




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


a_      						=   logging.get_logger(__name__)


a_      						=   {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
a_      						=   {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

a_      						=   {
    "RUCAIBox/mvp": 1024,
}




@lru_cache()
def     __SCREAMING_SNAKE_CASE      (     )				->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  (
        list(range(ord('''!'''					)    ,   ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)    ,   ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)    ,   ord('''ÿ'''					) + 1					)					)
    )
    __UpperCamelCase     						=  bs[:]
    __UpperCamelCase     						=  0
    for b in range(2**8					):
        if b not in bs:
            bs.append(lowercase_					)
            cs.append(2**8 + n					)
            n += 1
    __UpperCamelCase     						=  [chr(lowercase_					) for n in cs]
    return dict(zip(lowercase_    ,   lowercase_					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase     						=  set()
    __UpperCamelCase     						=  word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)					)
        __UpperCamelCase     						=  char
    return pairs


class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       int			=    VOCAB_FILES_NAMES
      lowerCAmelCase__	:       int			=    PRETRAINED_VOCAB_FILES_MAP
      lowerCAmelCase__	:       str			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCAmelCase__	:       str			=    ["input_ids", "attention_mask"]







      def __init__(   self    :       List[Any]       ,				snake_case    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       int="replace"       ,				snake_case    :       Optional[int]="<s>"       ,				snake_case    :       List[Any]="</s>"       ,				snake_case    :       str="</s>"       ,				snake_case    :       Optional[Any]="<s>"       ,				snake_case    :       Any="<unk>"       ,				snake_case    :       List[Any]="<pad>"       ,				snake_case    :       int="<mask>"       ,				snake_case    :       List[Any]=False       ,				**snake_case    :       Tuple       ,				):
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else bos_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else eos_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else sep_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else cls_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else unk_token
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCamelCase     						=  AddedToken(snake_case       ,				lstrip=snake_case       ,				rstrip=snake_case ) if isinstance(snake_case       ,				snake_case ) else mask_token

          super().__init__(
              errors=snake_case       ,				bos_token=snake_case       ,				eos_token=snake_case       ,				unk_token=snake_case       ,				sep_token=snake_case       ,				cls_token=snake_case       ,				pad_token=snake_case       ,				mask_token=snake_case       ,				add_prefix_space=snake_case       ,				**snake_case       ,				)
          with open(snake_case       ,				encoding='''utf-8''' ) as vocab_handle:
              __UpperCamelCase     						=  json.load(snake_case )
          __UpperCamelCase     						=  {v: k for k, v in self.encoder.items()}
          __UpperCamelCase     						=  errors  # how to handle errors in decoding
          __UpperCamelCase     						=  bytes_to_unicode()
          __UpperCamelCase     						=  {v: k for k, v in self.byte_encoder.items()}
          with open(snake_case       ,				encoding='''utf-8''' ) as merges_handle:
              __UpperCamelCase     						=  merges_handle.read().split('''\n''' )[1:-1]
          __UpperCamelCase     						=  [tuple(merge.split() ) for merge in bpe_merges]
          __UpperCamelCase     						=  dict(zip(snake_case       ,				range(len(snake_case ) ) ) )
          __UpperCamelCase     						=  {}
          __UpperCamelCase     						=  add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          __UpperCamelCase     						=  re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )







      @property
      def 					snake_case		(   self    :       List[str] ):
          return len(self.encoder )







      def 					snake_case		(   self    :       Dict ):
          return dict(self.encoder       ,				**self.added_tokens_encoder )







      def 					snake_case		(   self    :       Tuple       ,				snake_case    :       Union[str, Any] ):
          if token in self.cache:
              return self.cache[token]
          __UpperCamelCase     						=  tuple(snake_case )
          __UpperCamelCase     						=  get_pairs(snake_case )

          if not pairs:
              return token

          while True:
              __UpperCamelCase     						=  min(snake_case       ,				key=lambda snake_case : self.bpe_ranks.get(snake_case       ,				float('''inf''' ) ) )
              if bigram not in self.bpe_ranks:
                  break
              __UpperCamelCase						,					__UpperCamelCase     						=  bigram
              __UpperCamelCase     						=  []
              __UpperCamelCase     						=  0
              while i < len(snake_case ):
                  try:
                      __UpperCamelCase     						=  word.index(snake_case       ,				snake_case )
                  except ValueError:
                      new_word.extend(word[i:] )
                      break
                  else:
                      new_word.extend(word[i:j] )
                      __UpperCamelCase     						=  j

                  if word[i] == first and i < len(snake_case ) - 1 and word[i + 1] == second:
                      new_word.append(first + second )
                      i += 2
                  else:
                      new_word.append(word[i] )
                      i += 1
              __UpperCamelCase     						=  tuple(snake_case )
              __UpperCamelCase     						=  new_word
              if len(snake_case ) == 1:
                  break
              else:
                  __UpperCamelCase     						=  get_pairs(snake_case )
          __UpperCamelCase     						=  ''' '''.join(snake_case )
          __UpperCamelCase     						=  word
          return word







      def 					snake_case		(   self    :       List[str]       ,				snake_case    :       Any ):
          __UpperCamelCase     						=  []
          for token in re.findall(self.pat       ,				snake_case ):
              __UpperCamelCase     						=  ''''''.join(
                  self.byte_encoder[b] for b in token.encode('''utf-8''' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case ).split(''' ''' ) )
          return bpe_tokens







