






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
#
########################################################################


__lowerCAmelCase					   =       16
__lowerCAmelCase					   =       32


def 			__UpperCamelCase				(			lowercase_					:     Tuple      ):
       """simple docstring"""




       return int(x / 2**20      )




class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  def __enter__( self  ):






         """simple docstring"""


         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       _a					( self  ):






         """simple docstring"""


         return self.process.memory_info().rss







  def       _a					( self  ):






         """simple docstring"""


         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      ,  *UpperCamelCase__  ):






         """simple docstring"""


         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

  __lowerCAmelCase					   =       mocked_dataloaders  # noqa: F811


def 			__UpperCamelCase				(			lowercase_					:     List[Any]      ,					lowercase_					:     Tuple      ):
       """simple docstring"""




       if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,					SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					ranka_only=SCREAMING_SNAKE_CASE__      )      ,					optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=SCREAMING_SNAKE_CASE__      ,					ranka_only=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					)
       else:
              a_	    = Accelerator(fsdp_plugin=SCREAMING_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(SCREAMING_SNAKE_CASE__      )
              accelerator.init_trackers('fsdp_glue_no_trainer'      ,					SCREAMING_SNAKE_CASE__      )

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

       def tokenize_function(lowercase_					:     Optional[int]      ):
              # max_length=None => use the model max length (it's actually the default)
              a_	    = tokenizer(examples['sentence1']      ,					examples['sentence2']      ,					truncation=SCREAMING_SNAKE_CASE__      ,					max_length=SCREAMING_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(
                  SCREAMING_SNAKE_CASE__      ,					batched=SCREAMING_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(lowercase_					:     str      ):
              # 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(
                  SCREAMING_SNAKE_CASE__      ,					padding='longest'      ,					max_length=SCREAMING_SNAKE_CASE__      ,					pad_to_multiple_of=SCREAMING_SNAKE_CASE__      ,					return_tensors='pt'      ,					)

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

       set_seed(SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					low_cpu_mem_usage=SCREAMING_SNAKE_CASE__      )

       # New Code #
       # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
       a_	    = accelerator.prepare(SCREAMING_SNAKE_CASE__      )
       accelerator.print(SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					weight_decay=2E-4      )

       # Instantiate scheduler
       a_	    = get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE__      ,					num_warmup_steps=10      ,					num_training_steps=(len(SCREAMING_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_	    = accelerator.prepare(
           SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE__      )
                     # If resuming by step, we also need to know exactly how far into the DataLoader we went
                     a_	    = (num_epochs * len(SCREAMING_SNAKE_CASE__      )) - resume_step

    # Now we train the model
       for epoch in range(SCREAMING_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(SCREAMING_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(**SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE__      ,					SCREAMING_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      ,					SCREAMING_SNAKE_CASE__      )
                                          accelerator.save_state(SCREAMING_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=SCREAMING_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(SCREAMING_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(**SCREAMING_SNAKE_CASE__      )
                            a_	    = outputs.logits.argmax(dim=-1      )
                            a_	    = accelerator.gather_for_metrics((predictions, batch['labels'])      )
                            metric.add_batch(
                                predictions=SCREAMING_SNAKE_CASE__      ,					references=SCREAMING_SNAKE_CASE__      ,					)

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

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

       if args.with_tracking:
              accelerator.end_training()


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = argparse.ArgumentParser(description='Simple example of training script.'      )
       parser.add_argument(
           '--mixed_precision'      ,					type=SCREAMING_SNAKE_CASE__      ,					default=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					default=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					default=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__      ,					default='logs'      ,					help='Location on where to store experiment tracking logs`'      ,					)
       parser.add_argument(
           '--model_name_or_path'      ,					type=SCREAMING_SNAKE_CASE__      ,					help='Path to pretrained model or model identifier from huggingface.co/models.'      ,					required=SCREAMING_SNAKE_CASE__      ,					)
       a_	    = parser.parse_args()
       a_	    = {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
  main()












'''simple docstring'''







def 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




       if not isinstance(lowercase_      ,					lowercase_      ):
              raise ValueError('double_factorial() only accepts integral values'      )
       if num < 0:
              raise ValueError('double_factorial() not defined for negative values'      )
       a_	    = 1
       for i in range(lowercase_      ,					0      ,					-2      ):
              value *= i
       return value


if __name__ == "__main__":
  import doctest

  doctest.testmod()




