

'''simple docstring'''


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


lowerCAmelCase__      				=			logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase__      				=			{
    "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",
    },
}

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







@lru_cache()
def     SCREAMING_SNAKE_CASE(	)     ->     Any:
	UpperCAmelCase_		:   Tuple													=       (
	    list(range(ord('!'	) ,ord('~'	) + 1	)	) + list(range(ord('¡'	) ,ord('¬'	) + 1	)	) + list(range(ord('®'	) ,ord('ÿ'	) + 1	)	)
	)
	UpperCAmelCase_		:   Any													=       bs[:]
	UpperCAmelCase_		:   Dict													=       0
	for b in range(2**8	):
		if b not in bs:
			bs.append(UpperCamelCase	)
			cs.append(2**8 + n	)
			n += 1
	UpperCAmelCase_		:   str													=       [chr(UpperCamelCase	) for n in cs]
	return dict(zip(UpperCamelCase ,UpperCamelCase	)	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     List[str]:
	UpperCAmelCase_		:   Union[str, Any]													=       set()
	UpperCAmelCase_		:   Tuple													=       word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)	)
		UpperCAmelCase_		:   Optional[int]													=       char
	return pairs







class        lowercase    (	a_			):
							_lowerCamelCase		:   Any= VOCAB_FILES_NAMES
							_lowerCamelCase		:   Optional[int]= PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:   int= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:   Any= ["input_ids", "attention_mask"]


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case="replace"     ,      _snake_case="<s>"     ,      _snake_case="</s>"     ,      _snake_case="</s>"     ,      _snake_case="<s>"     ,      _snake_case="<unk>"     ,      _snake_case="<pad>"     ,      _snake_case="<mask>"     ,      _snake_case=False     ,      **_snake_case     ,      )				->					List[str]:
								UpperCAmelCase_		:   Any													=       AddedToken(_snake_case     ,      lstrip=_snake_case     ,      rstrip=_snake_case) if isinstance(_snake_case     ,      _snake_case) else bos_token
								UpperCAmelCase_		:   int													=       AddedToken(_snake_case     ,      lstrip=_snake_case     ,      rstrip=_snake_case) if isinstance(_snake_case     ,      _snake_case) else eos_token
								UpperCAmelCase_		:   int													=       AddedToken(_snake_case     ,      lstrip=_snake_case     ,      rstrip=_snake_case) if isinstance(_snake_case     ,      _snake_case) else sep_token
								UpperCAmelCase_		:   Optional[int]													=       AddedToken(_snake_case     ,      lstrip=_snake_case     ,      rstrip=_snake_case) if isinstance(_snake_case     ,      _snake_case) else cls_token
								UpperCAmelCase_		:   Optional[int]													=       AddedToken(_snake_case     ,      lstrip=_snake_case     ,      rstrip=_snake_case) if isinstance(_snake_case     ,      _snake_case) else unk_token
								UpperCAmelCase_		:   List[str]													=       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_		:   Tuple													=       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_		:   Optional[int]													=       json.load(_snake_case)
								UpperCAmelCase_		:   Dict													=       {v: k for k, v in self.encoder.items()}
								UpperCAmelCase_		:   str													=       errors  # how to handle errors in decoding
								UpperCAmelCase_		:   Union[str, Any]													=       bytes_to_unicode()
								UpperCAmelCase_		:   List[Any]													=       {v: k for k, v in self.byte_encoder.items()}
								with open(_snake_case     ,      encoding='utf-8') as merges_handle:
									UpperCAmelCase_		:   Optional[Any]													=       merges_handle.read().split('\n')[1:-1]
								UpperCAmelCase_		:   Tuple													=       [tuple(merge.split()) for merge in bpe_merges]
								UpperCAmelCase_		:   str													=       dict(zip(_snake_case     ,      range(len(_snake_case))))
								UpperCAmelCase_		:   List[str]													=       {}
								UpperCAmelCase_		:   Union[str, Any]													=       add_prefix_space

