

"""simple docstring"""






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										=		16
__lowerCamelCase										=		32





def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 return int(x / 2**20						)
class        _lowercase					:







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







     def __exit__(      self ,						*UpperCamelCase_       ):
      gc.collect()
      torch.cuda.empty_cache()
      __magic_name__     =      torch.cuda.memory_allocated()
      __magic_name__     =      torch.cuda.max_memory_allocated()
      __magic_name__     =      bamb(self.end - self.begin       )
      __magic_name__     =      bamb(self.peak - self.begin       )
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase = 16      ,   __UpperCamelCase = "bert-base-cased"      ,   __UpperCamelCase = 320      ,   __UpperCamelCase = 160      ,   ) ->      Union[str, Any]:
 __magic_name__     =      AutoTokenizer.from_pretrained(__UpperCamelCase						)
 __magic_name__     =      load_dataset(
     '''glue'''      ,   '''mrpc'''      ,   split={'''train''': f'''train[:{n_train}]''', '''validation''': f'''validation[:{n_val}]'''}						)

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

 def collate_fn(__UpperCamelCase						):
  # 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=128      ,   return_tensors='''pt'''						)
  return tokenizer.pad(__UpperCamelCase      ,   padding='''longest'''      ,   return_tensors='''pt'''						)

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

 return train_dataloader, eval_dataloader





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Any:
 # Initialize accelerator
 __magic_name__     =      Accelerator()

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

 set_seed(__UpperCamelCase						)
 __magic_name__   ,						__magic_name__     =      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)
 __magic_name__     =      AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase      ,   return_dict=__UpperCamelCase						)

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

 if accelerator.state.deepspeed_plugin is not None:
  __magic_name__     =      accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  __magic_name__     =      1
 __magic_name__     =      (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
 ):
  __magic_name__     =      get_linear_schedule_with_warmup(
      optimizer=__UpperCamelCase      ,   num_warmup_steps=0      ,   num_training_steps=__UpperCamelCase      ,   )
 else:
  __magic_name__     =      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.
 __magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__   ,						__magic_name__     =      accelerator.prepare(
     __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						)

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

 # Now we train the model
 __magic_name__     =      {}
 for epoch in range(__UpperCamelCase      ,   __UpperCamelCase						):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(__UpperCamelCase						):
    __magic_name__     =      model(**__UpperCamelCase						)
    __magic_name__     =      outputs.loss
    __magic_name__     =      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						)						)						)
  __magic_name__     =      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 	lowercase      (   ) ->      List[str]:
 __magic_name__     =      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=320      ,   help='''Number of training examples to use.'''      ,   )
 parser.add_argument(
     '''--n_val'''      ,   type=__UpperCamelCase      ,   default=160      ,   help='''Number of validation examples to use.'''      ,   )
 parser.add_argument(
     '''--num_epochs'''      ,   type=__UpperCamelCase      ,   default=1      ,   help='''Number of train epochs.'''      ,   )
 __magic_name__     =      parser.parse_args()
 __magic_name__     =      {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
 training_function(__UpperCamelCase      ,   __UpperCamelCase						)


if __name__ == "__main__":
 main()




"""simple docstring"""






def 	lowercase      (   ) ->      int:
 return 1





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else two_pence(x - 2						) + one_pence()





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else five_pence(x - 5						) + two_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else ten_pence(x - 10						) + five_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else twenty_pence(x - 20						) + ten_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else fifty_pence(x - 50						) + twenty_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else one_pound(x - 100						) + fifty_pence(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase						) ->      int:
 return 0 if x < 0 else two_pound(x - 200						) + one_pound(__UpperCamelCase						)





def 	lowercase      (   __UpperCamelCase = 200						) ->      int:
 return two_pound(__UpperCamelCase						)


if __name__ == "__main__":
 print(solution(int(input().strip())))

