

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__	:       Any			      =      16
UpperCAmelCase__	:       Union[str, Any]			      =      32






def    __lowercase (       _A     )       ->  Dict:
      return int(x / 2**20     )







class     a__    :







  """simple docstring"""




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







        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				torch.cuda.memory_allocated()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				psutil.Process()

        SCREAMING_SNAKE_CASE	: int					       =				self.cpu_mem_used()
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				True
        SCREAMING_SNAKE_CASE	: Tuple					       =				threading.Thread(target=self.peak_monitor_func						)
        SCREAMING_SNAKE_CASE	: Tuple					       =				True
        peak_monitor_thread.start()
        return self


  def 	_lowercase (     self      :	Any						)   ->str:
        """simple docstring"""







        return self.process.memory_info().rss


  def 	_lowercase (     self      :	Union[str, Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				-1

        while True:
              SCREAMING_SNAKE_CASE	: List[str]					       =				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      :	Tuple			,			*UpperCAmelCase__      :	Dict						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				False

        gc.collect()
        torch.cuda.empty_cache()
        SCREAMING_SNAKE_CASE	: Dict					       =				torch.cuda.memory_allocated()
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				torch.cuda.max_memory_allocated()
        SCREAMING_SNAKE_CASE	: Tuple					       =				bamb(self.end - self.begin						)
        SCREAMING_SNAKE_CASE	: Any					       =				bamb(self.peak - self.begin						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.cpu_mem_used()
        SCREAMING_SNAKE_CASE	: Tuple					       =				bamb(self.cpu_end - self.cpu_begin						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				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__	:       Any			      =      mocked_dataloaders  # noqa: F811






def    __lowercase (       _A  ,      _A     )       ->  List[Any]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""  ,      _A     ) == "1":
            SCREAMING_SNAKE_CASE	: Dict					       =				2

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

      # Initialize accelerator
      if args.with_tracking:
            SCREAMING_SNAKE_CASE	: Dict					       =				Accelerator(
                cpu=args.cpu  ,      mixed_precision=args.mixed_precision  ,      log_with="""wandb"""  ,      project_dir=args.logging_dir  ,      fsdp_plugin=_A  ,      )
      else:
            SCREAMING_SNAKE_CASE	: List[Any]					       =				Accelerator(fsdp_plugin=_A     )
      accelerator.print(accelerator.distributed_type     )

      if hasattr(args.checkpointing_steps  ,      """isdigit"""     ):
            if args.checkpointing_steps == "epoch":
                  SCREAMING_SNAKE_CASE	: Any					       =				args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  SCREAMING_SNAKE_CASE	: List[Any]					       =				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	: List[str]					       =				None
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE	: Tuple					       =				config["""lr"""]
      SCREAMING_SNAKE_CASE	: List[str]					       =				int(config["""num_epochs"""]     )
      SCREAMING_SNAKE_CASE	: Any					       =				int(config["""seed"""]     )
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				int(config["""batch_size"""]     )

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

      SCREAMING_SNAKE_CASE	: Tuple					       =				AutoTokenizer.from_pretrained(args.model_name_or_path     )
      SCREAMING_SNAKE_CASE	: Dict					       =				load_dataset("""glue"""  ,      """mrpc"""     )
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				evaluate.load("""glue"""  ,      """mrpc"""     )

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

      # If the batch size is too big we use gradient accumulation
      SCREAMING_SNAKE_CASE	: Tuple					       =				1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				batch_size // MAX_GPU_BATCH_SIZE
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				MAX_GPU_BATCH_SIZE

      def collate_fn(_A     ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE	: Optional[Any]					       =				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":
                  SCREAMING_SNAKE_CASE	: Tuple					       =				16
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE	: Any					       =				8
            else:
                  SCREAMING_SNAKE_CASE	: int					       =				None

            return tokenizer.pad(
                _A  ,      padding="""longest"""  ,      max_length=_A  ,      pad_to_multiple_of=_A  ,      return_tensors="""pt"""  ,      )

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				DataLoader(
          tokenized_datasets["""train"""]  ,      shuffle=_A  ,      collate_fn=_A  ,      batch_size=_A     )
      SCREAMING_SNAKE_CASE	: List[str]					       =				DataLoader(
          tokenized_datasets["""validation"""]  ,      shuffle=_A  ,      collate_fn=_A  ,      batch_size=_A     )

      set_seed(_A     )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE	: List[Any]					       =				AutoModelForSequenceClassification.from_pretrained(
          args.model_name_or_path  ,      return_dict=_A  ,      low_cpu_mem_usage=_A     )

      # New Code #
      # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				accelerator.prepare(_A     )
      accelerator.print(_A     )

      # 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	: Tuple					       =				torch.optim.AdamW(params=model.parameters()  ,      lr=_A  ,      weight_decay=2e-4     )

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE	: Tuple					       =				get_linear_schedule_with_warmup(
          optimizer=_A  ,      num_warmup_steps=10  ,      num_training_steps=(len(_A     ) * 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	: Dict					       =				accelerator.prepare(
          _A  ,      _A  ,      _A  ,      _A     )

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

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

    # Now we train the model
      for epoch in range(_A     ):
            # 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	: Union[str, Any]					       =				0
                  for step, batch in enumerate(_A     ):
                        # 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	: str					       =				model(**_A     )
                        SCREAMING_SNAKE_CASE	: Dict					       =				outputs.loss
                        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				loss / gradient_accumulation_steps
                        # We keep track of the loss at each epoch
                        if args.with_tracking:
                              total_loss += loss.detach().float()
                        accelerator.backward(_A     )
                        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(_A  ,      _A     ):
                              SCREAMING_SNAKE_CASE	: str					       =				F"step_{overall_step}"
                              if overall_step % checkpointing_steps == 0:
                                    if args.output_dir is not None:
                                          SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				os.path.join(args.output_dir  ,      _A     )
                                    accelerator.save_state(_A     )
        # 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=_A  ,      )

            # New Code #
            # context manager to track the peak memory usage during the evaluation
            with TorchTracemalloc() as tracemalloc:
                  model.eval()
                  for step, batch in enumerate(_A     ):
                        # 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	: List[str]					       =				model(**_A     )
                        SCREAMING_SNAKE_CASE	: List[Any]					       =				outputs.logits.argmax(dim=-1     )
                        SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
                        metric.add_batch(
                            predictions=_A  ,      references=_A  ,      )

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

                  if checkpointing_steps == "epoch":
                        SCREAMING_SNAKE_CASE	: List[str]					       =				F"epoch_{epoch}"
                        if args.output_dir is not None:
                              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				os.path.join(args.output_dir  ,      _A     )
                        accelerator.save_state(_A     )
        # 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=_A  ,      )

      if args.with_tracking:
            accelerator.end_training()






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


if __name__ == "__main__":
  main()







UpperCAmelCase__	:       Union[str, 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
"""

UpperCAmelCase__	:       List[Any]			      =      [{"""type""": """code""", """content""": INSTALL_CONTENT}]
UpperCAmelCase__	:       List[Any]			      =      {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}




