






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# 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)
#   - FSDP
#
# 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
#
########################################################################


__UpperCAmelCase      =				1_6
__UpperCAmelCase      =				3_2





def 						A_					(						lowercase_			)				->Dict:







 """simple docstring"""

 return int(x / 2**2_0			)
class 	a_:



     """simple docstring"""



     def __enter__(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		psutil.Process()

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		True
      SCREAMING_SNAKE_CASE				    =		threading.Thread(target=self.peak_monitor_func)
      SCREAMING_SNAKE_CASE				    =		True
      peak_monitor_thread.start()
      return self





     def     __UpperCamelCase						(		self					:	Any)							->	List[str]:

      """simple docstring"""
      return self.process.memory_info().rss





     def     __UpperCamelCase						(		self					:	List[str])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		-1

      while True:
       SCREAMING_SNAKE_CASE				    =		max(self.cpu_mem_used()  ,  self.cpu_peak)

       # can't sleep or will not catch the peak right (this comment is here on purpose)
       # time.sleep(0.001) # 1msec

       if not self.peak_monitoring:
        break





     def __exit__(		self					:	int  ,  *lowerCAmelCase__					:	Tuple)							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		False

      gc.collect()
      torch.cuda.empty_cache()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.memory_allocated()
      SCREAMING_SNAKE_CASE				    =		torch.cuda.max_memory_allocated()
      SCREAMING_SNAKE_CASE				    =		bamb(self.end - self.begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.peak - self.begin)

      SCREAMING_SNAKE_CASE				    =		self.cpu_mem_used()
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_end - self.cpu_begin)
      SCREAMING_SNAKE_CASE				    =		bamb(self.cpu_peak - self.cpu_begin)
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      __UpperCAmelCase      =				mocked_dataloaders  # noqa: F811





def 						A_					(						lowercase_	,  lowercase_			)				->Dict:







 """simple docstring"""

 if os.environ.get('TESTING_MOCKED_DATALOADERS'	,  lowercase_			) == "1":
  SCREAMING_SNAKE_CASE				    =		2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 SCREAMING_SNAKE_CASE				    =		FullyShardedDataParallelPlugin(
     state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_	,  ranka_only=lowercase_			)	,  )

 # Initialize accelerator
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		Accelerator(
      cpu=args.cpu	,  mixed_precision=args.mixed_precision	,  log_with='wandb'	,  project_dir=args.logging_dir	,  fsdp_plugin=lowercase_	,  )
 else:
  SCREAMING_SNAKE_CASE				    =		Accelerator(fsdp_plugin=lowercase_			)
 accelerator.print(accelerator.distributed_type			)

 if hasattr(args.checkpointing_steps	,  'isdigit'			):
  if args.checkpointing_steps == "epoch":
   SCREAMING_SNAKE_CASE				    =		args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   SCREAMING_SNAKE_CASE				    =		int(args.checkpointing_steps			)
  else:
   raise ValueError(
       f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
 else:
  SCREAMING_SNAKE_CASE				    =		None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 SCREAMING_SNAKE_CASE				    =		config['lr']
 SCREAMING_SNAKE_CASE				    =		int(config['num_epochs']			)
 SCREAMING_SNAKE_CASE				    =		int(config['seed']			)
 SCREAMING_SNAKE_CASE				    =		int(config['batch_size']			)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  SCREAMING_SNAKE_CASE				    =		vars(lowercase_			)
  accelerator.init_trackers('fsdp_glue_no_trainer'	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		AutoTokenizer.from_pretrained(args.model_name_or_path			)
 SCREAMING_SNAKE_CASE				    =		load_dataset('glue'	,  'mrpc'			)
 SCREAMING_SNAKE_CASE				    =		evaluate.load('glue'	,  'mrpc'			)

 def tokenize_function(lowercase_			):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE				    =		tokenizer(examples['sentence1']	,  examples['sentence2']	,  truncation=lowercase_	,  max_length=lowercase_			)
  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():
  SCREAMING_SNAKE_CASE				    =		datasets.map(
      lowercase_	,  batched=lowercase_	,  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
 SCREAMING_SNAKE_CASE				    =		tokenized_datasets.rename_column('label'	,  'labels'			)

