCase_							)						,						UpperCAmelCase_							)							)
						return torch.tensor(UpperCAmelCase_						,						dtype=torch.floataa							)






class 							lowercase__			(							lowercase		,      lowercase     ):
	lowercase__						       =    [e.name for e in KarrasDiffusionSchedulers]
	lowercase__						       =    2





	@register_to_config
	def __init__(				self      :       List[str]				,lowerCamelCase__      :       int = 1000				,lowerCamelCase__      :       float = 0.0_0_0_8_5				,lowerCamelCase__      :       float = 0.0_1_2				,lowerCamelCase__      :       str = "linear"				,lowerCamelCase__      :       Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase__      :       str = "epsilon"				,lowerCamelCase__      :       str = "linspace"				,lowerCamelCase__      :       int = 0				,):







							'''simple docstring'''
							if trained_betas is not None:
													_UpperCamelCase      :	Tuple				       =     torch.tensor(lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "linear":
													_UpperCamelCase      :	int				       =     torch.linspace(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				,dtype=torch.floataa				)
							elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													_UpperCamelCase      :	Dict				       =     (
													    torch.linspace(beta_start**0.5				,beta_end**0.5				,lowerCamelCase__				,dtype=torch.floataa				) ** 2
													)
							elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													_UpperCamelCase      :	Dict				       =     betas_for_alpha_bar(lowerCamelCase__				)
							else:
													raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'				)

							_UpperCamelCase      :	Tuple				       =     1.0 - self.betas
							_UpperCamelCase      :	Any				       =     torch.cumprod(self.alphas				,dim=0				)

							#  set all values
							self.set_timesteps(lowerCamelCase__				,lowerCamelCase__				,lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       Any				,lowerCamelCase__      :       Optional[int]=None				):







							'''simple docstring'''
							if schedule_timesteps is None:
													_UpperCamelCase      :	Optional[Any]				       =     self.timesteps

							_UpperCamelCase      :	List[str]				       =     (schedule_timesteps == timestep).nonzero()

							# The sigma index that is taken for the **very** first `step`
							# is always the second index (or the last index if there is only 1)
							# This way we can ensure we don't accidentally skip a sigma in
							# case we start in the middle of the denoising schedule (e.g. for image-to-image)
							if len(self._index_counter				) == 0:
													_UpperCamelCase      :	List[Any]				       =     1 if len(lowerCamelCase__				) > 1 else 0
							else:
													_UpperCamelCase      :	Optional[Any]				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
													_UpperCamelCase      :	str				       =     self._index_counter[timestep_int]

							return indices[pos].item()





	@property
	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     self.index_for_timestep(lowerCamelCase__				)

							if self.state_in_first_order:
													_UpperCamelCase      :	str				       =     self.sigmas[step_index]
							else:
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_interpol[step_index - 1]

							_UpperCamelCase      :	Optional[Any]				       =     sample / ((sigma**2 + 1) ** 0.5)
							return sample





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       int				,lowerCamelCase__      :       Union[str, torch.device] = None				,lowerCamelCase__      :       Optional[int] = None				,):







							'''simple docstring'''
							_UpperCamelCase      :	int				       =     num_inference_steps

							_UpperCamelCase      :	List[str]				       =     num_train_timesteps or self.config.num_train_timesteps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
													_UpperCamelCase      :	Optional[int]				       =     np.linspace(0				,num_train_timesteps - 1				,lowerCamelCase__				,dtype=lowerCamelCase__				)[::-1].copy()
							elif self.config.timestep_spacing == "leading":
													_UpperCamelCase      :	Optional[Any]				       =     num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	Tuple				       =     (np.arange(0				,lowerCamelCase__				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__				)
													timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
													_UpperCamelCase      :	Dict				       =     num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													_UpperCamelCase      :	int				       =     (np.arange(lowerCamelCase__				,0				,-step_ratio				)).round().copy().astype(lowerCamelCase__				)
													timesteps -= 1
							else:
													raise ValueError(
													    F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'				)

							_UpperCamelCase      :	List[str]				       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
							_UpperCamelCase      :	str				       =     torch.from_numpy(np.log(lowerCamelCase__				)				).to(lowerCamelCase__				)

