


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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				16
SCREAMING_SNAKE_CASE :   Dict							      =				32



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_ = 16				)					->		Union[str, Any]:
    _lowercase :							List[Any]   	=	AutoTokenizer.from_pretrained('bert-base-cased'				)
    _lowercase :							Optional[Any]   	=	load_dataset('glue'							,				'mrpc'				)

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

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

    # Instantiate dataloaders.
    _lowercase :							Any   	=	DataLoader(
        tokenized_datasets['train']							,				shuffle=lowerCamelCase_							,				collate_fn=lowerCamelCase_							,				batch_size=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	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

 SCREAMING_SNAKE_CASE :   Tuple							      =				mocked_dataloaders  # noqa: F811



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Tuple:
    # For testing only
    if os.environ.get('TESTING_MOCKED_DATALOADERS'							,				lowerCamelCase_				) == "1":
        _lowercase :							int   	=	2
    # New Code #
    _lowercase :							Dict   	=	int(args.gradient_accumulation_steps				)
    # Initialize accelerator
    _lowercase :							List[str]   	=	Accelerator(
        cpu=args.cpu							,				mixed_precision=args.mixed_precision							,				gradient_accumulation_steps=lowerCamelCase_				)
    if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
        raise NotImplementedError(
            'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'				)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _lowercase :							int   	=	config['lr']
    _lowercase :							int   	=	int(config['num_epochs']				)
    _lowercase :							Union[str, Any]   	=	int(config['seed']				)
    _lowercase :							Dict   	=	int(config['batch_size']				)

    _lowercase :							Union[str, Any]   	=	evaluate.load('glue'							,				'mrpc'				)

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

    # Instantiate optimizer
    _lowercase :							Union[str, Any]   	=	AdamW(params=model.parameters()							,				lr=lowerCamelCase_				)

    # Instantiate scheduler
    _lowercase :							int   	=	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.
    _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							Tuple   	=	accelerator.prepare(
        lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)

    # Now we train the model
    for epoch in range(lowerCamelCase_				):
        model.train()
        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_				):
                _lowercase :							Tuple   	=	model(**lowerCamelCase_				)
                _lowercase :							Optional[int]   	=	output.loss
                accelerator.backward(lowerCamelCase_				)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        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():
                _lowercase :							Tuple   	=	model(**lowerCamelCase_				)
            _lowercase :							List[str]   	=	outputs.logits.argmax(dim=-1				)
            _lowercase	,       _lowercase :							Union[str, Any]   	=	accelerator.gather_for_metrics((predictions, batch['labels'])				)
            metric.add_batch(
                predictions=lowerCamelCase_							,				references=lowerCamelCase_							,				)

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



def        UpperCamelCase_(   )					->		Optional[int]:
    _lowercase :							Tuple   	=	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('--cpu'							,				action='store_true'							,				help='If passed, will train on the CPU.'				)
    _lowercase :							int   	=	parser.parse_args()
    _lowercase :							Optional[Any]   	=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
    training_function(lowerCamelCase_							,				lowerCamelCase_				)


if __name__ == "__main__":
 main()






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict



@pytest.mark.parametrize(
    'files'							,				[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		List[str]:
    _lowercase :							Any   	=	tmp_path_factory.mktemp('dset_infos_dir'				)
    if "full:README.md" in files:
        with open(dataset_infos_dir / 'README.md'							,				'w'				) as f:
            f.write('---\ndataset_info:\n  dataset_size: 42\n---'				)
    if "empty:README.md" in files:
        with open(dataset_infos_dir / 'README.md'							,				'w'				) as f:
            f.write(''				)
    # we want to support dataset_infos.json for backward compatibility
    if "dataset_infos.json" in files:
        with open(dataset_infos_dir / 'dataset_infos.json'							,				'w'				) as f:
            f.write('{"default": {"dataset_size": 42}}'				)
    _lowercase :							Any   	=	DatasetInfosDict.from_directory(lowerCamelCase_				)
    assert dataset_infos
    assert dataset_infos["default"].dataset_size == 42



@pytest.mark.parametrize(
    'dataset_info'							,				[
        DatasetInfo(),
        DatasetInfo(
            description='foo'							,				features=Features({'a': Value('int32'				)}				)							,				builder_name='builder'							,				config_name='config'							,				version='1.0.0'							,				splits=[{'name': 'train'}]							,				download_size=42							,				),
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[int]:
    _lowercase :							Union[str, Any]   	=	str(lowerCamelCase_				)
    dataset_info.write_to_directory(lowerCamelCase_				)
    _lowercase :							List[str]   	=	DatasetInfo.from_directory(lowerCamelCase_				)
    assert dataset_info == reloaded
    assert os.path.exists(os.path.join(lowerCamelCase_							,				'dataset_info.json'				)				)



def        UpperCamelCase_(   )					->		int:
    _lowercase :							Tuple   	=	DatasetInfo(
        description='foo'							,				citation='bar'							,				homepage='https://foo.bar'							,				license='CC0'							,				features=Features({'a': Value('int32'				)}				)							,				post_processed={}							,				supervised_keys=()							,				task_templates=[]							,				builder_name='builder'							,				config_name='config'							,				version='1.0.0'							,				splits=[{'name': 'train', 'num_examples': 42}]							,				download_checksums={}							,				download_size=1337							,				post_processing_size=442							,				dataset_size=1234							,				size_in_bytes=1337 + 442 + 1234							,				)
    _lowercase :							Optional[int]   	=	dataset_info._to_yaml_dict()
    assert sorted(lowerCamelCase_				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        assert key in dataset_info_yaml_dict
        assert isinstance(dataset_info_yaml_dict[key]							,				(list, dict, int, str)				)
    _lowercase :							str   	=	yaml.safe_dump(lowerCamelCase_				)
    _lowercase :							str   	=	yaml.safe_load(lowerCamelCase_				)
    assert dataset_info_yaml_dict == reloaded



def        UpperCamelCase_(   )					->		int:
    _lowercase :							Tuple   	=	DatasetInfo()
    _lowercase :							Tuple   	=	dataset_info._to_yaml_dict()
    assert dataset_info_yaml_dict == {}



@pytest.mark.parametrize(
    'dataset_infos_dict'							,				[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}				),
        DatasetInfosDict({'my_config_name': DatasetInfo()}				),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'							,				features=Features({'a': Value('int32'				)}				)							,				builder_name='builder'							,				config_name='config'							,				version='1.0.0'							,				splits=[{'name': 'train'}]							,				download_size=42							,				)
            }				),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42				),
                'v2': DatasetInfo(dataset_size=1337				),
            }				),
    ]							,				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Dict:
    _lowercase :							Tuple   	=	str(lowerCamelCase_				)
    dataset_infos_dict.write_to_directory(lowerCamelCase_				)
    _lowercase :							Tuple   	=	DatasetInfosDict.from_directory(lowerCamelCase_				)

    # the config_name of the dataset_infos_dict take over the attribute
    for config_name, dataset_info in dataset_infos_dict.items():
        _lowercase :							Any   	=	config_name
        # the yaml representation doesn't include fields like description or citation
        # so we just test that we can recover what we can from the yaml
        _lowercase :							str   	=	DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
    assert dataset_infos_dict == reloaded

    if dataset_infos_dict:
        assert os.path.exists(os.path.join(lowerCamelCase_							,				'README.md'				)				)


