#!/usr/bin/env python
# Copyright 2023 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.
# See the License for the specific language governing permissions and

# /// script
# dependencies = [
#     "transformers @ git+https://github.com/huggingface/transformers.git",
#     "torch>=1.5.0",
#     "torchvision>=0.6.0",
#     "datasets>=1.8.0",
# ]
# ///

import argparse
import logging
import math
import os
from pathlib import Path

import datasets
import numpy as np
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import set_seed
from datasets import load_dataset
from huggingface_hub import HfApi
from torch.utils.data import DataLoader
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from tqdm.auto import tqdm

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    SchedulerType,
    get_scheduler,
)
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


""" Pre-training a 🤗 Transformers model for simple masked image modeling (SimMIM)
without using HuggingFace Trainer.
Any model supported by the AutoModelForMaskedImageModeling API can be used.
"""

logger = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.57.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")

MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def parse_args():
    parser = argparse.ArgumentParser(
        description="Finetune a transformers model on a simple Masked Image Modeling task"
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        default="cifar10",
        help="Name of a dataset from the datasets package",
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,
        help="The configuration name of the dataset to use (via the datasets library).",
    )
    parser.add_argument(
        "--image_column_name",
        type=str,
        default=None,
        help="The column name of the images in the files. If not set, will try to use 'image' or 'img'.",
    )
    parser.add_argument(
        "--train_dir",
        type=str,
        default=None,
        help="A folder containing the training data.",
    )
    parser.add_argument(
        "--validation_dir",
        type=None,
        default=None,
        help="A folder containing the validation data.",
    )
    parser.add_argument(
        "--train_val_split",
        type=float,
        default=0.15,
        help="Percent to split off of train for validation.",
    )
    parser.add_argument(
        "--mask_patch_size",
        type=int,
        default=32,
        help="The size of the square patches to use for masking.",
    )
    parser.add_argument(
        "--mask_ratio",
        type=float,
        default=0.6,
        help="Percentage of patches to mask.",
    )
    parser.add_argument(
        "--max_train_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--max_eval_samples",
        type=int,
        default=None,
        help=(
            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        ),
    )
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        default=None,
        help=(
            "The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a "
            "checkpoint identifier on the hub. "
            "Don't set if you want to train a model from scratch."
        ),
    )
    parser.add_argument(
        "--model_type",
        type=str,
        default=None,
        help="If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES),
    )
    parser.add_argument(
        "--config_name_or_path",
        type=str,
        default=None,
        help="Pretrained config name or path if not the same as model_name",
    )
    parser.add_argument(
        "--config_overrides",
        type=str,
        default=None,
        help=(
            "Override some existing default config settings when a model is trained from scratch. Example: "
            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
        ),
    )
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="Where do you want to store (cache) the pretrained models/datasets downloaded from the hub",
    )
    parser.add_argument(
        "--model_revision",
        type=str,
        default="main",
        help="The specific model version to use (can be a branch name, tag name or commit id).",
    )
    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(
        "--image_processor_name",
        type=str,
        default=None,
        help="Name or path of preprocessor config.",
    )
    parser.add_argument(
        "--token",
        type=str,
        default=None,
        help=(
            "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
            "generated when running `hf auth login` (stored in `~/.huggingface`)."
        ),
    )
    parser.add_argument(
        "--trust_remote_code",
        action="store_true",
        help=(
            "Whether to trust the execution of code from datasets/models defined on the Hub."
            " 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(
        "--image_size",
        type=int,
        default=None,
        help="The size (resolution) of each image. If not specified, will use `image_size` of the configuration.",
    )
    parser.add_argument(
        "--patch_size",
        type=int,
        default=None,
        help="The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.",
    )
    parser.add_argument(
        "--encoder_stride",
        type=int,
        default=None,
        help={"help": "Stride to use for the encoder."},
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
        help="Whether or not to push the model to the Hub.",
    )
    parser.add_argument(
        "--with_tracking",
        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."
        ),
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=None,
        help="A seed for reproducible training.",
    )
    parser.add_argument(
        "--per_device_train_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the training dataloader.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=5e-5,
        help="The initial learning rate for [`AdamW`] optimizer.",
    )
    parser.add_argument(
        "--weight_decay",
        type=float,
        default=0.0,
        help="Weight decay to use.",
    )
    parser.add_argument(
        "--num_train_epochs",
        type=float,
        default=3.0,
        help="Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).",
    )
    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(
        "--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(
        "--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(
        "--per_device_eval_batch_size",
        type=int,
        default=8,
        help="Batch size (per device) for the evaluation dataloader.",
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default=None,
        help="Where to store the final model.",
    )
    args = parser.parse_args()

