





"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_snake_case					:		int							=			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_snake_case					:		Optional[Any]							=			{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_snake_case					:		List[str]							=			{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_snake_case					:		Optional[Any]							=			sorted(arg_to_scheduler.keys())
_snake_case					:		Optional[Any]							=			'{' + ', '.join(arg_to_scheduler_choices) + '}'






class  _UpperCAmelCase       (     pl.LightningModule			):


					def __init__( self      :Optional[Any]					,	__UpperCamelCase      :argparse.Namespace					,	__UpperCamelCase      :Union[str, Any]=None					,	__UpperCamelCase      :Dict="base"					,	__UpperCamelCase      :Dict=None					,	__UpperCamelCase      :Union[str, Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :Union[str, Any]					,	):
												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(__UpperCamelCase		)
												A		=  0
												A		=  Path(self.hparams.output_dir		)
												A		=  self.hparams.cache_dir if self.hparams.cache_dir else None
												if config is None:
																			A		=  AutoConfig.from_pretrained(
																			    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,	**({"num_labels": num_labels} if num_labels is not None else {})					,	cache_dir=__UpperCamelCase					,	**__UpperCamelCase					,	)
												else:
																			A		=  config

												A		=  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
												for p in extra_model_params:
																			if getattr(self.hparams					,	__UpperCamelCase					,	__UpperCamelCase		):
																										assert hasattr(self.config					,	__UpperCamelCase		), f"model config doesn't have a `{p}` attribute"
																										setattr(self.config					,	__UpperCamelCase					,	getattr(self.hparams					,	__UpperCamelCase		)		)

												if tokenizer is None:
																			A		=  AutoTokenizer.from_pretrained(
																			    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,	cache_dir=__UpperCamelCase					,	)
												else:
																			A		=  tokenizer
												A		=  MODEL_MODES[mode]
												if model is None:
																			A		=  self.model_type.from_pretrained(
																			    self.hparams.model_name_or_path					,	from_tf=bool(".ckpt" in self.hparams.model_name_or_path		)					,	config=self.config					,	cache_dir=__UpperCamelCase					,	)
												else:
																			A		=  model


					def      lowerCamelCase	( self      :str					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Any		):
												A		=  self.model_type.from_pretrained(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :int		):
												A		=  arg_to_scheduler[self.hparams.lr_scheduler]
												A		=  get_schedule_func(
												    self.opt					,	num_warmup_steps=self.hparams.warmup_steps					,	num_training_steps=self.total_steps()		)
												A		=  {"scheduler": scheduler, "interval": "step", "frequency": 1}
												return scheduler


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  self.model
												A		=  ["bias", "LayerNorm.weight"]
												A		=  [
												    {
												        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
												        "weight_decay": self.hparams.weight_decay,
												    },
												    {
												        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)],
												        "weight_decay": 0.0,
												    },
												]
												if self.hparams.adafactor:
																			A		=  Adafactor(
																			    __UpperCamelCase					,	lr=self.hparams.learning_rate					,	scale_parameter=__UpperCamelCase					,	relative_step=__UpperCamelCase		)

												else:
																			A		=  AdamW(
																			    __UpperCamelCase					,	lr=self.hparams.learning_rate					,	eps=self.hparams.adam_epsilon		)
												A		=  optimizer

												A		=  self.get_lr_scheduler()

												return [optimizer], [scheduler]


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int		):
												return self.validation_step(__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :List[str]		):
												return self.validation_end(__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  max(1					,	self.hparams.gpus		)  # TODO: consider num_tpu_cores
												A		=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
												return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Tuple		):
												if mode == "test":
																			A		=  len(self.test_dataloader().dataset		)
												else:
																			A		=  self.get_dataloader("train"					,	self.hparams.train_batch_size					,	shuffle=__UpperCamelCase		)
																			A		=  len(self.train_dataloader().dataset		)


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :str					,	__UpperCamelCase      :int					,	__UpperCamelCase      :bool = False		):
												raise NotImplementedError("You must implement this for your task"		)


					def      lowerCamelCase	( self      :Tuple		):
												return self.train_loader


					def      lowerCamelCase	( self      :int		):
												return self.get_dataloader("dev"					,	self.hparams.eval_batch_size					,	shuffle=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Tuple		):
												return self.get_dataloader("test"					,	self.hparams.eval_batch_size					,	shuffle=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict		):
												return os.path.join(
												    self.hparams.data_dir					,	"cached_{}_{}_{}".format(
												        __UpperCamelCase					,	list(filter(__UpperCamelCase					,	self.hparams.model_name_or_path.split("/"		)		)		).pop()					,	str(self.hparams.max_seq_length		)					,	)					,	)


