code: 
  
  
  
  import argparse
  import os
  
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  
  from accelerate import Accelerator, DistributedType
  
  
  ########################################################################
  # This is a fully working simple example to use Accelerate
  #
  # This example trains a Bert base model on GLUE MRPC
  # in any of the following settings (with the same script):
  #   - single CPU or single GPU
  #   - multi GPUS (using PyTorch distributed mode)
  #   - (multi) TPUs
  #   - fp16 (mixed-precision) or fp32 (normal precision)
  #
  # This example also demonstrates the checkpointing and sharding capabilities
  #
  # To run it in each of these various modes, follow the instructions
  # in the readme for examples:
  # https://github.com/huggingface/accelerate/tree/main/examples
  #
  ########################################################################
  
  
  __A       					=		16
  __A       					=		32
  
  
  def  __a  (					lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      Optional[Any]       )   ->				int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if args.with_tracking:
  												UpperCAmelCase_=     Accelerator(
  												    cpu=args.cpu    ,mixed_precision=args.mixed_precision    ,log_with="""all"""    ,project_dir=args.project_dir       )
  						else:
  												UpperCAmelCase_=     Accelerator(cpu=args.cpu    ,mixed_precision=args.mixed_precision       )
  
  						if hasattr(args.checkpointing_steps    ,"""isdigit"""       ):
  												if args.checkpointing_steps == "epoch":
  																		UpperCAmelCase_=     args.checkpointing_steps
  												elif args.checkpointing_steps.isdigit():
  																		UpperCAmelCase_=     int(args.checkpointing_steps       )
  												else:
  																		raise ValueError(
  																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
  						else:
  												UpperCAmelCase_=     None
  						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  						UpperCAmelCase_=     config["""lr"""]
  						UpperCAmelCase_=     int(config["""num_epochs"""]       )
  						UpperCAmelCase_=     int(config["""seed"""]       )
  						UpperCAmelCase_=     int(config["""batch_size"""]       )
  
  						# We need to initialize the trackers we use, and also store our configuration
  						if args.with_tracking:
  												UpperCAmelCase_=     os.path.split(lowerCAmelCase_       )[-1].split("""."""       )[0]
  												accelerator.init_trackers(lowerCAmelCase_    ,lowerCAmelCase_       )
  
  						UpperCAmelCase_=     AutoTokenizer.from_pretrained("""bert-base-cased"""       )
  						UpperCAmelCase_=     load_dataset("""glue"""    ,"""mrpc"""       )
  						UpperCAmelCase_=     evaluate.load("""glue"""    ,"""mrpc"""       )
  
  						def tokenize_function(lowerCAmelCase_     :      Union[str, Any]       ):
  												# max_length=None => use the model max length (it's actually the default)
  												UpperCAmelCase_=     tokenizer(examples["""sentence1"""]    ,examples["""sentence2"""]    ,truncation=lowerCAmelCase_    ,max_length=lowerCAmelCase_       )
  												return outputs
  
  						# Apply the method we just defined to all the examples in all the splits of the dataset
  						# starting with the main process first:
  						with accelerator.main_process_first():
  												UpperCAmelCase_=     datasets.map(
  												    lowerCAmelCase_    ,batched=lowerCAmelCase_    ,remove_columns=["""idx""", """sentence1""", """sentence2"""]    ,)
  
  						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  						# transformers library
  						UpperCAmelCase_=     tokenized_datasets.rename_column("""label"""    ,"""labels"""       )
  
  						# If the batch size is too big we use gradient accumulation
  						UpperCAmelCase_=     1
  						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  												UpperCAmelCase_=     batch_size // MAX_GPU_BATCH_SIZE
  												UpperCAmelCase_=     MAX_GPU_BATCH_SIZE
  
  						def collate_fn(lowerCAmelCase_     :      str       ):
  												# On TPU it's best to pad everything to the same length or training will be very slow.
  												UpperCAmelCase_=     1_28 if accelerator.distributed_type == DistributedType.TPU else None
  												# When using mixed precision we want round multiples of 8/16
  												if accelerator.mixed_precision == "fp8":
  																		UpperCAmelCase_=     16
  												elif accelerator.mixed_precision != "no":
  																		UpperCAmelCase_=     8
  												else:
  																		UpperCAmelCase_=     None
  
  												return tokenizer.pad(
  												    lowerCAmelCase_    ,padding="""longest"""    ,max_length=lowerCAmelCase_    ,pad_to_multiple_of=lowerCAmelCase_    ,return_tensors="""pt"""    ,)
  
  						# Instantiate dataloaders.
  						UpperCAmelCase_=     DataLoader(
  						    tokenized_datasets["""train"""]    ,shuffle=lowerCAmelCase_    ,collate_fn=lowerCAmelCase_    ,batch_size=lowerCAmelCase_       )
  						UpperCAmelCase_=     DataLoader(
  						    tokenized_datasets["""validation"""]    ,shuffle=lowerCAmelCase_    ,collate_fn=lowerCAmelCase_    ,batch_size=lowerCAmelCase_       )
  
  						set_seed(lowerCAmelCase_       )
  
