



'''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


_A				:    List[Any]     						=1_6
_A				:    Tuple     						=3_2







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	return int(x / 2**20					)



class 		lowerCamelCase__      :




					'''simple docstring'''



					def __enter__(  self				:	int					)  ->       Tuple:



						'''simple docstring'''





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




					def __exit__(  self				:	int     ,  *UpperCamelCase_				:	Any					)  ->       List[str]:



						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						_lowercase					:       Union[str, Any]       =		torch.cuda.memory_allocated()
						_lowercase					:       Dict       =		torch.cuda.max_memory_allocated()
						_lowercase					:       List[Any]       =		bamb(self.end - self.begin					)
						_lowercase					:       Optional[Any]       =		bamb(self.peak - self.begin					)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 						__UpperCamelCase				(							_lowercase,				_lowercase = 16,				_lowercase = "bert-base-cased",				_lowercase = 320,				_lowercase = 160,				) ->			Optional[Any]:
	_lowercase					:       Optional[int]       =		AutoTokenizer.from_pretrained(UpperCamelCase__					)
	_lowercase					:       Dict       =		load_dataset(
	    'glue',				'mrpc',				split={'train': f'''train[:{n_train}]''', 'validation': f'''validation[:{n_val}]'''}					)

	def tokenize_function(_lowercase					):
		# max_length=None => use the model max length (it's actually the default)
		_lowercase					:       List[str]       =		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
	_lowercase					:       int       =		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
	_lowercase					:       Any       =		tokenized_datasets.rename_column('label',				'labels'					)

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

	return train_dataloader, eval_dataloader







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Optional[int]:
	# Initialize accelerator
	_lowercase					:       Optional[Any]       =		Accelerator()

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

	set_seed(UpperCamelCase__					)
	_lowercase  ,    _lowercase					:       List[Any]       =		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)
	_lowercase					:       List[Any]       =		AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__,				return_dict=UpperCamelCase__					)

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

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

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

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


if __name__ == "__main__":
						main()





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
