





'''simple docstring'''






import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A :			str							   =      16
A :			Dict							   =      32





def    lowercase_   (		lowercase__    ) ->str:
						return int(x / 2**20    )







class       lowerCamelCase       :


					def __enter__(       self						:	Any							):





											'''simple docstring'''





											gc.collect()
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
											_snake_case: Any	       =		torch.cuda.memory_allocated()
											_snake_case: List[Any]	       =		psutil.Process()

											_snake_case: List[str]	       =		self.cpu_mem_used()
											_snake_case: Any	       =		True
											_snake_case: Any	       =		threading.Thread(target=self.peak_monitor_func							)
											_snake_case: List[str]	       =		True
											peak_monitor_thread.start()
											return self


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





											return self.process.memory_info().rss


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		-1

											while True:
																	_snake_case: str	       =		max(self.cpu_mem_used()							,  self.cpu_peak							)

																	# can't sleep or will not catch the peak right (this comment is here on purpose)
																	# time.sleep(0.001) # 1msec

																	if not self.peak_monitoring:
																							break




					def __exit__(       self						:	List[str]							,  *__snake_case						:	List[Any]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		False

											gc.collect()
											torch.cuda.empty_cache()
											_snake_case: str	       =		torch.cuda.memory_allocated()
											_snake_case: int	       =		torch.cuda.max_memory_allocated()
											_snake_case: Optional[int]	       =		bamb(self.end - self.begin							)
											_snake_case: List[str]	       =		bamb(self.peak - self.begin							)

											_snake_case: Tuple	       =		self.cpu_mem_used()
											_snake_case: int	       =		bamb(self.cpu_end - self.cpu_begin							)
											_snake_case: Optional[Any]	       =		bamb(self.cpu_peak - self.cpu_begin							)
											# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		A :			List[Any]							   =      mocked_dataloaders  # noqa: F811





def    lowercase_   (		lowercase__					,	lowercase__    ) ->int:
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'					,	lowercase__    ) == "1":
												_snake_case: int	       =		2

						# New Code #
						# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
						_snake_case: Dict	       =		FullyShardedDataParallelPlugin(
						    state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase__					,	ranka_only=lowercase__    )					,	optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase__					,	ranka_only=lowercase__    )					,	)

						# Initialize accelerator
						if args.with_tracking:
												_snake_case: List[Any]	       =		Accelerator(
												    cpu=args.cpu					,	mixed_precision=args.mixed_precision					,	log_with='wandb'					,	project_dir=args.logging_dir					,	fsdp_plugin=lowercase__					,	)
						else:
												_snake_case: Dict	       =		Accelerator(fsdp_plugin=lowercase__    )
						accelerator.print(accelerator.distributed_type    )

						if hasattr(args.checkpointing_steps					,	'isdigit'    ):
												if args.checkpointing_steps == "epoch":
																		_snake_case: str	       =		args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		_snake_case: int	       =		int(args.checkpointing_steps    )
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
						else:
												_snake_case: Tuple	       =		None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_snake_case: Tuple	       =		config['lr']
						_snake_case: List[Any]	       =		int(config['num_epochs']    )
						_snake_case: List[Any]	       =		int(config['seed']    )
						_snake_case: Dict	       =		int(config['batch_size']    )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												_snake_case: Union[str, Any]	       =		vars(lowercase__    )
												accelerator.init_trackers('fsdp_glue_no_trainer'					,	lowercase__    )

						_snake_case: Dict	       =		AutoTokenizer.from_pretrained(args.model_name_or_path    )
						_snake_case: Union[str, Any]	       =		load_dataset('glue'					,	'mrpc'    )
						_snake_case: Optional[Any]	       =		evaluate.load('glue'					,	'mrpc'    )

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

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												_snake_case: Optional[Any]	       =		datasets.map(
												    lowercase__					,	batched=lowercase__					,	remove_columns=['idx', 'sentence1', 'sentence2']					,	)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						_snake_case: List[str]	       =		tokenized_datasets.rename_column('label'					,	'labels'    )

