



"""simple docstring"""







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

import sentencepiece as spm

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

UpperCAmelCase:  str			     =				logging.get_logger(__name__)

UpperCAmelCase:  Tuple			     =				{"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase:  int			     =				{
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
UpperCAmelCase:  Union[str, Any]			     =				{
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCAmelCase:  List[Any]			     =				"""▁"""

UpperCAmelCase     , UpperCAmelCase:  int			     =				"""[INST]""", """[/INST]"""
UpperCAmelCase     , UpperCAmelCase:  Union[str, Any]			     =				"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase:  Optional[int]			     =				"""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""





class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_							:       int						       =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_							:       Dict						       =  ["input_ids", "attention_mask"]






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_="<unk>"				,UpperCAmelCase_="<s>"				,UpperCAmelCase_="</s>"				,UpperCAmelCase_=None				,UpperCAmelCase_ = None				,UpperCAmelCase_=True				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=True				,**UpperCAmelCase_				,):
			_lowercase				: List[str]     =   {} if sp_model_kwargs is None else sp_model_kwargs
			_lowercase				: List[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else bos_token
			_lowercase				: Union[str, Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else eos_token
			_lowercase				: Optional[int]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else unk_token
			_lowercase				: Optional[Any]     =   AddedToken(UpperCAmelCase_				,lstrip=UpperCAmelCase_				,rstrip=UpperCAmelCase_   ) if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ) else pad_token
			super().__init__(
			    bos_token=UpperCAmelCase_				,eos_token=UpperCAmelCase_				,unk_token=UpperCAmelCase_				,pad_token=UpperCAmelCase_				,add_bos_token=UpperCAmelCase_				,add_eos_token=UpperCAmelCase_				,sp_model_kwargs=self.sp_model_kwargs				,clean_up_tokenization_spaces=UpperCAmelCase_				,legacy=UpperCAmelCase_				,**UpperCAmelCase_				,)
			if legacy:
					logger.warning_once(
					    f"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
					    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""   )
			_lowercase				: Tuple     =   legacy
			_lowercase				: Optional[int]     =   vocab_file
			_lowercase				: Any     =   add_bos_token
			_lowercase				: Union[str, Any]     =   add_eos_token
			_lowercase				: Union[str, Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.Load(UpperCAmelCase_   )






	def __getstate__(     self   ):
			_lowercase				: Union[str, Any]     =   self.__dict__.copy()
			_lowercase				: Optional[int]     =   None
			_lowercase				: Optional[Any]     =   self.sp_model.serialized_model_proto()
			return state






	def __setstate__(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   d
			_lowercase				: List[Any]     =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.sp_model.get_piece_size()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   {self.convert_ids_to_tokens(UpperCAmelCase_   ): i for i in range(self.vocab_size   )}
			vocab.update(self.added_tokens_encoder   )
			return vocab






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,**UpperCAmelCase_   ):
			# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
			# the beginning of the text
			if not self.legacy:
					_lowercase				: str     =   SPIECE_UNDERLINE + text.replace(UpperCAmelCase_				,""" """   )
			return super().tokenize(UpperCAmelCase_				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if not self.legacy:
					_lowercase				: Any     =   text.startswith(UpperCAmelCase_   )
					if is_first:
							_lowercase				: str     =   text[1:]

			_lowercase				: Dict     =   self.sp_model.encode(UpperCAmelCase_				,out_type=UpperCAmelCase_   )

			if not self.legacy and not is_first and not text.startswith(""" """   ) and tokens[0].startswith(UpperCAmelCase_   ):
					_lowercase				: List[str]     =   ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
			return tokens






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return self.sp_model.piece_to_id(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[str]     =   self.sp_model.IdToPiece(UpperCAmelCase_   )
			return token






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: List[Any]     =   []
			_lowercase				: Dict     =   """"""
			_lowercase				: List[Any]     =   False
			for i, token in enumerate(UpperCAmelCase_   ):
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
							if not prev_is_special and i != 0:
									out_string += " "
							out_string += self.sp_model.decode(UpperCAmelCase_   ) + token
							_lowercase				: Optional[Any]     =   True
							_lowercase				: str     =   []
					else:
							current_sub_tokens.append(UpperCAmelCase_   )
							_lowercase				: Optional[Any]     =   False
			out_string += self.sp_model.decode(UpperCAmelCase_   )
			return out_string






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			if not os.path.isdir(UpperCAmelCase_   ):
					logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
					return
			_lowercase				: str     =   os.path.join(
			    UpperCAmelCase_				,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

			if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCAmelCase_   ) and os.path.isfile(self.vocab_file   ):
					copyfile(self.vocab_file				,UpperCAmelCase_   )
			elif not os.path.isfile(self.vocab_file   ):
					with open(UpperCAmelCase_				,"""wb"""   ) as fi:
							_lowercase				: List[Any]     =   self.sp_model.serialized_model_proto()
							fi.write(UpperCAmelCase_   )

			return (out_vocab_file,)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			_lowercase				: Any     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: List[str]     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Union[str, Any]     =   bos_token_id + token_ids_a + eos_token_id

			if token_ids_a is not None:
					_lowercase				: int     =   output + bos_token_id + token_ids_a + eos_token_id

			return output






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None				,UpperCAmelCase_ = False   ):
			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=UpperCAmelCase_				,token_ids_a=UpperCAmelCase_				,already_has_special_tokens=UpperCAmelCase_   )

			_lowercase				: Tuple     =   [1] if self.add_bos_token else []
			_lowercase				: Dict     =   [1] if self.add_eos_token else []

			if token_ids_a is None:
					return bos_token_id + ([0] * len(UpperCAmelCase_   )) + eos_token_id
			return (
			    bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			    + bos_token_id
			    + ([0] * len(UpperCAmelCase_   ))
			    + eos_token_id
			)






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Union[str, Any]     =   [self.bos_token_id] if self.add_bos_token else []
			_lowercase				: str     =   [self.eos_token_id] if self.add_eos_token else []

			_lowercase				: Any     =   [0] * len(bos_token_id + token_ids_a + eos_token_id   )

			if token_ids_a is not None:
					output += [1] * len(bos_token_id + token_ids_a + eos_token_id   )

			return output






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Tuple     =   list(conversation.iter_texts()   )
			if not all([is_user for is_user, msg in dialogue[::2]]   ) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]]   ):
					raise ValueError(
					    """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""   )

			_lowercase				: List[int]     =   []
			if len(conversation.past_user_inputs   ) > 0:
					if not conversation.past_user_inputs[0].startswith(UpperCAmelCase_   ) or E_SYS not in conversation.past_user_inputs[0]:
							_lowercase				: Optional[int]     =   (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(UpperCAmelCase_   ) or E_SYS not in dialogue[0][1]:
					_lowercase				: List[str]     =   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            f"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """				,add_special_tokens=UpperCAmelCase_   )
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2]				,dialogue[1::2]   )
			    ]				,[]				,)
			if not (dialogue[-1][0]):
					raise ValueError(f"""Last message must be from user, got {dialogue[-1]["role"]}"""   )
			dialog_tokens += [self.bos_token_id] + self.encode(
			    f"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""				,add_special_tokens=UpperCAmelCase_   )
			return dialog_tokens






'''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