					@pl.utilities.rank_zero_only
					def      lowerCamelCase	( self      :List[str]					,	__UpperCamelCase      :Dict[str, Any]		):
												A		=  self.output_dir.joinpath("best_tfmr"		)
												A		=  self.step_count
												self.model.save_pretrained(__UpperCamelCase		)
												self.tokenizer.save_pretrained(__UpperCamelCase		)


					@staticmethod
					def      lowerCamelCase	( __UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :Tuple		):
												parser.add_argument(
												    "--model_name_or_path"					,	default=__UpperCamelCase					,	type=__UpperCamelCase					,	required=__UpperCamelCase					,	help="Path to pretrained model or model identifier from huggingface.co/models"					,	)
												parser.add_argument(
												    "--config_name"					,	default=""					,	type=__UpperCamelCase					,	help="Pretrained config name or path if not the same as model_name"		)
												parser.add_argument(
												    "--tokenizer_name"					,	default=__UpperCamelCase					,	type=__UpperCamelCase					,	help="Pretrained tokenizer name or path if not the same as model_name"					,	)
												parser.add_argument(
												    "--cache_dir"					,	default=""					,	type=__UpperCamelCase					,	help="Where do you want to store the pre-trained models downloaded from huggingface.co"					,	)
												parser.add_argument(
												    "--encoder_layerdrop"					,	type=__UpperCamelCase					,	help="Encoder layer dropout probability (Optional). Goes into model.config"					,	)
												parser.add_argument(
												    "--decoder_layerdrop"					,	type=__UpperCamelCase					,	help="Decoder layer dropout probability (Optional). Goes into model.config"					,	)
												parser.add_argument(
												    "--dropout"					,	type=__UpperCamelCase					,	help="Dropout probability (Optional). Goes into model.config"					,	)
												parser.add_argument(
												    "--attention_dropout"					,	type=__UpperCamelCase					,	help="Attention dropout probability (Optional). Goes into model.config"					,	)
												parser.add_argument("--learning_rate"					,	default=5e-5					,	type=__UpperCamelCase					,	help="The initial learning rate for Adam."		)
												parser.add_argument(
												    "--lr_scheduler"					,	default="linear"					,	choices=__UpperCamelCase					,	metavar=__UpperCamelCase					,	type=__UpperCamelCase					,	help="Learning rate scheduler"					,	)
												parser.add_argument("--weight_decay"					,	default=0.0					,	type=__UpperCamelCase					,	help="Weight decay if we apply some."		)
												parser.add_argument("--adam_epsilon"					,	default=1e-8					,	type=__UpperCamelCase					,	help="Epsilon for Adam optimizer."		)
												parser.add_argument("--warmup_steps"					,	default=0					,	type=__UpperCamelCase					,	help="Linear warmup over warmup_steps."		)
												parser.add_argument("--num_workers"					,	default=4					,	type=__UpperCamelCase					,	help="kwarg passed to DataLoader"		)
												parser.add_argument("--num_train_epochs"					,	dest="max_epochs"					,	default=3					,	type=__UpperCamelCase		)
												parser.add_argument("--train_batch_size"					,	default=32					,	type=__UpperCamelCase		)
												parser.add_argument("--eval_batch_size"					,	default=32					,	type=__UpperCamelCase		)
												parser.add_argument("--adafactor"					,	action="store_true"		)







class  _UpperCAmelCase       (     pl.Callback			):


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Tuple					,	__UpperCamelCase      :List[Any]		):
												A		=  trainer.lr_schedulers[0]["scheduler"]
												A		=  {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
												pl_module.logger.log_metrics(__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[str]					,	__UpperCamelCase      :pl.Trainer					,	__UpperCamelCase      :pl.LightningModule		):
												rank_zero_info("***** Validation results *****"		)
												A		=  trainer.callback_metrics
												# Log results
												for key in sorted(__UpperCamelCase		):
																			if key not in ["log", "progress_bar"]:
																										rank_zero_info("{} = {}\n".format(__UpperCamelCase					,	str(metrics[key]		)		)		)