 # If the batch size is too big we use gradient accumulation
 SCREAMING_SNAKE_CASE				    =		1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  SCREAMING_SNAKE_CASE				    =		batch_size // MAX_GPU_BATCH_SIZE
  SCREAMING_SNAKE_CASE				    =		MAX_GPU_BATCH_SIZE

 def collate_fn(lowercase_			):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  SCREAMING_SNAKE_CASE				    =		1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   SCREAMING_SNAKE_CASE				    =		1_6
  elif accelerator.mixed_precision != "no":
   SCREAMING_SNAKE_CASE				    =		8
  else:
   SCREAMING_SNAKE_CASE				    =		None

  return tokenizer.pad(
      lowercase_	,  padding='longest'	,  max_length=lowercase_	,  pad_to_multiple_of=lowercase_	,  return_tensors='pt'	,  )

 # Instantiate dataloaders.
 SCREAMING_SNAKE_CASE				    =		DataLoader(
     tokenized_datasets['train']	,  shuffle=lowercase_	,  collate_fn=lowercase_	,  batch_size=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		DataLoader(
     tokenized_datasets['validation']	,  shuffle=lowercase_	,  collate_fn=lowercase_	,  batch_size=lowercase_			)

 set_seed(lowercase_			)

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 SCREAMING_SNAKE_CASE				    =		AutoModelForSequenceClassification.from_pretrained(
     args.model_name_or_path	,  return_dict=lowercase_	,  low_cpu_mem_usage=lowercase_			)

 # New Code #
 # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
 SCREAMING_SNAKE_CASE				    =		accelerator.prepare(lowercase_			)
 accelerator.print(lowercase_			)

 # Instantiate optimizer
 # New Code #
 # For FSDP feature, at present it doesn't support multiple parameter groups,
 # so we need to create a single parameter group for the whole model
 SCREAMING_SNAKE_CASE				    =		torch.optim.AdamW(params=model.parameters()	,  lr=lowercase_	,  weight_decay=2e-4			)

 # Instantiate scheduler
 SCREAMING_SNAKE_CASE				    =		get_linear_schedule_with_warmup(
     optimizer=lowercase_	,  num_warmup_steps=1_0	,  num_training_steps=(len(lowercase_			) * num_epochs) // gradient_accumulation_steps	,  )

 # New Code #
 # For FSDP feature, prepare everything except the model as we have already prepared the model
 # before creating the optimizer
 # 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.
 SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		accelerator.prepare(
     lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)

 SCREAMING_SNAKE_CASE				    =		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			)
   SCREAMING_SNAKE_CASE				    =		os.path.basename(args.resume_from_checkpoint			)
  else:
   # Get the most recent checkpoint
   SCREAMING_SNAKE_CASE				    =		[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
   dirs.sort(key=os.path.getctime			)
   SCREAMING_SNAKE_CASE				    =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract `epoch_{i}` or `step_{i}`
  SCREAMING_SNAKE_CASE				    =		os.path.splitext(lowercase_			)[0]

  if "epoch" in training_difference:
   num_epochs -= int(training_difference.replace('epoch_'	,  ''			)			)
   SCREAMING_SNAKE_CASE				    =		None
  else:
   SCREAMING_SNAKE_CASE				    =		int(training_difference.replace('step_'	,  ''			)			)
   num_epochs -= resume_step // len(lowercase_			)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   SCREAMING_SNAKE_CASE				    =		(num_epochs * len(lowercase_			)) - resume_step

