



'''simple docstring'''

import argparse
import os

import evaluate
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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)
#
# 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
#
########################################################################


__lowerCAmelCase		     =  16
__lowerCAmelCase		     =  32



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_ = 16    )     -> Union[str, Any]:
		_a	: Any								=				AutoTokenizer.from_pretrained('bert-base-cased'    )
		_a	: int								=				load_dataset('glue'		,     'mrpc'    )

		def tokenize_function(lowerCAmelCase_    ):
				# max_length=None => use the model max length (it's actually the default)
				_a	: Dict								=				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
		# starting with the main process first:
		with accelerator.main_process_first():
				_a	: Tuple								=				datasets.map(
				    lowerCAmelCase_		,     batched=lowerCAmelCase_		,     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
		_a	: str								=				tokenized_datasets.rename_column('label'		,     'labels'    )

		def collate_fn(lowerCAmelCase_    ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				_a	: int								=				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":
						_a	: Tuple								=				16
				elif accelerator.mixed_precision != "no":
						_a	: str								=				8
				else:
						_a	: Tuple								=				None

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

		# Instantiate dataloaders.
		_a	: List[str]								=				DataLoader(
		    tokenized_datasets['train']		,     shuffle=lowerCAmelCase_		,     collate_fn=lowerCAmelCase_		,     batch_size=lowerCAmelCase_    )
		_a	: str								=				DataLoader(
		    tokenized_datasets['validation']		,     shuffle=lowerCAmelCase_		,     collate_fn=lowerCAmelCase_		,     batch_size=lowerCAmelCase_    )

		return train_dataloader, eval_dataloader


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

							__lowerCAmelCase		     =  mocked_dataloaders  # noqa: F811



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> Optional[int]:
		# For testing only
		if os.environ.get('TESTING_MOCKED_DATALOADERS'		,     lowerCAmelCase_    ) == "1":
				_a	: Optional[Any]								=				2
		# New Code #
		_a	: List[Any]								=				int(args.gradient_accumulation_steps    )
		_a	: str								=				int(args.local_sgd_steps    )
		# Initialize accelerator
		_a	: Union[str, Any]								=				Accelerator(
		    cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     gradient_accumulation_steps=lowerCAmelCase_    )
		if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
				raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'    )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		_a	: int								=				config['lr']
		_a	: str								=				int(config['num_epochs']    )
		_a	: List[Any]								=				int(config['seed']    )
		_a	: Optional[Any]								=				int(config['batch_size']    )

		_a	: Any								=				evaluate.load('glue'		,     'mrpc'    )

		set_seed(lowerCAmelCase_    )
		_a			,   _a	: Union[str, Any]								=				get_dataloaders(lowerCAmelCase_		,     lowerCAmelCase_    )
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		_a	: List[Any]								=				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'		,     return_dict=lowerCAmelCase_    )

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		_a	: Tuple								=				model.to(accelerator.device    )

		# Instantiate optimizer
		_a	: List[Any]								=				AdamW(params=model.parameters()		,     lr=lowerCAmelCase_    )

		# Instantiate scheduler
		_a	: Union[str, Any]								=				get_linear_schedule_with_warmup(
		    optimizer=lowerCAmelCase_		,     num_warmup_steps=100		,     num_training_steps=(len(lowerCAmelCase_    ) * num_epochs)		,     )

		# 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	: Any								=				accelerator.prepare(
		    lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Now we train the model
		for epoch in range(lowerCAmelCase_    ):
				model.train()
				with LocalSGD(
				    accelerator=lowerCAmelCase_		,     model=lowerCAmelCase_		,     local_sgd_steps=lowerCAmelCase_		,     enabled=local_sgd_steps is not None    ) as local_sgd:
						for step, batch in enumerate(lowerCAmelCase_    ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device    )
								# New code #
								# We use the new `accumulate` context manager to perform gradient accumulation
								# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
								with accelerator.accumulate(lowerCAmelCase_    ):
										_a	: Dict								=				model(**lowerCAmelCase_    )
										_a	: Any								=				output.loss
										accelerator.backward(lowerCAmelCase_    )
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										# LocalSGD-specific line
										local_sgd.step()

				model.eval()
				for step, batch in enumerate(lowerCAmelCase_    ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device    )
						with torch.no_grad():
								_a	: Any								=				model(**lowerCAmelCase_    )
						_a	: Any								=				outputs.logits.argmax(dim=-1    )
						_a			,   _a	: Dict								=				accelerator.gather_for_metrics((predictions, batch['labels'])    )
						metric.add_batch(
						    predictions=lowerCAmelCase_		,     references=lowerCAmelCase_		,     )

				_a	: List[Any]								=				metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f"""epoch {epoch}:"""		,     lowerCAmelCase_    )



def 	__lowerCamelCase	( )     -> str:
		_a	: List[str]								=				argparse.ArgumentParser(description='Simple example of training script.'    )
		parser.add_argument(
		    '--mixed_precision'		,     type=lowerCAmelCase_		,     default=lowerCAmelCase_		,     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.'		,     )
		# New Code #
		parser.add_argument(
		    '--gradient_accumulation_steps'		,     type=lowerCAmelCase_		,     default=1		,     help='The number of minibatches to be ran before gradients are accumulated.'		,     )
		parser.add_argument(
		    '--local_sgd_steps'		,     type=lowerCAmelCase_		,     default=8		,     help='Number of local SGD steps or None to disable local SGD'    )
		parser.add_argument('--cpu'		,     action='store_true'		,     help='If passed, will train on the CPU.'    )
		_a	: Tuple								=				parser.parse_args()
		_a	: int								=				{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowerCAmelCase_		,     lowerCAmelCase_    )


if __name__ == "__main__":
							main()









'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


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