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :pl.Trainer					,	__UpperCamelCase      :pl.LightningModule		):
												rank_zero_info("***** Test results *****"		)
												A		=  trainer.callback_metrics
												# Log and save results to file
												A		=  os.path.join(pl_module.hparams.output_dir					,	"test_results.txt"		)
												with open(__UpperCamelCase					,	"w"		) as writer:
																			for key in sorted(__UpperCamelCase		):
																										if key not in ["log", "progress_bar"]:
																																	rank_zero_info("{} = {}\n".format(__UpperCamelCase					,	str(metrics[key]		)		)		)
																																	writer.write("{} = {}\n".format(__UpperCamelCase					,	str(metrics[key]		)		)		)







def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    "--output_dir" ,  default=UpperCamelCase ,  type=UpperCamelCase ,  required=UpperCamelCase ,  help="The output directory where the model predictions and checkpoints will be written." ,  )
							parser.add_argument(
							    "--fp16" ,  action="store_true" ,  help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" ,  )

							parser.add_argument(
							    "--fp16_opt_level" ,  type=UpperCamelCase ,  default="O2" ,  help=(
							        "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
							        "See details at https://nvidia.github.io/apex/amp.html"
							    ) ,  )
							parser.add_argument("--n_tpu_cores" ,  dest="tpu_cores" ,  type=UpperCamelCase	)
							parser.add_argument("--max_grad_norm" ,  dest="gradient_clip_val" ,  default=1.0 ,  type=UpperCamelCase ,  help="Max gradient norm"	)
							parser.add_argument("--do_train" ,  action="store_true" ,  help="Whether to run training."	)
							parser.add_argument("--do_predict" ,  action="store_true" ,  help="Whether to run predictions on the test set."	)
							parser.add_argument(
							    "--gradient_accumulation_steps" ,  dest="accumulate_grad_batches" ,  type=UpperCamelCase ,  default=1 ,  help="Number of updates steps to accumulate before performing a backward/update pass." ,  )
							parser.add_argument("--seed" ,  type=UpperCamelCase ,  default=42 ,  help="random seed for initialization"	)
							parser.add_argument(
							    "--data_dir" ,  default=UpperCamelCase ,  type=UpperCamelCase ,  required=UpperCamelCase ,  help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." ,  )






def  A__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase=None ,  UpperCamelCase=True ,  UpperCamelCase=[] ,  UpperCamelCase=None ,  UpperCamelCase=None ,  **UpperCamelCase ,  ):
							pl.seed_everything(args.seed	)

							# init model
							A		=  Path(model.hparams.output_dir	)
							odir.mkdir(exist_ok=UpperCamelCase	)

							# add custom checkpoints
							if checkpoint_callback is None:
														A		=  pl.callbacks.ModelCheckpoint(
														    filepath=args.output_dir ,  prefix="checkpoint" ,  monitor="val_loss" ,  mode="min" ,  save_top_k=1	)
							if early_stopping_callback:
														extra_callbacks.append(UpperCamelCase	)
							if logging_callback is None:
														A		=  LoggingCallback()

							A		=  {}

							# TODO: remove with PyTorch 1.6 since pl uses native amp
							if args.fpaa:
														A		=  16
														A		=  args.fpaa_opt_level

							if args.gpus > 1:
														A		=  "ddp"

							A		=  args.accumulate_grad_batches
							A		=  extra_train_kwargs.get("accelerator" ,  UpperCamelCase	)
							A		=  extra_train_kwargs.get("profiler" ,  UpperCamelCase	)

							A		=  pl.Trainer.from_argparse_args(
							    UpperCamelCase ,  weights_summary=UpperCamelCase ,  callbacks=[logging_callback] + extra_callbacks ,  logger=UpperCamelCase ,  checkpoint_callback=UpperCamelCase ,  **UpperCamelCase ,  )

							if args.do_train:
														trainer.fit(UpperCamelCase	)

							return trainer









"""simple docstring"""
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 SchedulerMixin


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






@dataclass
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							42







class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :Tuple					,	__UpperCamelCase      :int = 40					,	__UpperCamelCase      :float = 0.002					,	__UpperCamelCase      :float = 80.0					,	__UpperCamelCase      :float = 0.5					,	__UpperCamelCase      :float = 1.0					,	__UpperCamelCase      :float = 7.0					,	__UpperCamelCase      :bool = True					,	):
												# standard deviation of the initial noise distribution
												A		=  sigma_max

