





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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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       : int			     =       16
_UpperCAmelCase       : Optional[int]			     =       32


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

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

    return tokenizer.pad(
        UpperCamelCase__		,			padding='longest'		,			max_length=UpperCamelCase__		,			pad_to_multiple_of=UpperCamelCase__		,			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


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    _UpperCAmelCase       : Union[str, Any]			     =       mocked_dataloaders  # noqa: F811


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:





  if os.environ.get('TESTING_MOCKED_DATALOADERS'		,			UpperCamelCase__				) == "1":
    lowerCamelCase__					:   Union[str, Any]      				=	2
  # Initialize accelerator
  lowerCamelCase__					:   Dict      				=	Accelerator(cpu=args.cpu		,			mixed_precision=args.mixed_precision				)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase__					:   Union[str, Any]      				=	config['lr']
  lowerCamelCase__					:   Union[str, Any]      				=	int(config['num_epochs']				)
  lowerCamelCase__					:   Any      				=	int(config['seed']				)
  lowerCamelCase__					:   Union[str, Any]      				=	int(config['batch_size']				)

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

  # If the batch size is too big we use gradient accumulation
  lowerCamelCase__					:   Optional[Any]      				=	1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    lowerCamelCase__					:   List[str]      				=	batch_size // MAX_GPU_BATCH_SIZE
    lowerCamelCase__					:   str      				=	MAX_GPU_BATCH_SIZE

  set_seed(UpperCamelCase__				)
  lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	get_dataloaders(UpperCamelCase__		,			UpperCamelCase__				)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase__					:   Dict      				=	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__					:   Optional[Any]      				=	model.to(accelerator.device				)

  # Instantiate optimizer
  lowerCamelCase__					:   Any      				=	AdamW(params=model.parameters()		,			lr=UpperCamelCase__				)

  # Instantiate scheduler
  lowerCamelCase__					:   int      				=	get_linear_schedule_with_warmup(
      optimizer=UpperCamelCase__		,			num_warmup_steps=100		,			num_training_steps=(len(UpperCamelCase__				) * num_epochs) // gradient_accumulation_steps		,			)

  # 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__					:   int      				=	accelerator.prepare(
      UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__				)

  # Now we train the model
  for epoch in range(UpperCamelCase__				):
    model.train()
    for step, batch in enumerate(UpperCamelCase__				):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device				)
      lowerCamelCase__					:   Tuple      				=	model(**UpperCamelCase__				)
      lowerCamelCase__					:   Dict      				=	outputs.loss
      lowerCamelCase__					:   Any      				=	loss / gradient_accumulation_steps
      accelerator.backward(UpperCamelCase__				)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    lowerCamelCase__					:   int      				=	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__					:   str      				=	model(**UpperCamelCase__				)
      lowerCamelCase__					:   List[str]      				=	outputs.logits.argmax(dim=-1				)
      lowerCamelCase__				,							lowerCamelCase__					:   int      				=	accelerator.gather((predictions, batch['labels'])				)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(UpperCamelCase__				) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          lowerCamelCase__					:   Optional[int]      				=	predictions[: len(eval_dataloader.dataset				) - samples_seen]
          lowerCamelCase__					:   Any      				=	references[: len(eval_dataloader.dataset				) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=UpperCamelCase__		,			references=UpperCamelCase__		,			)

    lowerCamelCase__					:   str      				=	metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}:"""		,			UpperCamelCase__				)


def  SCREAMING_SNAKE_CASE   ( )    ->  str:
  lowerCamelCase__					:   str      				=	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.'		,			)
  parser.add_argument('--cpu'		,			action='store_true'		,			help='If passed, will train on the CPU.'				)
  lowerCamelCase__					:   Any      				=	parser.parse_args()
  lowerCamelCase__					:   int      				=	{'lr': 2e-5, 'num_epochs': 3, '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





