
import math


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Tuple:
    assert isinstance(_snake_case		,    _snake_case		) and (
        number >= 0
    ), "'number' must been an int and positive"

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or not number % 2:
        # Negatives, 0, 1 and all even numbers are not primes
        return False

    __lowercase						:	Union[str, Any]					=				range(3		,    int(math.sqrt(_snake_case		) + 1		)		,    2		)
    return not any(not number % i for i in odd_numbers		)





def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase=1		,    **__lowerCAmelCase		)       ->  Union[str, Any]:
    __lowercase						:	Union[str, Any]					=				factor * value
    __lowercase						:	Any					=				value

    while not is_prime(_snake_case		):
        value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

    if value == first_value_val:
        return next_prime(value + 1		,    **_snake_case		)
    return value





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					:			Union[str, Any]  				=			16
_lowerCAmelCase					:			List[str]  				=			32






def        UpperCamelCase_(							_snake_case   :  Accelerator       ,     _snake_case   :  int = 16	):




			"""simple docstring"""






			__a        =AutoTokenizer.from_pretrained('bert-base-cased'	)
			__a        =load_dataset('glue'       ,     'mrpc'	)

			def tokenize_function(_snake_case   :  Optional[int]	):
						# max_length=None => use the model max length (it's actually the default)
						__a        =tokenizer(examples['sentence1']       ,     examples['sentence2']       ,     truncation=_snake_case       ,     max_length=_snake_case	)
						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        =datasets.map(
						    _snake_case       ,     batched=_snake_case       ,     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        =tokenized_datasets.rename_column('label'       ,     'labels'	)

			def collate_fn(_snake_case   :  List[Any]	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a        =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        =16
						elif accelerator.mixed_precision != "no":
									__a        =8
						else:
									__a        =None

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

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

			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					:			List[Any]  				=			mocked_dataloaders  # noqa: F811






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,     _snake_case	) == "1":
						__a        =2
			# New Code #
			__a        =int(args.gradient_accumulation_steps	)
			__a        =int(args.local_sgd_steps	)
			# Initialize accelerator
			__a        =Accelerator(
			    cpu=args.cpu       ,     mixed_precision=args.mixed_precision       ,     gradient_accumulation_steps=_snake_case	)
			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        =config['lr']
			__a        =int(config['num_epochs']	)
			__a        =int(config['seed']	)
			__a        =int(config['batch_size']	)

			__a        =evaluate.load('glue'       ,     'mrpc'	)

			set_seed(_snake_case	)
			__a      ,    __a        =get_dataloaders(_snake_case       ,     _snake_case	)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a        =AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,     return_dict=_snake_case	)

			# 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        =model.to(accelerator.device	)

			# Instantiate optimizer
			__a        =AdamW(params=model.parameters()       ,     lr=_snake_case	)

			# Instantiate scheduler
			__a        =get_linear_schedule_with_warmup(
			    optimizer=_snake_case       ,     num_warmup_steps=100       ,     num_training_steps=(len(_snake_case	) * 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        =accelerator.prepare(
			    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			# Now we train the model
			for epoch in range(_snake_case	):
						model.train()
						with LocalSGD(
						    accelerator=_snake_case       ,     model=_snake_case       ,     local_sgd_steps=_snake_case       ,     enabled=local_sgd_steps is not None	) as local_sgd:
									for step, batch in enumerate(_snake_case	):
												# 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(_snake_case	):
															__a        =model(**_snake_case	)
															__a        =output.loss
															accelerator.backward(_snake_case	)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

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

						__a        =metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'       ,     _snake_case	)






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =argparse.ArgumentParser(description='Simple example of training script.'	)
			parser.add_argument(
			    '--mixed_precision'       ,     type=_snake_case       ,     default=_snake_case       ,     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=_snake_case       ,     default=1       ,     help='The number of minibatches to be ran before gradients are accumulated.'       ,     )
			parser.add_argument(
			    '--local_sgd_steps'       ,     type=_snake_case       ,     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        =parser.parse_args()
			__a        ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_snake_case       ,     _snake_case	)


if __name__ == "__main__":
						main()