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       Union[str, Any] ):
          return self.encoder.get(snake_case       ,				self.encoder.get(self.unk_token ) )







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       List[str] ):
          return self.decoder.get(snake_case )







      def 					snake_case		(   self    :       str       ,				snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  ''''''.join(snake_case )
          __UpperCamelCase     						=  bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''       ,				errors=self.errors )
          return text







      def 					snake_case		(   self    :       str       ,				snake_case    :       str       ,				snake_case    :       Optional[str] = None ):
          if not os.path.isdir(snake_case ):
              logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
              return
          __UpperCamelCase     						=  os.path.join(
              snake_case       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
          __UpperCamelCase     						=  os.path.join(
              snake_case       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

          with open(snake_case       ,				'''w'''       ,				encoding='''utf-8''' ) as f:
              f.write(json.dumps(self.encoder       ,				indent=2       ,				sort_keys=snake_case       ,				ensure_ascii=snake_case ) + '''\n''' )

          __UpperCamelCase     						=  0
          with open(snake_case       ,				'''w'''       ,				encoding='''utf-8''' ) as writer:
              writer.write('''#version: 0.2\n''' )
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,				key=lambda snake_case : kv[1] ):
                  if index != token_index:
                      logger.warning(
                          F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                          ''' Please check that the tokenizer is not corrupted!''' )
                      __UpperCamelCase     						=  token_index
                  writer.write(''' '''.join(snake_case ) + '''\n''' )
                  index += 1

          return vocab_file, merge_file







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None ):
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCamelCase     						=  [self.cls_token_id]
          __UpperCamelCase     						=  [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep







      def 					snake_case		(   self    :       Any       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None       ,				snake_case    :       bool = False ):
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=snake_case       ,				token_ids_a=snake_case       ,				already_has_special_tokens=snake_case )

          if token_ids_a is None:
              return [1] + ([0] * len(snake_case )) + [1]
          return [1] + ([0] * len(snake_case )) + [1, 1] + ([0] * len(snake_case )) + [1]







      def 					snake_case		(   self    :       int       ,				snake_case    :       List[int]       ,				snake_case    :       Optional[List[int]] = None ):
          __UpperCamelCase     						=  [self.sep_token_id]
          __UpperCamelCase     						=  [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 + sep + token_ids_a + sep ) * [0]







      def 					snake_case		(   self    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       int=False       ,				**snake_case    :       Any ):
          __UpperCamelCase     						=  kwargs.pop('''add_prefix_space'''       ,				self.add_prefix_space )
          if (is_split_into_words or add_prefix_space) and (len(snake_case ) > 0 and not text[0].isspace()):
              __UpperCamelCase     						=  ''' ''' + text
          return (text, kwargs)








'''simple docstring'''
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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


lowerCAmelCase :  Any      =      logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				42
				lowerCAmelCase_			       =				None







def 						A_(			A		:						Any						,			A		:						Optional[Any]=0.999						,			A		:						Tuple="cosine"						,			):
							if alpha_transform_type == "cosine":

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

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(A		:						Optional[Any]):
																					return math.exp(t * -12.0)

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

							UpperCamelCase					       =			[]
							for i in range(A):
														UpperCamelCase					       =			i / num_diffusion_timesteps
														UpperCamelCase					       =			(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(A) / alpha_bar_fn(A)						,			A))
							return torch.tensor(A						,			dtype=torch.floataa)





class        SCREAMING_SNAKE_CASE__	(			snake_case_				,    snake_case_):
				lowerCAmelCase_			       =				[e.name for e in KarrasDiffusionSchedulers]
				lowerCAmelCase_			       =				1







				@register_to_config
				def __init__(    self		,							A_ = 1000		,							A_ = 0.0_001		,							A_ = 0.02		,							A_ = "linear"		,							A_ = None		,							A_ = "epsilon"		,							A_ = "linear"		,							A_ = False		,							A_ = "linspace"		,							A_ = 0		,							)->						Optional[Any]:







											'''simple docstring'''






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

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

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

											# setable values
											UpperCamelCase					       =			None
											UpperCamelCase					       =			np.linspace(0		,							num_train_timesteps - 1		,							A_		,							dtype=A_			)[::-1].copy()
											UpperCamelCase					       =			torch.from_numpy(A_			)
											UpperCamelCase					       =			False
											UpperCamelCase					       =			use_karras_sigmas







