code: 
  
  
  
  
  
  
  """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
  
  
  lowercase__      =				logging.get_logger(__name__)
  
  
  @dataclass
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       int      =							42
  		a_			:       Optional[Any]      =							42
  		a_			:       int      =							None
  		a_			:       List[str]      =							None
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				return json.dumps(dataclasses.asdict(self			)		,    indent=2			) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=UpperCAmelCase_    )
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Union[str, Any]      =							42
  		a_			:       List[str]      =							None
  		a_			:       Optional[Any]      =							None
  		a_			:       Union[str, Any]      =							None
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				return json.dumps(dataclasses.asdict(self			)			) + "\n"
  
  
  
  
  
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			int			):
  				raise NotImplementedError()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			List[str]			):
  				raise NotImplementedError()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple		,    a_							:			List[Any]			):
  				raise NotImplementedError()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]		,    a_							:			Union[str, Any]			):
  				raise NotImplementedError()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				raise NotImplementedError()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str		,    a_							:			Tuple			):
  				if len(self.get_labels()			) > 1:
  						lowerCAmelCase_     :							Optional[Any]             =    self.get_labels()[int(example.label			)]
  				return example
  
  
  
  
  
  		@classmethod
  		def      lowerCamelCase (     cls							:			Union[str, Any]		,    a_							:			List[str]		,    a_							:			List[Any]=None			):
  				with open(a_		,    "r"		,    encoding="utf-8-sig"			) as f:
  						return list(csv.reader(a_		,    delimiter="\t"		,    quotechar=a_			)			)
  
  
  
  
  
  
  
  class 	__lowerCamelCase       (				UpperCAmelCase_    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Union[str, Any]		,    a_							:			Union[str, Any]=None		,    a_							:			Dict=None		,    a_							:			List[Any]="classification"		,    a_							:			Optional[Any]=False			):
  				lowerCAmelCase_     :							List[Any]             =    [] if labels is None else labels
  				lowerCAmelCase_     :							Dict             =    [] if examples is None else examples
  				lowerCAmelCase_     :							Optional[Any]             =    mode
  				lowerCAmelCase_     :							str             =    verbose
  
  
  
  
  
  		def __len__(     self							:			Optional[Any]			):
  				return len(self.examples			)
  
  
  
  
  
  		def __getitem__(     self							:			Union[str, Any]		,    a_							:			Optional[Any]			):
  				if isinstance(a_		,    a_			):
  						return SingleSentenceClassificationProcessor(labels=self.labels		,    examples=self.examples[idx]			)
  				return self.examples[idx]
  
  
  
  
  
  		@classmethod
  		def      lowerCamelCase (     cls							:			Any		,    a_							:			Any		,    a_							:			Tuple=""		,    a_							:			List[str]=0		,    a_							:			Optional[int]=1		,    a_							:			Dict=None		,    a_							:			Dict=False		,    **a_							:			str			):
  				lowerCAmelCase_     :							Optional[Any]             =    cls(**a_			)
  				processor.add_examples_from_csv(
  				    a_		,    split_name=a_		,    column_label=a_		,    column_text=a_		,    column_id=a_		,    skip_first_row=a_		,    overwrite_labels=a_		,    overwrite_examples=a_		,    )
  				return processor
  
  
  
  
  
  		@classmethod
  		def      lowerCamelCase (     cls							:			Tuple		,    a_							:			Tuple		,    a_							:			Dict=None		,    **a_							:			int			):
  				lowerCAmelCase_     :							Optional[Any]             =    cls(**a_			)
  				processor.add_examples(a_		,    labels=a_			)
  				return processor
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[str]		,    a_							:			Union[str, Any]		,    a_							:			Optional[Any]=""		,    a_							:			str=0		,    a_							:			Union[str, Any]=1		,    a_							:			Optional[int]=None		,    a_							:			Union[str, Any]=False		,    a_							:			Union[str, Any]=False		,    a_							:			Union[str, Any]=False		,    ):
  				lowerCAmelCase_     :							Optional[Any]             =    self._read_tsv(a_			)
  				if skip_first_row:
  						lowerCAmelCase_     :							List[str]             =    lines[1:]
  				lowerCAmelCase_     :							List[str]             =    []
  				lowerCAmelCase_     :							Optional[Any]             =    []
  				lowerCAmelCase_     :							Optional[int]             =    []
  				for i, line in enumerate(a_			):
  						texts.append(line[column_text]			)
  						labels.append(line[column_label]			)
  						if column_id is not None:
  								ids.append(line[column_id]			)
  						else:
  								lowerCAmelCase_     :							Optional[int]             =    f'''{split_name}-{i}''' if split_name else str(a_			)
  								ids.append(a_			)
  
