



'''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    return int(x / 2**20      )






class 	lowerCAmelCase_:
  '''simple docstring'''




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






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







def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = 16							,   UpperCamelCase = "bert-base-cased"							,   UpperCamelCase = 320							,   UpperCamelCase = 160							,   ):



    """simple docstring"""





    lowerCAmelCase__			:  Dict											= AutoTokenizer.from_pretrained(UpperCamelCase      )
    lowerCAmelCase__			:  List[Any]											= 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)
        lowerCAmelCase__			:  Optional[Any]											= 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
    lowerCAmelCase__			:  Dict											= 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
    lowerCAmelCase__			:  Dict											= 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.
    lowerCAmelCase__			:  Union[str, Any]											= DataLoader(
        tokenized_datasets["""train"""]							,   shuffle=UpperCamelCase							,   collate_fn=UpperCamelCase							,   batch_size=UpperCamelCase      )
    lowerCAmelCase__			:  Tuple											= DataLoader(
        tokenized_datasets["""validation"""]							,   shuffle=UpperCamelCase							,   collate_fn=UpperCamelCase							,   batch_size=UpperCamelCase      )

    return train_dataloader, eval_dataloader
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= Accelerator()

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

    set_seed(UpperCamelCase      )
    lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= 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)
    lowerCAmelCase__			:  Any											= AutoModelForSequenceClassification.from_pretrained(UpperCamelCase							,   return_dict=UpperCamelCase      )

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

    if accelerator.state.deepspeed_plugin is not None:
        lowerCAmelCase__			:  List[str]											= accelerator.state.deepspeed_plugin.deepspeed_config[
            """gradient_accumulation_steps"""
        ]
    else:
        lowerCAmelCase__			:  Dict											= 1
    lowerCAmelCase__			:  Union[str, Any]											= (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
    ):
        lowerCAmelCase__			:  Optional[Any]											= get_linear_schedule_with_warmup(
            optimizer=UpperCamelCase							,   num_warmup_steps=0							,   num_training_steps=UpperCamelCase							,   )
    else:
        lowerCAmelCase__			:  Optional[int]											= 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.
    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[int]											= accelerator.prepare(
        UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

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

    # Now we train the model
    lowerCAmelCase__			:  Union[str, Any]											= {}
    for epoch in range(UpperCamelCase							,   UpperCamelCase      ):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(UpperCamelCase      ):
                lowerCAmelCase__			:  int											= model(**UpperCamelCase      )
                lowerCAmelCase__			:  Dict											= outputs.loss
                lowerCAmelCase__			:  List[Any]											= 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      )      )      )
        lowerCAmelCase__			:  Tuple											= 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        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  Any											= 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."""							,   )
    lowerCAmelCase__			:  int											= parser.parse_args()
    lowerCAmelCase__			:  str											= {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
    training_function(UpperCamelCase							,   UpperCamelCase      )


if __name__ == "__main__":
 main()







'''simple docstring'''





from ..utils import DummyObject, requires_backends






class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				int           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[int]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		str:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				str           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Union[str, Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				int           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Tuple:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Dict           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Dict:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[str]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Dict           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Any:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Union[str, Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[str]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Union[str, Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[str]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Any:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[str]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Dict           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Union[str, Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		str:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[int]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				int           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Dict:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				str           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Union[str, Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Any:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[int]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		str:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Any           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		int:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[str]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Tuple           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		str:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[int]:
      requires_backends(self  ,["""sentencepiece"""]				)







class 	lowerCAmelCase_(    metaclass=SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  __lowercase							:				List[Any]           =				['''sentencepiece''']




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      requires_backends(self  ,["""sentencepiece"""]				)


