# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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""" Finetuning 🤗 Transformers model for object detection with Accelerate."""

import argparse
import json
import logging
import math
import os
from functools import partial
from pathlib import Path
from typing import Any, List, Mapping, Tuple, Union

import albumentations as A
import datasets
import numpy as np
import torch
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from torchmetrics.detection.mean_ap import MeanAveragePrecision
from tqdm.auto import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForObjectDetection,
    SchedulerType,
    get_scheduler,
)
from transformers.image_processing_utils import BatchFeature
from transformers.image_transforms import center_to_corners_format
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.42.0.dev0")

logging.basicConfig(level=logging.INFO)
logger = get_logger(__name__)

require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")


# Copied from examples/pytorch/object-detection/run_object_detection.format_image_annotations_as_coco
def format_image_annotations_as_coco(
    image_id: str, categories: List[int], areas: List[float], bboxes: List[Tuple[float]]
) -> dict:
    """Format one set of image annotations to the COCO format

    Args:
        image_id (str): image id. e.g. "0001"
        categories (List[int]): list of categories/class labels corresponding to provided bounding boxes
        areas (List[float]): list of corresponding areas to provided bounding boxes
        bboxes (List[Tuple[float]]): list of bounding boxes provided in COCO format
            ([center_x, center_y, width, height] in absolute coordinates)

    Returns:
        dict: {
            "image_id": image id,
            "annotations": list of formatted annotations
        }
    """
    annotations = []
    for category, area, bbox in zip(categories, areas, bboxes):
        formatted_annotation = {
            "image_id": image_id,
            "category_id": category,
            "iscrowd": 0,
            "area": area,
            "bbox": list(bbox),
        }
        annotations.append(formatted_annotation)

    return {
        "image_id": image_id,
        "annotations": annotations,
    }


# Copied from examples/pytorch/object-detection/run_object_detection.convert_bbox_yolo_to_pascal
def convert_bbox_yolo_to_pascal(boxes: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
    """
    Convert bounding boxes from YOLO format (x_center, y_center, width, height) in range [0, 1]
    to Pascal VOC format (x_min, y_min, x_max, y_max) in absolute coordinates.

    Args:
        boxes (torch.Tensor): Bounding boxes in YOLO format
        image_size (Tuple[int, int]): Image size in format (height, width)

    Returns:
        torch.Tensor: Bounding boxes in Pascal VOC format (x_min, y_min, x_max, y_max)
    """
    # convert center to corners format
    boxes = center_to_corners_format(boxes)

    # convert to absolute coordinates
    height, width = image_size
    boxes = boxes * torch.tensor([[width, height, width, height]])

    return boxes


# Copied from examples/pytorch/object-detection/run_object_detection.augment_and_transform_batch
def augment_and_transform_batch(
    examples: Mapping[str, Any], transform: A.Compose, image_processor: AutoImageProcessor
) -> BatchFeature:
    """Apply augmentations and format annotations in COCO format for object detection task"""

    images = []
    annotations = []
    for image_id, image, objects in zip(examples["image_id"], examples["image"], examples["objects"]):
        image = np.array(image.convert("RGB"))

        # apply augmentations
        output = transform(image=image, bboxes=objects["bbox"], category=objects["category"])
        images.append(output["image"])

        # format annotations in COCO format
        formatted_annotations = format_image_annotations_as_coco(
            image_id, output["category"], objects["area"], output["bboxes"]
        )
        annotations.append(formatted_annotations)

    # Apply the image processor transformations: resizing, rescaling, normalization
    result = image_processor(images=images, annotations=annotations, return_tensors="pt")

    return result


# Copied from examples/pytorch/object-detection/run_object_detection.collate_fn
def collate_fn(batch: List[BatchFeature]) -> Mapping[str, Union[torch.Tensor, List[Any]]]:
    data = {}
    data["pixel_values"] = torch.stack([x["pixel_values"] for x in batch])
    data["labels"] = [x["labels"] for x in batch]
    if "pixel_mask" in batch[0]:
        data["pixel_mask"] = torch.stack([x["pixel_mask"] for x in batch])
    return data


