
"""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


_lowercase											=			logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowercase											=			{
    '''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
_lowercase											=			{
    '''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
}
_lowercase											=			sorted(arg_to_scheduler.keys())
_lowercase											=			'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''







class 			lowerCAmelCase_			( pl.LightningModule      ):



						'''simple docstring'''


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

							self.save_hyperparameters(A_ )
							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=A_					,**A_					,)
							else:
								A  				=				config

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

							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=A_					,)
							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=A_					,)
							else:
								A  				=				model


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,*A_       : Any					,**A_       : Optional[int] )       ->				Optional[Any]:
							A  				=				self.model_type.from_pretrained(*A_					,**A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				Tuple:
							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   _SCREAMING_SNAKE_CASE (	self       : Any )       ->				int:
							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(
								    A_					,lr=self.hparams.learning_rate					,scale_parameter=A_					,relative_step=A_ )

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

							A  				=				self.get_lr_scheduler()

							return [optimizer], [scheduler]


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : Optional[int]					,A_       : Any )       ->				Dict:
							return self.validation_step(A_					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : int )       ->				Optional[Any]:
							return self.validation_end(A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				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   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : int )       ->				Optional[int]:
							if mode == "test":
								A  				=				len(self.test_dataloader().dataset )
							else:
								A  				=				self.get_dataloader('train'					,self.hparams.train_batch_size					,shuffle=A_ )
								A  				=				len(self.train_dataloader().dataset )


						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : str					,A_       : int					,A_       : bool = False )       ->				Tuple:
							raise NotImplementedError('You must implement this for your task' )


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Optional[int]:
							return self.train_loader


						def   _SCREAMING_SNAKE_CASE (	self       : int )       ->				Optional[int]:
							return self.get_dataloader('dev'					,self.hparams.eval_batch_size					,shuffle=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Any:
							return self.get_dataloader('test'					,self.hparams.eval_batch_size					,shuffle=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : List[str] )       ->				int:
							return os.path.join(
							    self.hparams.data_dir					,'cached_{}_{}_{}'.format(
							        A_					,list(filter(A_					,self.hparams.model_name_or_path.split('/' ) ) ).pop()					,str(self.hparams.max_seq_length )					,)					,)


						@pl.utilities.rank_zero_only
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Dict[str, Any] )       ->				None:
							A  				=				self.output_dir.joinpath('best_tfmr' )
							A  				=				self.step_count
							self.model.save_pretrained(A_ )
							self.tokenizer.save_pretrained(A_ )




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







class 			lowerCAmelCase_			( pl.Callback      ):



						'''simple docstring'''


						def   _SCREAMING_SNAKE_CASE (	self       : str					,A_       : Optional[Any]					,A_       : Tuple )       ->				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(A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : pl.Trainer					,A_       : pl.LightningModule )       ->				Union[str, Any]:
							rank_zero_info('***** Validation results *****' )
							A  				=				trainer.callback_metrics
							# Log results
							for key in sorted(A_ ):
								if key not in ["log", "progress_bar"]:
									rank_zero_info('{} = {}\n'.format(A_					,str(metrics[key] ) ) )




						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : pl.Trainer					,A_       : pl.LightningModule )       ->				Any:
							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(A_					,'w' ) as writer:
								for key in sorted(A_ ):
									if key not in ["log", "progress_bar"]:
										rank_zero_info('{} = {}\n'.format(A_					,str(metrics[key] ) ) )
										writer.write('{} = {}\n'.format(A_					,str(metrics[key] ) ) )






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       List[Any]       ):
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    '--output_dir'		, default=snake_case__		, type=snake_case__		, required=snake_case__		, 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=snake_case__		, 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=snake_case__       )
	parser.add_argument('--max_grad_norm'		, dest='gradient_clip_val'		, default=1.0		, type=snake_case__		, 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=snake_case__		, default=1		, help='Number of updates steps to accumulate before performing a backward/update pass.'		, )
	parser.add_argument('--seed'		, type=snake_case__		, default=42		, help='random seed for initialization'       )
	parser.add_argument(
	    '--data_dir'		, default=snake_case__		, type=snake_case__		, required=snake_case__		, help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'		, )






def  _snake_case			(						snake_case__				:       BaseTransformer		, snake_case__				:       argparse.Namespace		, snake_case__				:       Any=None		, snake_case__				:       List[str]=True		, snake_case__				:       int=[]		, snake_case__				:       List[str]=None		, snake_case__				:       Union[str, Any]=None		, **snake_case__				:       Any		, ):
	pl.seed_everything(args.seed       )

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

	# 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(snake_case__       )
	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'		, snake_case__       )
	A  				=				extra_train_kwargs.get('profiler'		, snake_case__       )

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

	if args.do_train:
		trainer.fit(snake_case__       )

	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


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







@dataclass
class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      torch.FloatTensor







class 			lowerCAmelCase_			( _lowercase						,   _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      Dict =      1


						@register_to_config
						def __init__(	self       : List[Any]					,A_       : int = 40					,A_       : float = 0.0_02					,A_       : float = 80.0					,A_       : float = 0.5					,A_       : float = 1.0					,A_       : float = 7.0					,A_       : bool = True					,)       ->				List[str]:
							# standard deviation of the initial noise distribution
							A  				=				sigma_max

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

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


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : List[Any]					,A_       : List[Any]=None )       ->				List[Any]:
							if schedule_timesteps is None:
								A  				=				self.timesteps

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


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor] )       ->				torch.FloatTensor:
							# Get sigma corresponding to timestep
							if isinstance(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )
							A  				=				self.index_for_timestep(A_ )
							A  				=				self.sigmas[step_idx]

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

							A  				=				True
							return sample


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Union[float, np.ndarray] )       ->				Tuple:
							if not isinstance(A_					,np.ndarray ):
								A  				=				np.array(A_					,dtype=np.floataa )

							A  				=				1000 * 0.25 * np.log(sigmas + 1e-44 )

							return timesteps


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : Optional[int] = None					,A_       : Union[str, torch.device] = None					,A_       : Optional[List[int]] = None					,)       ->				Optional[Any]:
							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(A_ ) ):
									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(A_					,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					,A_ ) * 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(A_ )
							A  				=				self.sigma_to_t(A_ )

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

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


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				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   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Dict )       ->				Any:
							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   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Optional[Any] )       ->				Dict:
							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   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor					,A_       : Union[float, torch.FloatTensor]					,A_       : torch.FloatTensor					,A_       : Optional[torch.Generator] = None					,A_       : bool = True					,)       ->				Union[CMStochasticIterativeSchedulerOutput, 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'
								        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(A_					,torch.Tensor ):
								A  				=				timestep.to(self.timesteps.device )

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

							A  				=				self.index_for_timestep(A_ )

							# 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(A_ )

							# 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=A_ )
							else:
								A  				=				torch.zeros_like(A_ )
							A  				=				noise * self.config.s_noise

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

							# 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=A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : int					,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
							A  				=				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
								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       : List[str] )       ->				List[Any]:
							return self.config.num_train_timesteps