    # Now we train the model
 for epoch in range(lowercase_			):
  # New Code #
  # context manager to track the peak memory usage during the training epoch
  with TorchTracemalloc() as tracemalloc:
   model.train()
   if args.with_tracking:
    SCREAMING_SNAKE_CASE				    =		0
   for step, batch in enumerate(lowercase_			):
    # We need to skip steps until we reach the resumed step
    if args.resume_from_checkpoint and epoch == 0:
     if resume_step is not None and step < resume_step:
      pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    SCREAMING_SNAKE_CASE				    =		outputs.loss
    SCREAMING_SNAKE_CASE				    =		loss / gradient_accumulation_steps
    # We keep track of the loss at each epoch
    if args.with_tracking:
     total_loss += loss.detach().float()
    accelerator.backward(lowercase_			)
    if step % gradient_accumulation_steps == 0:
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()
     # accelerator.print(lr_scheduler.get_lr())

    overall_step += 1

    if isinstance(lowercase_	,  lowercase_			):
     SCREAMING_SNAKE_CASE				    =		f'''step_{overall_step}'''
     if overall_step % checkpointing_steps == 0:
      if args.output_dir is not None:
       SCREAMING_SNAKE_CASE				    =		os.path.join(args.output_dir	,  lowercase_			)
      accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the train (max): {}'.format(
          tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   model.eval()
   for step, batch in enumerate(lowercase_			):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device			)
    with torch.no_grad():
     SCREAMING_SNAKE_CASE				    =		model(**lowercase_			)
    SCREAMING_SNAKE_CASE				    =		outputs.logits.argmax(dim=-1			)
    SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		accelerator.gather_for_metrics((predictions, batch['labels'])			)
    metric.add_batch(
        predictions=lowercase_	,  references=lowercase_	,  )

   SCREAMING_SNAKE_CASE				    =		metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:'''	,  lowercase_			)
   if args.with_tracking:
    accelerator.log(
        {
            'accuracy': eval_metric['accuracy'],
            'f1': eval_metric['f1'],
            'train_loss': total_loss.item() / len(lowercase_			),
        }	,  step=lowercase_	,  )

   if checkpointing_steps == "epoch":
    SCREAMING_SNAKE_CASE				    =		f'''epoch_{epoch}'''
    if args.output_dir is not None:
     SCREAMING_SNAKE_CASE				    =		os.path.join(args.output_dir	,  lowercase_			)
    accelerator.save_state(lowercase_			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
  accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin			)			)			)
  accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used			)			)
  accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked			)			)
  accelerator.print(
      'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
  # Logging the peak memory usage of the GPU to the tracker
  if args.with_tracking:
   accelerator.log(
       {
           'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin			),
       }	,  step=lowercase_	,  )

 if args.with_tracking:
  accelerator.end_training()





def 						A_					(						)				->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		argparse.ArgumentParser(description='Simple example of training script.'			)
 parser.add_argument(
     '--mixed_precision'	,  type=lowercase_	,  default=lowercase_	,  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=lowercase_	,  default=lowercase_	,  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=lowercase_	,  default=lowercase_	,  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=lowercase_	,  default='.'	,  help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,  )
 parser.add_argument(
     '--logging_dir'	,  type=lowercase_	,  default='logs'	,  help='Location on where to store experiment tracking logs`'	,  )
 parser.add_argument(
     '--model_name_or_path'	,  type=lowercase_	,  help='Path to pretrained model or model identifier from huggingface.co/models.'	,  required=lowercase_	,  )
 SCREAMING_SNAKE_CASE				    =		parser.parse_args()
 SCREAMING_SNAKE_CASE				    =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
 training_function(lowercase_	,  lowercase_			)


if __name__ == "__main__":
      main()












import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				str  					='''hf-internal-testing/tiny-stable-diffusion-pipe'''
     __snake_case		:				Union[str, Any]  					=1
     __snake_case		:				str  					=77
     __snake_case		:				int  					=32
     __snake_case		:				List[str]  					=4
     __snake_case		:				Any  					=64
     __snake_case		:				Optional[int]  					=torch.manual_seed(0	)





