





"""simple docstring"""




import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_	 =logging.get_logger(__name__)
UpperCamelCase_	 ={"""vocab_file""": """spiece.model"""}

UpperCamelCase_	 ={
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
}

UpperCamelCase_	 ={
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

UpperCamelCase_	 ="""▁"""




class 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=       ['''input_ids''', '''token_type_ids''']






				def __init__(					self						:       Union[str, Any],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       int=False,					lowerCAmelCase__						:       Tuple=True,					lowerCAmelCase__						:       List[str]=True,					lowerCAmelCase__						:       Tuple="<unk>",					lowerCAmelCase__						:       List[Any]="[SEP]",					lowerCAmelCase__						:       int="<pad>",					lowerCAmelCase__						:       int="[CLS]",					lowerCAmelCase__						:       int="[MASK]",					lowerCAmelCase__						:       Optional[Dict[str, Any]] = None,					**lowerCAmelCase__						:       Union[str, Any],					)      -> None:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       (
										    AddedToken(lowerCAmelCase__,					lstrip=lowerCAmelCase__,					rstrip=lowerCAmelCase__,					normalized=lowerCAmelCase__				)
										    if isinstance(lowerCAmelCase__,					lowerCAmelCase__				)
										    else mask_token
										)

										_UpperCamelCase     :   Any      =       {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=lowerCAmelCase__,					remove_space=lowerCAmelCase__,					keep_accents=lowerCAmelCase__,					unk_token=lowerCAmelCase__,					sep_token=lowerCAmelCase__,					pad_token=lowerCAmelCase__,					cls_token=lowerCAmelCase__,					mask_token=lowerCAmelCase__,					sp_model_kwargs=self.sp_model_kwargs,					**lowerCAmelCase__,					)

										_UpperCamelCase     :   List[Any]      =       do_lower_case
										_UpperCamelCase     :   Tuple      =       remove_space
										_UpperCamelCase     :   Dict      =       keep_accents
										_UpperCamelCase     :   Dict      =       vocab_file

										_UpperCamelCase     :   str      =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(lowerCAmelCase__				)






				@property
				def 							snake_case      (					self						:       str				)      -> Tuple:




										'''simple docstring'''



										return len(self.sp_model				)






				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       {self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab






				def __getstate__(					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       self.__dict__.copy()
										_UpperCamelCase     :   Optional[Any]      =       None
										return state






				def __setstate__(					self						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any]				)      -> Dict:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       d

										# for backward compatibility
										if not hasattr(self,					'''sp_model_kwargs'''				):
																_UpperCamelCase     :   Optional[Any]      =       {}

										_UpperCamelCase     :   int      =       spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(self.vocab_file				)






				def 							snake_case      (					self						:       List[str],					lowerCAmelCase__						:       Optional[Any]				)      -> Dict:




										'''simple docstring'''



										if self.remove_space:
																_UpperCamelCase     :   Any      =       ''' '''.join(inputs.strip().split()				)
										else:
																_UpperCamelCase     :   int      =       inputs
										_UpperCamelCase     :   Optional[Any]      =       outputs.replace('''``''',					'''"'''				).replace('''\'\'''',					'''"'''				)

										if not self.keep_accents:
																_UpperCamelCase     :   Union[str, Any]      =       unicodedata.normalize('''NFKD''',					lowerCAmelCase__				)
																_UpperCamelCase     :   Dict      =       ''''''.join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__				)]				)
										if self.do_lower_case:
																_UpperCamelCase     :   Dict      =       outputs.lower()

										return outputs






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       str				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       self.preprocess_text(lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       self.sp_model.encode(lowerCAmelCase__,					out_type=lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       []
										for piece in pieces:
																if len(lowerCAmelCase__				) > 1 and piece[-1] == str(''','''				) and piece[-2].isdigit():
																						_UpperCamelCase     :   Optional[Any]      =       self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__,					''''''				)				)
																						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																												if len(cur_pieces[0]				) == 1:
																																		_UpperCamelCase     :   Optional[int]      =       cur_pieces[1:]
																												else:
																																		_UpperCamelCase     :   Optional[int]      =       cur_pieces[0][1:]
																						cur_pieces.append(piece[-1]				)
																						new_pieces.extend(lowerCAmelCase__				)
																else:
																						new_pieces.append(lowerCAmelCase__				)

										return new_pieces






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Tuple				)      -> Tuple:




										'''simple docstring'''



										return self.sp_model.PieceToId(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       List[str]				)      -> Dict:




										'''simple docstring'''



										return self.sp_model.IdToPiece(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Union[str, Any]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       []
										_UpperCamelCase     :   Optional[int]      =       ''''''
										_UpperCamelCase     :   Optional[int]      =       False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(lowerCAmelCase__				) + token
																						_UpperCamelCase     :   Optional[int]      =       True
																						_UpperCamelCase     :   Union[str, Any]      =       []
																else:
																						current_sub_tokens.append(lowerCAmelCase__				)
																						_UpperCamelCase     :   int      =       False
										out_string += self.sp_model.decode(lowerCAmelCase__				)
										return out_string.strip()






