

"""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
#
########################################################################


lowerCamelCase									= 16
lowerCamelCase									= 32




def       UpperCAmelCase__    (      _A				:			Optional[Any]							):





  '''simple docstring'''







  return int(x / 2**20							)







class 						__magic_name__      :

   '''simple docstring'''




   def __enter__(    self	)							->   Tuple:






     """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 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     return self.process.memory_info().rss



   def 			_UpperCAmelCase			(    self	)							->   int:






     """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,   *lowercase_	)							->   List[str]:






     """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__    (      _A				:			Dict     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,				_A							) == "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=_A     ,				ranka_only=_A							)     ,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=_A     ,				ranka_only=_A							)     ,				)

  # 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=_A     ,				)
  else:
    a__  =Accelerator(fsdp_plugin=_A							)
  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(_A							)
    accelerator.init_trackers('''fsdp_glue_no_trainer'''     ,				_A							)

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

  def tokenize_function(_A				:			Optional[int]							):
    # max_length=None => use the model max length (it's actually the default)
    a__  =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():
    a__  =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
  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(_A				:			Any							):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a__  =1_28 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(
        _A     ,				padding='''longest'''     ,				max_length=_A     ,				pad_to_multiple_of=_A     ,				return_tensors='''pt'''     ,				)

  # Instantiate dataloaders.
  a__  =DataLoader(
      tokenized_datasets['''train''']     ,				shuffle=_A     ,				collate_fn=_A     ,				batch_size=_A							)
  a__  =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)
  a__  =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
  a__  =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
  a__  =torch.optim.AdamW(params=model.parameters()     ,				lr=_A     ,				weight_decay=2E-4							)

  # Instantiate scheduler
  a__  =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.
  a__,				a__,				a__,				a__  =accelerator.prepare(
      _A     ,				_A     ,				_A     ,				_A							)

  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(_A							)[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(_A							)
      # If resuming by step, we also need to know exactly how far into the DataLoader we went
      a__  =(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:
        a__  =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							)
        a__  =model(**_A							)
        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(_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							):
          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     ,				_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():
          a__  =model(**_A							)
        a__  =outputs.logits.argmax(dim=-1							)
        a__,				a__  =accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
        metric.add_batch(
            predictions=_A     ,				references=_A     ,				)

      a__  =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":
        a__  =F"""epoch_{epoch}"""
        if args.output_dir is not None:
          a__  =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       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =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     ,				)
  a__  =parser.parse_args()
  a__  ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_A     ,				_A							)


if __name__ == "__main__":
       main()



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase									= {
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaubertForMultipleChoice''',
           '''FlaubertForQuestionAnswering''',
           '''FlaubertForQuestionAnsweringSimple''',
           '''FlaubertForSequenceClassification''',
           '''FlaubertForTokenClassification''',
           '''FlaubertModel''',
           '''FlaubertWithLMHeadModel''',
           '''FlaubertPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFFlaubertForMultipleChoice''',
           '''TFFlaubertForQuestionAnsweringSimple''',
           '''TFFlaubertForSequenceClassification''',
           '''TFFlaubertForTokenClassification''',
           '''TFFlaubertModel''',
           '''TFFlaubertPreTrainedModel''',
           '''TFFlaubertWithLMHeadModel''',
       ]


if TYPE_CHECKING:
       from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
       from .tokenization_flaubert import FlaubertTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )

else:
       import sys

       lowerCamelCase									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
