
'''simple docstring'''
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						fname.split(os.path.sep       )[-1]
    return re.search(r"^(.*)_\d+\.jpg$",		SCREAMING_SNAKE_CASE_       ).groups()[0]






class    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_ ,       lowercase_=None ,       lowercase_=None):
     snake_case_ :    Optional[int]     			=						file_names
     snake_case_ :    str     			=						image_transform
     snake_case_ :    Tuple     			=						label_to_id





 def __len__(    self):
     return len(self.file_names)





 def __getitem__(    self ,       lowercase_):
     snake_case_ :    Tuple     			=						self.file_names[idx]
     snake_case_ :    int     			=						PIL.Image.open(lowercase_)
     snake_case_ :    Optional[Any]     			=						raw_image.convert("RGB")
     if self.image_transform is not None:
         snake_case_ :    Any     			=						self.image_transform(lowercase_)
     snake_case_ :    Dict     			=						extract_label(lowercase_)
     if self.label_to_id is not None:
         snake_case_ :    Optional[Any]     			=						self.label_to_id[label]
     return {"image": image, "label": label}


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if args.with_tracking:
        snake_case_ :    List[str]     			=						Accelerator(
            cpu=args.cpu,		mixed_precision=args.mixed_precision,		log_with="all",		project_dir=args.project_dir       )
    else:
        snake_case_ :    List[str]     			=						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_ :    Union[str, Any]     			=						config['lr']
    snake_case_ :    str     			=						int(config["num_epochs"]       )
    snake_case_ :    List[Any]     			=						int(config["seed"]       )
    snake_case_ :    Optional[int]     			=						int(config["batch_size"]       )
    snake_case_ :    List[str]     			=						config['image_size']
    if not isinstance(SCREAMING_SNAKE_CASE_,		(list, tuple)       ):
        snake_case_ :    str     			=						(image_size, image_size)

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps,		"isdigit"       ):
        if args.checkpointing_steps == "epoch":
            snake_case_ :    List[Any]     			=						args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            snake_case_ :    List[Any]     			=						int(args.checkpointing_steps       )
        else:
            raise ValueError(
                f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'       )
    else:
        snake_case_ :    Union[str, Any]     			=						None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        snake_case_ :    Optional[Any]     			=						os.path.split(SCREAMING_SNAKE_CASE_       )[-1].split("."       )[0]
        accelerator.init_trackers(SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       )

    # Grab all the image filenames
    snake_case_ :    List[Any]     			=						[os.path.join(args.data_dir,		SCREAMING_SNAKE_CASE_       ) for fname in os.listdir(args.data_dir       ) if fname.endswith(".jpg"       )]

    # Build the label correspondences
    snake_case_ :    Optional[int]     			=						[extract_label(SCREAMING_SNAKE_CASE_       ) for fname in file_names]
    snake_case_ :    Tuple     			=						list(set(SCREAMING_SNAKE_CASE_       )       )
    id_to_label.sort()
    snake_case_ :    int     			=						{lbl: i for i, lbl in enumerate(SCREAMING_SNAKE_CASE_       )}

    # Set the seed before splitting the data.
    np.random.seed(SCREAMING_SNAKE_CASE_       )
    torch.manual_seed(SCREAMING_SNAKE_CASE_       )
    torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_       )

    # Split our filenames between train and validation
    snake_case_ :    Any     			=						np.random.permutation(len(SCREAMING_SNAKE_CASE_       )       )
    snake_case_ :    List[str]     			=						int(0.8 * len(SCREAMING_SNAKE_CASE_       )       )
    snake_case_ :    str     			=						random_perm[:cut]
    snake_case_ :    Tuple     			=						random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    snake_case_ :    int     			=						Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE_,		scale=(0.5, 1.0)       ), ToTensor()]       )
    snake_case_ :    Optional[Any]     			=						PetsDataset(
        [file_names[i] for i in train_split],		image_transform=SCREAMING_SNAKE_CASE_,		label_to_id=SCREAMING_SNAKE_CASE_       )

