"""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	:	Optional[int]=					16
_a	:	List[str]=					32
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[str]    )				->     int:

	'''simple docstring'''





	return int(x / 2**20    )

class 						UpperCamelCase     :



				def __enter__(self							: Tuple)       ->      Union[str, Any]:
					gc.collect()
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
					__snake_case     :				Dict 						=					torch.cuda.memory_allocated()
					__snake_case     :				Any 						=					psutil.Process()

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



				def    _lowercase	(self							: List[Any])       ->      int:
					return self.process.memory_info().rss



				def    _lowercase	(self							: Tuple)       ->      Dict:
					__snake_case     :				Dict 						=					-1

					while True:
						__snake_case     :				int 						=					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							: Union[str, Any]      ,    *_A							: Union[str, Any])       ->      Any:
					__snake_case     :				Union[str, Any] 						=					False

					gc.collect()
					torch.cuda.empty_cache()
					__snake_case     :				Dict 						=					torch.cuda.memory_allocated()
					__snake_case     :				Any 						=					torch.cuda.max_memory_allocated()
					__snake_case     :				Tuple 						=					bamb(self.end - self.begin)
					__snake_case     :				List[Any] 						=					bamb(self.peak - self.begin)

					__snake_case     :				Tuple 						=					self.cpu_mem_used()
					__snake_case     :				List[Any] 						=					bamb(self.cpu_end - self.cpu_begin)
					__snake_case     :				Tuple 						=					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	:	Optional[Any]=					mocked_dataloaders  # noqa: F811
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]     ,				UpperCAmelCase_       :							Union[str, Any]    )				->     str:

	'''simple docstring'''





	if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,				UpperCAmelCase_    ) == "1":
		__snake_case     :				Dict 						=					2

	# New Code #
	# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
	__snake_case     :				Optional[Any] 						=					FullyShardedDataParallelPlugin(
	    state_dict_config=FullStateDictConfig(offload_to_cpu=UpperCAmelCase_     ,				ranka_only=UpperCAmelCase_    )     ,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=UpperCAmelCase_     ,				ranka_only=UpperCAmelCase_    )     ,				)

	# Initialize accelerator
	if args.with_tracking:
		__snake_case     :				Any 						=					Accelerator(
		    cpu=args.cpu     ,				mixed_precision=args.mixed_precision     ,				log_with='wandb'     ,				project_dir=args.logging_dir     ,				fsdp_plugin=UpperCAmelCase_     ,				)
	else:
		__snake_case     :				Any 						=					Accelerator(fsdp_plugin=UpperCAmelCase_    )
	accelerator.print(accelerator.distributed_type    )

	if hasattr(args.checkpointing_steps     ,				'isdigit'    ):
		if args.checkpointing_steps == "epoch":
			__snake_case     :				Optional[int] 						=					args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			__snake_case     :				Any 						=					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     :				str 						=					None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__snake_case     :				str 						=					config['lr']
	__snake_case     :				int 						=					int(config['num_epochs']    )
	__snake_case     :				Any 						=					int(config['seed']    )
	__snake_case     :				str 						=					int(config['batch_size']    )

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

	__snake_case     :				int 						=					AutoTokenizer.from_pretrained(args.model_name_or_path    )
	__snake_case     :				Tuple 						=					load_dataset('glue'     ,				'mrpc'    )
	__snake_case     :				Tuple 						=					evaluate.load('glue'     ,				'mrpc'    )

	def tokenize_function(UpperCAmelCase_       :							List[Any]    ):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case     :				Optional[int] 						=					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
	# starting with the main process first:
	with accelerator.main_process_first():
		__snake_case     :				Dict 						=					datasets.map(
		    UpperCAmelCase_     ,				batched=UpperCAmelCase_     ,				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     :				Optional[int] 						=					tokenized_datasets.rename_column('label'     ,				'labels'    )

	# If the batch size is too big we use gradient accumulation
	__snake_case     :				Optional[Any] 						=					1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		__snake_case     :				Union[str, Any] 						=					batch_size // MAX_GPU_BATCH_SIZE
		__snake_case     :				str 						=					MAX_GPU_BATCH_SIZE

	def collate_fn(UpperCAmelCase_       :							Union[str, Any]    ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		__snake_case     :				int 						=					1_28 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[Any] 						=					16
		elif accelerator.mixed_precision != "no":
			__snake_case     :				Union[str, Any] 						=					8
		else:
			__snake_case     :				Union[str, Any] 						=					None

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

	# Instantiate dataloaders.
	__snake_case     :				Optional[Any] 						=					DataLoader(
	    tokenized_datasets['train']     ,				shuffle=UpperCAmelCase_     ,				collate_fn=UpperCAmelCase_     ,				batch_size=UpperCAmelCase_    )
	__snake_case     :				str 						=					DataLoader(
	    tokenized_datasets['validation']     ,				shuffle=UpperCAmelCase_     ,				collate_fn=UpperCAmelCase_     ,				batch_size=UpperCAmelCase_    )

	set_seed(UpperCAmelCase_    )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case     :				Dict 						=					AutoModelForSequenceClassification.from_pretrained(
	    args.model_name_or_path     ,				return_dict=UpperCAmelCase_     ,				low_cpu_mem_usage=UpperCAmelCase_    )