							_UpperCamelCase      :	int				       =     np.interp(lowerCamelCase__				,np.arange(0				,len(lowerCamelCase__				)				)				,lowerCamelCase__				)
							_UpperCamelCase      :	Any				       =     np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
							_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(device=lowerCamelCase__				)

							# compute up and down sigmas
							_UpperCamelCase      :	Dict				       =     sigmas.roll(-1				)
							_UpperCamelCase      :	Tuple				       =     0.0
							_UpperCamelCase      :	Optional[Any]				       =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
							_UpperCamelCase      :	List[str]				       =     (sigmas_next**2 - sigmas_up**2) ** 0.5
							_UpperCamelCase      :	Tuple				       =     0.0

							# compute interpolated sigmas
							_UpperCamelCase      :	Any				       =     sigmas.log().lerp(sigmas_down.log()				,0.5				).exp()
							_UpperCamelCase      :	Tuple				       =     0.0

							# set sigmas
							_UpperCamelCase      :	Tuple				       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
							_UpperCamelCase      :	Tuple				       =     torch.cat(
							    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)
							_UpperCamelCase      :	Optional[int]				       =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2				), sigmas_up[-1:]]				)
							_UpperCamelCase      :	List[str]				       =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2				), sigmas_down[-1:]]				)

							if str(lowerCamelCase__				).startswith('mps'				):
													# mps does not support float64
													_UpperCamelCase      :	int				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	Dict				       =     torch.from_numpy(lowerCamelCase__				).to(lowerCamelCase__				)

							_UpperCamelCase      :	List[str]				       =     self.sigma_to_t(lowerCamelCase__				).to(lowerCamelCase__				,dtype=timesteps.dtype				)
							_UpperCamelCase      :	List[Any]				       =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,dim=-1				).flatten()

							_UpperCamelCase      :	int				       =     torch.cat([timesteps[:1], interleaved_timesteps]				)

							_UpperCamelCase      :	Optional[int]				       =     None

							# for exp beta schedules, such as the one for `pipeline_shap_e.py`
							# we need an index counter
							_UpperCamelCase      :	Optional[Any]				       =     defaultdict(lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       List[Any]				,lowerCamelCase__      :       Optional[Any]				):







							'''simple docstring'''
							# get log sigma
							_UpperCamelCase      :	Union[str, Any]				       =     sigma.log()

							# get distribution
							_UpperCamelCase      :	str				       =     log_sigma - self.log_sigmas[:, None]

							# get sigmas range
							_UpperCamelCase      :	List[str]				       =     dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
							_UpperCamelCase      :	Any				       =     low_idx + 1

							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[low_idx]
							_UpperCamelCase      :	Optional[int]				       =     self.log_sigmas[high_idx]

							# interpolate sigmas
							_UpperCamelCase      :	Optional[int]				       =     (low - log_sigma) / (low - high)
							_UpperCamelCase      :	Any				       =     w.clamp(0				,1				)

							# transform interpolation to time range
							_UpperCamelCase      :	int				       =     (1 - w) * low_idx + w * high_idx
							_UpperCamelCase      :	Dict				       =     t.view(sigma.shape				)
							return t





	@property
	def 			UpperCamelCase_						(				self      :       Any				):







							'''simple docstring'''
							return self.sample is None





	def 			UpperCamelCase_						(				self      :       Tuple				,lowerCamelCase__      :       Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase__      :       Union[float, torch.FloatTensor]				,lowerCamelCase__      :       Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase__      :       Optional[torch.Generator] = None				,lowerCamelCase__      :       bool = True				,):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     self.index_for_timestep(lowerCamelCase__				)

							# advance index counter by 1
							_UpperCamelCase      :	int				       =     timestep.cpu().item() if torch.is_tensor(lowerCamelCase__				) else timestep
							self._index_counter[timestep_int] += 1

							if self.state_in_first_order:
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas[step_index]
													_UpperCamelCase      :	str				       =     self.sigmas_interpol[step_index]
													_UpperCamelCase      :	Optional[int]				       =     self.sigmas_up[step_index]
													_UpperCamelCase      :	List[Any]				       =     self.sigmas_down[step_index - 1]
							else:
													# 2nd order / KPDM2's method
													_UpperCamelCase      :	List[Any]				       =     self.sigmas[step_index - 1]
													_UpperCamelCase      :	Dict				       =     self.sigmas_interpol[step_index - 1]
													_UpperCamelCase      :	List[str]				       =     self.sigmas_up[step_index - 1]
													_UpperCamelCase      :	Optional[Any]				       =     self.sigmas_down[step_index - 1]