												A		=  np.linspace(0					,	1					,	__UpperCamelCase		)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												# setable values
												A		=  None
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  False
												A		=  False


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Dict=None		):
												if schedule_timesteps is None:
																			A		=  self.timesteps

												A		=  (schedule_timesteps == timestep).nonzero()
												return indices.item()


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]		):
												# Get sigma corresponding to timestep
												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)
												A		=  self.index_for_timestep(__UpperCamelCase		)
												A		=  self.sigmas[step_idx]

												A		=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												A		=  True
												return sample


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Union[float, np.ndarray]		):
												if not isinstance(__UpperCamelCase					,	np.ndarray		):
																			A		=  np.array(__UpperCamelCase					,	dtype=np.floataa		)

												A		=  10_00 * 0.25 * np.log(sigmas + 1e-44		)

												return timesteps


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Union[str, torch.device] = None					,	__UpperCamelCase      :Optional[List[int]] = None					,	):
												if num_inference_steps is None and timesteps is None:
																			raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."		)

												if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."		)

												# Follow DDPMScheduler custom timesteps logic
												if timesteps is not None:
																			for i in range(1					,	len(__UpperCamelCase		)		):
																										if timesteps[i] >= timesteps[i - 1]:
																																	raise ValueError("`timesteps` must be in descending order."		)

																			if timesteps[0] >= self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`timesteps` must start before `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps}."		)

																			A		=  np.array(__UpperCamelCase					,	dtype=np.intaa		)
																			A		=  True
												else:
																			if num_inference_steps > self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																										    f" maximal {self.config.num_train_timesteps} timesteps."		)

																			A		=  num_inference_steps

																			A		=  self.config.num_train_timesteps // self.num_inference_steps
																			A		=  (np.arange(0					,	__UpperCamelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
																			A		=  False

												# Map timesteps to Karras sigmas directly for multistep sampling
												# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
												A		=  self.config.num_train_timesteps
												A		=  timesteps[::-1].copy()
												A		=  ramp / (num_train_timesteps - 1)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												A		=  np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
												A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)

												if str(__UpperCamelCase		).startswith("mps"		):
																			# mps does not support float64
																			A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase					,	dtype=torch.floataa		)
												else:
																			A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.config.rho
												A		=  sigma_min ** (1 / rho)
												A		=  sigma_max ** (1 / rho)
												A		=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / (sigma**2 + sigma_data**2)
												A		=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Tuple		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
												A		=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = True					,	):

												if (
												    isinstance(__UpperCamelCase					,	__UpperCamelCase		)
												    or isinstance(__UpperCamelCase					,	torch.IntTensor		)
												    or isinstance(__UpperCamelCase					,	torch.LongTensor		)
												):
																			raise ValueError(
																			    (
																			        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																			        f" `{self.__class__}.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(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)

												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.index_for_timestep(__UpperCamelCase		)

												# sigma_next corresponds to next_t in original implementation
												A		=  self.sigmas[step_index]
												if step_index + 1 < self.config.num_train_timesteps:
																			A		=  self.sigmas[step_index + 1]
												else:
																			# Set sigma_next to sigma_min
																			A		=  self.sigmas[-1]

												# Get scalings for boundary conditions
												A,     A		=  self.get_scalings_for_boundary_condition(__UpperCamelCase		)

												# 1. Denoise model output using boundary conditions
												A		=  c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																			A		=  denoised.clamp(-1					,	1		)

												# 2. Sample z ~ N(0, s_noise^2 * I)
												# Noise is not used for onestep sampling.
												if len(self.timesteps		) > 1:
																			A		=  randn_tensor(
																			    model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__UpperCamelCase		)
												else:
																			A		=  torch.zeros_like(__UpperCamelCase		)
												A		=  noise * self.config.s_noise

												A		=  sigma_next.clamp(min=__UpperCamelCase					,	max=__UpperCamelCase		)

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												A		=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																			return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCamelCase		)


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

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

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

												A		=  original_samples + noise * sigma
												return noisy_samples


					def __len__( self      :str		):
												return self.config.num_train_timesteps