						# If the batch size is too big we use gradient accumulation
						_snake_case: Dict	       =		1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												_snake_case: Dict	       =		batch_size // MAX_GPU_BATCH_SIZE
												_snake_case: Tuple	       =		MAX_GPU_BATCH_SIZE

						def collate_fn(lowercase__    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												_snake_case: Dict	       =		128 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		_snake_case: Optional[int]	       =		16
												elif accelerator.mixed_precision != "no":
																		_snake_case: Tuple	       =		8
												else:
																		_snake_case: List[str]	       =		None

												return tokenizer.pad(
												    lowercase__					,	padding='longest'					,	max_length=lowercase__					,	pad_to_multiple_of=lowercase__					,	return_tensors='pt'					,	)

						# Instantiate dataloaders.
						_snake_case: int	       =		DataLoader(
						    tokenized_datasets['train']					,	shuffle=lowercase__					,	collate_fn=lowercase__					,	batch_size=lowercase__    )
						_snake_case: int	       =		DataLoader(
						    tokenized_datasets['validation']					,	shuffle=lowercase__					,	collate_fn=lowercase__					,	batch_size=lowercase__    )

						set_seed(lowercase__    )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_snake_case: List[Any]	       =		AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path					,	return_dict=lowercase__					,	low_cpu_mem_usage=lowercase__    )

						# New Code #
						# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
						_snake_case: List[Any]	       =		accelerator.prepare(lowercase__    )
						accelerator.print(lowercase__    )

						# Instantiate optimizer
						# New Code #
						# For FSDP feature, at present it doesn't support multiple parameter groups,
						# so we need to create a single parameter group for the whole model
						_snake_case: Optional[int]	       =		torch.optim.AdamW(params=model.parameters()					,	lr=lowercase__					,	weight_decay=2e-4    )

						# Instantiate scheduler
						_snake_case: Optional[Any]	       =		get_linear_schedule_with_warmup(
						    optimizer=lowercase__					,	num_warmup_steps=10					,	num_training_steps=(len(lowercase__    ) * num_epochs) // gradient_accumulation_steps					,	)

						# New Code #
						# For FSDP feature, prepare everything except the model as we have already prepared the model
						# before creating the optimizer
						# 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.
						_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: str	       =		accelerator.prepare(
						    lowercase__					,	lowercase__					,	lowercase__					,	lowercase__    )

						_snake_case: Tuple	       =		0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
																		accelerator.load_state(args.resume_from_checkpoint    )
																		_snake_case: int	       =		os.path.basename(args.resume_from_checkpoint    )
												else:
																		# Get the most recent checkpoint
																		_snake_case: str	       =		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime    )
																		_snake_case: str	       =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												_snake_case: str	       =		os.path.splitext(lowercase__    )[0]

												if "epoch" in training_difference:
																		num_epochs -= int(training_difference.replace('epoch_'					,	''    )    )
																		_snake_case: Tuple	       =		None
												else:
																		_snake_case: Optional[Any]	       =		int(training_difference.replace('step_'					,	''    )    )
																		num_epochs -= resume_step // len(lowercase__    )
																		# If resuming by step, we also need to know exactly how far into the DataLoader we went
																		_snake_case: Optional[int]	       =		(num_epochs * len(lowercase__    )) - resume_step

    # Now we train the model
						for epoch in range(lowercase__    ):
												# New Code #
												# context manager to track the peak memory usage during the training epoch
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		if args.with_tracking:
																								_snake_case: Optional[Any]	       =		0
																		for step, batch in enumerate(lowercase__    ):
																								# We need to skip steps until we reach the resumed step
																								if args.resume_from_checkpoint and epoch == 0:
																														if resume_step is not None and step < resume_step:
																																				pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device    )
																								_snake_case: Optional[Any]	       =		model(**lowercase__    )
																								_snake_case: Optional[Any]	       =		outputs.loss
																								_snake_case: Dict	       =		loss / gradient_accumulation_steps
																								# We keep track of the loss at each epoch
																								if args.with_tracking:
																														total_loss += loss.detach().float()
																								accelerator.backward(lowercase__    )
																								if step % gradient_accumulation_steps == 0:
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()
																														# accelerator.print(lr_scheduler.get_lr())

