





import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


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


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

    def tokenize_function(_UpperCAmelCase				):
        # max_length=None => use the model max length (it's actually the default)
        lowerCamelCase__					:   List[str]      				=	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
    lowerCamelCase__					:   List[Any]      				=	datasets.map(
        _UpperCAmelCase		,			batched=_UpperCAmelCase		,			remove_columns=['idx', 'sentence1', 'sentence2']		,			load_from_cache_file=_UpperCAmelCase				)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    lowerCamelCase__					:   int      				=	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.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_UpperCAmelCase		,			padding='max_length'		,			max_length=128		,			return_tensors='pt'				)
        return tokenizer.pad(_UpperCAmelCase		,			padding='longest'		,			return_tensors='pt'				)

    # Instantiate dataloaders.
    lowerCamelCase__					:   Union[str, Any]      				=	DataLoader(
        tokenized_datasets['train']		,			shuffle=_UpperCAmelCase		,			collate_fn=_UpperCAmelCase		,			batch_size=_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	DataLoader(
        tokenized_datasets['validation']		,			shuffle=_UpperCAmelCase		,			collate_fn=_UpperCAmelCase		,			batch_size=_UpperCAmelCase				)

    return train_dataloader, eval_dataloader


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    # Initialize accelerator
    lowerCamelCase__					:   int      				=	Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCamelCase__					:   List[str]      				=	config['lr']
    lowerCamelCase__					:   List[str]      				=	int(config['num_epochs']				)
    lowerCamelCase__					:   List[str]      				=	int(config['seed']				)
    lowerCamelCase__					:   int      				=	int(config['batch_size']				)
    lowerCamelCase__					:   Optional[int]      				=	args.model_name_or_path

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

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__					:   Optional[Any]      				=	AutoModelForSequenceClassification.from_pretrained(_UpperCAmelCase		,			return_dict=_UpperCAmelCase				)

    # Instantiate optimizer
    lowerCamelCase__					:   Optional[int]      				=	(
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    lowerCamelCase__					:   List[Any]      				=	optimizer_cls(params=model.parameters()		,			lr=_UpperCAmelCase				)

    if accelerator.state.deepspeed_plugin is not None:
        lowerCamelCase__					:   str      				=	accelerator.state.deepspeed_plugin.deepspeed_config[
            'gradient_accumulation_steps'
        ]
    else:
        lowerCamelCase__					:   Union[str, Any]      				=	1
    lowerCamelCase__					:   Optional[int]      				=	(len(_UpperCAmelCase				) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        lowerCamelCase__					:   int      				=	get_linear_schedule_with_warmup(
            optimizer=_UpperCAmelCase		,			num_warmup_steps=0		,			num_training_steps=_UpperCAmelCase		,			)
    else:
        lowerCamelCase__					:   List[str]      				=	DummyScheduler(_UpperCAmelCase		,			total_num_steps=_UpperCAmelCase		,			warmup_num_steps=0				)

    # 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				)

    # We need to keep track of how many total steps we have iterated over
    lowerCamelCase__					:   Dict      				=	0
    # We also need to keep track of the stating epoch so files are named properly
    lowerCamelCase__					:   Optional[int]      				=	0

    # Now we train the model
    lowerCamelCase__					:   str      				=	evaluate.load('glue'		,			'mrpc'				)
    lowerCamelCase__					:   Optional[Any]      				=	0
    lowerCamelCase__					:   int      				=	{}
    for epoch in range(_UpperCAmelCase		,			_UpperCAmelCase				):
        model.train()
        for step, batch in enumerate(_UpperCAmelCase				):
            lowerCamelCase__					:   Dict      				=	model(**_UpperCAmelCase				)
            lowerCamelCase__					:   Optional[Any]      				=	outputs.loss
            lowerCamelCase__					:   Dict      				=	loss / gradient_accumulation_steps
            accelerator.backward(_UpperCAmelCase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1

        model.eval()
        lowerCamelCase__					:   str      				=	0
        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__					:   Union[str, Any]      				=	model(**_UpperCAmelCase				)
            lowerCamelCase__					:   Optional[Any]      				=	outputs.logits.argmax(dim=-1				)
            # It is slightly faster to call this once, than multiple times
            lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	accelerator.gather(
                (predictions, batch['labels'])				)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                if step == len(_UpperCAmelCase				) - 1:
                    lowerCamelCase__					:   Optional[int]      				=	predictions[: len(eval_dataloader.dataset				) - samples_seen]
                    lowerCamelCase__					:   Any      				=	references[: len(eval_dataloader.dataset				) - samples_seen]
                else:
                    samples_seen += references.shape[0]
            metric.add_batch(
                predictions=_UpperCAmelCase		,			references=_UpperCAmelCase		,			)