  						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
  						UpperCAmelCase_=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""    ,return_dict=lowerCAmelCase_       )
  
  						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  						UpperCAmelCase_=     model.to(accelerator.device       )
  
  						# Instantiate optimizer
  						UpperCAmelCase_=     AdamW(params=model.parameters()    ,lr=lowerCAmelCase_       )
  
  						# Instantiate scheduler
  						UpperCAmelCase_=     get_linear_schedule_with_warmup(
  						    optimizer=lowerCAmelCase_    ,num_warmup_steps=1_00    ,num_training_steps=(len(lowerCAmelCase_       ) * num_epochs) // gradient_accumulation_steps    ,)
  
  						# Prepare everything
  						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  						# prepare method.
  						UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     accelerator.prepare(
  						    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )
  
  						# We need to keep track of how many total steps we have iterated over
  						UpperCAmelCase_=     0
  						# We also need to keep track of the stating epoch so files are named properly
  						UpperCAmelCase_=     0
  
  						# Potentially load in the weights and states from a previous save
  						if args.resume_from_checkpoint:
  												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""       )
  																		accelerator.load_state(args.resume_from_checkpoint       )
  																		UpperCAmelCase_=     os.path.basename(args.resume_from_checkpoint       )
  												else:
  																		# Get the most recent checkpoint
  																		UpperCAmelCase_=     [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
  																		dirs.sort(key=os.path.getctime       )
  																		UpperCAmelCase_=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  												# Extract `epoch_{i}` or `step_{i}`
  												UpperCAmelCase_=     os.path.splitext(lowerCAmelCase_       )[0]
  
  												if "epoch" in training_difference:
  																		UpperCAmelCase_=     int(training_difference.replace("""epoch_"""    ,""""""       )       ) + 1
  																		UpperCAmelCase_=     None
  												else:
  																		UpperCAmelCase_=     int(training_difference.replace("""step_"""    ,""""""       )       )
  																		UpperCAmelCase_=     resume_step // len(lowerCAmelCase_       )
  																		resume_step -= starting_epoch * len(lowerCAmelCase_       )
  
      # Now we train the model
  						for epoch in range(lowerCAmelCase_    ,lowerCAmelCase_       ):
  												model.train()
  												if args.with_tracking:
  																		UpperCAmelCase_=     0
  												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
  																		# We need to skip steps until we reach the resumed step
  																		UpperCAmelCase_=     accelerator.skip_first_batches(lowerCAmelCase_    ,lowerCAmelCase_       )
  																		overall_step += resume_step
  												else:
  																		# After the first iteration though, we need to go back to the original dataloader
  																		UpperCAmelCase_=     train_dataloader
  												for step, batch in enumerate(lowerCAmelCase_       ):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		batch.to(accelerator.device       )
  																		UpperCAmelCase_=     model(**lowerCAmelCase_       )
  																		UpperCAmelCase_=     outputs.loss
  																		UpperCAmelCase_=     loss / gradient_accumulation_steps
  																		# We keep track of the loss at each epoch
  																		if args.with_tracking:
  																								total_loss += loss.detach().float()
  																		accelerator.backward(lowerCAmelCase_       )
  																		if step % gradient_accumulation_steps == 0:
  																								optimizer.step()
  																								lr_scheduler.step()
  																								optimizer.zero_grad()
  
  																		overall_step += 1
  
  																		if isinstance(lowerCAmelCase_    ,lowerCAmelCase_       ):
  																								UpperCAmelCase_=     F"""step_{overall_step}"""
  																								if overall_step % checkpointing_steps == 0:
  																														if args.output_dir is not None:
  																																				UpperCAmelCase_=     os.path.join(args.output_dir    ,lowerCAmelCase_       )
  																														accelerator.save_state(lowerCAmelCase_       )
  
  												model.eval()
  												for step, batch in enumerate(lowerCAmelCase_       ):
  																		# We could avoid this line since we set the accelerator with `device_placement=True`.
  																		batch.to(accelerator.device       )
  																		with torch.no_grad():
  																								UpperCAmelCase_=     model(**lowerCAmelCase_       )
  																		UpperCAmelCase_=     outputs.logits.argmax(dim=-1       )
  																		UpperCAmelCase_,     UpperCAmelCase_=     accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
  																		metric.add_batch(
  																		    predictions=lowerCAmelCase_    ,references=lowerCAmelCase_    ,)
  
