
'''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


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


_A						      =				16
_A						      =				32



def 		lowercase_    (     A__				,	A__ = 16	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      AutoTokenizer.from_pretrained("bert-base-cased"	)
    snake_case				  =      load_dataset("glue"				,	"mrpc"	)

    def tokenize_function(A__	):
        # max_length=None => use the model max length (it's actually the default)
        snake_case				  =      tokenizer(examples["sentence1"]				,	examples["sentence2"]				,	truncation=A__				,	max_length=A__	)
        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				  =      datasets.map(
            A__				,	batched=A__				,	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				  =      tokenized_datasets.rename_column("label"				,	"labels"	)

    def collate_fn(A__	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case				  =      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				  =      16
        elif accelerator.mixed_precision != "no":
            snake_case				  =      8
        else:
            snake_case				  =      None

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

    # Instantiate dataloaders.
    snake_case				  =      DataLoader(
        tokenized_datasets["train"]				,	shuffle=A__				,	collate_fn=A__				,	batch_size=A__	)
    snake_case				  =      DataLoader(
        tokenized_datasets["validation"]				,	shuffle=A__				,	collate_fn=A__				,	batch_size=A__	)

    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						      =				mocked_dataloaders  # noqa: F811



def 		lowercase_    (     A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

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

    snake_case				  =      evaluate.load("glue"				,	"mrpc"	)

    # If the batch size is too big we use gradient accumulation
    snake_case				  =      1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        snake_case				  =      batch_size // MAX_GPU_BATCH_SIZE
        snake_case				  =      MAX_GPU_BATCH_SIZE

    set_seed(A__	)
    snake_case  ,							snake_case				  =      get_dataloaders(A__				,	A__	)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case				  =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,	return_dict=A__	)

    # 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				  =      model.to(accelerator.device	)

    # Instantiate optimizer
    snake_case				  =      AdamW(params=model.parameters()				,	lr=A__	)

    # Instantiate scheduler
    snake_case				  =      get_linear_schedule_with_warmup(
        optimizer=A__				,	num_warmup_steps=100				,	num_training_steps=(len(A__	) * 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.
    snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case				  =      accelerator.prepare(
        A__				,	A__				,	A__				,	A__				,	A__	)

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

        model.eval()
        snake_case				  =      0
        for step, batch in enumerate(A__	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            with torch.no_grad():
                snake_case				  =      model(**A__	)
            snake_case				  =      outputs.logits.argmax(dim=-1	)
            snake_case  ,							snake_case				  =      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(A__	) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    snake_case				  =      predictions[: len(eval_dataloader.dataset	) - samples_seen]
                    snake_case				  =      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=A__				,	references=A__				,	)

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



def 		lowercase_    (     )							->							Any:
    """simple docstring"""

    snake_case				  =      argparse.ArgumentParser(description="Simple example of training script."	)
    parser.add_argument(
        "--mixed_precision"				,	type=A__				,	default=A__				,	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."	)
    snake_case				  =      parser.parse_args()
    snake_case				  =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(A__				,	A__	)


if __name__ == "__main__":
   main()



import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def 		lowercase_    (     A__				,	A__=()				,	A__=None				,	A__="no"				,	A__="29500"	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      False
    snake_case				  =      False
    if any(key.startswith("KAGGLE"	) for key in os.environ.keys()	):
        snake_case				  =      True
    elif "IPython" in sys.modules:
        snake_case				  =      "google.colab" in str(sys.modules["IPython"].get_ipython()	)

    try:
        snake_case				  =      PrecisionType(mixed_precision.lower()	)
    except ValueError:
        raise ValueError(
            F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'	)

    if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"				,	A__	) is not None):
        # TPU launch
        import torch_xla.distributed.xla_multiprocessing as xmp

        if len(AcceleratorState._shared_state	) > 0:
            raise ValueError(
                "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
                "your training function. Restart your notebook and make sure no cells initializes an "
                "`Accelerator`."	)
        if num_processes is None:
            snake_case				  =      8

        snake_case				  =      PrepareForLaunch(A__				,	distributed_type="TPU"	)
        print(F'Launching a training on {num_processes} TPU cores.'	)
        xmp.spawn(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
    elif in_colab:
        # No need for a distributed launch otherwise as it's either CPU or one GPU.
        if torch.cuda.is_available():
            print("Launching training on one GPU."	)
        else:
            print("Launching training on one CPU."	)
        function(*A__	)
    else:
        if num_processes is None:
            raise ValueError(
                "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."	)

        if num_processes > 1:
            # Multi-GPU launch
            from torch.multiprocessing import start_processes
            from torch.multiprocessing.spawn import ProcessRaisedException

            if len(AcceleratorState._shared_state	) > 0:
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
                    "inside your training function. Restart your notebook and make sure no cells initializes an "
                    "`Accelerator`."	)

            if torch.cuda.is_initialized():
                raise ValueError(
                    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
                    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
                    "function."	)

            # torch.distributed will expect a few environment variable to be here. We set the ones common to each
            # process here (the other ones will be set be the launcher).
            with patch_environment(
                world_size=A__				,	master_addr="127.0.01"				,	master_port=A__				,	mixed_precision=A__	):
                snake_case				  =      PrepareForLaunch(A__				,	distributed_type="MULTI_GPU"	)
                print(F'Launching training on {num_processes} GPUs.'	)
                try:
                    start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)
                except ProcessRaisedException as e:
                    if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                        raise RuntimeError(
                            "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
                            "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
                            "Please review your imports and test them when running the `notebook_launcher()` to identify "
                            "which one is problematic."	) from e

        else:
            # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
            if is_mps_available():
                snake_case				  =      "1"
                print("Launching training on MPS."	)
            elif torch.cuda.is_available():
                print("Launching training on one GPU."	)
            else:
                print("Launching training on CPU."	)
            function(*A__	)





def 		lowercase_    (     A__				,	A__=()				,	A__=2	)							->							Dict:
    """simple docstring"""

    from torch.multiprocessing import start_processes

    with tempfile.NamedTemporaryFile() as tmp_file:
        # torch.distributed will expect a few environment variable to be here. We set the ones common to each
        # process here (the other ones will be set be the launcher).
        with patch_environment(
            world_size=A__				,	master_addr="127.0.01"				,	master_port="29500"				,	accelerate_mixed_precision="no"				,	accelerate_debug_rdv_file=tmp_file.name				,	accelerate_use_cpu="yes"				,	):
            snake_case				  =      PrepareForLaunch(A__				,	debug=A__	)
            start_processes(A__				,	args=A__				,	nprocs=A__				,	start_method="fork"	)