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								UpperCAmelCase_		:   Optional[int]													=       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)				->					Optional[int]:
								return dict(self.encoder     ,      **self.added_tokens_encoder)


							def     _snake_case				(      self     ,      _snake_case)				->					str:
								if token in self.cache:
									return self.cache[token]
								UpperCAmelCase_		:   Optional[int]													=       tuple(_snake_case)
								UpperCAmelCase_		:   List[Any]													=       get_pairs(_snake_case)

								if not pairs:
									return token

								while True:
									UpperCAmelCase_		:   str													=       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_		:   Tuple													=       bigram
									UpperCAmelCase_		:   List[str]													=       []
									UpperCAmelCase_		:   Union[str, Any]													=       0
									while i < len(_snake_case):
										try:
											UpperCAmelCase_		:   Optional[int]													=       word.index(_snake_case     ,      _snake_case)
										except ValueError:
											new_word.extend(word[i:])
											break
										else:
											new_word.extend(word[i:j])
											UpperCAmelCase_		:   Union[str, Any]													=       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_		:   Any													=       tuple(_snake_case)
									UpperCAmelCase_		:   Dict													=       new_word
									if len(_snake_case) == 1:
										break
									else:
										UpperCAmelCase_		:   Any													=       get_pairs(_snake_case)
								UpperCAmelCase_		:   int													=       ' '.join(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       word
								return word


							def     _snake_case				(      self     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Union[str, Any]													=       []
								for token in re.findall(self.pat     ,      _snake_case):
									UpperCAmelCase_		:   Tuple													=       ''.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     ,      _snake_case)				->					List[str]:
								return self.encoder.get(_snake_case     ,      self.encoder.get(self.unk_token))


							def     _snake_case				(      self     ,      _snake_case)				->					List[str]:
								return self.decoder.get(_snake_case)


							def     _snake_case				(      self     ,      _snake_case)				->					Dict:
								UpperCAmelCase_		:   Dict													=       ''.join(_snake_case)
								UpperCAmelCase_		:   str													=       bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'     ,      errors=self.errors)
								return text


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return
								UpperCAmelCase_		:   Optional[int]													=       os.path.join(
								    _snake_case     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:   Optional[int]													=       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_		:   Union[str, Any]													=       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_		:   Dict													=       token_index
										writer.write(' '.join(_snake_case) + '\n')
										index += 1

								return vocab_file, merge_file


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					List[int]:
								if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase_		:   str													=       [self.cls_token_id]
								UpperCAmelCase_		:   Any													=       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = False)				->					List[int]:
								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     ,      _snake_case     ,      _snake_case = None)				->					List[int]:
								UpperCAmelCase_		:   Tuple													=       [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 + sep + token_ids_a + sep) * [0]





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case=False     ,      **_snake_case)				->					Tuple:
								UpperCAmelCase_		:   List[Any]													=       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_		:   Dict													=       ' ' + 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__      				=			logging.get_logger(__name__)  # pylint: disable=invalid-name



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        lowercase    (	a_			):
							_lowerCamelCase		:   torch.FloatTensor
							_lowerCamelCase		:   Optional[torch.FloatTensor]= None







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=0.999 ,UpperCamelCase="cosine" ,)     ->     List[str]:
	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_		:   Optional[int]													=       []
	for i in range(UpperCamelCase	):
		UpperCAmelCase_		:   Optional[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    (	a_,       a_			):
							_lowerCamelCase		:   List[str]= [e.name for e in KarrasDiffusionSchedulers]
							_lowerCamelCase		:   Optional[int]= 1


							@register_to_config
							def __init__(      self     ,      _snake_case = 1000     ,      _snake_case = 0.0_001     ,      _snake_case = 0.02     ,      _snake_case = "linear"     ,      _snake_case = None     ,      _snake_case = "epsilon"     ,      _snake_case = "linspace"     ,      _snake_case = 0     ,      )				->					List[str]:
								if trained_betas is not None:
									UpperCAmelCase_		:   List[str]													=       torch.tensor(_snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "linear":
									UpperCAmelCase_		:   List[Any]													=       torch.linspace(_snake_case     ,      _snake_case     ,      _snake_case     ,      dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									UpperCAmelCase_		:   List[Any]													=       (
									    torch.linspace(beta_start**0.5     ,      beta_end**0.5     ,      _snake_case     ,      dtype=torch.floataa) ** 2
									)
								elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									UpperCAmelCase_		:   int													=       betas_for_alpha_bar(_snake_case)
								else:
									raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

								UpperCAmelCase_		:   List[Any]													=       1.0 - self.betas
								UpperCAmelCase_		:   Union[str, Any]													=       torch.cumprod(self.alphas     ,      dim=0)

								UpperCAmelCase_		:   Union[str, Any]													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   int													=       np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case)

								# setable values
								UpperCAmelCase_		:   List[str]													=       None
								UpperCAmelCase_		:   Union[str, Any]													=       np.linspace(0     ,      num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[::-1].copy()
								UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       False