    # Sanity checks
    data_files = {}
    if args.train_dir is not None:
        data_files["train"] = args.train_dir
    if args.validation_dir is not None:
        data_files["val"] = args.validation_dir
    args.data_files = data_files if data_files else None

    if args.push_to_hub:
        assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."

    return args


class MaskGenerator:
    """
    A class to generate boolean masks for the pretraining task.

    A mask is a 1D tensor of shape (model_patch_size**2,) where the value is either 0 or 1,
    where 1 indicates "masked".
    """

    def __init__(self, input_size=192, mask_patch_size=32, model_patch_size=4, mask_ratio=0.6):
        self.input_size = input_size
        self.mask_patch_size = mask_patch_size
        self.model_patch_size = model_patch_size
        self.mask_ratio = mask_ratio

        if self.input_size % self.mask_patch_size != 0:
            raise ValueError("Input size must be divisible by mask patch size")
        if self.mask_patch_size % self.model_patch_size != 0:
            raise ValueError("Mask patch size must be divisible by model patch size")

        self.rand_size = self.input_size // self.mask_patch_size
        self.scale = self.mask_patch_size // self.model_patch_size

        self.token_count = self.rand_size**2
        self.mask_count = int(np.ceil(self.token_count * self.mask_ratio))

    def __call__(self):
        mask_idx = np.random.permutation(self.token_count)[: self.mask_count]
        mask = np.zeros(self.token_count, dtype=int)
        mask[mask_idx] = 1

        mask = mask.reshape((self.rand_size, self.rand_size))
        mask = mask.repeat(self.scale, axis=0).repeat(self.scale, axis=1)

        return torch.tensor(mask.flatten())


def collate_fn(examples):
    pixel_values = torch.stack([example["pixel_values"] for example in examples])
    mask = torch.stack([example["mask"] for example in examples])
    return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def main():
    args = parse_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,
    )

    # Make one log on every process with the configuration for debugging.
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        level=logging.INFO,
    )
    logger.info(accelerator.state)
    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.
    if args.seed is not None:
        set_seed(args.seed)

    # 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()

    # Initialize our dataset.
    ds = load_dataset(
        args.dataset_name,
        args.dataset_config_name,
        data_files=args.data_files,
        cache_dir=args.cache_dir,
        token=args.token,
        trust_remote_code=args.trust_remote_code,
    )

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

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config_kwargs = {
        "cache_dir": args.cache_dir,
        "revision": args.model_revision,
        "token": args.token,
        "trust_remote_code": args.trust_remote_code,
    }
    if args.config_name_or_path:
        config = AutoConfig.from_pretrained(args.config_name_or_path, **config_kwargs)
    elif args.model_name_or_path:
        config = AutoConfig.from_pretrained(args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if args.config_overrides is not None:
            logger.info(f"Overriding config: {args.config_overrides}")
            config.update_from_string(args.config_overrides)
            logger.info(f"New config: {config}")

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
    if hasattr(config, "decoder_type"):
        config.decoder_type = "simmim"

    # adapt config
    args.image_size = args.image_size if args.image_size is not None else config.image_size
    args.patch_size = args.patch_size if args.patch_size is not None else config.patch_size
    args.encoder_stride = args.encoder_stride if args.encoder_stride is not None else config.encoder_stride

    config.update(
        {
            "image_size": args.image_size,
            "patch_size": args.patch_size,
            "encoder_stride": args.encoder_stride,
        }
    )

