






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a    :   List[Any]        =			{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _a    :   List[Any]        =			[
     """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """VanForImageClassification""",
     """VanModel""",
     """VanPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_van import (
      VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      VanForImageClassification,
      VanModel,
      VanPreTrainedModel,
  )

else:
 import sys

 _a    :   Tuple        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)











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


_a    :   List[Any]        =			16
_a    :   Tuple        =			32


def 				snake_case__							(							UpperCAmelCase							:  Accelerator    ,			UpperCAmelCase							:  int = 1_6       ):
    lowerCAmelCase__   :Tuple      							=				AutoTokenizer.from_pretrained("bert-base-cased"       )
    lowerCAmelCase__   :Any      							=				load_dataset("glue"    ,			"mrpc"       )

    def tokenize_function(UpperCAmelCase							:  Dict       ):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__   :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__   :Any      							=				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							:  Any       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        lowerCAmelCase__   :Optional[int]      							=				1_2_8 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__   :int      							=				1_6
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__   :Optional[int]      							=				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__   :Dict      							=				DataLoader(
        tokenized_datasets["train"]    ,			shuffle=UpperCAmelCase    ,			collate_fn=UpperCAmelCase    ,			batch_size=UpperCAmelCase       )
    lowerCAmelCase__   :Optional[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

 _a    :   List[Any]        =			mocked_dataloaders  # noqa: F811


def 				snake_case__							(							UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  str       ):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,			UpperCAmelCase       ) == "1":
        lowerCAmelCase__   :Tuple      							=				2
    # New Code #
    lowerCAmelCase__   :Tuple      							=				int(args.gradient_accumulation_steps       )
    lowerCAmelCase__   :str      							=				int(args.local_sgd_steps       )
    # Initialize accelerator
    lowerCAmelCase__   :Optional[Any]      							=				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__   :Union[str, Any]      							=				config["lr"]
    lowerCAmelCase__   :int      							=				int(config["num_epochs"]       )
    lowerCAmelCase__   :List[Any]      							=				int(config["seed"]       )
    lowerCAmelCase__   :int      							=				int(config["batch_size"]       )

    lowerCAmelCase__   :Tuple      							=				evaluate.load("glue"    ,			"mrpc"       )

    set_seed(UpperCAmelCase       )
    lowerCAmelCase__							,lowerCAmelCase__   :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__   :Any      							=				model.to(accelerator.device       )

    # Instantiate optimizer
    lowerCAmelCase__   :Optional[int]      							=				AdamW(params=model.parameters()    ,			lr=UpperCAmelCase       )

    # Instantiate scheduler
    lowerCAmelCase__   :List[Any]      							=				get_linear_schedule_with_warmup(
        optimizer=UpperCAmelCase    ,			num_warmup_steps=1_0_0    ,			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__   :Tuple      							=				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__   :List[str]      							=				model(**UpperCAmelCase       )
                    lowerCAmelCase__   :List[Any]      							=				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__   :Dict      							=				model(**UpperCAmelCase       )
            lowerCAmelCase__   :List[Any]      							=				outputs.logits.argmax(dim=-1       )
            lowerCAmelCase__							,lowerCAmelCase__   :List[Any]      							=				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 				snake_case__							(							):
    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__   :Tuple      							=				parser.parse_args()
    lowerCAmelCase__   :Any      							=				{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(UpperCAmelCase    ,			UpperCAmelCase       )


if __name__ == "__main__":
 main()



