





'''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
#
########################################################################


A   : List[str]             =			16
A   : Optional[Any]             =			32





def       lowercase_      (		lowercase__				,  lowercase__ = 16						)   ->int:
						_snake_case:   Any  					=		AutoTokenizer.from_pretrained('bert-base-cased'						)
						_snake_case:   List[str]  					=		load_dataset('glue'				,  'mrpc'						)

						def tokenize_function(lowercase__						):
												# max_length=None => use the model max length (it's actually the default)
												_snake_case:   Union[str, Any]  					=		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:   Union[str, 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:   Optional[int]  					=		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.
												_snake_case:   str  					=		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:   Optional[Any]  					=		8
												else:
																		_snake_case:   int  					=		None

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

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

						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

		A   : Optional[int]             =			mocked_dataloaders  # noqa: F811





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Dict:
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'				,  lowercase__						) == "1":
												_snake_case:   Union[str, Any]  					=		2
						# New Code #
						_snake_case:   Optional[int]  					=		int(args.gradient_accumulation_steps						)
						_snake_case:   Optional[int]  					=		int(args.local_sgd_steps						)
						# Initialize accelerator
						_snake_case:   List[str]  					=		Accelerator(
						    cpu=args.cpu				,  mixed_precision=args.mixed_precision				,  gradient_accumulation_steps=lowercase__						)
						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
						_snake_case:   Optional[int]  					=		config['lr']
						_snake_case:   List[str]  					=		int(config['num_epochs']						)
						_snake_case:   List[Any]  					=		int(config['seed']						)
						_snake_case:   List[Any]  					=		int(config['batch_size']						)

						_snake_case:   Dict  					=		evaluate.load('glue'				,  'mrpc'						)

						set_seed(lowercase__						)
						_snake_case					,						_snake_case:   int  					=		get_dataloaders(lowercase__				,  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('bert-base-cased'				,  return_dict=lowercase__						)

						# 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).
						_snake_case:   Tuple  					=		model.to(accelerator.device						)

						# Instantiate optimizer
						_snake_case:   str  					=		AdamW(params=model.parameters()				,  lr=lowercase__						)

						# Instantiate scheduler
						_snake_case:   Any  					=		get_linear_schedule_with_warmup(
						    optimizer=lowercase__				,  num_warmup_steps=100				,  num_training_steps=(len(lowercase__						) * 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.
						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case:   Tuple  					=		accelerator.prepare(
						    lowercase__				,  lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)

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

												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:   Union[str, Any]  					=		model(**lowercase__						)
																		_snake_case:   Dict  					=		outputs.logits.argmax(dim=-1						)
																		_snake_case					,						_snake_case:   Optional[Any]  					=		accelerator.gather_for_metrics((predictions, batch['labels'])						)
																		metric.add_batch(
																		    predictions=lowercase__				,  references=lowercase__				,  )

												_snake_case:   Union[str, Any]  					=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''				,  lowercase__						)





def       lowercase_      (		)   ->Optional[Any]:
						_snake_case:   Optional[int]  					=		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.'				,  )
						# New Code #
						parser.add_argument(
						    '--gradient_accumulation_steps'				,  type=lowercase__				,  default=1				,  help='The number of minibatches to be ran before gradients are accumulated.'				,  )
						parser.add_argument(
						    '--local_sgd_steps'				,  type=lowercase__				,  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.'						)
						_snake_case:   Union[str, Any]  					=		parser.parse_args()
						_snake_case:   Optional[int]  					=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(lowercase__				,  lowercase__						)


if __name__ == "__main__":
		main()












'''simple docstring'''






def       lowercase_      (		lowercase__ = 1				,  lowercase__ = 1000						)   ->int:
						_snake_case:   int  					=		1
						_snake_case:   Union[str, Any]  					=		0

						for divide_by_number in range(lowercase__				,  digit + 1						):
												_snake_case:   list[int]  					=		[]
												_snake_case:   int  					=		numerator
												for _ in range(1				,  digit + 1						):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(lowercase__						):
																														_snake_case:   Union[str, Any]  					=		len(lowercase__						)
																														_snake_case:   Tuple  					=		divide_by_number
																		else:
																								has_been_divided.append(lowercase__						)
																								_snake_case:   Union[str, Any]  					=		now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()