  				return self.add_examples(
  				    a_		,    a_		,    a_		,    overwrite_labels=a_		,    overwrite_examples=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			int		,    a_							:			Optional[int]=None		,    a_							:			Any=None		,    a_							:			str=False		,    a_							:			Any=False			):
  				if labels is not None and len(a_			) != len(a_			):
  						raise ValueError(
  						    f'''Text and labels have mismatched lengths {len(a_			)} and {len(a_			)}'''			)
  				if ids is not None and len(a_			) != len(a_			):
  						raise ValueError(f'''Text and ids have mismatched lengths {len(a_			)} and {len(a_			)}'''			)
  				if ids is None:
  						lowerCAmelCase_     :							int             =    [None] * len(a_			)
  				if labels is None:
  						lowerCAmelCase_     :							List[str]             =    [None] * len(a_			)
  				lowerCAmelCase_     :							Tuple             =    []
  				lowerCAmelCase_     :							Optional[Any]             =    set()
  				for text_or_text_and_label, label, guid in zip(a_		,    a_		,    a_			):
  						if isinstance(a_		,    (tuple, list)			) and label is None:
  								lowerCAmelCase_       ,			lowerCAmelCase_     :							Any             =    text_or_text_and_label
  						else:
  								lowerCAmelCase_     :							Union[str, Any]             =    text_or_text_and_label
  						added_labels.add(a_			)
  						examples.append(InputExample(guid=a_		,    text_a=a_		,    text_b=a_		,    label=a_			)			)
  
  				# Update examples
  				if overwrite_examples:
  						lowerCAmelCase_     :							Union[str, Any]             =    examples
  				else:
  						self.examples.extend(a_			)
  
  				# Update labels
  				if overwrite_labels:
  						lowerCAmelCase_     :							List[Any]             =    list(a_			)
  				else:
  						lowerCAmelCase_     :							List[str]             =    list(set(self.labels			).union(a_			)			)
  
  				return self.examples
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]		,    a_							:			Union[str, Any]		,    a_							:			str=None		,    a_							:			Union[str, Any]=False		,    a_							:			List[str]=0		,    a_							:			Optional[Any]=True		,    a_							:			Tuple=None		,    ):
  				if max_length is None:
  						lowerCAmelCase_     :							List[str]             =    tokenizer.max_len
  
  				lowerCAmelCase_     :							List[Any]             =    {label: i for i, label in enumerate(self.labels			)}
  
  				lowerCAmelCase_     :							List[Any]             =    []
  				for ex_index, example in enumerate(self.examples			):
  						if ex_index % 1_00_00 == 0:
  								logger.info(f'''Tokenizing example {ex_index}'''			)
  
  						lowerCAmelCase_     :							List[Any]             =    tokenizer.encode(
  						    example.text_a		,    add_special_tokens=a_		,    max_length=min(a_		,    tokenizer.max_len			)		,    )
  						all_input_ids.append(a_			)
  
  				lowerCAmelCase_     :							Dict             =    max(len(a_			) for input_ids in all_input_ids			)
  
  				lowerCAmelCase_     :							Optional[Any]             =    []
  				for ex_index, (input_ids, example) in enumerate(zip(a_		,    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.
  						lowerCAmelCase_     :							Any             =    [1 if mask_padding_with_zero else 0] * len(a_			)
  
  						# Zero-pad up to the sequence length.
  						lowerCAmelCase_     :							int             =    batch_length - len(a_			)
  						if pad_on_left:
  								lowerCAmelCase_     :							List[Any]             =    ([pad_token] * padding_length) + input_ids
  								lowerCAmelCase_     :							Dict             =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
  						else:
  								lowerCAmelCase_     :							Any             =    input_ids + ([pad_token] * padding_length)
  								lowerCAmelCase_     :							Optional[int]             =    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
  						if len(a_			) != batch_length:
  								raise ValueError(f'''Error with input length {len(a_			)} vs {batch_length}'''			)
  						if len(a_			) != batch_length:
  								raise ValueError(f'''Error with input length {len(a_			)} vs {batch_length}'''			)
  
  						if self.mode == "classification":
  								lowerCAmelCase_     :							List[str]             =    label_map[example.label]
  						elif self.mode == "regression":
  								lowerCAmelCase_     :							Any             =    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(a_			) for x in input_ids]			)}'''			)
  								logger.info(f'''attention_mask: {" ".join([str(a_			) for x in attention_mask]			)}'''			)
  								logger.info(f'''label: {example.label} (id = {label})'''			)
  
  						features.append(InputFeatures(input_ids=a_		,    attention_mask=a_		,    label=a_			)			)
  
  				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)
  
  						lowerCAmelCase_     :							Tuple             =    tf.data.Dataset.from_generator(
  						    a_		,    ({"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
  
  						lowerCAmelCase_     :							List[str]             =    torch.tensor([f.input_ids for f in features]		,    dtype=torch.long			)
  						lowerCAmelCase_     :							Optional[Any]             =    torch.tensor([f.attention_mask for f in features]		,    dtype=torch.long			)
  						if self.mode == "classification":
  								lowerCAmelCase_     :							int             =    torch.tensor([f.label for f in features]		,    dtype=torch.long			)
  						elif self.mode == "regression":
  								lowerCAmelCase_     :							Union[str, Any]             =    torch.tensor([f.label for f in features]		,    dtype=torch.float			)
  
  						lowerCAmelCase_     :							Optional[Any]             =    TensorDataset(a_		,    a_		,    a_			)
  						return dataset
  				else:
  						raise ValueError("return_tensors should be one of 'tf' or 'pt'"			)
  