    # For evaluation, we use a deterministic Resize
    snake_case_ :    List[str]     			=						Compose([Resize(SCREAMING_SNAKE_CASE_       ), ToTensor()]       )
    snake_case_ :    str     			=						PetsDataset([file_names[i] for i in eval_split],		image_transform=SCREAMING_SNAKE_CASE_,		label_to_id=SCREAMING_SNAKE_CASE_       )

    # Instantiate dataloaders.
    snake_case_ :    Dict     			=						DataLoader(SCREAMING_SNAKE_CASE_,		shuffle=SCREAMING_SNAKE_CASE_,		batch_size=SCREAMING_SNAKE_CASE_,		num_workers=4       )
    snake_case_ :    Union[str, Any]     			=						DataLoader(SCREAMING_SNAKE_CASE_,		shuffle=SCREAMING_SNAKE_CASE_,		batch_size=SCREAMING_SNAKE_CASE_,		num_workers=4       )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case_ :    Tuple     			=						create_model("resnet50d",		pretrained=SCREAMING_SNAKE_CASE_,		num_classes=len(SCREAMING_SNAKE_CASE_       )       )

    # 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_ :    List[Any]     			=						model.to(accelerator.device       )

    # Freezing the base model
    for param in model.parameters():
        snake_case_ :    Any     			=						False
    for param in model.get_classifier().parameters():
        snake_case_ :    List[Any]     			=						True

    # We normalize the batches of images to be a bit faster.
    snake_case_ :    Optional[int]     			=						torch.tensor(model.default_cfg["mean"]       )[None, :, None, None].to(accelerator.device       )
    snake_case_ :    int     			=						torch.tensor(model.default_cfg["std"]       )[None, :, None, None].to(accelerator.device       )

    # Instantiate optimizer
    snake_case_ :    Optional[int]     			=						torch.optim.Adam(params=model.parameters(),		lr=lr / 2_5       )

    # Instantiate learning rate scheduler
    snake_case_ :    str     			=						OneCycleLR(optimizer=SCREAMING_SNAKE_CASE_,		max_lr=SCREAMING_SNAKE_CASE_,		epochs=SCREAMING_SNAKE_CASE_,		steps_per_epoch=len(SCREAMING_SNAKE_CASE_       )       )

    # 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_ :    List[str]     			=						accelerator.prepare(
        SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       )
    # We need to keep track of how many total steps we have iterated over
    snake_case_ :    str     			=						0
    # We also need to keep track of the starting epoch so files are named properly
    snake_case_ :    List[str]     			=						0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'       )
            accelerator.load_state(args.resume_from_checkpoint       )
            snake_case_ :    List[Any]     			=						os.path.basename(args.resume_from_checkpoint       )
        else:
            # Get the most recent checkpoint
            snake_case_ :    str     			=						[f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
            dirs.sort(key=os.path.getctime       )
            snake_case_ :    str     			=						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        snake_case_ :    Optional[int]     			=						os.path.splitext(SCREAMING_SNAKE_CASE_       )[0]

        if "epoch" in training_difference:
            snake_case_ :    Union[str, Any]     			=						int(training_difference.replace("epoch_",		""       )       ) + 1
            snake_case_ :    Tuple     			=						None
        else:
            snake_case_ :    Any     			=						int(training_difference.replace("step_",		""       )       )
            snake_case_ :    int     			=						resume_step // len(SCREAMING_SNAKE_CASE_       )
            resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_       )