    # create image processor
    if args.image_processor_name:
        image_processor = AutoImageProcessor.from_pretrained(args.image_processor_name, **config_kwargs)
    elif args.model_name_or_path:
        image_processor = AutoImageProcessor.from_pretrained(args.model_name_or_path, **config_kwargs)
    else:
        IMAGE_PROCESSOR_TYPES = {
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        image_processor = IMAGE_PROCESSOR_TYPES[args.model_type]()

    # create model
    if args.model_name_or_path:
        model = AutoModelForMaskedImageModeling.from_pretrained(
            args.model_name_or_path,
            from_tf=bool(".ckpt" in args.model_name_or_path),
            config=config,
            cache_dir=args.cache_dir,
            revision=args.model_revision,
            token=args.token,
            trust_remote_code=args.trust_remote_code,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedImageModeling.from_config(
            config,
            token=args.token,
            trust_remote_code=args.trust_remote_code,
        )

    column_names = ds["train"].column_names

    if args.image_column_name is not None:
        image_column_name = args.image_column_name
    elif "image" in column_names:
        image_column_name = "image"
    elif "img" in column_names:
        image_column_name = "img"
    else:
        image_column_name = column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    transforms = Compose(
        [
            Lambda(lambda img: img.convert("RGB")),
            RandomResizedCrop(args.image_size, scale=(0.67, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
        ]
    )

    # create mask generator
    mask_generator = MaskGenerator(
        input_size=args.image_size,
        mask_patch_size=args.mask_patch_size,
        model_patch_size=args.patch_size,
        mask_ratio=args.mask_ratio,
    )

    def preprocess_images(examples):
        """Preprocess a batch of images by applying transforms + creating a corresponding mask, indicating
        which patches to mask."""

        examples["pixel_values"] = [transforms(image) for image in examples[image_column_name]]
        examples["mask"] = [mask_generator() for i in range(len(examples[image_column_name]))]

        return examples

    if args.max_train_samples is not None:
        ds["train"] = ds["train"].shuffle(seed=args.seed).select(range(args.max_train_samples))
    # Set the training transforms
    ds["train"].set_transform(preprocess_images)

    if args.max_eval_samples is not None:
        ds["validation"] = ds["validation"].shuffle(seed=args.seed).select(range(args.max_eval_samples))
    # Set the validation transforms
    ds["validation"].set_transform(preprocess_images)

    # DataLoaders creation:
    train_dataloader = DataLoader(
        ds["train"],
        shuffle=True,
        collate_fn=collate_fn,
        batch_size=args.per_device_train_batch_size,
    )
    eval_dataloader = DataLoader(
        ds["validation"],
        collate_fn=collate_fn,
        batch_size=args.per_device_eval_batch_size,
    )

    # Optimizer
    # Split weights in two groups, one with weight decay and the other not.
    no_decay = ["bias", "LayerNorm.weight"]
    optimizer_grouped_parameters = [
        {
            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
            "weight_decay": args.weight_decay,
        },
        {
            "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
            "weight_decay": 0.0,
        },
    ]
    optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)

    # Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
    # shorter in multiprocess)

    # 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, eval_dataloader, lr_scheduler = accelerator.prepare(
        model,
        optimizer,
        train_dataloader,
        eval_dataloader,
        lr_scheduler,
    )

    # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
    if accelerator.distributed_type == DistributedType.TPU:
        model.tie_weights()

    # 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)

    # 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)

    # 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("mim_no_trainer", experiment_config)

    # Train!
    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(ds['train'])}")
    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(int(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 and accelerator.sync_gradients:
                    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 completed_steps >= args.max_train_steps:
                break

        model.eval()
        losses = []
        for step, batch in enumerate(eval_dataloader):
            with torch.no_grad():
                outputs = model(**batch)

            loss = outputs.loss
            losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))

        losses = torch.cat(losses)
        eval_loss = torch.mean(losses)

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

        if args.with_tracking:
            accelerator.log(
                {
                    "eval_loss": eval_loss,
                    "train_loss": total_loss.item() / len(train_dataloader),
                    "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)

    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:
            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,
                )

    accelerator.wait_for_everyone()
    accelerator.end_training()


if __name__ == "__main__":
    main()