  
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  import math
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  @dataclass
  class 	__lowerCamelCase       (				A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       torch.FloatTensor
  		a_			:       torch.FloatTensor
  
  
  
  
  
  
  
  def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:
  
  		"""simple docstring"""
  
  
  
  
  
  
  
  		if alpha_transform_type == "cosine":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
  
  		elif alpha_transform_type == "exp":
  
  				def alpha_bar_fn(__UpperCamelCase ):
  						return math.exp(t * -12.0 )
  
  		else:
  				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
  
  		lowerCAmelCase_     :							List[str]             =    []
  		for i in range(__UpperCamelCase ):
  				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
  				lowerCAmelCase_     :							Optional[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 	__lowerCamelCase       (				A__				,							A__    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Tuple      =							1
  
  
  
  
  
  		@register_to_config
  		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
  				if trained_betas is not None:
  						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
  				elif beta_schedule == "linear":
  						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
  				elif beta_schedule == "scaled_linear":
  						# this schedule is very specific to the latent diffusion model.
  						lowerCAmelCase_     :							Optional[Any]             =    (
  						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
  						)
  				elif beta_schedule == "squaredcos_cap_v2":
  						# Glide cosine schedule
  						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
  				elif beta_schedule == "sigmoid":
  						# GeoDiff sigmoid schedule
  						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
  						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
  				else:
  						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
  				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)
  
  				# standard deviation of the initial noise distribution
  				lowerCAmelCase_     :							List[str]             =    1.0
  
  				# setable values
  				lowerCAmelCase_     :							Dict             =    None
  				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)
  
  				lowerCAmelCase_     :							int             =    eta
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
  				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
  				lowerCAmelCase_     :							Dict             =    num_inference_steps
  
  				lowerCAmelCase_     :							List[str]             =    []
  
  				lowerCAmelCase_     :							Dict             =    {}
  				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
  						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1
  
  				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
  				while t >= 1:
  						lowerCAmelCase_     :							int             =    t - 1
  						timesteps.append(a_			)
  
  						if jumps.get(a_		,    0			) > 0:
  								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
  								for _ in range(a_			):
  										lowerCAmelCase_     :							Any             =    t + 1
  										timesteps.append(a_			)
  
  				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
  				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
  				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps
  
  				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
  				lowerCAmelCase_     :							str             =    1 - alpha_prod_t_prev
  
  				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
  				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
  				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
  				# variance to pred_sample
  				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
  				# without eta.
  				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
  				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  				return variance
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
  				lowerCAmelCase_     :							Any             =    timestep
  				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps
  
  				# 1. compute alphas, betas
  				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
  				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
  				lowerCAmelCase_     :							Optional[Any]             =    1 - alpha_prod_t
  
  				# 2. compute predicted original sample from predicted noise also called
  				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
  
  				# 3. Clip "predicted x_0"
  				if self.config.clip_sample:
  						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -1		,    1			)
  
  				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
  				# substitute formula (7) in the algorithm coming from DDPM paper
  				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
  				# DDIM schedule gives the same results as DDPM with eta = 1.0
  				# Noise is being reused in 7. and 8., but no impact on quality has
  				# been observed.
  
  				# 5. Add noise
  				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
  				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
  				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5
  
  				lowerCAmelCase_     :							Optional[int]             =    0
  				if t > 0 and self.eta > 0:
  						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise
  
  				# 6. compute "direction pointing to x_t" of formula (12)
  				# from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
  
  				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  				lowerCAmelCase_     :							Dict             =    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance
  
  				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Union[str, Any]             =    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise
  
  				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
  				lowerCAmelCase_     :							Tuple             =    mask * prev_known_part + (1.0 - mask) * prev_unknown_part
  
  				if not return_dict:
  						return (
  						    pred_prev_sample,
  						    pred_original_sample,
  						)
  
  				return RePaintSchedulerOutput(prev_sample=a_		,    pred_original_sample=a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
  				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps
  
  				for i in range(a_			):
  						lowerCAmelCase_     :							Optional[Any]             =    self.betas[timestep + i]
  						if sample.device.type == "mps":
  								# randn does not work reproducibly on mps
  								lowerCAmelCase_     :							str             =    randn_tensor(sample.shape		,    dtype=sample.dtype		,    generator=a_			)
  								lowerCAmelCase_     :							List[str]             =    noise.to(sample.device			)
  						else:
  								lowerCAmelCase_     :							Tuple             =    randn_tensor(sample.shape		,    generator=a_		,    device=sample.device		,    dtype=sample.dtype			)
  
  						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
  						lowerCAmelCase_     :							int             =    (1 - beta) ** 0.5 * sample + beta**0.5 * noise
  
  				return sample
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
  				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)
  
  
  
  
  
  		def __len__(     self							:			Union[str, Any]			):
  				return self.config.num_train_timesteps
  