def nested_to_cpu(objects):
    """Move nested tesnors in objects to CPU if they are on GPU"""
    if isinstance(objects, torch.Tensor):
        return objects.cpu()
    elif isinstance(objects, Mapping):
        return type(objects)({k: nested_to_cpu(v) for k, v in objects.items()})
    elif isinstance(objects, (list, tuple)):
        return type(objects)([nested_to_cpu(v) for v in objects])
    elif isinstance(objects, (np.ndarray, str, int, float, bool)):
        return objects
    raise ValueError(f"Unsupported type {type(objects)}")


def evaluation_loop(
    model: torch.nn.Module,
    image_processor: AutoImageProcessor,
    accelerator: Accelerator,
    dataloader: DataLoader,
    id2label: Mapping[int, str],
) -> dict:
    model.eval()
    metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True)

    for step, batch in enumerate(tqdm(dataloader, disable=not accelerator.is_local_main_process)):
        with torch.no_grad():
            outputs = model(**batch)

        # For metric computation we need to collect ground truth and predicted boxes in the same format

        # 1. Collect predicted boxes, classes, scores
        # image_processor convert boxes from YOLO format to Pascal VOC format
        # ([x_min, y_min, x_max, y_max] in absolute coordinates)
        image_size = torch.stack([example["orig_size"] for example in batch["labels"]], dim=0)
        predictions = image_processor.post_process_object_detection(outputs, threshold=0.0, target_sizes=image_size)
        predictions = nested_to_cpu(predictions)

        # 2. Collect ground truth boxes in the same format for metric computation
        # Do the same, convert YOLO boxes to Pascal VOC format
        target = []
        for label in batch["labels"]:
            label = nested_to_cpu(label)
            boxes = convert_bbox_yolo_to_pascal(label["boxes"], label["orig_size"])
            labels = label["class_labels"]
            target.append({"boxes": boxes, "labels": labels})

        metric.update(predictions, target)

    metrics = metric.compute()

    # Replace list of per class metrics with separate metric for each class
    classes = metrics.pop("classes")
    map_per_class = metrics.pop("map_per_class")
    mar_100_per_class = metrics.pop("mar_100_per_class")
    for class_id, class_map, class_mar in zip(classes, map_per_class, mar_100_per_class):
        class_name = id2label[class_id.item()]
        metrics[f"map_{class_name}"] = class_map
        metrics[f"mar_100_{class_name}"] = class_mar

    # Convert metrics to float
    metrics = {k: round(v.item(), 4) for k, v in metrics.items()}

    return metrics


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model for object detection task")
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to a pretrained model or model identifier from huggingface.co/models.",
        default="facebook/detr-resnet-50",
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        help="Name of the dataset on the hub.",
        default="cppe-5",
    )
    parser.add_argument(
        "--train_val_split",
        type=float,
        default=0.15,
        help="Fraction of the dataset to be used for validation.",
    )
    parser.add_argument(
        "--ignore_mismatched_sizes",
        action="store_true",
        help="Ignore mismatched sizes between the model and the dataset.",
    )
    parser.add_argument(
        "--image_square_size",
        type=int,
        default=1333,
        help="Image longest size will be resized to this value, then image will be padded to square.",
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        help="Path to a folder in which the model and dataset will be cached.",
    )
    parser.add_argument(
        "--use_auth_token",
        action="store_true",
        help="Whether to use an authentication token to access the model repository.",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=4,
        help="Number of workers to use for the dataloaders.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--adam_beta1",
        type=float,
        default=0.9,
        help="Beta1 for AdamW optimizer",
    )
    parser.add_argument(
        "--adam_beta2",
        type=float,
        default=0.999,
        help="Beta2 for AdamW optimizer",
    )
    parser.add_argument(
        "--adam_epsilon",
        type=float,
        default=1e-8,
        help="Epsilon for AdamW optimizer",
    )
    parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
    parser.add_argument(
        "--max_train_steps",
        type=int,
        default=None,
        help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--lr_scheduler_type",
        type=SchedulerType,
        default="linear",
        help="The scheduler type to use.",
        choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
    )
    parser.add_argument(
        "--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument(
        "--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
    )
    parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--trust_remote_code",
        type=bool,
        default=False,
        help=(
            "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
            "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
            "execute code present on the Hub on your local machine."
        ),
    )
    parser.add_argument(
        "--checkpointing_steps",
        type=str,
        default=None,
        help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
    )
    parser.add_argument(
        "--resume_from_checkpoint",
        type=str,
        default=None,
        help="If the training should continue from a checkpoint folder.",
    )
    parser.add_argument(
        "--with_tracking",
        required=False,
        action="store_true",
        help="Whether to enable experiment trackers for logging.",
    )
    parser.add_argument(
        "--report_to",
        type=str,
        default="all",
        help=(
            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
            ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
            "Only applicable when `--with_tracking` is passed."
        ),
    )
    args = parser.parse_args()

