
'''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__						=			1_6
lowercase__						=			3_2




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:


  '''simple docstring'''


  return int(x / 2**20 )






class   snake_case__					:







  """simple docstring"""

  def __enter__(			self							: Optional[int]			)	-> Optional[int]:






    """simple docstring"""







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

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



  def 			lowerCAmelCase							(			self							: List[str]			)	-> Union[str, Any]:






    """simple docstring"""







    return self.process.memory_info().rss



  def 			lowerCAmelCase							(			self							: int			)	-> Union[str, Any]:






    """simple docstring"""







    snake_case     :					Union[str, Any]     =	-1

    while True:
      snake_case     :					Tuple     =	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__							: str			)	-> str:






    """simple docstring"""







    snake_case     :					Tuple     =	False

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

    snake_case     :					Optional[int]     =	self.cpu_mem_used()
    snake_case     :					List[str]     =	bamb(self.cpu_end - self.cpu_begin			)
    snake_case     :					str     =	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__						=			mocked_dataloaders  # noqa: F811




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


  '''simple docstring'''


  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,					SCREAMING_SNAKE_CASE__ ) == "1":
    snake_case     :					List[str]     =	2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  snake_case     :					List[Any]     =	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:
    snake_case     :					Optional[int]     =	Accelerator(
        cpu=args.cpu   ,					mixed_precision=args.mixed_precision   ,					log_with='''wandb'''   ,					project_dir=args.logging_dir   ,					fsdp_plugin=SCREAMING_SNAKE_CASE__   ,					)
  else:
    snake_case     :					str     =	Accelerator(fsdp_plugin=SCREAMING_SNAKE_CASE__ )
  accelerator.print(accelerator.distributed_type )

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

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

  snake_case     :					Any     =	AutoTokenizer.from_pretrained(args.model_name_or_path )
  snake_case     :					List[Any]     =	load_dataset('''glue'''   ,					'''mrpc''' )
  snake_case     :					Any     =	evaluate.load('''glue'''   ,					'''mrpc''' )

  def tokenize_function(SCREAMING_SNAKE_CASE__ ):
    # max_length=None => use the model max length (it's actually the default)
    snake_case     :					List[str]     =	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():
    snake_case     :					Dict     =	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
  snake_case     :					int     =	tokenized_datasets.rename_column('''label'''   ,					'''labels''' )

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

  def collate_fn(SCREAMING_SNAKE_CASE__ ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    snake_case     :					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":
      snake_case     :					Any     =	16
    elif accelerator.mixed_precision != "no":
      snake_case     :					List[Any]     =	8
    else:
      snake_case     :					List[str]     =	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.
  snake_case     :					Any     =	DataLoader(
      tokenized_datasets['''train''']   ,					shuffle=SCREAMING_SNAKE_CASE__   ,					collate_fn=SCREAMING_SNAKE_CASE__   ,					batch_size=SCREAMING_SNAKE_CASE__ )
  snake_case     :					Dict     =	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)
  snake_case     :					Optional[Any]     =	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
  snake_case     :					Union[str, Any]     =	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
  snake_case     :					Optional[Any]     =	torch.optim.AdamW(params=model.parameters()   ,					lr=SCREAMING_SNAKE_CASE__   ,					weight_decay=2E-4 )

  # Instantiate scheduler
  snake_case     :					int     =	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.
  snake_case     :					str     =	accelerator.prepare(
      SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )

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

    if "epoch" in training_difference:
      num_epochs -= int(training_difference.replace('''epoch_'''   ,					'''''' ) )
      snake_case     :					Dict     =	None
    else:
      snake_case     :					Optional[int]     =	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
      snake_case     :					str     =	(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:
        snake_case     :					Any     =	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 )
        snake_case     :					str     =	model(**SCREAMING_SNAKE_CASE__ )
        snake_case     :					Optional[int]     =	outputs.loss
        snake_case     :					Tuple     =	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__ ):
          snake_case     :					Dict     =	F'step_{overall_step}'
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              snake_case     :					Union[str, Any]     =	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():
          snake_case     :					Optional[Any]     =	model(**SCREAMING_SNAKE_CASE__ )
        snake_case     :					List[str]     =	outputs.logits.argmax(dim=-1 )
        snake_case     :					Tuple     =	accelerator.gather_for_metrics((predictions, batch['''labels''']) )
        metric.add_batch(
            predictions=SCREAMING_SNAKE_CASE__   ,					references=SCREAMING_SNAKE_CASE__   ,					)

      snake_case     :					List[Any]     =	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":
        snake_case     :					Optional[Any]     =	F'epoch_{epoch}'
        if args.output_dir is not None:
          snake_case     :					int     =	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	(					)     ->       List[str]:


  '''simple docstring'''


  snake_case     :					str     =	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__   ,					)
  snake_case     :					str     =	parser.parse_args()
  snake_case     :					Optional[Any]     =	{'''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'''
from typing import TYPE_CHECKING

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


lowercase__						=			{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			["FlavaFeatureExtractor"]
						lowercase__						=			["FlavaImageProcessor"]
						lowercase__						=			["FlavaProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			[
						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FlavaForPreTraining",
						    "FlavaImageCodebook",
						    "FlavaImageModel",
						    "FlavaModel",
						    "FlavaMultimodalModel",
						    "FlavaPreTrainedModel",
						    "FlavaTextModel",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

						lowercase__						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


