





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


a__: Tuple							   =						16
a__: List[Any]							   =						32


def       UpperCamelCase__(			UpperCamelCase__     :			Any    )->Any:
       return int(x / 2**20    )




class        SCREAMING_SNAKE_CASE__					:



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




 def __exit__(				self,*__lowerCamelCase					):
        gc.collect()
        torch.cuda.empty_cache()
        A__		   =		torch.cuda.memory_allocated()
        A__		   =		torch.cuda.max_memory_allocated()
        A__		   =		bamb(self.end - self.begin					)
        A__		   =		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					,	)->str:
       A__		   =		AutoTokenizer.from_pretrained(UpperCamelCase__    )
       A__		   =		load_dataset(
           '''glue'''					,	'''mrpc'''					,	split={'''train''': f"train[:{n_train}]", '''validation''': f"validation[:{n_val}]"}    )

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

       def collate_fn(UpperCamelCase__     :			Optional[int]    ):
              # 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.
       A__		   =		DataLoader(
           tokenized_datasets['''train''']					,	shuffle=UpperCamelCase__					,	collate_fn=UpperCamelCase__					,	batch_size=UpperCamelCase__    )
       A__		   =		DataLoader(
           tokenized_datasets['''validation''']					,	shuffle=UpperCamelCase__					,	collate_fn=UpperCamelCase__					,	batch_size=UpperCamelCase__    )

       return train_dataloader, eval_dataloader


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Dict    )->List[str]:
       # Initialize accelerator
       A__		   =		Accelerator()

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

       set_seed(UpperCamelCase__    )
       A__   ,      A__		   =		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)
       A__		   =		AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__					,	return_dict=UpperCamelCase__    )

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

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

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

       # Now we train the model
       A__		   =		{}
       for epoch in range(UpperCamelCase__					,	UpperCamelCase__    ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(UpperCamelCase__    ):
                            A__		   =		model(**UpperCamelCase__    )
                            A__		   =		outputs.loss
                            A__		   =		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    )    )    )
              A__		   =		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__(			)->int:
       A__		   =		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.'''					,	)
       A__		   =		parser.parse_args()
       A__		   =		{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
       training_function(UpperCamelCase__					,	UpperCamelCase__    )


if __name__ == "__main__":
       main()












import pytest

import datasets


# Import fixture modules as plugins
a__: Dict							   =						['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']


def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]					,	UpperCamelCase__     :			Tuple    )->List[str]:
       # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
       for item in items:
              if any(marker in item.keywords for marker in ['''integration''', '''unit''']    ):
                     continue
              item.add_marker(pytest.mark.unit    )


def       UpperCamelCase__(			UpperCamelCase__     :			Dict    )->List[Any]:
       config.addinivalue_line('''markers'''					,	'''torchaudio_latest: mark test to run with torchaudio>=0.12'''    )


@pytest.fixture(autouse=UpperCamelCase__    )
def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			int    )->Union[str, Any]:
       # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
       A__		   =		tmp_path_factory.getbasetemp() / '''cache'''
       A__		   =		test_hf_cache_home / '''datasets'''
       A__		   =		test_hf_cache_home / '''metrics'''
       A__		   =		test_hf_cache_home / '''modules'''
       monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''					,	str(UpperCamelCase__    )    )
       monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''					,	str(UpperCamelCase__    )    )
       monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''					,	str(UpperCamelCase__    )    )
       A__		   =		test_hf_datasets_cache / '''downloads'''
       monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''					,	str(UpperCamelCase__    )    )
       A__		   =		test_hf_datasets_cache / '''downloads''' / '''extracted'''
       monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''					,	str(UpperCamelCase__    )    )


@pytest.fixture(autouse=UpperCamelCase__					,	scope='''session'''    )
def       UpperCamelCase__(			)->int:
       datasets.disable_progress_bar()


@pytest.fixture(autouse=UpperCamelCase__    )
def       UpperCamelCase__(			UpperCamelCase__     :			str    )->Tuple:
       # don't take tests into account when counting downloads
       monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''					,	UpperCamelCase__    )


@pytest.fixture
def       UpperCamelCase__(			UpperCamelCase__     :			List[Any]    )->Optional[Any]:
       # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
       # To be removed once SQLAlchemy 2.0 supported
       monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''					,	UpperCamelCase__    )