																								overall_step += 1

																								if isinstance(lowercase__					,	lowercase__    ):
																														_snake_case: Optional[Any]	       =		F'''step_{overall_step}'''
																														if overall_step % checkpointing_steps == 0:
																																				if args.output_dir is not None:
																																										_snake_case: Optional[Any]	       =		os.path.join(args.output_dir					,	lowercase__    )
																																				accelerator.save_state(lowercase__    )
        # New Code #
        # 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    )    )    )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin    ),
																		    }					,	step=lowercase__					,	)

												# New Code #
												# context manager to track the peak memory usage during the evaluation
												with TorchTracemalloc() as tracemalloc:
																		model.eval()
																		for step, batch in enumerate(lowercase__    ):
																								# We could avoid this line since we set the accelerator with `device_placement=True`.
																								batch.to(accelerator.device    )
																								with torch.no_grad():
																														_snake_case: str	       =		model(**lowercase__    )
																								_snake_case: Optional[int]	       =		outputs.logits.argmax(dim=-1    )
																								_snake_case       ,    _snake_case: Dict	       =		accelerator.gather_for_metrics((predictions, batch['labels'])    )
																								metric.add_batch(
																								    predictions=lowercase__					,	references=lowercase__					,	)

																		_snake_case: Dict	       =		metric.compute()
																		# Use accelerator.print to print only on the main process.
																		accelerator.print(F'''epoch {epoch}:'''					,	lowercase__    )
																		if args.with_tracking:
																								accelerator.log(
																								    {
																								        'accuracy': eval_metric['accuracy'],
																								        'f1': eval_metric['f1'],
																								        'train_loss': total_loss.item() / len(lowercase__    ),
																								    }					,	step=lowercase__					,	)

																		if checkpointing_steps == "epoch":
																								_snake_case: Any	       =		F'''epoch_{epoch}'''
																								if args.output_dir is not None:
																														_snake_case: int	       =		os.path.join(args.output_dir					,	lowercase__    )
																								accelerator.save_state(lowercase__    )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
												accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin    )    )    )
												accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used    )    )
												accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked    )    )
												accelerator.print(
												    'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
												# Logging the peak memory usage of the GPU to the tracker
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin    ),
																		    }					,	step=lowercase__					,	)

						if args.with_tracking:
												accelerator.end_training()





def    lowercase_   (		) ->Union[str, Any]:
						_snake_case: Optional[Any]	       =		argparse.ArgumentParser(description='Simple example of training script.'    )
						parser.add_argument(
						    '--mixed_precision'					,	type=lowercase__					,	default=lowercase__					,	choices=['no', 'fp16', 'bf16', 'fp8']					,	help='Whether to use mixed precision. Choose'
						    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
						    'and an Nvidia Ampere GPU.'					,	)
						parser.add_argument('--cpu'					,	action='store_true'					,	help='If passed, will train on the CPU.'    )
						parser.add_argument(
						    '--checkpointing_steps'					,	type=lowercase__					,	default=lowercase__					,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'					,	)
						parser.add_argument(
						    '--resume_from_checkpoint'					,	type=lowercase__					,	default=lowercase__					,	help='If the training should continue from a checkpoint folder.'					,	)
						parser.add_argument(
						    '--with_tracking'					,	action='store_true'					,	help='Whether to load in all available experiment trackers from the environment and use them for logging.'					,	)
						parser.add_argument(
						    '--output_dir'					,	type=lowercase__					,	default='.'					,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'					,	)
						parser.add_argument(
						    '--logging_dir'					,	type=lowercase__					,	default='logs'					,	help='Location on where to store experiment tracking logs`'					,	)
						parser.add_argument(
						    '--model_name_or_path'					,	type=lowercase__					,	help='Path to pretrained model or model identifier from huggingface.co/models.'					,	required=lowercase__					,	)
						_snake_case: Dict	       =		parser.parse_args()
						_snake_case: Dict	       =		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(lowercase__					,	lowercase__    )


if __name__ == "__main__":
		main()












'''simple docstring'''






def    lowercase_   (		lowercase__ = 400_0000    ) ->int:

						if n <= 1:
												return 0
						_snake_case: Union[str, Any]	       =		0
						_snake_case: List[Any]	       =		2
						_snake_case: Any	       =		0
						while 4 * b + a <= n:
												_snake_case       ,    _snake_case: int	       =		b, 4 * b + a
												count += a
						return count + b


if __name__ == "__main__":
		print(F'{solution() = }')