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  lowerCAmelCase__					:	str=0.99_99)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		UNetaDConditionModel.from_pretrained(self.model_id  ,  subfolder='unet')
      SCREAMING_SNAKE_CASE				    =		unet.to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		EMAModel(unet.parameters()  ,  decay=lowerCAmelCase__  ,  model_cls=lowerCAmelCase__  ,  model_config=unet.config)
      return unet, ema_unet





     def     __UpperCamelCase						(		self					:	List[str])							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		torch.randn(
          self.batch_size  ,  self.num_in_channels  ,  self.latent_height  ,  self.latent_width  ,  generator=self.generator).to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		torch.randint(0  ,  1_0_0_0  ,  size=(self.batch_size,)  ,  generator=self.generator).to(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		torch.randn(
          self.batch_size  ,  self.prompt_length  ,  self.text_encoder_hidden_dim  ,  generator=self.generator).to(lowerCAmelCase__)
      return noisy_latents, timesteps, encoder_hidden_states





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	Tuple)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{}
      for k, param in unet.state_dict().items():
       SCREAMING_SNAKE_CASE				    =		torch.randn_like(lowerCAmelCase__) + (param * torch.randn_like(lowerCAmelCase__))
       updated_state_dict.update({k: updated_param})
      unet.load_state_dict(lowerCAmelCase__)
      return unet





     def     __UpperCamelCase						(		self					:	Optional[int])							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      # Take the first (hypothetical) EMA step.
      ema_unet.step(unet.parameters())
      assert ema_unet.optimization_step == 1

      # Take two more.
      for _ in range(2):
       ema_unet.step(unet.parameters())
      assert ema_unet.optimization_step == 3





     def     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      # Since the `unet` is not being updated (i.e., backprop'd)
      # there won't be any difference between the `params` of `unet`
      # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
      ema_unet.step(unet.parameters())
      SCREAMING_SNAKE_CASE				    =		list(unet.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)

      # The above holds true even if we call `ema.step()` multiple times since
      # `unet` params are still not being updated.
      for _ in range(4):
       ema_unet.step(unet.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Any)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      # Here we simulate the parameter updates for `unet`. Since there might
      # be some parameters which are initialized to zero we take extra care to
      # initialize their values to something non-zero before the multiplication.
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)

      # Take the EMA step.
      ema_unet.step(unet_pseudo_updated_step_one.parameters())

      # Now the EMA'd parameters won't be equal to the original model parameters.
      SCREAMING_SNAKE_CASE				    =		list(unet_pseudo_updated_step_one.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert ~torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)

      # Ensure this is the case when we take multiple EMA steps.
      for _ in range(4):
       ema_unet.step(unet.parameters())
      for s_param, param in zip(ema_unet.shadow_params  ,  lowerCAmelCase__):
       assert ~torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	Dict:

      """simple docstring"""
      # If we call EMA step after a backpropagation consecutively for two times,
      # the shadow params from those two steps should be different.
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()

      # First backprop + EMA
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_one.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      # Second backprop + EMA
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_two.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      for step_one, step_two in zip(lowerCAmelCase__  ,  lowerCAmelCase__):
       assert ~torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	List[str])							->	Optional[Any]:

      """simple docstring"""
      # If there's no decay even if there are backprops, EMA steps
      # won't take any effect i.e., the shadow params would remain the
      # same.
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models(decay=0.0)
      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_one.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      SCREAMING_SNAKE_CASE				    =		self.simulate_backprop(lowerCAmelCase__)
      ema_unet.step(unet_step_two.parameters())
      SCREAMING_SNAKE_CASE				    =		ema_unet.shadow_params

      for step_one, step_two in zip(lowerCAmelCase__  ,  lowerCAmelCase__):
       assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__)





     @skip_mps
     def     __UpperCamelCase						(		self					:	Optional[int])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_models()
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.get_dummy_inputs()

      with tempfile.TemporaryDirectory() as tmpdir:
       ema_unet.save_pretrained(lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		UNetaDConditionModel.from_pretrained(lowerCAmelCase__  ,  model_cls=lowerCAmelCase__)
       SCREAMING_SNAKE_CASE				    =		loaded_unet.to(unet.device)

      # Since no EMA step has been performed the outputs should match.
      SCREAMING_SNAKE_CASE				    =		unet(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__).sample
      SCREAMING_SNAKE_CASE				    =		loaded_unet(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__).sample

      assert torch.allclose(lowerCAmelCase__  ,  lowerCAmelCase__  ,  atol=1e-4)




