






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


snake_case    				=		16
snake_case    				=		32


def   lowerCamelCase__			(     lowercase						,     lowercase = 16				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      int							=    AutoTokenizer.from_pretrained("bert-base-cased"				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    load_dataset("glue"						,     "mrpc"				)

  def tokenize_function(lowercase				):
    # max_length=None => use the model max length (it's actually the default)
    SCREAMING_SNAKE_CASE       :      Dict							=    tokenizer(examples["sentence1"]						,     examples["sentence2"]						,     truncation=lowercase						,     max_length=lowercase				)
    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():
    SCREAMING_SNAKE_CASE       :      List[Any]							=    datasets.map(
        lowercase						,     batched=lowercase						,     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
  SCREAMING_SNAKE_CASE       :      Dict							=    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.
    SCREAMING_SNAKE_CASE       :      Union[str, 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":
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    16
    elif accelerator.mixed_precision != "no":
      SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    8
    else:
      SCREAMING_SNAKE_CASE       :      Tuple							=    None

    return tokenizer.pad(
        lowercase						,     padding="longest"						,     max_length=lowercase						,     pad_to_multiple_of=lowercase						,     return_tensors="pt"						,     )

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE       :      Dict							=    DataLoader(
      tokenized_datasets["train"]						,     shuffle=lowercase						,     collate_fn=lowercase						,     batch_size=lowercase				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    DataLoader(
      tokenized_datasets["validation"]						,     shuffle=lowercase						,     collate_fn=lowercase						,     batch_size=lowercase				)

  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

   snake_case    				=		mocked_dataloaders  # noqa: F811


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  if os.environ.get("TESTING_MOCKED_DATALOADERS"						,     lowercase				) == "1":
    SCREAMING_SNAKE_CASE       :      List[Any]							=    2
  # New Code #
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    int(args.gradient_accumulation_steps				)
  SCREAMING_SNAKE_CASE       :      str							=    int(args.local_sgd_steps				)
  # Initialize accelerator
  SCREAMING_SNAKE_CASE       :      List[Any]							=    Accelerator(
      cpu=args.cpu						,     mixed_precision=args.mixed_precision						,     gradient_accumulation_steps=lowercase				)
  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
  SCREAMING_SNAKE_CASE       :      Dict							=    config["lr"]
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    int(config["num_epochs"]				)
  SCREAMING_SNAKE_CASE       :      Any							=    int(config["seed"]				)
  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    int(config["batch_size"]				)

  SCREAMING_SNAKE_CASE       :      Dict							=    evaluate.load("glue"						,     "mrpc"				)

  set_seed(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    get_dataloaders(lowercase						,     lowercase				)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,     return_dict=lowercase				)

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

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE       :      Dict							=    AdamW(params=model.parameters()						,     lr=lowercase				)

  # Instantiate scheduler
  SCREAMING_SNAKE_CASE       :      List[str]							=    get_linear_schedule_with_warmup(
      optimizer=lowercase						,     num_warmup_steps=100						,     num_training_steps=(len(lowercase				) * 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.
  SCREAMING_SNAKE_CASE       :      Any							=    accelerator.prepare(
      lowercase						,     lowercase						,     lowercase						,     lowercase						,     lowercase				)

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

    model.eval()
    for step, batch in enumerate(lowercase				):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device				)
      with torch.no_grad():
        SCREAMING_SNAKE_CASE       :      List[Any]							=    model(**lowercase				)
      SCREAMING_SNAKE_CASE       :      str							=    outputs.logits.argmax(dim=-1				)
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    accelerator.gather_for_metrics((predictions, batch["labels"])				)
      metric.add_batch(
          predictions=lowercase						,     references=lowercase						,     )

    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F'''epoch {epoch}:'''						,     lowercase				)


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    argparse.ArgumentParser(description="Simple example of training script."				)
  parser.add_argument(
      "--mixed_precision"						,     type=lowercase						,     default=lowercase						,     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=lowercase						,     default=1						,     help="The number of minibatches to be ran before gradients are accumulated."						,     )
  parser.add_argument(
      "--local_sgd_steps"						,     type=lowercase						,     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."				)
  SCREAMING_SNAKE_CASE       :      Tuple							=    parser.parse_args()
  SCREAMING_SNAKE_CASE       :      str							=    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(lowercase						,     lowercase				)


if __name__ == "__main__":
   main()














from ..utils import DummyObject, requires_backends


class  SCREAMING_SNAKE_CASE			(			metaclass=lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	['''keras_nlp''']


     def __init__(				self       :	Dict					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Dict      ):
       requires_backends(self					, ["keras_nlp"]      )






