

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


__A   :     List[Any]      =			16
__A   :     int      =			32
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> List[str]:



    '''simple docstring'''






    return int(x / 2**20				)






class    __A				:






    def __enter__(	self			:    Any    ):
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowerCAmelCase    			=      torch.cuda.memory_allocated()
        lowerCAmelCase    			=      psutil.Process()

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






    def 	lowercase__ (	self			:    Any    ):
        return self.process.memory_info().rss






    def 	lowercase__ (	self			:    List[Any]    ):
        lowerCAmelCase    			=      -1

        while True:
            lowerCAmelCase    			=      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    ,				*UpperCAmelCase_			:    Optional[Any]    ):
        lowerCAmelCase    			=      False

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

        lowerCAmelCase    			=      self.cpu_mem_used()
        lowerCAmelCase    			=      bamb(self.cpu_end - self.cpu_begin    )
        lowerCAmelCase    			=      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

 __A   :     int      =			mocked_dataloaders  # noqa: F811
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase				)					-> List[Any]:



    '''simple docstring'''






    if os.environ.get('TESTING_MOCKED_DATALOADERS',      _UpperCAmelCase				) == "1":
        lowerCAmelCase    			=      2

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

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

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

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

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

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

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

    def collate_fn(_UpperCAmelCase				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase    			=      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":
            lowerCAmelCase    			=      16
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase    			=      8
        else:
            lowerCAmelCase    			=      None

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

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

    set_seed(_UpperCAmelCase				)

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

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

    # 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
    lowerCAmelCase    			=      torch.optim.AdamW(params=model.parameters(),      lr=_UpperCAmelCase,      weight_decay=2e-4				)

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

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

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

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

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

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

            if checkpointing_steps == "epoch":
                lowerCAmelCase    			=      f"epoch_{epoch}"
                if args.output_dir is not None:
                    lowerCAmelCase    			=      os.path.join(args.output_dir,      _UpperCAmelCase				)
                accelerator.save_state(_UpperCAmelCase				)
        # 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=_UpperCAmelCase,      )

    if args.with_tracking:
        accelerator.end_training()
def 		SCREAMING_SNAKE_CASE__				(							)					-> Union[str, Any]:



    '''simple docstring'''






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


if __name__ == "__main__":
 main()








__A   :     List[Any]      =			'''
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

__A   :     Dict      =			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
__A   :     Tuple      =			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