    # Sanity checks
    if args.push_to_hub or args.with_tracking:
        if args.output_dir is None:
            raise ValueError(
                "Need an `output_dir` to create a repo when `--push_to_hub` or `with_tracking` is specified."
            )

    if args.output_dir is not None:
        os.makedirs(args.output_dir, exist_ok=True)

    return args


def main():
    args = parse_args()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_object_detection_no_trainer", args)

    # Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
    # If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
    # in the environment
    accelerator_log_kwargs = {}

    if args.with_tracking:
        accelerator_log_kwargs["log_with"] = args.report_to
        accelerator_log_kwargs["project_dir"] = args.output_dir

    accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)

    logger.info(accelerator.state, main_process_only=False)
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_info()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()

    # If passed along, set the training seed now.
    # We set device_specific to True as we want different data augmentation per device.
    if args.seed is not None:
        set_seed(args.seed, device_specific=True)

    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            # Retrieve of infer repo_name
            repo_name = args.hub_model_id
            if repo_name is None:
                repo_name = Path(args.output_dir).absolute().name
            # Create repo and retrieve repo_id
            api = HfApi()
            repo_id = api.create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id

            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
    accelerator.wait_for_everyone()

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)

    # If we don't have a validation split, split off a percentage of train as validation.
    args.train_val_split = None if "validation" in dataset.keys() else args.train_val_split
    if isinstance(args.train_val_split, float) and args.train_val_split > 0.0:
        split = dataset["train"].train_test_split(args.train_val_split, seed=args.seed)
        dataset["train"] = split["train"]
        dataset["validation"] = split["test"]

    # Get dataset categories and prepare mappings for label_name <-> label_id
    categories = dataset["train"].features["objects"].feature["category"].names
    id2label = dict(enumerate(categories))
    label2id = {v: k for k, v in id2label.items()}

    # ------------------------------------------------------------------------------------------------
    # Load pretrained config, model and image processor
    # ------------------------------------------------------------------------------------------------

    common_pretrained_args = {
        "cache_dir": args.cache_dir,
        "token": args.hub_token,
        "trust_remote_code": args.trust_remote_code,
    }
    config = AutoConfig.from_pretrained(
        args.model_name_or_path, label2id=label2id, id2label=id2label, **common_pretrained_args
    )
    model = AutoModelForObjectDetection.from_pretrained(
        args.model_name_or_path,
        config=config,
        ignore_mismatched_sizes=args.ignore_mismatched_sizes,
        **common_pretrained_args,
    )
    image_processor = AutoImageProcessor.from_pretrained(
        args.model_name_or_path,
        # At this moment we recommend using external transform to pad and resize images.
        # It`s faster and yields much better results for object-detection models.
        do_pad=False,
        do_resize=False,
        # We will save image size parameter in config just for reference
        size={"longest_edge": args.image_square_size},
        **common_pretrained_args,
    )

    # ------------------------------------------------------------------------------------------------
    # Define image augmentations and dataset transforms
    # ------------------------------------------------------------------------------------------------
    max_size = args.image_square_size
    basic_transforms = [
        A.LongestMaxSize(max_size=max_size),
        A.PadIfNeeded(max_size, max_size, border_mode=0, value=(128, 128, 128), position="top_left"),
    ]
    train_augment_and_transform = A.Compose(
        [
            A.Compose(
                [
                    A.SmallestMaxSize(max_size=max_size, p=1.0),
                    A.RandomSizedBBoxSafeCrop(height=max_size, width=max_size, p=1.0),
                ],
                p=0.2,
            ),
            A.OneOf(
                [
                    A.Blur(blur_limit=7, p=0.5),
                    A.MotionBlur(blur_limit=7, p=0.5),
                    A.Defocus(radius=(1, 5), alias_blur=(0.1, 0.25), p=0.1),
                ],
                p=0.1,
            ),
            A.Perspective(p=0.1),
            A.HorizontalFlip(p=0.5),
            A.RandomBrightnessContrast(p=0.5),
            A.HueSaturationValue(p=0.1),
            *basic_transforms,
        ],
        bbox_params=A.BboxParams(format="coco", label_fields=["category"], clip=True, min_area=25),
    )
    validation_transform = A.Compose(
        basic_transforms,
        bbox_params=A.BboxParams(format="coco", label_fields=["category"], clip=True),
    )

