





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
#
########################################################################


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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase = 16				)    ->  str:
    lowerCamelCase__					:   Optional[int]      				=	AutoTokenizer.from_pretrained('bert-base-cased'				)
    lowerCamelCase__					:   Any      				=	load_dataset('glue'		,			'mrpc'				)

    def tokenize_function(_UpperCAmelCase				):
        # max_length=None => use the model max length (it's actually the default)
        lowerCamelCase__					:   Union[str, Any]      				=	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():
        lowerCamelCase__					:   Optional[int]      				=	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
    lowerCamelCase__					:   List[str]      				=	tokenized_datasets.rename_column('label'		,			'labels'				)

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

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

    # Instantiate dataloaders.
    lowerCamelCase__					:   Optional[int]      				=	DataLoader(
        tokenized_datasets['train']		,			shuffle=_UpperCAmelCase		,			collate_fn=_UpperCAmelCase		,			batch_size=_UpperCAmelCase				)
    lowerCamelCase__					:   Tuple      				=	DataLoader(
        tokenized_datasets['validation']		,			shuffle=_UpperCAmelCase		,			collate_fn=_UpperCAmelCase		,			batch_size=_UpperCAmelCase				)

    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

    _UpperCAmelCase       : str			     =       mocked_dataloaders  # noqa: F811


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[str]:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'		,			_UpperCAmelCase				) == "1":
        lowerCamelCase__					:   str      				=	2
    # New Code #
    lowerCamelCase__					:   List[Any]      				=	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=_UpperCAmelCase				)
    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__					:   List[Any]      				=	config['lr']
    lowerCamelCase__					:   int      				=	int(config['num_epochs']				)
    lowerCamelCase__					:   List[Any]      				=	int(config['seed']				)
    lowerCamelCase__					:   List[Any]      				=	int(config['batch_size']				)

    lowerCamelCase__					:   str      				=	evaluate.load('glue'		,			'mrpc'				)

    set_seed(_UpperCAmelCase				)
    lowerCamelCase__				,							lowerCamelCase__					:   Optional[int]      				=	get_dataloaders(_UpperCAmelCase		,			_UpperCAmelCase				)
    # 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=_UpperCAmelCase				)

    # 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__					:   Dict      				=	model.to(accelerator.device				)

    # Instantiate optimizer
    lowerCamelCase__					:   int      				=	AdamW(params=model.parameters()		,			lr=_UpperCAmelCase				)

    # Instantiate scheduler
    lowerCamelCase__					:   Dict      				=	get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase		,			num_warmup_steps=100		,			num_training_steps=(len(_UpperCAmelCase				) * 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__					:   str      				=	accelerator.prepare(
        _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

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

        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():
                lowerCamelCase__					:   List[Any]      				=	model(**_UpperCAmelCase				)
            lowerCamelCase__					:   List[Any]      				=	outputs.logits.argmax(dim=-1				)
            lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	accelerator.gather_for_metrics((predictions, batch['labels'])				)
            metric.add_batch(
                predictions=_UpperCAmelCase		,			references=_UpperCAmelCase		,			)

        lowerCamelCase__					:   Optional[int]      				=	metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""		,			_UpperCAmelCase				)


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


if __name__ == "__main__":
    main()












import enum
import shutil
import sys


_UpperCAmelCase     ,_UpperCAmelCase       : List[Any]			     =       shutil.get_terminal_size()

_UpperCAmelCase       : Union[str, Any]			     =       {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}

class      lowerCAmelCase							(      enum.Enum    ):
 UpperCAmelCase__									=	0
 UpperCAmelCase__									=	1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  int:
    sys.stdout.write(str(_UpperCAmelCase				) + end				)
    sys.stdout.flush()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  Union[str, Any]:
    forceWrite(F"""\u001b[{color}m{content}\u001b[0m"""		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    forceWrite('\r'				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""				)


def  SCREAMING_SNAKE_CASE   ( )    ->  int:
    forceWrite(' ' * TERMINAL_WIDTH				)
    reset_cursor()


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    reset_cursor()
    forceWrite('-' * TERMINAL_WIDTH				)





