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


_a   :      str       =				16
_a   :      Any       =				32
def 					a__ ( a :					Optional[Any]		):

  """simple docstring"""







  return int(x / 2**20		)






class   _UpperCAmelCase					:
    def __enter__(     self  ):
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      _snake_case  :    Optional[Any]            = torch.cuda.memory_allocated()
      _snake_case  :    int            = psutil.Process()

      _snake_case  :    Any            = self.cpu_mem_used()
      _snake_case  :    Any            = True
      _snake_case  :    Tuple            = threading.Thread(target=self.peak_monitor_func  )
      _snake_case  :    Any            = True
      peak_monitor_thread.start()
      return self
    def        lowerCamelCase__						(     self  ):
      return self.process.memory_info().rss
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = -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    ,     *snake_case_  ):
      _snake_case  :    Optional[int]            = False

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

      _snake_case  :    Optional[Any]            = self.cpu_mem_used()
      _snake_case  :    Union[str, Any]            = bamb(self.cpu_end - self.cpu_begin  )
      _snake_case  :    Union[str, 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

    _a   :      Tuple       =				mocked_dataloaders  # noqa: F811
def 					a__ ( a :					Union[str, Any]							,  a :					List[str]		):

  """simple docstring"""







  if os.environ.get("TESTING_MOCKED_DATALOADERS"							,  a		) == "1":
    _snake_case  :    Any            = 2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  _snake_case  :    Union[str, Any]            = 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:
    _snake_case  :    Optional[Any]            = Accelerator(
        cpu=args.cpu							,  mixed_precision=args.mixed_precision							,  log_with="wandb"							,  project_dir=args.logging_dir							,  fsdp_plugin=a							,  )
  else:
    _snake_case  :    str            = Accelerator(fsdp_plugin=a		)
  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  :    Optional[int]            = 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  :    List[Any]            = 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  :    Optional[Any]            = int(config["seed"]		)
  _snake_case  :    int            = int(config["batch_size"]		)

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

  _snake_case  :    List[str]            = AutoTokenizer.from_pretrained(args.model_name_or_path		)
  _snake_case  :    Optional[Any]            = load_dataset("glue"							,  "mrpc"		)
  _snake_case  :    Any            = evaluate.load("glue"							,  "mrpc"		)

  def tokenize_function(a :					Any		):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case  :    Optional[Any]            = 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():
    _snake_case  :    Optional[int]            = 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
  _snake_case  :    List[str]            = tokenized_datasets.rename_column("label"							,  "labels"		)

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

  def collate_fn(a :					Dict		):
    # 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  :    Any            = 8
    else:
      _snake_case  :    List[Any]            = None

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

  # Instantiate dataloaders.
  _snake_case  :    List[Any]            = DataLoader(
      tokenized_datasets["train"]							,  shuffle=a							,  collate_fn=a							,  batch_size=a		)
  _snake_case  :    List[Any]            = 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)
  _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
  _snake_case  :    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
  _snake_case  :    Any            = torch.optim.AdamW(params=model.parameters()							,  lr=a							,  weight_decay=2e-4		)

  # Instantiate scheduler
  _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.
  _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case  :    Optional[int]            = accelerator.prepare(
      a							,  a							,  a							,  a		)

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

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

      _snake_case  :    Dict            = 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":
        _snake_case  :    Optional[int]            = f'epoch_{epoch}'
        if args.output_dir is not None:
          _snake_case  :    Tuple            = 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 					a__ ( ):

  """simple docstring"""







  _snake_case  :    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							,  )
  _snake_case  :    List[str]            = parser.parse_args()
  _snake_case  :    Any            = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(a							,  a		)


if __name__ == "__main__":
    main()








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


UpperCAmelCase_       : Union[str, Any]							=    logging.get_logger(__name__)





class   lowercase__     (				__A	):
  __UpperCamelCase						  =	"""vision-text-dual-encoder"""
  __UpperCamelCase						  =	True







  def __init__(						self     ,    _lowercase=512     ,    _lowercase=2.6592     ,    **_lowercase    ):
       super().__init__(**_lowercase    )

       if "vision_config" not in kwargs:
            raise ValueError("""`vision_config` can not be `None`."""    )

       if "text_config" not in kwargs:
            raise ValueError("""`text_config` can not be `None`."""    )

       lowerCAmelCase_       :							Dict			 =		kwargs.pop("""vision_config"""    )
       lowerCAmelCase_       :							Optional[int]			 =		kwargs.pop("""text_config"""    )

       lowerCAmelCase_       :							Tuple			 =		vision_config.pop("""model_type"""    )
       lowerCAmelCase_       :							str			 =		text_config.pop("""model_type"""    )

       if vision_model_type == "clip":
            lowerCAmelCase_       :							Dict			 =		AutoConfig.for_model(_lowercase     ,    **_lowercase    ).vision_config
       elif vision_model_type == "clip_vision_model":
            lowerCAmelCase_       :							Any			 =		CLIPVisionConfig(**_lowercase    )
       else:
            lowerCAmelCase_       :							Tuple			 =		AutoConfig.for_model(_lowercase     ,    **_lowercase    )

       lowerCAmelCase_       :							List[Any]			 =		AutoConfig.for_model(_lowercase     ,    **_lowercase    )

       lowerCAmelCase_       :							Union[str, Any]			 =		projection_dim
       lowerCAmelCase_       :							Optional[int]			 =		logit_scale_init_value







  @classmethod
  def 						UpperCAmelCase__		(						cls     ,    _lowercase     ,    _lowercase     ,    **_lowercase    ):
       return cls(vision_config=vision_config.to_dict()     ,    text_config=text_config.to_dict()     ,    **_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							int			 =		copy.deepcopy(self.__dict__    )
       lowerCAmelCase_       :							List[str]			 =		self.vision_config.to_dict()
       lowerCAmelCase_       :							List[str]			 =		self.text_config.to_dict()
       lowerCAmelCase_       :							str			 =		self.__class__.model_type
       return output
