




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


lowerCamelCase__			:       int         =							16
lowerCamelCase__			:       List[str]         =							32

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict							) ->			Tuple:
      return int(x / 2**20							)





class 	lowerCamelCase_							:


 '''simple docstring'''







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




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


def  UpperCAmelCase_							(				__UpperCAmelCase						:     Accelerator      ,      __UpperCAmelCase						:     int = 16      ,      __UpperCAmelCase						:     str = "bert-base-cased"      ,      __UpperCAmelCase						:     int = 3_20      ,      __UpperCAmelCase						:     int = 1_60      ,      ) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      AutoTokenizer.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      load_dataset(
          'glue'      ,      'mrpc'      ,      split={'train': f"train[:{n_train}]", 'validation': f"validation[:{n_val}]"}							)

      def tokenize_function(__UpperCAmelCase						:     Dict							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_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
      SCREAMING_SNAKE_CASE_     						=      datasets.map(
          __UpperCAmelCase      ,      batched=__UpperCAmelCase      ,      remove_columns=['idx', 'sentence1', 'sentence2']      ,      load_from_cache_file=__UpperCAmelCase							)

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

      def collate_fn(__UpperCAmelCase						:     Tuple							):
            # 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(__UpperCAmelCase      ,      padding='max_length'      ,      max_length=1_28      ,      return_tensors='pt'							)
            return tokenizer.pad(__UpperCAmelCase      ,      padding='longest'      ,      return_tensors='pt'							)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE_     						=      DataLoader(
          tokenized_datasets['train']      ,      shuffle=__UpperCAmelCase      ,      collate_fn=__UpperCAmelCase      ,      batch_size=__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      DataLoader(
          tokenized_datasets['validation']      ,      shuffle=__UpperCAmelCase      ,      collate_fn=__UpperCAmelCase      ,      batch_size=__UpperCAmelCase							)

      return train_dataloader, eval_dataloader

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Any      ,      __UpperCAmelCase						:     List[Any]							) ->			Union[str, Any]:
      # Initialize accelerator
      SCREAMING_SNAKE_CASE_     						=      Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE_     						=      config['lr']
      SCREAMING_SNAKE_CASE_     						=      int(config['num_epochs']							)
      SCREAMING_SNAKE_CASE_     						=      int(config['seed']							)
      SCREAMING_SNAKE_CASE_     						=      int(config['batch_size']							)
      SCREAMING_SNAKE_CASE_     						=      args.model_name_or_path

      set_seed(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      get_dataloaders(__UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      args.n_train      ,      args.n_val							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE_     						=      AutoModelForSequenceClassification.from_pretrained(__UpperCAmelCase      ,      return_dict=__UpperCAmelCase							)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE_     						=      (
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      SCREAMING_SNAKE_CASE_     						=      optimizer_cls(params=model.parameters()      ,      lr=__UpperCAmelCase							)

      if accelerator.state.deepspeed_plugin is not None:
            SCREAMING_SNAKE_CASE_     						=      accelerator.state.deepspeed_plugin.deepspeed_config[
                'gradient_accumulation_steps'
            ]
      else:
            SCREAMING_SNAKE_CASE_     						=      1
      SCREAMING_SNAKE_CASE_     						=      (len(__UpperCAmelCase							) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            SCREAMING_SNAKE_CASE_     						=      get_linear_schedule_with_warmup(
                optimizer=__UpperCAmelCase      ,      num_warmup_steps=0      ,      num_training_steps=__UpperCAmelCase      ,      )
      else:
            SCREAMING_SNAKE_CASE_     						=      DummyScheduler(__UpperCAmelCase      ,      total_num_steps=__UpperCAmelCase      ,      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.
      SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      accelerator.prepare(
          __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase      ,      __UpperCAmelCase							)

      # We need to keep track of how many total steps we have iterated over
      SCREAMING_SNAKE_CASE_     						=      0
      # We also need to keep track of the stating epoch so files are named properly
      SCREAMING_SNAKE_CASE_     						=      0

      # Now we train the model
      SCREAMING_SNAKE_CASE_     						=      {}
      for epoch in range(__UpperCAmelCase      ,      __UpperCAmelCase							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(__UpperCAmelCase							):
                        SCREAMING_SNAKE_CASE_     						=      model(**__UpperCAmelCase							)
                        SCREAMING_SNAKE_CASE_     						=      outputs.loss
                        SCREAMING_SNAKE_CASE_     						=      loss / gradient_accumulation_steps
                        accelerator.backward(__UpperCAmelCase							)
                        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							)							)							)
            SCREAMING_SNAKE_CASE_     						=      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(__UpperCAmelCase      ,      __UpperCAmelCase							)

def  UpperCAmelCase_							(				) ->			List[str]:
      SCREAMING_SNAKE_CASE_     						=      argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'							)
      parser.add_argument(
          '--model_name_or_path'      ,      type=__UpperCAmelCase      ,      default='bert-base-cased'      ,      help='Path to pretrained model or model identifier from huggingface.co/models.'      ,      required=__UpperCAmelCase      ,      )
      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(
          '--peak_memory_upper_bound'      ,      type=__UpperCAmelCase      ,      default=__UpperCAmelCase      ,      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=__UpperCAmelCase      ,      default=3_20      ,      help='Number of training examples to use.'      ,      )
      parser.add_argument(
          '--n_val'      ,      type=__UpperCAmelCase      ,      default=1_60      ,      help='Number of validation examples to use.'      ,      )
      parser.add_argument(
          '--num_epochs'      ,      type=__UpperCAmelCase      ,      default=1      ,      help='Number of train epochs.'      ,      )
      SCREAMING_SNAKE_CASE_     						=      parser.parse_args()
      SCREAMING_SNAKE_CASE_     						=      {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
      training_function(__UpperCAmelCase      ,      __UpperCAmelCase							)


if __name__ == "__main__":
   main()





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			list:
      return [
          txt[:a] + txt[a].upper() + txt[a + 1 :]
          for a in range(len(__UpperCAmelCase							)							)
          if txt[a].isalpha()
      ]


if __name__ == "__main__":
   __import__('doctest').testmod()