    # Make transform functions for batch and apply for dataset splits
    train_transform_batch = partial(
        augment_and_transform_batch, transform=train_augment_and_transform, image_processor=image_processor
    )
    validation_transform_batch = partial(
        augment_and_transform_batch, transform=validation_transform, image_processor=image_processor
    )

    with accelerator.main_process_first():
        train_dataset = dataset["train"].with_transform(train_transform_batch)
        valid_dataset = dataset["validation"].with_transform(validation_transform_batch)
        test_dataset = dataset["test"].with_transform(validation_transform_batch)

    dataloader_common_args = {
        "num_workers": args.dataloader_num_workers,
        "collate_fn": collate_fn,
    }
    train_dataloader = DataLoader(
        train_dataset, shuffle=True, batch_size=args.per_device_train_batch_size, **dataloader_common_args
    )
    valid_dataloader = DataLoader(
        valid_dataset, shuffle=False, batch_size=args.per_device_eval_batch_size, **dataloader_common_args
    )
    test_dataloader = DataLoader(
        test_dataset, shuffle=False, batch_size=args.per_device_eval_batch_size, **dataloader_common_args
    )

    # ------------------------------------------------------------------------------------------------
    # Define optimizer, scheduler and prepare everything with the accelerator
    # ------------------------------------------------------------------------------------------------

    # Optimizer
    optimizer = torch.optim.AdamW(
        list(model.parameters()),
        lr=args.learning_rate,
        betas=[args.adam_beta1, args.adam_beta2],
        eps=args.adam_epsilon,
    )

    # Figure out how many steps we should save the Accelerator states
    checkpointing_steps = args.checkpointing_steps
    if checkpointing_steps is not None and checkpointing_steps.isdigit():
        checkpointing_steps = int(checkpointing_steps)

    # Scheduler and math around the number of training steps.
    overrode_max_train_steps = False
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if args.max_train_steps is None:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
        overrode_max_train_steps = True

    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps * accelerator.num_processes,
        num_training_steps=args.max_train_steps
        if overrode_max_train_steps
        else args.max_train_steps * accelerator.num_processes,
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, valid_dataloader, test_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, test_dataloader, lr_scheduler
    )

    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
    if overrode_max_train_steps:
        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
    # Afterwards we recalculate our number of training epochs
    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    # We need to initialize the trackers we use, and also store our configuration.
    # The trackers initializes automatically on the main process.
    if args.with_tracking:
        experiment_config = vars(args)
        # TensorBoard cannot log Enums, need the raw value
        experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
        accelerator.init_trackers("object_detection_no_trainer", experiment_config)

    # ------------------------------------------------------------------------------------------------
    # Run training with evaluation on each epoch
    # ------------------------------------------------------------------------------------------------

    total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps

    logger.info("***** Running training *****")
    logger.info(f"  Num examples = {len(train_dataset)}")
    logger.info(f"  Num Epochs = {args.num_train_epochs}")
    logger.info(f"  Instantaneous batch size per device = {args.per_device_train_batch_size}")
    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
    logger.info(f"  Total optimization steps = {args.max_train_steps}")

    # Only show the progress bar once on each machine.
    progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
    completed_steps = 0
    starting_epoch = 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 != "":
            checkpoint_path = args.resume_from_checkpoint
            path = os.path.basename(args.resume_from_checkpoint)
        else:
            # Get the most recent checkpoint
            dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
            dirs.sort(key=os.path.getctime)
            path = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            checkpoint_path = path
            path = os.path.basename(checkpoint_path)

        accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
        accelerator.load_state(checkpoint_path)
        # Extract `epoch_{i}` or `step_{i}`
        training_difference = os.path.splitext(path)[0]

        if "epoch" in training_difference:
            starting_epoch = int(training_difference.replace("epoch_", "")) + 1
            resume_step = None
            completed_steps = starting_epoch * num_update_steps_per_epoch
        else:
            # need to multiply `gradient_accumulation_steps` to reflect real steps
            resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
            starting_epoch = resume_step // len(train_dataloader)
            completed_steps = resume_step // args.gradient_accumulation_steps
            resume_step -= starting_epoch * len(train_dataloader)