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[int],					lowerCAmelCase__						:       bool = False,					lowerCAmelCase__						:       bool = None,					lowerCAmelCase__						:       bool = True,					**lowerCAmelCase__						:       str,					)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       kwargs.pop('''use_source_tokenizer''',					lowerCAmelCase__				)

										_UpperCamelCase     :   Dict      =       self.convert_ids_to_tokens(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										_UpperCamelCase     :   str      =       []
										_UpperCamelCase     :   str      =       []
										for token in filtered_tokens:
																if skip_special_tokens and token in self.all_special_ids:
																						continue
																if token in self.added_tokens_encoder:
																						if current_sub_text:
																												sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__				)				)
																												_UpperCamelCase     :   str      =       []
																						sub_texts.append(lowerCAmelCase__				)
																else:
																						current_sub_text.append(lowerCAmelCase__				)
										if current_sub_text:
																sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__				)				)

										# Mimic the behavior of the Rust tokenizer:
										# No space after <unk>
										if spaces_between_special_tokens:
																_UpperCamelCase     :   Optional[int]      =       re.sub(R'''(<unk>) ''',					R'''\1''',					''' '''.join(lowerCAmelCase__				)				)
										else:
																_UpperCamelCase     :   int      =       ''''''.join(lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[Any]      =       (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																_UpperCamelCase     :   str      =       self.clean_up_tokenization(lowerCAmelCase__				)
																return clean_text
										else:
																return text






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       List[int],					lowerCAmelCase__						:       Optional[List[int]] = None				)      -> List[int]:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       [self.sep_token_id]
										_UpperCamelCase     :   Any      =       [self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       List[int],					lowerCAmelCase__						:       Optional[List[int]] = None,					lowerCAmelCase__						:       bool = False				)      -> List[int]:




										'''simple docstring'''



										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase__,					token_ids_a=lowerCAmelCase__,					already_has_special_tokens=lowerCAmelCase__				)

										if token_ids_a is not None:
																return [1] + ([0] * len(lowerCAmelCase__				)) + [1] + ([0] * len(lowerCAmelCase__				)) + [1]
										return [1] + ([0] * len(lowerCAmelCase__				)) + [1]






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[int],					lowerCAmelCase__						:       Optional[List[int]] = None				)      -> List[int]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       [self.sep_token_id]
										_UpperCamelCase     :   Union[str, Any]      =       [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 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       Optional[str] = None				)      -> Tuple[str]:




										'''simple docstring'''



										if not os.path.isdir(lowerCAmelCase__				):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																return
										_UpperCamelCase     :   Any      =       os.path.join(
										    lowerCAmelCase__,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				) and os.path.isfile(self.vocab_file				):
																copyfile(self.vocab_file,					lowerCAmelCase__				)
										elif not os.path.isfile(self.vocab_file				):
																with open(lowerCAmelCase__,					'''wb'''				) as fi:
																						_UpperCamelCase     :   Optional[int]      =       self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase__				)

										return (out_vocab_file,)








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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       torch.FloatTensor
       UpperCamelCase__    :       Optional[torch.FloatTensor]		   =		None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=0.9_99  ,					_UpperCAmelCase="cosine"  ,					):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_UpperCAmelCase							):
            return math.cos((t + 0.0_08) / 1.0_08 * 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}'							)

    __a					=							[]
    for i in range(_UpperCAmelCase							):
        __a					=							i / num_diffusion_timesteps
        __a					=							(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        _A			(      __UpperCAmelCase ,__UpperCAmelCase       ):
       UpperCamelCase__    :       List[Any]		   =		[e.name for e in KarrasDiffusionSchedulers]
       UpperCamelCase__    :       Optional[Any]		   =		1



       @register_to_config
       def __init__(						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	int = 1_000	,						__SCREAMING_SNAKE_CASE				:	float = 0.00_01	,						__SCREAMING_SNAKE_CASE				:	float = 0.02	,						__SCREAMING_SNAKE_CASE				:	str = "linear"	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[np.ndarray, List[float]]] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[bool] = False	,						__SCREAMING_SNAKE_CASE				:	str = "epsilon"	,						__SCREAMING_SNAKE_CASE				:	str = "linspace"	,						__SCREAMING_SNAKE_CASE				:	int = 0	,						):


           '''simple docstring'''

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

           __a					=							1.0 - self.betas
           __a					=							torch.cumprod(self.alphas	,						dim=0)

           __a					=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           __a					=							np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
           __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE)

           # setable values
           __a					=							None
           __a					=							use_karras_sigmas
           self.set_timesteps(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)
           __a					=							[]
           __a					=							False



       @property
       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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



       def 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	Union[float, torch.FloatTensor]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor):
               __a					=							timestep.to(self.timesteps.device)
           __a					=							(self.timesteps == timestep).nonzero().item()
           __a					=							self.sigmas[step_index]
           __a					=							sample / ((sigma**2 + 1) ** 0.5)
           __a					=							True
           return sample



