



"""simple docstring"""







import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCAmelCase:  str			     =				logging.get_logger(__name__)





@dataclass
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str
	SCREAMING_SNAKE_CASE_							:       str
	SCREAMING_SNAKE_CASE_							:       Optional[str]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[str]						       =  None






	def    lowerCamelCase__					(     self   ):
			return json.dumps(dataclasses.asdict(self   )				,indent=2   ) + "\n"







@dataclass(frozen=snake_case       )
class   UpperCamelCase	:




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[int]
	SCREAMING_SNAKE_CASE_							:       Optional[List[int]]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[List[int]]						       =  None
	SCREAMING_SNAKE_CASE_							:       Optional[Union[int, float]]						       =  None






	def    lowerCamelCase__					(     self   ):
			return json.dumps(dataclasses.asdict(self   )   ) + "\n"







class   UpperCamelCase	:




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self   ):
			raise NotImplementedError()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if len(self.get_labels()   ) > 1:
					_lowercase				: Optional[int]     =   self.get_labels()[int(example.label   )]
			return example






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=None   ):
			with open(UpperCAmelCase_				,"""r"""				,encoding="""utf-8-sig"""   ) as f:
					return list(csv.reader(UpperCAmelCase_				,delimiter="""\t"""				,quotechar=UpperCAmelCase_   )   )







class   UpperCamelCase	(     snake_case       ):




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_="classification"				,UpperCAmelCase_=False   ):
			_lowercase				: int     =   [] if labels is None else labels
			_lowercase				: int     =   [] if examples is None else examples
			_lowercase				: Union[str, Any]     =   mode
			_lowercase				: Optional[Any]     =   verbose






	def __len__(     self   ):
			return len(self.examples   )






	def __getitem__(     self				,UpperCAmelCase_   ):
			if isinstance(UpperCAmelCase_				,UpperCAmelCase_   ):
					return SingleSentenceClassificationProcessor(labels=self.labels				,examples=self.examples[idx]   )
			return self.examples[idx]






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=""				,UpperCAmelCase_=0				,UpperCAmelCase_=1				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,**UpperCAmelCase_   ):
			_lowercase				: int     =   cls(**UpperCAmelCase_   )
			processor.add_examples_from_csv(
			    UpperCAmelCase_				,split_name=UpperCAmelCase_				,column_label=UpperCAmelCase_				,column_text=UpperCAmelCase_				,column_id=UpperCAmelCase_				,skip_first_row=UpperCAmelCase_				,overwrite_labels=UpperCAmelCase_				,overwrite_examples=UpperCAmelCase_				,)
			return processor






	@classmethod
	def    lowerCamelCase__					(     cls				,UpperCAmelCase_				,UpperCAmelCase_=None				,**UpperCAmelCase_   ):
			_lowercase				: int     =   cls(**UpperCAmelCase_   )
			processor.add_examples(UpperCAmelCase_				,labels=UpperCAmelCase_   )
			return processor






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=""				,UpperCAmelCase_=0				,UpperCAmelCase_=1				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,UpperCAmelCase_=False				,):
			_lowercase				: Dict     =   self._read_tsv(UpperCAmelCase_   )
			if skip_first_row:
					_lowercase				: Union[str, Any]     =   lines[1:]
			_lowercase				: int     =   []
			_lowercase				: Optional[int]     =   []
			_lowercase				: int     =   []
			for i, line in enumerate(UpperCAmelCase_   ):
					texts.append(line[column_text]   )
					labels.append(line[column_label]   )
					if column_id is not None:
							ids.append(line[column_id]   )
					else:
							_lowercase				: Tuple     =   f"""{split_name}-{i}""" if split_name else str(UpperCAmelCase_   )
							ids.append(UpperCAmelCase_   )