							@property
							def     _snake_case				(      self)				->					Optional[Any]:
								# 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     _snake_case				(      self     ,      _snake_case     ,      _snake_case)				->					torch.FloatTensor:
								if isinstance(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   List[Any]													=       timestep.to(self.timesteps.device)
								UpperCAmelCase_		:   int													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       sample / ((sigma**2 + 1) ** 0.5)
								UpperCAmelCase_		:   Optional[Any]													=       True
								return sample


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       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_		:   List[Any]													=       np.linspace(0     ,      self.config.num_train_timesteps - 1     ,      _snake_case     ,      dtype=_snake_case)[
									    ::-1
									].copy()
								elif self.config.timestep_spacing == "leading":
									UpperCAmelCase_		:   Any													=       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_		:   List[Any]													=       (np.arange(0     ,      _snake_case) * step_ratio).round()[::-1].copy().astype(_snake_case)
									timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
									UpperCAmelCase_		:   List[Any]													=       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_		:   Any													=       (np.arange(self.config.num_train_timesteps     ,      0     ,      -step_ratio)).round().copy().astype(_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'.""")

								UpperCAmelCase_		:   Any													=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								UpperCAmelCase_		:   Dict													=       np.interp(_snake_case     ,      np.arange(0     ,      len(_snake_case))     ,      _snake_case)
								UpperCAmelCase_		:   Optional[int]													=       np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								UpperCAmelCase_		:   int													=       torch.from_numpy(_snake_case).to(device=_snake_case)
								if str(_snake_case).startswith('mps'):
									# mps does not support float64
									UpperCAmelCase_		:   Optional[int]													=       torch.from_numpy(_snake_case).to(_snake_case     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       torch.from_numpy(_snake_case).to(device=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = True     ,      )				->					Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

								if (
								    isinstance(_snake_case     ,      _snake_case)
								    or isinstance(_snake_case     ,      torch.IntTensor)
								    or isinstance(_snake_case     ,      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(_snake_case     ,      torch.Tensor):
									UpperCAmelCase_		:   Optional[int]													=       timestep.to(self.timesteps.device)

								UpperCAmelCase_		:   List[Any]													=       (self.timesteps == timestep).nonzero().item()
								UpperCAmelCase_		:   int													=       self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
									UpperCAmelCase_		:   Tuple													=       sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									UpperCAmelCase_		:   str													=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`""")

								UpperCAmelCase_		:   Dict													=       self.sigmas[step_index]
								UpperCAmelCase_		:   Union[str, Any]													=       self.sigmas[step_index + 1]
								UpperCAmelCase_		:   str													=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
								UpperCAmelCase_		:   Any													=       (sigma_to**2 - sigma_up**2) ** 0.5

								# 2. Convert to an ODE derivative
								UpperCAmelCase_		:   Dict													=       (sample - pred_original_sample) / sigma

								UpperCAmelCase_		:   Tuple													=       sigma_down - sigma

								UpperCAmelCase_		:   List[str]													=       sample + derivative * dt

								UpperCAmelCase_		:   Any													=       model_output.device
								UpperCAmelCase_		:   List[Any]													=       randn_tensor(model_output.shape     ,      dtype=model_output.dtype     ,      device=_snake_case     ,      generator=_snake_case)

								UpperCAmelCase_		:   Union[str, Any]													=       prev_sample + noise * sigma_up

								if not return_dict:
									return (prev_sample,)

								return EulerAncestralDiscreteSchedulerOutput(
								    prev_sample=_snake_case     ,      pred_original_sample=_snake_case)


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      )				->					torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								UpperCAmelCase_		:   List[str]													=       self.sigmas.to(device=original_samples.device     ,      dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
									# mps does not support float64
									UpperCAmelCase_		:   str													=       self.timesteps.to(original_samples.device     ,      dtype=torch.floataa)
									UpperCAmelCase_		:   List[Any]													=       timesteps.to(original_samples.device     ,      dtype=torch.floataa)
								else:
									UpperCAmelCase_		:   List[Any]													=       self.timesteps.to(original_samples.device)
									UpperCAmelCase_		:   str													=       timesteps.to(original_samples.device)

								UpperCAmelCase_		:   Optional[Any]													=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								UpperCAmelCase_		:   Any													=       sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
									UpperCAmelCase_		:   Union[str, Any]													=       sigma.unsqueeze(-1)

								UpperCAmelCase_		:   int													=       original_samples + noise * sigma
								return noisy_samples





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