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           def lms_derivative(__SCREAMING_SNAKE_CASE				:	str):
               __a					=							1.0
               for k in range(__SCREAMING_SNAKE_CASE):
                   if current_order == k:
                       continue
                   prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
               return prod

           __a					=							integrate.quad(__SCREAMING_SNAKE_CASE	,						self.sigmas[t]	,						self.sigmas[t + 1]	,						epsrel=1E-4)[0]

           return integrated_coeff



       def 						_lowerCamelCase     (						self				:	int	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Union[str, torch.device] = None):


           '''simple docstring'''

           __a					=							num_inference_steps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
               __a					=							np.linspace(0	,						self.config.num_train_timesteps - 1	,						__SCREAMING_SNAKE_CASE	,						dtype=__SCREAMING_SNAKE_CASE)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               __a					=							self.config.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
               __a					=							(np.arange(0	,						__SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               __a					=							self.config.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
               __a					=							(np.arange(self.config.num_train_timesteps	,						0	,						-step_ratio)).round().copy().astype(__SCREAMING_SNAKE_CASE)
               timesteps -= 1
           else:
               raise ValueError(
                   F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.')

           __a					=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           __a					=							np.log(__SCREAMING_SNAKE_CASE)
           __a					=							np.interp(__SCREAMING_SNAKE_CASE	,						np.arange(0	,						len(__SCREAMING_SNAKE_CASE))	,						__SCREAMING_SNAKE_CASE)

           if self.use_karras_sigmas:
               __a					=							self._convert_to_karras(in_sigmas=__SCREAMING_SNAKE_CASE)
               __a					=							np.array([self._sigma_to_t(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE) for sigma in sigmas])

           __a					=							np.concatenate([sigmas, [0.0]]).astype(np.floataa)

           __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               # mps does not support float64
               __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa)
           else:
               __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)

           __a					=							[]



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							np.log(__SCREAMING_SNAKE_CASE)

           # get distribution
           __a					=							log_sigma - log_sigmas[:, np.newaxis]

           # get sigmas range
           __a					=							np.cumsum((dists >= 0)	,						axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
           __a					=							low_idx + 1

           __a					=							log_sigmas[low_idx]
           __a					=							log_sigmas[high_idx]

           # interpolate sigmas
           __a					=							(low - log_sigma) / (low - high)
           __a					=							np.clip(__SCREAMING_SNAKE_CASE	,						0	,						1)

           # transform interpolation to time range
           __a					=							(1 - w) * low_idx + w * high_idx
           __a					=							t.reshape(sigma.shape)
           return t



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor):


           '''simple docstring'''

           __a					=							in_sigmas[-1].item()
           __a					=							in_sigmas[0].item()

           __a					=							7.0  # 7.0 is the value used in the paper
           __a					=							np.linspace(0	,						1	,						self.num_inference_steps)
           __a					=							sigma_min ** (1 / rho)
           __a					=							sigma_max ** (1 / rho)
           __a					=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	Union[float, torch.FloatTensor]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	int = 4	,						__SCREAMING_SNAKE_CASE				:	bool = True	,						):


           '''simple docstring'''

           if not self.is_scale_input_called:
               warnings.warn(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.''')

           if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor):
               __a					=							timestep.to(self.timesteps.device)
           __a					=							(self.timesteps == timestep).nonzero().item()
           __a					=							self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               __a					=							sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               __a					=							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
               __a					=							model_output
           else:
               raise ValueError(
                   F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

           # 2. Convert to an ODE derivative
           __a					=							(sample - pred_original_sample) / sigma
           self.derivatives.append(__SCREAMING_SNAKE_CASE)
           if len(self.derivatives) > order:
               self.derivatives.pop(0)

           # 3. Compute linear multistep coefficients
           __a					=							min(step_index + 1	,						__SCREAMING_SNAKE_CASE)
           __a					=							[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE) for curr_order in range(__SCREAMING_SNAKE_CASE)]

           # 4. Compute previous sample based on the derivatives path
           __a					=							sample + sum(
               coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE	,						reversed(self.derivatives)))

           if not return_dict:
               return (prev_sample,)

           return LMSDiscreteSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE	,						pred_original_sample=__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						):


           '''simple docstring'''

           __a					=							self.sigmas.to(device=original_samples.device	,						dtype=original_samples.dtype)
           if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE):
               # mps does not support float64
               __a					=							self.timesteps.to(original_samples.device	,						dtype=torch.floataa)
               __a					=							timesteps.to(original_samples.device	,						dtype=torch.floataa)
           else:
               __a					=							self.timesteps.to(original_samples.device)
               __a					=							timesteps.to(original_samples.device)

           __a					=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

           __a					=							original_samples + noise * sigma
           return noisy_samples





       def __len__(						self				:	List[str]):


           '''simple docstring'''

           return self.config.num_train_timesteps