			return self.add_examples(
			    UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,overwrite_labels=UpperCAmelCase_				,overwrite_examples=UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=False   ):
			if labels is not None and len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
					raise ValueError(
					    f"""Text and labels have mismatched lengths {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )
			if ids is not None and len(UpperCAmelCase_   ) != len(UpperCAmelCase_   ):
					raise ValueError(f"""Text and ids have mismatched lengths {len(UpperCAmelCase_   )} and {len(UpperCAmelCase_   )}"""   )
			if ids is None:
					_lowercase				: Any     =   [None] * len(UpperCAmelCase_   )
			if labels is None:
					_lowercase				: List[str]     =   [None] * len(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   []
			_lowercase				: int     =   set()
			for text_or_text_and_label, label, guid in zip(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   ):
					if isinstance(UpperCAmelCase_				,(tuple, list)   ) and label is None:
							_lowercase     ,  _lowercase				: Dict     =   text_or_text_and_label
					else:
							_lowercase				: Optional[int]     =   text_or_text_and_label
					added_labels.add(UpperCAmelCase_   )
					examples.append(InputExample(guid=UpperCAmelCase_				,text_a=UpperCAmelCase_				,text_b=UpperCAmelCase_				,label=UpperCAmelCase_   )   )

			# Update examples
			if overwrite_examples:
					_lowercase				: Any     =   examples
			else:
					self.examples.extend(UpperCAmelCase_   )

			# Update labels
			if overwrite_labels:
					_lowercase				: List[str]     =   list(UpperCAmelCase_   )
			else:
					_lowercase				: Optional[int]     =   list(set(self.labels   ).union(UpperCAmelCase_   )   )

			return self.examples






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,UpperCAmelCase_=0				,UpperCAmelCase_=True				,UpperCAmelCase_=None				,):
			if max_length is None:
					_lowercase				: Any     =   tokenizer.max_len

			_lowercase				: List[Any]     =   {label: i for i, label in enumerate(self.labels   )}

			_lowercase				: Union[str, Any]     =   []
			for ex_index, example in enumerate(self.examples   ):
					if ex_index % 1_00_00 == 0:
							logger.info(f"""Tokenizing example {ex_index}"""   )

					_lowercase				: Optional[int]     =   tokenizer.encode(
					    example.text_a				,add_special_tokens=UpperCAmelCase_				,max_length=min(UpperCAmelCase_				,tokenizer.max_len   )				,)
					all_input_ids.append(UpperCAmelCase_   )

			_lowercase				: str     =   max(len(UpperCAmelCase_   ) for input_ids in all_input_ids   )

			_lowercase				: Optional[Any]     =   []
			for ex_index, (input_ids, example) in enumerate(zip(UpperCAmelCase_				,self.examples   )   ):
					if ex_index % 1_00_00 == 0:
							logger.info(f"""Writing example {ex_index}/{len(self.examples   )}"""   )
					# The mask has 1 for real tokens and 0 for padding tokens. Only real
					# tokens are attended to.
					_lowercase				: Any     =   [1 if mask_padding_with_zero else 0] * len(UpperCAmelCase_   )

					# Zero-pad up to the sequence length.
					_lowercase				: Dict     =   batch_length - len(UpperCAmelCase_   )
					if pad_on_left:
							_lowercase				: Dict     =   ([pad_token] * padding_length) + input_ids
							_lowercase				: Any     =   ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
					else:
							_lowercase				: Optional[Any]     =   input_ids + ([pad_token] * padding_length)
							_lowercase				: int     =   attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

					if len(UpperCAmelCase_   ) != batch_length:
							raise ValueError(f"""Error with input length {len(UpperCAmelCase_   )} vs {batch_length}"""   )
					if len(UpperCAmelCase_   ) != batch_length:
							raise ValueError(f"""Error with input length {len(UpperCAmelCase_   )} vs {batch_length}"""   )

					if self.mode == "classification":
							_lowercase				: Dict     =   label_map[example.label]
					elif self.mode == "regression":
							_lowercase				: Optional[int]     =   float(example.label   )
					else:
							raise ValueError(self.mode   )

					if ex_index < 5 and self.verbose:
							logger.info("""*** Example ***"""   )
							logger.info(f"""guid: {example.guid}"""   )
							logger.info(f"""input_ids: {" ".join([str(UpperCAmelCase_   ) for x in input_ids]   )}"""   )
							logger.info(f"""attention_mask: {" ".join([str(UpperCAmelCase_   ) for x in attention_mask]   )}"""   )
							logger.info(f"""label: {example.label} (id = {label})"""   )

					features.append(InputFeatures(input_ids=UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,label=UpperCAmelCase_   )   )

			if return_tensors is None:
					return features
			elif return_tensors == "tf":
					if not is_tf_available():
							raise RuntimeError("""return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"""   )
					import tensorflow as tf

					def gen():
							for ex in features:
									yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