    # Now we train the model
    for epoch in range(SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       ):
        model.train()
        if args.with_tracking:
            snake_case_ :    Tuple     			=						0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            snake_case_ :    str     			=						accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            snake_case_ :    List[Any]     			=						train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            snake_case_ :    Optional[int]     			=						{k: v.to(accelerator.device       ) for k, v in batch.items()}
            snake_case_ :    Dict     			=						(batch['image'] - mean) / std
            snake_case_ :    Any     			=						model(SCREAMING_SNAKE_CASE_       )
            snake_case_ :    Any     			=						torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE_,		batch["label"]       )
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(SCREAMING_SNAKE_CASE_       )
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       ):
                snake_case_ :    Optional[Any]     			=						f'step_{overall_step}'
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        snake_case_ :    Union[str, Any]     			=						os.path.join(args.output_dir,		SCREAMING_SNAKE_CASE_       )
                    accelerator.save_state(SCREAMING_SNAKE_CASE_       )
        model.eval()
        snake_case_ :    Optional[int]     			=						0
        snake_case_ :    Optional[int]     			=						0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE_       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            snake_case_ :    Tuple     			=						{k: v.to(accelerator.device       ) for k, v in batch.items()}
            snake_case_ :    str     			=						(batch['image'] - mean) / std
            with torch.no_grad():
                snake_case_ :    List[str]     			=						model(SCREAMING_SNAKE_CASE_       )
            snake_case_ :    Dict     			=						outputs.argmax(dim=-1       )
            snake_case_ :    Optional[Any]     			=						accelerator.gather_for_metrics((predictions, batch["label"])       )
            snake_case_ :    Optional[Any]     			=						predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        snake_case_ :    List[Any]     			=						accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}: {1_0_0 * eval_metric:.2f}'       )
        if args.with_tracking:
            accelerator.log(
                {
                    "accuracy": 1_0_0 * eval_metric,
                    "train_loss": total_loss.item() / len(SCREAMING_SNAKE_CASE_       ),
                    "epoch": epoch,
                },		step=SCREAMING_SNAKE_CASE_,		)
        if checkpointing_steps == "epoch":
            snake_case_ :    List[str]     			=						f'epoch_{epoch}'
            if args.output_dir is not None:
                snake_case_ :    int     			=						os.path.join(args.output_dir,		SCREAMING_SNAKE_CASE_       )
            accelerator.save_state(SCREAMING_SNAKE_CASE_       )

    if args.with_tracking:
        accelerator.end_training()

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument("--data_dir",		required=SCREAMING_SNAKE_CASE_,		help="The data folder on disk."       )
    parser.add_argument("--fp16",		action="store_true",		help="If passed, will use FP16 training."       )
    parser.add_argument(
        "--mixed_precision",		type=SCREAMING_SNAKE_CASE_,		default=SCREAMING_SNAKE_CASE_,		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."       )
    parser.add_argument(
        "--checkpointing_steps",		type=SCREAMING_SNAKE_CASE_,		default=SCREAMING_SNAKE_CASE_,		help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.",		)
    parser.add_argument(
        "--output_dir",		type=SCREAMING_SNAKE_CASE_,		default=".",		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",		)
    parser.add_argument(
        "--resume_from_checkpoint",		type=SCREAMING_SNAKE_CASE_,		default=SCREAMING_SNAKE_CASE_,		help="If the training should continue from a checkpoint folder.",		)
    parser.add_argument(
        "--with_tracking",		action="store_true",		help="Whether to load in all available experiment trackers from the environment and use them for logging.",		)
    parser.add_argument(
        "--project_dir",		type=SCREAMING_SNAKE_CASE_,		default="logs",		help="Location on where to store experiment tracking logs` and relevent project information",		)
    snake_case_ :    Optional[int]     			=						parser.parse_args()
    snake_case_ :    List[Any]     			=						{'lr': 3E-2, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 6_4, 'image_size': 2_2_4}
    training_function(SCREAMING_SNAKE_CASE_,		SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  main()








'''simple docstring'''
import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
    snake_case_ :    int     			=						DatasetDict(
        {
            "train": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "validation": dataset["train"].select(__SCREAMING_SNAKE_CASE       ),
            "test": dataset["validation"],
        }       )

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

    def collate_fn(__SCREAMING_SNAKE_CASE       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case_ :    Dict     			=						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":
            snake_case_ :    Optional[int]     			=						1_6
        elif accelerator.mixed_precision != "no":
            snake_case_ :    Tuple     			=						8
        else:
            snake_case_ :    Union[str, Any]     			=						None

