




"""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
#
########################################################################


lowercase__     						=						16
lowercase__     						=						32







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

				def tokenize_function(lowercase__		):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase     :			Tuple    =    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():
								_lowerCamelCase     :			Dict    =    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
				_lowerCamelCase     :			List[Any]    =    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.
								_lowerCamelCase     :			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":
												_lowerCamelCase     :			Optional[int]    =    16
								elif accelerator.mixed_precision != "no":
												_lowerCamelCase     :			Optional[int]    =    8
								else:
												_lowerCamelCase     :			Optional[Any]    =    None

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

				# Instantiate dataloaders.
				_lowerCamelCase     :			Any    =    DataLoader(
				    tokenized_datasets['train']							,    shuffle=snake_case_							,    collate_fn=snake_case_							,    batch_size=snake_case_		)
				_lowerCamelCase     :			List[str]    =    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

			lowercase__     						=						mocked_dataloaders  # noqa: F811







def      _snake_case			(				lowercase__							,    lowercase__		):




				if os.environ.get('TESTING_MOCKED_DATALOADERS'							,    snake_case_		) == "1":
								_lowerCamelCase     :			Any    =    2
				# New Code #
				_lowerCamelCase     :			Optional[int]    =    int(args.gradient_accumulation_steps		)
				_lowerCamelCase     :			Optional[Any]    =    int(args.local_sgd_steps		)
				# Initialize accelerator
				_lowerCamelCase     :			Optional[int]    =    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
				_lowerCamelCase     :			Any    =    config['lr']
				_lowerCamelCase     :			Optional[int]    =    int(config['num_epochs']		)
				_lowerCamelCase     :			Tuple    =    int(config['seed']		)
				_lowerCamelCase     :			Union[str, Any]    =    int(config['batch_size']		)

				_lowerCamelCase     :			int    =    evaluate.load('glue'							,    'mrpc'		)

				set_seed(snake_case_		)
				_lowerCamelCase, _lowerCamelCase     :			str    =    get_dataloaders(snake_case_							,    snake_case_		)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_lowerCamelCase     :			List[str]    =    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).
				_lowerCamelCase     :			Optional[int]    =    model.to(accelerator.device		)

				# Instantiate optimizer
				_lowerCamelCase     :			Tuple    =    AdamW(params=model.parameters()							,    lr=snake_case_		)

				# Instantiate scheduler
				_lowerCamelCase     :			int    =    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.
				_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase     :			Any    =    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_		):
																				_lowerCamelCase     :			Dict    =    model(**snake_case_		)
																				_lowerCamelCase     :			Dict    =    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():
																_lowerCamelCase     :			str    =    model(**snake_case_		)
												_lowerCamelCase     :			str    =    outputs.logits.argmax(dim=-1		)
												_lowerCamelCase, _lowerCamelCase     :			Union[str, Any]    =    accelerator.gather_for_metrics((predictions, batch['labels'])		)
												metric.add_batch(
												    predictions=snake_case_							,    references=snake_case_							,    )

								_lowerCamelCase     :			Any    =    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''							,    snake_case_		)







def      _snake_case			(				):
				_lowerCamelCase     :			Tuple    =    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.'		)
				_lowerCamelCase     :			List[Any]    =    parser.parse_args()
				_lowerCamelCase     :			Union[str, Any]    =    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(snake_case_							,    snake_case_		)


if __name__ == "__main__":
			main()
'''simple docstring'''





from __future__ import annotations





def 	lowerCAmelCase_				(					snake_case_       : list					,    snake_case_       : int | None = None					,    snake_case_       : int | None = None			)    ->      None:


     '''simple docstring'''




     if start is None:
          UpperCAmelCase_           =   0

     if end is None:
          UpperCAmelCase_           =   len(snake_case_			) - 1

     if start >= end:
          return

     UpperCAmelCase_           =   (start + end) // 2

     slowsort(snake_case_					,    snake_case_					,    snake_case_			)
     slowsort(snake_case_					,    mid + 1					,    snake_case_			)

     if sequence[end] < sequence[mid]:
          UpperCAmelCase_    , UpperCAmelCase_           =   sequence[mid], sequence[end]

     slowsort(snake_case_					,    snake_case_					,    end - 1			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




