

'''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 		_A     (    A__      ):






 """simple docstring"""



 return int(x / 2**20      )






class 			lowercase_	:




  """simple docstring"""




  def __enter__(      self   :     List[str]			):
   gc.collect()
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
   __lowercase							  = torch.cuda.memory_allocated()
   return self






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


def 		_A     (    A__     ,				A__ = 16     ,				A__ = "bert-base-cased"     ,				A__ = 320     ,				A__ = 160     ,				):






 """simple docstring"""



 __lowercase							  = AutoTokenizer.from_pretrained(A__      )
 __lowercase							  = load_dataset(
     '''glue'''     ,				'''mrpc'''     ,				split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}      )

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

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowercase							  = datasets.map(
     A__     ,				batched=A__     ,				remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,				load_from_cache_file=A__      )

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

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

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

 return train_dataloader, eval_dataloader


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = Accelerator()

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

 set_seed(A__      )
 __lowercase  ,	__lowercase							  = get_dataloaders(A__     ,				A__     ,				A__     ,				args.n_train     ,				args.n_val      )

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

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

 if accelerator.state.deepspeed_plugin is not None:
  __lowercase							  = accelerator.state.deepspeed_plugin.deepspeed_config[
      '''gradient_accumulation_steps'''
  ]
 else:
  __lowercase							  = 1
 __lowercase							  = (len(A__      ) * 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							  = get_linear_schedule_with_warmup(
      optimizer=A__     ,				num_warmup_steps=0     ,				num_training_steps=A__     ,				)
 else:
  __lowercase							  = DummyScheduler(A__     ,				total_num_steps=A__     ,				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							  = accelerator.prepare(
     A__     ,				A__     ,				A__     ,				A__     ,				A__      )

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

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


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''      )
 parser.add_argument(
     '''--model_name_or_path'''     ,				type=A__     ,				default='''bert-base-cased'''     ,				help='''Path to pretrained model or model identifier from huggingface.co/models.'''     ,				required=A__     ,				)
 parser.add_argument(
     '''--output_dir'''     ,				type=A__     ,				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=A__     ,				default=A__     ,				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=A__     ,				default=320     ,				help='''Number of training examples to use.'''     ,				)
 parser.add_argument(
     '''--n_val'''     ,				type=A__     ,				default=160     ,				help='''Number of validation examples to use.'''     ,				)
 parser.add_argument(
     '''--num_epochs'''     ,				type=A__     ,				default=1     ,				help='''Number of train epochs.'''     ,				)
 __lowercase							  = parser.parse_args()
 __lowercase							  = {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
 training_function(A__     ,				A__      )


if __name__ == "__main__":
 main()








'''simple docstring'''

import functools





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			len(_lowerCamelCase       )
    A__				=			len(_lowerCamelCase       )

    @functools.cache
    def min_distance(_lowerCamelCase					:					int						,       _lowerCamelCase					:					int       ) -> int:
        # if first word index is overflow - delete all from the second word
        if indexa >= len_worda:
            return len_worda - indexa
        # if second word index is overflow - delete all from the first word
        if indexa >= len_worda:
            return len_worda - indexa
        A__				=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
        return min(
            1 + min_distance(indexa + 1						,       _lowerCamelCase       )						,       1 + min_distance(_lowerCamelCase						,       indexa + 1       )						,       diff + min_distance(indexa + 1						,       indexa + 1       )						,       )

    return min_distance(0						,       0       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