  												UpperCAmelCase_=     metric.compute()
  												# Use accelerator.print to print only on the main process.
  												accelerator.print(F"""epoch {epoch}:"""    ,lowerCAmelCase_       )
  												if args.with_tracking:
  																		accelerator.log(
  																		    {
  																		        """accuracy""": eval_metric["""accuracy"""],
  																		        """f1""": eval_metric["""f1"""],
  																		        """train_loss""": total_loss.item() / len(lowerCAmelCase_       ),
  																		        """epoch""": epoch,
  																		    }    ,step=lowerCAmelCase_    ,)
  
  												if checkpointing_steps == "epoch":
  																		UpperCAmelCase_=     F"""epoch_{epoch}"""
  																		if args.output_dir is not None:
  																								UpperCAmelCase_=     os.path.join(args.output_dir    ,lowerCAmelCase_       )
  																		accelerator.save_state(lowerCAmelCase_       )
  
  						if args.with_tracking:
  												accelerator.end_training()
  
  
  def  __a  (					)   ->				Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     argparse.ArgumentParser(description="""Simple example of training script."""       )
  						parser.add_argument(
  						    """--mixed_precision"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,choices=["""no""", """fp16""", """bf16""", """fp8"""]    ,help="""Whether to use mixed precision. Choose"""
  						    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
  						    """and an Nvidia Ampere GPU."""    ,)
  						parser.add_argument("""--cpu"""    ,action="""store_true"""    ,help="""If passed, will train on the CPU."""       )
  						parser.add_argument(
  						    """--checkpointing_steps"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""    ,)
  						parser.add_argument(
  						    """--resume_from_checkpoint"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""If the training should continue from a checkpoint folder."""    ,)
  						parser.add_argument(
  						    """--with_tracking"""    ,action="""store_true"""    ,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""    ,)
  						parser.add_argument(
  						    """--output_dir"""    ,type=lowerCAmelCase_    ,default="""."""    ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""    ,)
  						parser.add_argument(
  						    """--project_dir"""    ,type=lowerCAmelCase_    ,default="""logs"""    ,help="""Location on where to store experiment tracking logs` and relevent project information"""    ,)
  						UpperCAmelCase_=     parser.parse_args()
  						UpperCAmelCase_=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  						training_function(lowerCAmelCase_    ,lowerCAmelCase_       )
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
style_context: 
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  
  @flax.struct.dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      CommonSchedulerState
  	a__   :      jnp.ndarray
  
  	# setable values
  	a__   :      jnp.ndarray
  	a__   :      jnp.ndarray
  	a__   :      Optional[int]	     =							None
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
  							return cls(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      DDIMSchedulerState
  
  
  
  
  
  
  class 	lowercase		(				snake_case__				,					snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	a__   :      jnp.dtype
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return True
  
  
  
  
  	@register_to_config
  	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     dtype
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
  							if common is None:
  													UpperCAmelCase_=     CommonSchedulerState.create(self  )
  
  							# At every step in ddim, we are looking into the previous alphas_cumprod
  							# For the final step, there is no previous alphas_cumprod because we are already at 0
  							# `set_alpha_to_one` decides whether we set this parameter simply to one or
  							# whether we use the final alpha of the "non-previous" one.
  							UpperCAmelCase_=     (
  							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  							)
  
  							# standard deviation of the initial noise distribution
  							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )
  
  							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]
  
  							return DDIMSchedulerState.create(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
  							return sample
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
  							UpperCAmelCase_=     self.config.num_train_timesteps // num_inference_steps
  							# creates integer timesteps by multiplying by ratio
  							# rounding to avoid issues when num_inference_step is power of 3
  							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  							return state.replace(
  							    num_inference_steps=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
  							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(
  							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
  							UpperCAmelCase_=     1 - alpha_prod_t
  							UpperCAmelCase_=     1 - alpha_prod_t_prev
  
  							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  							return variance
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
  							if state.num_inference_steps is None:
  													raise ValueError(
  													    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )
  
  							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  							# Ideally, read DDIM paper in-detail understanding
  
  							# Notation (<variable name> -> <name in paper>
  							# - pred_noise_t -> e_theta(x_t, t)
  							# - pred_original_sample -> f_theta(x_t, t) or x_0
  							# - std_dev_t -> sigma_t
  							# - eta -> η
  							# - pred_sample_direction -> "direction pointing to x_t"
  							# - pred_prev_sample -> "x_t-1"
  
  							# 1. get previous step value (=t-1)
  							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  							UpperCAmelCase_=     state.common.alphas_cumprod
  							UpperCAmelCase_=     state.final_alpha_cumprod
  
  							# 2. compute alphas, betas
  							UpperCAmelCase_=     alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     1 - alpha_prod_t
  
  							# 3. compute predicted original sample from predicted noise also called
  							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							if self.config.prediction_type == "epsilon":
  													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  													UpperCAmelCase_=     model_output
  							elif self.config.prediction_type == "sample":
  													UpperCAmelCase_=     model_output
  													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  							elif self.config.prediction_type == "v_prediction":
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  							else:
  													raise ValueError(
  													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
  													    """ `v_prediction`"""  )
  
  							# 4. compute variance: "sigma_t(η)" -> see formula (16)
  							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     eta * variance ** 0.5
  
  							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  							if not return_dict:
  													return (prev_sample, state)
  
  							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
  							return self.config.num_train_timesteps
  
  
  
  
  