



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   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]							)     ->    Dict:
   return int(x / 2**20							)




class   lowerCamelCase__  :



 """simple docstring"""

 def __enter__(						self				:				Dict			)      ->  List[str]:
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
    _A						     =					torch.cuda.memory_allocated()
    _A						     =					psutil.Process()

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

 def 						snake_case_			(						self				:				Any			)      ->  Union[str, Any]:
    return self.process.memory_info().rss

 def 						snake_case_			(						self				:				Dict			)      ->  List[Any]:
    _A						     =					-1

    while True:
       _A						     =					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				:				Optional[Any] ,	*__lowerCAmelCase				:				Optional[int]			)      ->  Optional[Any]:
    _A						     =					False

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

    _A						     =					self.cpu_mem_used()
    _A						     =					bamb(self.cpu_end - self.cpu_begin			)
    _A						     =					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   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]  ,      _snake_case  :Optional[Any]							)     ->    Optional[int]:







   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,      _snake_case							) == "1":
      _A						     =					2

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

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

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

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

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

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

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

   def collate_fn(_snake_case  :int							):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _A						     =					128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _A						     =					16
      elif accelerator.mixed_precision != "no":
         _A						     =					8
      else:
         _A						     =					None

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

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

   set_seed(_snake_case							)

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

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

   # 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
   _A						     =					torch.optim.AdamW(params=model.parameters()  ,      lr=_snake_case  ,      weight_decay=2E-4							)

   # Instantiate scheduler
   _A						     =					get_linear_schedule_with_warmup(
       optimizer=_snake_case  ,      num_warmup_steps=10  ,      num_training_steps=(len(_snake_case							) * 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.
   _A     ,						_A     ,						_A     ,						_A						     =					accelerator.prepare(
       _snake_case  ,      _snake_case  ,      _snake_case  ,      _snake_case							)

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

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

    # Now we train the model
   for epoch in range(_snake_case							):
      # New Code #
      # context manager to track the peak memory usage during the training epoch
      with TorchTracemalloc() as tracemalloc:
         model.train()
         if args.with_tracking:
            _A						     =					0
         for step, batch in enumerate(_snake_case							):
            # 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							)
            _A						     =					model(**_snake_case							)
            _A						     =					outputs.loss
            _A						     =					loss / gradient_accumulation_steps
            # We keep track of the loss at each epoch
            if args.with_tracking:
               total_loss += loss.detach().float()
            accelerator.backward(_snake_case							)
            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(_snake_case  ,      _snake_case							):
               _A						     =					F'''step_{overall_step}'''
               if overall_step % checkpointing_steps == 0:
                  if args.output_dir is not None:
                     _A						     =					os.path.join(args.output_dir  ,      _snake_case							)
                  accelerator.save_state(_snake_case							)
        # 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=_snake_case  ,      )

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

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

         if checkpointing_steps == "epoch":
            _A						     =					F'''epoch_{epoch}'''
            if args.output_dir is not None:
               _A						     =					os.path.join(args.output_dir  ,      _snake_case							)
            accelerator.save_state(_snake_case							)
        # 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=_snake_case  ,      )

   if args.with_tracking:
      accelerator.end_training()

def   SCREAMING_SNAKE_CASE_       (  )     ->    Tuple:
   _A						     =					argparse.ArgumentParser(description='''Simple example of training script.'''							)
   parser.add_argument(
       '''--mixed_precision'''  ,      type=_snake_case  ,      default=_snake_case  ,      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=_snake_case  ,      default=_snake_case  ,      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=_snake_case  ,      default=_snake_case  ,      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=_snake_case  ,      default='''.'''  ,      help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,      )
   parser.add_argument(
       '''--logging_dir'''  ,      type=_snake_case  ,      default='''logs'''  ,      help='''Location on where to store experiment tracking logs`'''  ,      )
   parser.add_argument(
       '''--model_name_or_path'''  ,      type=_snake_case  ,      help='''Path to pretrained model or model identifier from huggingface.co/models.'''  ,      required=_snake_case  ,      )
   _A						     =					parser.parse_args()
   _A						     =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_snake_case  ,      _snake_case							)


if __name__ == "__main__":
   main()








def   SCREAMING_SNAKE_CASE_       (  _snake_case  :list							)     ->    list:
   if len(_snake_case							) <= 1:
      return data
   else:
      return [
          *quick_sort([e for e in data[1:] if e <= data[0]]							),
          data[0],
          *quick_sort([e for e in data[1:] if e > data[0]]							),
      ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