				@property
				def 		UpperCAmelCase_  (    self			)->						Any:







											'''simple docstring'''






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

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







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						torch.FloatTensor:







											'''simple docstring'''






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

											UpperCamelCase					       =			sample / ((sigma**2 + 1) ** 0.5)

											UpperCamelCase					       =			True
											return sample







				def 		UpperCAmelCase_  (    self		,							A_		,							A_ = None			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			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":
																		UpperCamelCase					       =			np.linspace(0		,							self.config.num_train_timesteps - 1		,							A_		,							dtype=A_			)[
																		    ::-1
																		].copy()
											elif self.config.timestep_spacing == "leading":
																		UpperCamelCase					       =			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
																		UpperCamelCase					       =			(np.arange(0		,							A_			) * step_ratio).round()[::-1].copy().astype(A_			)
																		timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																		UpperCamelCase					       =			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
																		UpperCamelCase					       =			(np.arange(self.config.num_train_timesteps		,							0		,							-step_ratio			)).round().copy().astype(A_			)
																		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					       =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
											UpperCamelCase					       =			np.log(A_			)

											if self.config.interpolation_type == "linear":
																		UpperCamelCase					       =			np.interp(A_		,							np.arange(0		,							len(A_			)			)		,							A_			)
											elif self.config.interpolation_type == "log_linear":
																		UpperCamelCase					       =			torch.linspace(np.log(sigmas[-1]			)		,							np.log(sigmas[0]			)		,							num_inference_steps + 1			).exp()
											else:
																		raise ValueError(
																		    F'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																		    ' \'linear\' or \'log_linear\''			)

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

											UpperCamelCase					       =			np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
											UpperCamelCase					       =			torch.from_numpy(A_			).to(device=A_			)
											if str(A_			).startswith('mps'			):
																		# mps does not support float64
																		UpperCamelCase					       =			torch.from_numpy(A_			).to(A_		,							dtype=torch.floataa			)
											else:
																		UpperCamelCase					       =			torch.from_numpy(A_			).to(device=A_			)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			np.log(A_			)

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

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

											UpperCamelCase					       =			log_sigmas[low_idx]
											UpperCamelCase					       =			log_sigmas[high_idx]

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

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







				def 		UpperCAmelCase_  (    self		,							A_		,							A_			)->						torch.FloatTensor:







											'''simple docstring'''






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

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







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							A_ = 0.0		,							A_ = 0.0		,							A_ = float('inf'			)		,							A_ = 1.0		,							A_ = None		,							A_ = True		,							)->						Union[EulerDiscreteSchedulerOutput, Tuple]:







											'''simple docstring'''






											if (
											    isinstance(A_		,							A_			)
											    or isinstance(A_		,							torch.IntTensor			)
											    or isinstance(A_		,							torch.LongTensor			)
											):
																		raise ValueError(
																		    (
																		        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
																		        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
																		        ' one of the `scheduler.timesteps` as a timestep.'
																		    )		,							)

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

											if isinstance(A_		,							torch.Tensor			):
																		UpperCamelCase					       =			timestep.to(self.timesteps.device			)

											UpperCamelCase					       =			(self.timesteps == timestep).nonzero().item()
											UpperCamelCase					       =			self.sigmas[step_index]

											UpperCamelCase					       =			min(s_churn / (len(self.sigmas			) - 1)		,							2**0.5 - 1			) if s_tmin <= sigma <= s_tmax else 0.0

											UpperCamelCase					       =			randn_tensor(
											    model_output.shape		,							dtype=model_output.dtype		,							device=model_output.device		,							generator=A_			)

											UpperCamelCase					       =			noise * s_noise
											UpperCamelCase					       =			sigma * (gamma + 1)

											if gamma > 0:
																		UpperCamelCase					       =			sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											# NOTE: "original_sample" should not be an expected prediction_type but is left in for
											# backwards compatibility
											if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																		UpperCamelCase					       =			model_output
											elif self.config.prediction_type == "epsilon":
																		UpperCamelCase					       =			sample - sigma_hat * model_output
											elif self.config.prediction_type == "v_prediction":
																		# * c_out + input * c_skip
																		UpperCamelCase					       =			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											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
											UpperCamelCase					       =			(sample - pred_original_sample) / sigma_hat

											UpperCamelCase					       =			self.sigmas[step_index + 1] - sigma_hat

											UpperCamelCase					       =			sample + derivative * dt

											if not return_dict:
																		return (prev_sample,)

											return EulerDiscreteSchedulerOutput(prev_sample=A_		,							pred_original_sample=A_			)







				def 		UpperCAmelCase_  (    self		,							A_		,							A_		,							A_		,							)->						torch.FloatTensor:







											'''simple docstring'''






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

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

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

											UpperCamelCase					       =			original_samples + noise * sigma
											return noisy_samples







				def __len__(    self			)->						Optional[int]:







											'''simple docstring'''






											return self.config.num_train_timesteps