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

    # Instantiate dataloaders.
    snake_case_ :    Optional[int]     			=						DataLoader(
        tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						DataLoader(
        tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Tuple     			=						DataLoader(
        tokenized_datasets["test"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=__SCREAMING_SNAKE_CASE       )

    return train_dataloader, eval_dataloader, test_dataloader

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						[]
    # Download the dataset
    snake_case_ :    Tuple     			=						load_dataset("glue",		"mrpc"       )
    # Create our splits
    snake_case_ :    Union[str, Any]     			=						StratifiedKFold(n_splits=int(args.num_folds       )       )
    # Initialize accelerator
    snake_case_ :    Optional[Any]     			=						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_ :    Optional[Any]     			=						config["lr"]
    snake_case_ :    str     			=						int(config["num_epochs"]       )
    snake_case_ :    Tuple     			=						int(config["seed"]       )
    snake_case_ :    Optional[Any]     			=						int(config["batch_size"]       )

    snake_case_ :    List[Any]     			=						evaluate.load("glue",		"mrpc"       )

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

    set_seed(__SCREAMING_SNAKE_CASE       )

    # New Code #
    # Create our folds:
    snake_case_ :    int     			=						kfold.split(np.zeros(datasets["train"].num_rows       ),		datasets["train"]["label"]       )
    snake_case_ :    Optional[int]     			=						[]
    # Iterate over them
    for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE       ):
        snake_case_			, snake_case_			, snake_case_ :    Tuple     			=						get_fold_dataloaders(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		)
        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        snake_case_ :    Optional[int]     			=						AutoModelForSequenceClassification.from_pretrained("bert-base-cased",		return_dict=__SCREAMING_SNAKE_CASE       )

        # 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_ :    Union[str, Any]     			=						model.to(accelerator.device       )

        # Instantiate optimizer
        snake_case_ :    Any     			=						AdamW(params=model.parameters(),		lr=__SCREAMING_SNAKE_CASE       )

        # Instantiate scheduler
        snake_case_ :    List[str]     			=						get_linear_schedule_with_warmup(
            optimizer=__SCREAMING_SNAKE_CASE,		num_warmup_steps=1_0_0,		num_training_steps=(len(__SCREAMING_SNAKE_CASE       ) * 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_ :    str     			=						accelerator.prepare(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

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

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

            snake_case_ :    str     			=						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:',		__SCREAMING_SNAKE_CASE       )

        # New Code #
        # We also run predictions on the test set at the very end
        snake_case_ :    Any     			=						[]
        for step, batch in enumerate(__SCREAMING_SNAKE_CASE       ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device       )
            with torch.no_grad():
                snake_case_ :    int     			=						model(**__SCREAMING_SNAKE_CASE       )
            snake_case_ :    Tuple     			=						outputs.logits
            snake_case_			, snake_case_ :    List[Any]     			=						accelerator.gather_for_metrics((predictions, batch["labels"])       )
            fold_predictions.append(predictions.cpu()       )
            if i == 0:
                # We need all of the test predictions
                test_references.append(references.cpu()       )
        # Use accelerator.print to print only on the main process.
        test_predictions.append(torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )       )
        # We now need to release all our memory and get rid of the current model, optimizer, etc
        accelerator.free_memory()
    # New Code #
    # Finally we check the accuracy of our folded results:
    snake_case_ :    List[Any]     			=						torch.cat(__SCREAMING_SNAKE_CASE,		dim=0       )
    snake_case_ :    Any     			=						torch.stack(__SCREAMING_SNAKE_CASE,		dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
    snake_case_ :    Tuple     			=						metric.compute(predictions=__SCREAMING_SNAKE_CASE,		references=__SCREAMING_SNAKE_CASE       )
    accelerator.print("Average test metrics from all folds:",		__SCREAMING_SNAKE_CASE       )

def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						argparse.ArgumentParser(description="Simple example of training script."       )
    parser.add_argument(
        "--mixed_precision",		type=__SCREAMING_SNAKE_CASE,		default=__SCREAMING_SNAKE_CASE,		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."       )
    # New Code #
    parser.add_argument("--num_folds",		type=__SCREAMING_SNAKE_CASE,		default=3,		help="The number of splits to perform across the dataset"       )
    snake_case_ :    List[Any]     			=						parser.parse_args()
    snake_case_ :    Optional[Any]     			=						{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
    training_function(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  main()




