


"""simple docstring"""



def 						__lowerCAmelCase		(		lowercase						:						list[int]				)			->		list[list[int]]:


    """simple docstring"""





    snake_case					:   List[str]         =					[]
    if len(lowercase				) == 1:
        return [nums.copy()]
    for _ in range(len(lowercase				)				):
        snake_case					:   Any         =					nums.pop(0				)
        snake_case					:   Optional[Any]         =					permute(lowercase				)
        for perm in permutations:
            perm.append(lowercase				)
        result.extend(lowercase				)
        nums.append(lowercase				)
    return result







def 						__lowerCAmelCase		(		lowercase						:						int				)			->		Dict:


    """simple docstring"""





    def backtrack(lowercase						:						str				):
        if start == len(lowercase				) - 1:
            output.append(nums[:]				)
        else:
            for i in range(lowercase		,							len(lowercase				)				):
                snake_case      ,snake_case					:   str         =					nums[i], nums[start]
                backtrack(start + 1				)
                snake_case      ,snake_case					:   int         =					nums[i], nums[start]  # backtrack

    snake_case					:   Tuple         =					[]
    backtrack(0				)
    return output


if __name__ == "__main__":
  import doctest

  # use res to print the data in permute2 function
  __snake_case         =					permutea([1, 2, 3])
  print(res)
  doctest.testmod()









"""simple docstring"""



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						:						Accelerator		,							lowercase						:						int = 16				)			->		Any:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
    snake_case					:   Optional[int]         =					load_dataset("glue"		,							"mrpc"				)

    def tokenize_function(lowercase						:						int				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[str]         =					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():
        snake_case					:   Tuple         =					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
    snake_case					:   Optional[Any]         =					tokenized_datasets.rename_column("label"		,							"labels"				)

    def collate_fn(lowercase						:						Optional[int]				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case					:   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":
            snake_case					:   Dict         =					16
        elif accelerator.mixed_precision != "no":
            snake_case					:   Union[str, Any]         =					8
        else:
            snake_case					:   Any         =					None

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

    # Instantiate dataloaders.
    snake_case					:   Dict         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   str         =					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						:						List[str]		,							lowercase						:						Any				)			->		int:


    """simple docstring"""





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

    snake_case					:   Optional[int]         =					evaluate.load("glue"		,							"mrpc"				)

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

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Any         =					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.
    snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   int         =					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				):
                    snake_case					:   Optional[int]         =					model(**lowercase				)
                    snake_case					:   List[Any]         =					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():
                snake_case					:   List[str]         =					model(**lowercase				)
            snake_case					:   str         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   Dict         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

        snake_case					:   int         =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,							lowercase				)







def 						__lowerCAmelCase		(		)			->		int:


    """simple docstring"""





    snake_case					:   Optional[int]         =					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."				)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   List[Any]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()