					_lowercase				: Tuple     =   tf.data.Dataset.from_generator(
					    UpperCAmelCase_				,({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)				,({"""input_ids""": tf.TensorShape([None]   ), """attention_mask""": tf.TensorShape([None]   )}, tf.TensorShape([]   ))				,)
					return dataset
			elif return_tensors == "pt":
					if not is_torch_available():
							raise RuntimeError("""return_tensors set to 'pt' but PyTorch can't be imported"""   )
					import torch
					from torch.utils.data import TensorDataset

					_lowercase				: int     =   torch.tensor([f.input_ids for f in features]				,dtype=torch.long   )
					_lowercase				: str     =   torch.tensor([f.attention_mask for f in features]				,dtype=torch.long   )
					if self.mode == "classification":
							_lowercase				: Tuple     =   torch.tensor([f.label for f in features]				,dtype=torch.long   )
					elif self.mode == "regression":
							_lowercase				: Any     =   torch.tensor([f.label for f in features]				,dtype=torch.float   )

					_lowercase				: Any     =   TensorDataset(UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_   )
					return dataset
			else:
					raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""   )






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


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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			torch.FloatTensor
  SCREAMING_SNAKE_CASE__					:			Optional[torch.FloatTensor]					    =   None





def 				__a							(       __A			,							__A=0.999			,							__A="cosine"			,							):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    SCREAMING_SNAKE_CASE__         =    []
    for i in range(__A						):
        SCREAMING_SNAKE_CASE__         =    i / num_diffusion_timesteps
        SCREAMING_SNAKE_CASE__         =    (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  UpperCamelCase_      (	_snake_case		,  _snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   [e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   1



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

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

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

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



  @property
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Dict:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

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



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]						)       ->			torch.FloatTensor:
      if isinstance(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)
      SCREAMING_SNAKE_CASE__         =    (self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    sample / ((sigma**2 + 1) ** 0.5)
      SCREAMING_SNAKE_CASE__         =    True
      return sample



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					int  ,	A_     :					Union[str, torch.device] = None						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    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":
          SCREAMING_SNAKE_CASE__         =    np.linspace(0  ,	self.config.num_train_timesteps - 1  ,	A_  ,	dtype=A_						)[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          SCREAMING_SNAKE_CASE__         =    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
          SCREAMING_SNAKE_CASE__         =    (np.arange(0  ,	A_						) * step_ratio).round()[::-1].copy().astype(A_						)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          SCREAMING_SNAKE_CASE__         =    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
          SCREAMING_SNAKE_CASE__         =    (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'."						)

      SCREAMING_SNAKE_CASE__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      SCREAMING_SNAKE_CASE__         =    np.interp(A_  ,	np.arange(0  ,	len(A_						)						)  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)
      if str(A_						).startswith("mps"						):
          # mps does not support float64
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(A_  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]  ,	A_     :					torch.FloatTensor  ,	A_     :					Optional[torch.Generator] = None  ,	A_     :					bool = True  ,	)       ->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

      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						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)

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

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

      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index + 1]
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 - sigma_up**2) ** 0.5

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

      SCREAMING_SNAKE_CASE__         =    sigma_down - sigma

      SCREAMING_SNAKE_CASE__         =    sample + derivative * dt

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

      SCREAMING_SNAKE_CASE__         =    prev_sample + noise * sigma_up

      if not return_dict:
          return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=A_  ,	pred_original_sample=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	)       ->			torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      SCREAMING_SNAKE_CASE__         =    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
          SCREAMING_SNAKE_CASE__         =    self.timesteps.to(original_samples.device  ,	dtype=torch.floataa						)
          SCREAMING_SNAKE_CASE__         =    timesteps.to(original_samples.device  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    self.timesteps.to(original_samples.device						)
          SCREAMING_SNAKE_CASE__         =    timesteps.to(original_samples.device						)

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

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

      SCREAMING_SNAKE_CASE__         =    original_samples + noise * sigma
      return noisy_samples







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


