





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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Dict:
    return int(x / 2**20					)

class   lowerCAmelCase				:
 def __enter__( self    : List[str]      )     ->      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    : Optional[int]     ,	*UpperCAmelCase    : List[Any]      )     ->      List[Any]:
     gc.collect()
     torch.cuda.empty_cache()
     lowerCamelCase__       : Any   					=						torch.cuda.memory_allocated()
     lowerCamelCase__       : Union[str, Any]   					=						torch.cuda.max_memory_allocated()
     lowerCamelCase__       : str   					=						bamb(self.end - self.begin      )
     lowerCamelCase__       : int   					=						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		,      )     ->	Tuple:
    lowerCamelCase__       : int   					=						AutoTokenizer.from_pretrained(_lowerCAmelCase					)
    lowerCamelCase__       : int   					=						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__       : Tuple   					=						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
    lowerCamelCase__       : List[str]   					=						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
    lowerCamelCase__       : List[str]   					=						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(_lowerCAmelCase		,      padding='max_length'		,      max_length=128		,      return_tensors='pt'					)
        return tokenizer.pad(_lowerCAmelCase		,      padding='longest'		,      return_tensors='pt'					)

    # Instantiate dataloaders.
    lowerCamelCase__       : Dict   					=						DataLoader(
        tokenized_datasets['train']		,      shuffle=_lowerCAmelCase		,      collate_fn=_lowerCAmelCase		,      batch_size=_lowerCAmelCase					)
    lowerCamelCase__       : Optional[int]   					=						DataLoader(
        tokenized_datasets['validation']		,      shuffle=_lowerCAmelCase		,      collate_fn=_lowerCAmelCase		,      batch_size=_lowerCAmelCase					)

    return train_dataloader, eval_dataloader


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    # Initialize accelerator
    lowerCamelCase__       : Tuple   					=						Accelerator()

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

    set_seed(_lowerCAmelCase					)
    lowerCamelCase__       : 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)
    lowerCamelCase__       : Optional[int]   					=						AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase		,      return_dict=_lowerCAmelCase					)

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

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

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

    # Now we train the model
    lowerCamelCase__       : Optional[int]   					=						{}
    for epoch in range(_lowerCAmelCase		,      _lowerCAmelCase					):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(_lowerCAmelCase					):
                lowerCamelCase__       : List[Any]   					=						model(**_lowerCAmelCase					)
                lowerCamelCase__       : Optional[int]   					=						outputs.loss
                lowerCamelCase__       : Union[str, Any]   					=						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					)					)					)
        lowerCamelCase__       : str   					=						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 			SCREAMING_SNAKE_CASE (			)     ->	List[str]:
    lowerCamelCase__       : List[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.'		,      )
    lowerCamelCase__       : int   					=						parser.parse_args()
    lowerCamelCase__       : Tuple   					=						{"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
    training_function(_lowerCAmelCase		,      _lowerCAmelCase					)


if __name__ == "__main__":
    main()










'''simple docstring'''





import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       int       		= logging.get_logger()

          # the current default level is logging.WARNING
          snake_case__    :       str       		= logging.get_verbosity()

          logging.set_verbosity_error()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          logging.set_verbosity_warning()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          logging.set_verbosity_info()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          logging.set_verbosity_debug()
          self.assertEqual(logger.getEffectiveLevel()					,	logging.get_verbosity()		)

          # restore to the original level
          logging.set_verbosity(snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       List[Any]       		= logging.get_verbosity()

          snake_case__    :       Any       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          snake_case__    :       int       		= """Testing 1, 2, 3"""

          # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
          if level_origin <= logging.WARNING:
                with CaptureLogger(snake_case_		) as cl:
                      logger.warning(snake_case_		)
                self.assertEqual(cl.out					,	msg + """\n"""		)

          # this is setting the level for all of `transformers.*` loggers
          logging.set_verbosity_error()

          # should not be able to log warnings
          with CaptureLogger(snake_case_		) as cl:
                logger.warning(snake_case_		)
          self.assertEqual(cl.out					,	""""""		)

          # should be able to log warnings again
          logging.set_verbosity_warning()
          with CaptureLogger(snake_case_		) as cl:
                logger.warning(snake_case_		)
          self.assertEqual(cl.out					,	msg + """\n"""		)

          # restore to the original level
          logging.set_verbosity(snake_case_		)




    @mockenv(TRANSFORMERS_VERBOSITY="""error"""		)
    def lowerCamelCase			(       self   :	Any		):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          # this action activates the env var
          snake_case__    :       Union[str, Any]       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)

          snake_case__    :       int       		= os.getenv("""TRANSFORMERS_VERBOSITY"""					,	snake_case_		)
          snake_case__    :       List[Any]       		= logging.log_levels[env_level_str]

          snake_case__    :       str       		= logging.get_verbosity()
          self.assertEqual(
              snake_case_					,	snake_case_					,	f"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"					,	)

          # restore to the original level
          snake_case__    :       List[Any]       		= """"""
          transformers.utils.logging._reset_library_root_logger()




    @mockenv(TRANSFORMERS_VERBOSITY="""super-error"""		)
    def lowerCamelCase			(       self   :	int		):
          # reset for the env var to take effect, next time some logger call is made
          transformers.utils.logging._reset_library_root_logger()
          snake_case__    :       List[Any]       		= logging.logging.getLogger()
          with CaptureLogger(snake_case_		) as cl:
                # this action activates the env var
                logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""					,	cl.out		)

          # no need to restore as nothing was changed




    def lowerCamelCase			(       self   :	int		):
          # testing `logger.warning_advice()`
          transformers.utils.logging._reset_library_root_logger()

          snake_case__    :       Optional[int]       		= logging.get_logger("""transformers.models.bart.tokenization_bart"""		)
          snake_case__    :       Tuple       		= """Testing 1, 2, 3"""

          with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""		):
                # nothing should be logged as env var disables this method
                with CaptureLogger(snake_case_		) as cl:
                      logger.warning_advice(snake_case_		)
                self.assertEqual(cl.out					,	""""""		)

          with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""		):
                # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
                with CaptureLogger(snake_case_		) as cl:
                      logger.warning_advice(snake_case_		)
                self.assertEqual(cl.out					,	msg + """\n"""		)





def 		__snake_case(							)						->					List[str]:
      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()
