





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__       :     Any          =16
A__       :     Optional[Any]          =32
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      return int(x / 2**20						)




class 				UpperCAmelCase  :




      def __enter__(    self    :				Union[str, Any]						)       ->   str:
            gc.collect()
            torch.cuda.empty_cache()
            torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
            _lowerCAmelCase							  =							torch.cuda.memory_allocated()
            _lowerCAmelCase							  =							psutil.Process()

            _lowerCAmelCase							  =							self.cpu_mem_used()
            _lowerCAmelCase							  =							True
            _lowerCAmelCase							  =							threading.Thread(target=self.peak_monitor_func						)
            _lowerCAmelCase							  =							True
            peak_monitor_thread.start()
            return self




      def        lowercase__					(    self    :				List[str]						)       ->   Tuple:
            return self.process.memory_info().rss




      def        lowercase__					(    self    :				Dict						)       ->   Any:
            _lowerCAmelCase							  =							-1

            while True:
                  _lowerCAmelCase							  =							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    :				Optional[Any]    ,    *__snake_case    :				List[Any]						)       ->   Tuple:
            _lowerCAmelCase							  =							False

            gc.collect()
            torch.cuda.empty_cache()
            _lowerCAmelCase							  =							torch.cuda.memory_allocated()
            _lowerCAmelCase							  =							torch.cuda.max_memory_allocated()
            _lowerCAmelCase							  =							bamb(self.end - self.begin						)
            _lowerCAmelCase							  =							bamb(self.peak - self.begin						)

            _lowerCAmelCase							  =							self.cpu_mem_used()
            _lowerCAmelCase							  =							bamb(self.cpu_end - self.cpu_begin						)
            _lowerCAmelCase							  =							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__       :     Union[str, Any]          =mocked_dataloaders  # noqa: F811
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			UpperCAmelCase_						) == "1":
            _lowerCAmelCase							  =							2

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

      # Initialize accelerator
      if args.with_tracking:
            _lowerCAmelCase							  =							Accelerator(
                cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			log_with="""wandb"""							,			project_dir=args.logging_dir							,			fsdp_plugin=UpperCAmelCase_							,			)
      else:
            _lowerCAmelCase							  =							Accelerator(fsdp_plugin=UpperCAmelCase_						)
      accelerator.print(accelerator.distributed_type						)

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

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

      _lowerCAmelCase							  =							AutoTokenizer.from_pretrained(args.model_name_or_path						)
      _lowerCAmelCase							  =							load_dataset("""glue"""							,			"""mrpc"""						)
      _lowerCAmelCase							  =							evaluate.load("""glue"""							,			"""mrpc"""						)

      def tokenize_function(lowerCAmelCase						):
            # max_length=None => use the model max length (it's actually the default)
            _lowerCAmelCase							  =							tokenizer(examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=UpperCAmelCase_							,			max_length=UpperCAmelCase_						)
            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():
            _lowerCAmelCase							  =							datasets.map(
                UpperCAmelCase_							,			batched=UpperCAmelCase_							,			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
      _lowerCAmelCase							  =							tokenized_datasets.rename_column("""label"""							,			"""labels"""						)

      # If the batch size is too big we use gradient accumulation
      _lowerCAmelCase							  =							1
      if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
            _lowerCAmelCase							  =							batch_size // MAX_GPU_BATCH_SIZE
            _lowerCAmelCase							  =							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.
            _lowerCAmelCase							  =							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":
                  _lowerCAmelCase							  =							16
            elif accelerator.mixed_precision != "no":
                  _lowerCAmelCase							  =							8
            else:
                  _lowerCAmelCase							  =							None

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

      # Instantiate dataloaders.
      _lowerCAmelCase							  =							DataLoader(
          tokenized_datasets["""train"""]							,			shuffle=UpperCAmelCase_							,			collate_fn=UpperCAmelCase_							,			batch_size=UpperCAmelCase_						)
      _lowerCAmelCase							  =							DataLoader(
          tokenized_datasets["""validation"""]							,			shuffle=UpperCAmelCase_							,			collate_fn=UpperCAmelCase_							,			batch_size=UpperCAmelCase_						)

      set_seed(UpperCAmelCase_						)