    # update the progress_bar if load from checkpoint
    progress_bar.update(completed_steps)

    for epoch in range(starting_epoch, args.num_train_epochs):
        model.train()
        if args.with_tracking:
            total_loss = 0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We skip the first `n` batches in the dataloader when resuming from a checkpoint
            active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
        else:
            active_dataloader = train_dataloader

        for step, batch in enumerate(active_dataloader):
            with accelerator.accumulate(model):
                outputs = model(**batch)
                loss = outputs.loss
                # We keep track of the loss at each epoch
                if args.with_tracking:
                    total_loss += loss.detach().float()
                accelerator.backward(loss)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                completed_steps += 1

            if isinstance(checkpointing_steps, int):
                if completed_steps % checkpointing_steps == 0:
                    output_dir = f"step_{completed_steps}"
                    if args.output_dir is not None:
                        output_dir = os.path.join(args.output_dir, output_dir)
                    accelerator.save_state(output_dir)

                    if args.push_to_hub and epoch < args.num_train_epochs - 1:
                        accelerator.wait_for_everyone()
                        unwrapped_model = accelerator.unwrap_model(model)
                        unwrapped_model.save_pretrained(
                            args.output_dir,
                            is_main_process=accelerator.is_main_process,
                            save_function=accelerator.save,
                        )
                        if accelerator.is_main_process:
                            image_processor.save_pretrained(args.output_dir)
                            api.upload_folder(
                                commit_message=f"Training in progress epoch {epoch}",
                                folder_path=args.output_dir,
                                repo_id=repo_id,
                                repo_type="model",
                                token=args.hub_token,
                            )

            if completed_steps >= args.max_train_steps:
                break

        logger.info("***** Running evaluation *****")
        metrics = evaluation_loop(model, image_processor, accelerator, valid_dataloader, id2label)

        logger.info(f"epoch {epoch}: {metrics}")

        if args.with_tracking:
            accelerator.log(
                {
                    "train_loss": total_loss.item() / len(train_dataloader),
                    **metrics,
                    "epoch": epoch,
                    "step": completed_steps,
                },
                step=completed_steps,
            )

        if args.push_to_hub and epoch < args.num_train_epochs - 1:
            accelerator.wait_for_everyone()
            unwrapped_model = accelerator.unwrap_model(model)
            unwrapped_model.save_pretrained(
                args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
            )
            if accelerator.is_main_process:
                image_processor.save_pretrained(args.output_dir)
                api.upload_folder(
                    commit_message=f"Training in progress epoch {epoch}",
                    folder_path=args.output_dir,
                    repo_id=repo_id,
                    repo_type="model",
                    token=args.hub_token,
                )

        if args.checkpointing_steps == "epoch":
            output_dir = f"epoch_{epoch}"
            if args.output_dir is not None:
                output_dir = os.path.join(args.output_dir, output_dir)
            accelerator.save_state(output_dir)

    # ------------------------------------------------------------------------------------------------
    # Run evaluation on test dataset and save the model
    # ------------------------------------------------------------------------------------------------

    logger.info("***** Running evaluation on test dataset *****")
    metrics = evaluation_loop(model, image_processor, accelerator, test_dataloader, id2label)
    metrics = {f"test_{k}": v for k, v in metrics.items()}

    logger.info(f"Test metrics: {metrics}")

    if args.with_tracking:
        accelerator.end_training()

    if args.output_dir is not None:
        accelerator.wait_for_everyone()
        unwrapped_model = accelerator.unwrap_model(model)
        unwrapped_model.save_pretrained(
            args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
        )
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                json.dump(metrics, f, indent=2)

            image_processor.save_pretrained(args.output_dir)

            if args.push_to_hub:
                api.upload_folder(
                    commit_message="End of training",
                    folder_path=args.output_dir,
                    repo_id=repo_id,
                    repo_type="model",
                    token=args.hub_token,
                    ignore_patterns=["epoch_*"],
                )


if __name__ == "__main__":
    main()
