




import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCAmelCase	:						List[str]			=     16
_UpperCAmelCase	:						List[Any]			=     32



def       UpperCAmelCase__  (     lowerCamelCase   ):
  return int(x / 2**20   )




class    __lowerCAmelCase       :






       def __enter__(   self:					Tuple				):
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         lowercase    :Tuple      	=						torch.cuda.memory_allocated()
         return self






       def __exit__(   self:					List[Any]       ,					*_lowerCAmelCase:					Dict				):
         gc.collect()
         torch.cuda.empty_cache()
         lowercase    :Optional[Any]      	=						torch.cuda.memory_allocated()
         lowercase    :str      	=						torch.cuda.max_memory_allocated()
         lowercase    :Union[str, Any]      	=						bamb(self.end - self.begin				)
         lowercase    :Dict      	=						bamb(self.peak - self.begin				)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = 16,			lowerCamelCase = "bert-base-cased",			lowerCamelCase = 320,			lowerCamelCase = 160,			):
  lowercase    :List[str]      	=						AutoTokenizer.from_pretrained(lowerCamelCase   )
  lowercase    :Optional[int]      	=						load_dataset(
      "glue",			"mrpc",			split={"train": F"train[:{n_train}]", "validation": F"validation[:{n_val}]"}   )

  def tokenize_function(lowerCamelCase   ):
    # max_length=None => use the model max length (it's actually the default)
    lowercase    :Any      	=						tokenizer(examples["sentence1"],			examples["sentence2"],			truncation=lowerCamelCase,			max_length=lowerCamelCase   )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  lowercase    :Optional[int]      	=						datasets.map(
      lowerCamelCase,			batched=lowerCamelCase,			remove_columns=["idx", "sentence1", "sentence2"],			load_from_cache_file=lowerCamelCase   )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  lowercase    :Tuple      	=						tokenized_datasets.rename_column("label",			"labels"   )

  def collate_fn(lowerCamelCase   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(lowerCamelCase,			padding="max_length",			max_length=128,			return_tensors="pt"   )
    return tokenizer.pad(lowerCamelCase,			padding="longest",			return_tensors="pt"   )

  # Instantiate dataloaders.
  lowercase    :Union[str, Any]      	=						DataLoader(
      tokenized_datasets["train"],			shuffle=lowerCamelCase,			collate_fn=lowerCamelCase,			batch_size=lowerCamelCase   )
  lowercase    :List[Any]      	=						DataLoader(
      tokenized_datasets["validation"],			shuffle=lowerCamelCase,			collate_fn=lowerCamelCase,			batch_size=lowerCamelCase   )

  return train_dataloader, eval_dataloader



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  # Initialize accelerator
  lowercase    :Optional[Any]      	=						Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowercase    :Tuple      	=						config["lr"]
  lowercase    :Optional[Any]      	=						int(config["num_epochs"]   )
  lowercase    :int      	=						int(config["seed"]   )
  lowercase    :Optional[int]      	=						int(config["batch_size"]   )
  lowercase    :Tuple      	=						args.model_name_or_path

  set_seed(lowerCamelCase   )
  lowercase	,    lowercase    :List[str]      	=						get_dataloaders(lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			args.n_train,			args.n_val   )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowercase    :Dict      	=						AutoModelForSequenceClassification.from_pretrained(lowerCamelCase,			return_dict=lowerCamelCase   )

  # Instantiate optimizer
  lowercase    :Optional[Any]      	=						(
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  lowercase    :List[str]      	=						optimizer_cls(params=model.parameters(),			lr=lowerCamelCase   )

  if accelerator.state.deepspeed_plugin is not None:
    lowercase    :List[str]      	=						accelerator.state.deepspeed_plugin.deepspeed_config[
        "gradient_accumulation_steps"
    ]
  else:
    lowercase    :Any      	=						1
  lowercase    :List[str]      	=						(len(lowerCamelCase   ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    lowercase    :Any      	=						get_linear_schedule_with_warmup(
        optimizer=lowerCamelCase,			num_warmup_steps=0,			num_training_steps=lowerCamelCase,			)
  else:
    lowercase    :Union[str, Any]      	=						DummyScheduler(lowerCamelCase,			total_num_steps=lowerCamelCase,			warmup_num_steps=0   )

  # Prepare everything
  # 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.
  lowercase	,    lowercase	,    lowercase	,    lowercase	,    lowercase    :Optional[Any]      	=						accelerator.prepare(
      lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase,			lowerCamelCase   )

  # We need to keep track of how many total steps we have iterated over
  lowercase    :Any      	=						0
  # We also need to keep track of the stating epoch so files are named properly
  lowercase    :Union[str, Any]      	=						0

  # Now we train the model
  lowercase    :int      	=						{}
  for epoch in range(lowerCamelCase,			lowerCamelCase   ):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(lowerCamelCase   ):
        lowercase    :List[Any]      	=						model(**lowerCamelCase   )
        lowercase    :Dict      	=						outputs.loss
        lowercase    :str      	=						loss / gradient_accumulation_steps
        accelerator.backward(lowerCamelCase   )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

        overall_step += 1

        # 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   )   )   )
    lowercase    :Optional[int]      	=						tracemalloc.peaked + bamb(tracemalloc.begin   )
    if args.peak_memory_upper_bound is not None:
      assert (
          train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
      ), "Peak memory usage exceeded the upper bound"

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
    with open(os.path.join(args.output_dir,			"peak_memory_utilization.json"   ),			"w"   ) as f:
      json.dump(lowerCamelCase,			lowerCamelCase   )



def       UpperCAmelCase__  (     ):
  lowercase    :str      	=						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
  parser.add_argument(
      "--model_name_or_path",			type=lowerCamelCase,			default="bert-base-cased",			help="Path to pretrained model or model identifier from huggingface.co/models.",			required=lowerCamelCase,			)
  parser.add_argument(
      "--output_dir",			type=lowerCamelCase,			default=".",			help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",			)
  parser.add_argument(
      "--peak_memory_upper_bound",			type=lowerCamelCase,			default=lowerCamelCase,			help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.",			)
  parser.add_argument(
      "--n_train",			type=lowerCamelCase,			default=320,			help="Number of training examples to use.",			)
  parser.add_argument(
      "--n_val",			type=lowerCamelCase,			default=160,			help="Number of validation examples to use.",			)
  parser.add_argument(
      "--num_epochs",			type=lowerCamelCase,			default=1,			help="Number of train epochs.",			)
  lowercase    :Tuple      	=						parser.parse_args()
  lowercase    :Optional[int]      	=						{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
  training_function(lowerCamelCase,			lowerCamelCase   )


if __name__ == "__main__":
     main()









import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment




@require_huggingface_suite
class    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :List[str]      	=						inspect.getfile(accelerate.test_utils				)
         lowercase    :List[Any]      	=						os.path.sep.join(
             mod_file.split(os.path.sep				)[:-1] + ["scripts", "external_deps", "test_metrics.py"]				)

         from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

         lowercase    :str      	=						test_metrics






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         debug_launcher(self.test_metrics.main       ,					num_processes=1				)






       @require_cpu
       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         debug_launcher(self.test_metrics.main				)






       @require_single_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         self.test_metrics.main()






       @require_multi_gpu
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         print(F"Found {torch.cuda.device_count()} devices."				)
         lowercase    :str      	=						["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
         with patch_environment(omp_num_threads=1				):
           execute_subprocess_async(_lowerCAmelCase       ,					env=os.environ.copy()				)