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

        if best_performance < eval_metric["accuracy"]:
            lowerCamelCase__					:   Dict      				=	eval_metric['accuracy']

    if args.performance_lower_bound is not None:
        assert (
            args.performance_lower_bound <= best_performance
        ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir		,			'all_results.json'				)		,			'w'				) as f:
            json.dump(_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   List[Any]      				=	argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'				)
    parser.add_argument(
        '--model_name_or_path'		,			type=_UpperCAmelCase		,			default='bert-base-cased'		,			help='Path to pretrained model or model identifier from huggingface.co/models.'		,			required=_UpperCAmelCase		,			)
    parser.add_argument(
        '--output_dir'		,			type=_UpperCAmelCase		,			default='.'		,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,			)
    parser.add_argument(
        '--performance_lower_bound'		,			type=_UpperCAmelCase		,			default=_UpperCAmelCase		,			help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'		,			)
    parser.add_argument(
        '--num_epochs'		,			type=_UpperCAmelCase		,			default=3		,			help='Number of train epochs.'		,			)
    lowerCamelCase__					:   Tuple      				=	parser.parse_args()
    lowerCamelCase__					:   Dict      				=	{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
    training_function(_UpperCAmelCase		,			_UpperCAmelCase				)


if __name__ == "__main__":
    main()












import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_UpperCAmelCase       : Union[str, Any]			     =       logging.get_logger(__name__)

class      lowerCAmelCase							:
 UpperCAmelCase__									=	None


@experimental
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

    return _map_with_joblib(_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   int      				=	num_proc if num_proc <= len(_UpperCAmelCase				) else len(_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[int]      				=	[]  # We organize the splits ourselve (contiguous splits)
    for index in range(_UpperCAmelCase				):
        lowerCamelCase__					:   str      				=	len(_UpperCAmelCase				) // num_proc
        lowerCamelCase__					:   List[str]      				=	len(_UpperCAmelCase				) % num_proc
        lowerCamelCase__					:   Tuple      				=	div * index + min(_UpperCAmelCase		,			_UpperCAmelCase				)
        lowerCamelCase__					:   int      				=	start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)				)

    if len(_UpperCAmelCase				) != sum(len(i[1]				) for i in split_kwds				):
        raise ValueError(
            F"""Error dividing inputs iterable among processes. """
            F"""Total number of objects {len(_UpperCAmelCase				)}, """
            F"""length: {sum(len(i[1]				) for i in split_kwds				)}"""				)

    logger.info(
        F"""Spawning {num_proc} processes for {len(_UpperCAmelCase				)} objects in slices of {[len(i[1]				) for i in split_kwds]}"""				)
    lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	None, None
    if not disable_tqdm:
        lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	(RLock(),), tqdm.set_lock
    with Pool(_UpperCAmelCase		,			initargs=_UpperCAmelCase		,			initializer=_UpperCAmelCase				) as pool:
        lowerCamelCase__					:   Any      				=	pool.map(_UpperCAmelCase		,			_UpperCAmelCase				)
    logger.info(F"""Finished {num_proc} processes"""				)
    lowerCamelCase__					:   Union[str, Any]      				=	[obj for proc_res in mapped for obj in proc_res]
    logger.info(F"""Unpacked {len(_UpperCAmelCase				)} objects"""				)

    return mapped


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  int:
    # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
    # and it requires monkey-patching joblib internal classes which is subject to change
    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name		,			n_jobs=_UpperCAmelCase				):
        return joblib.Parallel()(
            joblib.delayed(_UpperCAmelCase				)((function, obj, types, None, True, None)				) for obj in iterable				)


@experimental
@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  Tuple:
    lowerCamelCase__					:   List[str]      				=	backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        lowerCamelCase__					:   str      				=	None