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

	# 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     :				int 						=					torch.optim.AdamW(params=model.parameters()     ,				lr=UpperCAmelCase_     ,				weight_decay=2E-4    )

	# Instantiate scheduler
	__snake_case     :				Optional[int] 						=					get_linear_schedule_with_warmup(
	    optimizer=UpperCAmelCase_     ,				num_warmup_steps=10     ,				num_training_steps=(len(UpperCAmelCase_    ) * 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     :				Optional[Any] 						=					accelerator.prepare(
	    UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_     ,				UpperCAmelCase_    )

	__snake_case     :				List[Any] 						=					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     :				Tuple 						=					os.path.basename(args.resume_from_checkpoint    )
		else:
			# Get the most recent checkpoint
			__snake_case     :				Dict 						=					[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
			dirs.sort(key=os.path.getctime    )
			__snake_case     :				Optional[int] 						=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		__snake_case     :				Dict 						=					os.path.splitext(UpperCAmelCase_    )[0]

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

    # Now we train the model
	for epoch in range(UpperCAmelCase_    ):
		# 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     :				int 						=					0
			for step, batch in enumerate(UpperCAmelCase_    ):
				# 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     :				int 						=					model(**UpperCAmelCase_    )
				__snake_case     :				List[str] 						=					outputs.loss
				__snake_case     :				List[Any] 						=					loss / gradient_accumulation_steps
				# We keep track of the loss at each epoch
				if args.with_tracking:
					total_loss += loss.detach().float()
				accelerator.backward(UpperCAmelCase_    )
				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(UpperCAmelCase_     ,				UpperCAmelCase_    ):
					__snake_case     :				List[str] 						=					F"step_{overall_step}"
					if overall_step % checkpointing_steps == 0:
						if args.output_dir is not None:
							__snake_case     :				Union[str, Any] 						=					os.path.join(args.output_dir     ,				UpperCAmelCase_    )
						accelerator.save_state(UpperCAmelCase_    )
        # 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=UpperCAmelCase_     ,				)

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

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

			if checkpointing_steps == "epoch":
				__snake_case     :				List[str] 						=					F"epoch_{epoch}"
				if args.output_dir is not None:
					__snake_case     :				Any 						=					os.path.join(args.output_dir     ,				UpperCAmelCase_    )
				accelerator.save_state(UpperCAmelCase_    )
        # 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=UpperCAmelCase_     ,				)

	if args.with_tracking:
		accelerator.end_training()
def     __UpperCAmelCase							(       )				->     int:

	'''simple docstring'''





	__snake_case     :				str 						=					argparse.ArgumentParser(description='Simple example of training script.'    )
	parser.add_argument(
	    '--mixed_precision'     ,				type=UpperCAmelCase_     ,				default=UpperCAmelCase_     ,				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=UpperCAmelCase_     ,				default=UpperCAmelCase_     ,				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=UpperCAmelCase_     ,				default=UpperCAmelCase_     ,				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=UpperCAmelCase_     ,				default='.'     ,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'     ,				)
	parser.add_argument(
	    '--logging_dir'     ,				type=UpperCAmelCase_     ,				default='logs'     ,				help='Location on where to store experiment tracking logs`'     ,				)
	parser.add_argument(
	    '--model_name_or_path'     ,				type=UpperCAmelCase_     ,				help='Path to pretrained model or model identifier from huggingface.co/models.'     ,				required=UpperCAmelCase_     ,				)
	__snake_case     :				Union[str, Any] 						=					parser.parse_args()
	__snake_case     :				str 						=					{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(UpperCAmelCase_     ,				UpperCAmelCase_    )


if __name__ == "__main__":
	main()






"""simple docstring"""







_a	:	Optional[Any]=					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[Any]    )				->     List[Any]:

	'''simple docstring'''





	return c.isdigit()
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     List[Any]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					[]

	# iterate over the string in reverse order
	for c in expression.split()[::-1]:
		# push operand to stack
		if is_operand(UpperCAmelCase_    ):
			stack.append(int(UpperCAmelCase_    )    )

		else:
			# pop values from stack can calculate the result
			# push the result onto the stack again
			__snake_case     :				Optional[Any] 						=					stack.pop()
			__snake_case     :				int 						=					stack.pop()
			stack.append(calc[c](UpperCAmelCase_     ,				UpperCAmelCase_    )    )

	return stack.pop()


# Driver code
if __name__ == "__main__":
	_a	:	Union[str, Any]=					"+ 9 * 2 6"
	print(evaluate(test_expression))

	_a	:	List[Any]=					"/ * 10 2 + 4 1 "
	print(evaluate(test_expression))





