





"""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__					:     List[Any]							=				1_6
a__					:     int							=				3_2





def 							a__			(  _UpperCAmelCase	)    ->  List[str]:

 '''simple docstring'''






 return int(x / 2**2_0	)



class      lowercase_				:

       def __enter__(			self     ):






        '''simple docstring'''

        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        snake_case__    							=				torch.cuda.memory_allocated()
        snake_case__    							=				psutil.Process()

        snake_case__    							=				self.cpu_mem_used()
        snake_case__    							=				True
        snake_case__    							=				threading.Thread(target=self.peak_monitor_func     )
        snake_case__    							=				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'''

        snake_case__    							=				-1

        while True:
         snake_case__    							=				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   ,					*__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				False

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

        snake_case__    							=				self.cpu_mem_used()
        snake_case__    							=				bamb(self.cpu_end - self.cpu_begin     )
        snake_case__    							=				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__					:     int							=				mocked_dataloaders  # noqa: F811





def 							a__			(  _UpperCAmelCase	,     _UpperCAmelCase	)    ->  List[Any]:

 '''simple docstring'''






 if os.environ.get("TESTING_MOCKED_DATALOADERS"	,     _UpperCAmelCase	) == "1":
  snake_case__    							=				2

 # New Code #
 # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
 snake_case__    							=				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:
  snake_case__    							=				Accelerator(
      cpu=args.cpu	,     mixed_precision=args.mixed_precision	,     log_with="wandb"	,     project_dir=args.logging_dir	,     fsdp_plugin=_UpperCAmelCase	,     )
 else:
  snake_case__    							=				Accelerator(fsdp_plugin=_UpperCAmelCase	)
 accelerator.print(accelerator.distributed_type	)

 if hasattr(args.checkpointing_steps	,     "isdigit"	):
  if args.checkpointing_steps == "epoch":
   snake_case__    							=				args.checkpointing_steps
  elif args.checkpointing_steps.isdigit():
   snake_case__    							=				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__    							=				None
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 snake_case__    							=				config['lr']
 snake_case__    							=				int(config["num_epochs"]	)
 snake_case__    							=				int(config["seed"]	)
 snake_case__    							=				int(config["batch_size"]	)

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

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

 def tokenize_function(_UpperCAmelCase	):
  # max_length=None => use the model max length (it's actually the default)
  snake_case__    							=				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():
  snake_case__    							=				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
 snake_case__    							=				tokenized_datasets.rename_column("label"	,     "labels"	)

 # If the batch size is too big we use gradient accumulation
 snake_case__    							=				1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  snake_case__    							=				batch_size // MAX_GPU_BATCH_SIZE
  snake_case__    							=				MAX_GPU_BATCH_SIZE

 def collate_fn(_UpperCAmelCase	):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  snake_case__    							=				1_2_8 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__    							=				1_6
  elif accelerator.mixed_precision != "no":
   snake_case__    							=				8
  else:
   snake_case__    							=				None

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

 # Instantiate dataloaders.
 snake_case__    							=				DataLoader(
     tokenized_datasets["train"]	,     shuffle=_UpperCAmelCase	,     collate_fn=_UpperCAmelCase	,     batch_size=_UpperCAmelCase	)
 snake_case__    							=				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)
 snake_case__    							=				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
 snake_case__    							=				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
 snake_case__    							=				torch.optim.AdamW(params=model.parameters()	,     lr=_UpperCAmelCase	,     weight_decay=2e-4	)

 # Instantiate scheduler
 snake_case__    							=				get_linear_schedule_with_warmup(
     optimizer=_UpperCAmelCase	,     num_warmup_steps=1_0	,     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.
 snake_case__    							=				accelerator.prepare(
     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)

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

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

   snake_case__    							=				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":
    snake_case__    							=				f"epoch_{epoch}"
    if args.output_dir is not None:
     snake_case__    							=				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 							a__			(  )    ->  Union[str, Any]:

 '''simple docstring'''






 snake_case__    							=				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	,     )
 snake_case__    							=				parser.parse_args()
 snake_case__    							=				{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
 training_function(_UpperCAmelCase	,     _UpperCAmelCase	)


if __name__ == "__main__":
   main()








"""simple docstring"""


def 							a__			(  _UpperCAmelCase	)    ->  int:

 '''simple docstring'''






 return int(_UpperCAmelCase	) if x - int(_UpperCAmelCase	) <= 0 else int(_UpperCAmelCase	) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