							# currently only gamma=0 is supported. This usually works best anyways.
							# We can support gamma in the future but then need to scale the timestep before
							# passing it to the model which requires a change in API
							_UpperCamelCase      :	int				       =     0
							_UpperCamelCase      :	Union[str, Any]				       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

							_UpperCamelCase      :	Union[str, Any]				       =     model_output.device
							_UpperCamelCase      :	Dict				       =     randn_tensor(model_output.shape				,dtype=model_output.dtype				,device=lowerCamelCase__				,generator=lowerCamelCase__				)

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
													_UpperCamelCase      :	Union[str, Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	str				       =     sample - sigma_input * model_output
							elif self.config.prediction_type == "v_prediction":
													_UpperCamelCase      :	Optional[Any]				       =     sigma_hat if self.state_in_first_order else sigma_interpol
													_UpperCamelCase      :	Optional[Any]				       =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
													    sample / (sigma_input**2 + 1)
													)
							elif self.config.prediction_type == "sample":
													raise NotImplementedError('prediction_type not implemented yet: sample'				)
							else:
													raise ValueError(
													    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'				)

							if self.state_in_first_order:
													# 2. Convert to an ODE derivative for 1st order
													_UpperCamelCase      :	Dict				       =     (sample - pred_original_sample) / sigma_hat
													# 3. delta timestep
													_UpperCamelCase      :	Optional[Any]				       =     sigma_interpol - sigma_hat

													# store for 2nd order step
													_UpperCamelCase      :	Union[str, Any]				       =     sample
													_UpperCamelCase      :	Any				       =     dt
													_UpperCamelCase      :	Optional[Any]				       =     sample + derivative * dt
							else:
													# DPM-Solver-2
													# 2. Convert to an ODE derivative for 2nd order
													_UpperCamelCase      :	Tuple				       =     (sample - pred_original_sample) / sigma_interpol
													# 3. delta timestep
													_UpperCamelCase      :	int				       =     sigma_down - sigma_hat

													_UpperCamelCase      :	Dict				       =     self.sample
													_UpperCamelCase      :	Tuple				       =     None

													_UpperCamelCase      :	Dict				       =     sample + derivative * dt
													_UpperCamelCase      :	int				       =     prev_sample + noise * sigma_up

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCamelCase__				)





	def 			UpperCamelCase_						(				self      :       Dict				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,lowerCamelCase__      :       torch.FloatTensor				,):







							'''simple docstring'''
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_UpperCamelCase      :	Optional[int]				       =     self.sigmas.to(device=original_samples.device				,dtype=original_samples.dtype				)
							if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__				):
													# mps does not support float64
													_UpperCamelCase      :	Any				       =     self.timesteps.to(original_samples.device				,dtype=torch.floataa				)
													_UpperCamelCase      :	Dict				       =     timesteps.to(original_samples.device				,dtype=torch.floataa				)
							else:
													_UpperCamelCase      :	List[str]				       =     self.timesteps.to(original_samples.device				)
													_UpperCamelCase      :	str				       =     timesteps.to(original_samples.device				)

							_UpperCamelCase      :	Optional[int]				       =     [self.index_for_timestep(lowerCamelCase__				,lowerCamelCase__				) for t in timesteps]

							_UpperCamelCase      :	Any				       =     sigmas[step_indices].flatten()
							while len(sigma.shape				) < len(original_samples.shape				):
													_UpperCamelCase      :	Optional[Any]				       =     sigma.unsqueeze(-1				)

							_UpperCamelCase      :	Dict				       =     original_samples + noise * sigma
							return noisy_samples





	def __len__(				self      :       List[str]				):







							'''simple docstring'''
							return self.config.num_train_timesteps