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

      # New Code #
      # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
      _lowerCAmelCase							  =							accelerator.prepare(UpperCAmelCase_						)
      accelerator.print(UpperCAmelCase_						)

      # 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
      _lowerCAmelCase							  =							torch.optim.AdamW(params=model.parameters()							,			lr=UpperCAmelCase_							,			weight_decay=2e-4						)

      # Instantiate scheduler
      _lowerCAmelCase							  =							get_linear_schedule_with_warmup(
          optimizer=UpperCAmelCase_							,			num_warmup_steps=10							,			num_training_steps=(len(UpperCAmelCase_						) * 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.
      _lowerCAmelCase							  =							accelerator.prepare(
          UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_						)

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

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

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

            # New Code #
            # context manager to track the peak memory usage during the evaluation
            with TorchTracemalloc() as tracemalloc:
                  model.eval()
                  for step, batch in enumerate(UpperCAmelCase_						):
                        # We could avoid this line since we set the accelerator with `device_placement=True`.
                        batch.to(accelerator.device						)
                        with torch.no_grad():
                              _lowerCAmelCase							  =							model(**UpperCAmelCase_						)
                        _lowerCAmelCase							  =							outputs.logits.argmax(dim=-1						)
                        _lowerCAmelCase							  =							accelerator.gather_for_metrics((predictions, batch["""labels"""])						)
                        metric.add_batch(
                            predictions=UpperCAmelCase_							,			references=UpperCAmelCase_							,			)

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

                  if checkpointing_steps == "epoch":
                        _lowerCAmelCase							  =							f"epoch_{epoch}"
                        if args.output_dir is not None:
                              _lowerCAmelCase							  =							os.path.join(args.output_dir							,			UpperCAmelCase_						)
                        accelerator.save_state(UpperCAmelCase_						)
        # 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=UpperCAmelCase_							,			)

      if args.with_tracking:
            accelerator.end_training()
def      UpperCamelCase__   (	):





      """simple docstring"""


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


if __name__ == "__main__":
      main()













'''simple docstring'''






class 				UpperCAmelCase  :




      def __init__(    self    :				Optional[Any]						)       ->   List[str]:
            _lowerCAmelCase							  =							[0]
            _lowerCAmelCase							  =							0




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            _lowerCAmelCase							  =							self.__heap[i]
            while i // 2 > 0:
                  if self.__heap[i] > self.__heap[i // 2]:
                        _lowerCAmelCase							  =							self.__heap[i // 2]
                        _lowerCAmelCase							  =							temporary
                  i //= 2




      def        lowercase__					(    self    :				Optional[Any]    ,    __snake_case    :				int						)       ->   None:
            self.__heap.append(__snake_case						)
            self.__size += 1
            self.__swap_up(self.__size						)




      def        lowercase__					(    self    :				str    ,    __snake_case    :				int						)       ->   None:
            while self.__size >= 2 * i:
                  if 2 * i + 1 > self.__size:
                        _lowerCAmelCase							  =							2 * i
                  else:
                        if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                              _lowerCAmelCase							  =							2 * i
                        else:
                              _lowerCAmelCase							  =							2 * i + 1
                  _lowerCAmelCase							  =							self.__heap[i]
                  if self.__heap[i] < self.__heap[bigger_child]:
                        _lowerCAmelCase							  =							self.__heap[bigger_child]
                        _lowerCAmelCase							  =							temporary
                  _lowerCAmelCase							  =							bigger_child




      def        lowercase__					(    self    :				Tuple						)       ->   int:
            _lowerCAmelCase							  =							self.__heap[1]
            _lowerCAmelCase							  =							self.__heap[self.__size]
            self.__size -= 1
            self.__heap.pop()
            self.__swap_down(1						)
            return max_value




      @property
      def        lowercase__					(    self    :				List[Any]						)       ->   str:
            return self.__heap[1:]




      def __len__(    self    :				Optional[Any]						)       ->   str:
            return self.__size


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A__       :     Tuple          =BinaryHeap()
      binary_heap.insert(6)
      binary_heap.insert(10)
      binary_heap.insert(15)
      binary_heap.insert(12)
      # pop root(max-values because it is max heap)
      print(binary_heap.pop())  # 15
      print(binary_heap.pop())  # 12
      # get the list and size after operations
      print(binary_heap.get_list)
      print(len(binary_heap))






