






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()









'''simple docstring'''







import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

lowercase__  :	Optional[Any]            =      32





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_ = 16						):
    UpperCamelCase__	     =      AutoTokenizer.from_pretrained("bert-base-cased"						)
    UpperCamelCase__	     =      load_dataset("glue"       ,	"mrpc"						)

    def tokenize_function(lowerCAmelCase_						):
        # 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"						)

    def collate_fn(lowerCAmelCase_						):
        # 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"       ,	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_						)

    return train_dataloader, eval_dataloader


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

      lowercase__  :	Dict            =      mocked_dataloaders  # noqa: F811





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,	lowerCAmelCase_						) == "1":
        UpperCamelCase__	     =      2
    # Initialize accelerator
    UpperCamelCase__	     =      Accelerator(cpu=args.cpu       ,	mixed_precision=args.mixed_precision						)
    # 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"]						)

    UpperCamelCase__	     =      evaluate.load("glue"       ,	"mrpc"						)

    # New Code #
    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase_						)						)
    def inner_training_loop(lowerCAmelCase_						):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        UpperCamelCase__	     =      observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCamelCase__	     =      num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        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_						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      get_dataloaders(lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Instantiate scheduler
        UpperCamelCase__	     =      get_linear_schedule_with_warmup(
            optimizer=lowerCAmelCase_       ,	num_warmup_steps=1_00       ,	num_training_steps=(len(lowerCAmelCase_						) * num_epochs)       ,	)

        # 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_						)

        # Now we train the model
        for epoch in range(lowerCAmelCase_						):
            model.train()
            for step, batch in enumerate(lowerCAmelCase_						):
                # And perform gradient accumulation
                with accelerator.accumulate(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
                    accelerator.backward(lowerCAmelCase_						)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            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_						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()





def     snake_case__			():
    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."						)
    UpperCamelCase__	     =      parser.parse_args()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    UpperCamelCase__	     =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
    training_function(lowerCAmelCase_       ,	lowerCAmelCase_						)


if __name__ == "__main__":
      main()




