




'''simple docstring'''






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


lowercase						:   List[Any]      						=							16
lowercase						:   Any      						=							32




def 		a_(			lowerCamelCase   )					->Union[str, Any]:






       '''simple docstring'''




       return int(x / 2**20   )

class 	SCREAMING_SNAKE_CASE_		:



 def __enter__(	self			):






        '''simple docstring'''




        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowercase	:		Tuple    =   torch.cuda.memory_allocated()
        lowercase	:		Union[str, Any]    =   psutil.Process()

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



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self.process.memory_info().rss



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Union[str, Any]    =   -1

        while True:
               lowercase	:		Any    =   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 ,		*A			):






        '''simple docstring'''




        lowercase	:		Tuple    =   False

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

        lowercase	:		Optional[Any]    =   self.cpu_mem_used()
        lowercase	:		int    =   bamb(self.cpu_end - self.cpu_begin			)
        lowercase	:		Dict    =   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

      lowercase						:   int      						=							mocked_dataloaders  # noqa: F811




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Any:






       '''simple docstring'''




       if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,					lowerCamelCase   ) == "1":
              lowercase	:		Dict    =   2

       # New Code #
       # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
       lowercase	:		Optional[int]    =   FullyShardedDataParallelPlugin(
           state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCamelCase ,					ranka_only=lowerCamelCase   ) ,					optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCamelCase ,					ranka_only=lowerCamelCase   ) ,					)

       # Initialize accelerator
       if args.with_tracking:
              lowercase	:		Optional[int]    =   Accelerator(
                  cpu=args.cpu ,					mixed_precision=args.mixed_precision ,					log_with='''wandb''' ,					project_dir=args.logging_dir ,					fsdp_plugin=lowerCamelCase ,					)
       else:
              lowercase	:		Union[str, Any]    =   Accelerator(fsdp_plugin=lowerCamelCase   )
       accelerator.print(accelerator.distributed_type   )

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

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

       lowercase	:		Optional[int]    =   AutoTokenizer.from_pretrained(args.model_name_or_path   )
       lowercase	:		List[str]    =   load_dataset('''glue''' ,					'''mrpc'''   )
       lowercase	:		Optional[int]    =   evaluate.load('''glue''' ,					'''mrpc'''   )

       def tokenize_function(lowerCamelCase   ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase	:		Union[str, Any]    =   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():
              lowercase	:		Tuple    =   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
       lowercase	:		Union[str, Any]    =   tokenized_datasets.rename_column('''label''' ,					'''labels'''   )

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

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

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

       # Instantiate dataloaders.
       lowercase	:		List[str]    =   DataLoader(
           tokenized_datasets['''train'''] ,					shuffle=lowerCamelCase ,					collate_fn=lowerCamelCase ,					batch_size=lowerCamelCase   )
       lowercase	:		Any    =   DataLoader(
           tokenized_datasets['''validation'''] ,					shuffle=lowerCamelCase ,					collate_fn=lowerCamelCase ,					batch_size=lowerCamelCase   )

       set_seed(lowerCamelCase   )

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

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

       # 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
       lowercase	:		Tuple    =   torch.optim.AdamW(params=model.parameters() ,					lr=lowerCamelCase ,					weight_decay=2E-4   )

       # Instantiate scheduler
       lowercase	:		Optional[Any]    =   get_linear_schedule_with_warmup(
           optimizer=lowerCamelCase ,					num_warmup_steps=10 ,					num_training_steps=(len(lowerCamelCase   ) * 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.
       lowercase,  lowercase,  lowercase,  lowercase	:		Union[str, Any]    =   accelerator.prepare(
           lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )

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

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

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

              # New Code #
              # context manager to track the peak memory usage during the evaluation
              with TorchTracemalloc() as tracemalloc:
                     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():
                                   lowercase	:		int    =   model(**lowerCamelCase   )
                            lowercase	:		List[Any]    =   outputs.logits.argmax(dim=-1   )
                            lowercase,  lowercase	:		List[Any]    =   accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
                            metric.add_batch(
                                predictions=lowerCamelCase ,					references=lowerCamelCase ,					)

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

                     if checkpointing_steps == "epoch":
                            lowercase	:		Any    =   f"""epoch_{epoch}"""
                            if args.output_dir is not None:
                                   lowercase	:		Optional[Any]    =   os.path.join(args.output_dir ,					lowerCamelCase   )
                            accelerator.save_state(lowerCamelCase   )
        # 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=lowerCamelCase ,					)

       if args.with_tracking:
              accelerator.end_training()




def 		a_(			)					->Optional[int]:






       '''simple docstring'''




       lowercase	:		int    =   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.'''   )
       parser.add_argument(
           '''--checkpointing_steps''' ,					type=lowerCamelCase ,					default=lowerCamelCase ,					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=lowerCamelCase ,					default=lowerCamelCase ,					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=lowerCamelCase ,					default='''.''' ,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,					)
       parser.add_argument(
           '''--logging_dir''' ,					type=lowerCamelCase ,					default='''logs''' ,					help='''Location on where to store experiment tracking logs`''' ,					)
       parser.add_argument(
           '''--model_name_or_path''' ,					type=lowerCamelCase ,					help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,					required=lowerCamelCase ,					)
       lowercase	:		List[str]    =   parser.parse_args()
       lowercase	:		List[Any]    =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowerCamelCase ,					lowerCamelCase   )


if __name__ == "__main__":
      main()







'''simple docstring'''

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

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





