# coding=utf-8
# Copyright 2022 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
# limitations under the License.
""" Finetuning any 🤗 Transformers model supported by AutoModelForSemanticSegmentation for semantic segmentation."""

import argparse
import json
import math
import os
import random
from pathlib import Path

import datasets
import numpy as np
import torch
from datasets import load_dataset, load_metric
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.transforms import functional
from tqdm.auto import tqdm

import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import set_seed
from huggingface_hub import Repository, hf_hub_download
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForSemanticSegmentation,
    SchedulerType,
    default_data_collator,
    get_scheduler,
)
from transformers.utils import get_full_repo_name
from transformers.utils.versions import require_version


logger = get_logger(__name__)

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


def pad_if_smaller(img, size, fill=0):
    min_size = min(img.size)
    if min_size < size:
        original_width, original_height = img.size
        pad_height = size - original_height if original_height < size else 0
        pad_width = size - original_width if original_width < size else 0
        img = functional.pad(img, (0, 0, pad_width, pad_height), fill=fill)
    return img


class Compose:
    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, image, target):
        for t in self.transforms:
            image, target = t(image, target)
        return image, target


class Identity:
    def __init__(self):
        pass

    def __call__(self, image, target):
        return image, target


class Resize:
    def __init__(self, size):
        self.size = size

    def __call__(self, image, target):
        image = functional.resize(image, self.size)
        target = functional.resize(target, self.size, interpolation=transforms.InterpolationMode.NEAREST)
        return image, target


class RandomResize:
    def __init__(self, min_size, max_size=None):
        self.min_size = min_size
        if max_size is None:
            max_size = min_size
        self.max_size = max_size

    def __call__(self, image, target):
        size = random.randint(self.min_size, self.max_size)
        image = functional.resize(image, size)
        target = functional.resize(target, size, interpolation=transforms.InterpolationMode.NEAREST)
        return image, target


class RandomCrop:
    def __init__(self, size):
        self.size = size

    def __call__(self, image, target):
        image = pad_if_smaller(image, self.size)
        target = pad_if_smaller(target, self.size, fill=255)
        crop_params = transforms.RandomCrop.get_params(image, (self.size, self.size))
        image = functional.crop(image, *crop_params)
        target = functional.crop(target, *crop_params)
        return image, target


class RandomHorizontalFlip:
    def __init__(self, flip_prob):
        self.flip_prob = flip_prob

    def __call__(self, image, target):
        if random.random() < self.flip_prob:
            image = functional.hflip(image)
            target = functional.hflip(target)
        return image, target


class PILToTensor:
    def __call__(self, image, target):
        image = functional.pil_to_tensor(image)
        target = torch.as_tensor(np.array(target), dtype=torch.int64)
        return image, target


class ConvertImageDtype:
    def __init__(self, dtype):
        self.dtype = dtype

    def __call__(self, image, target):
        image = functional.convert_image_dtype(image, self.dtype)
        return image, target


class Normalize:
    def __init__(self, mean, std):
        self.mean = mean
        self.std = std

    def __call__(self, image, target):
        image = functional.normalize(image, mean=self.mean, std=self.std)
        return image, target


class ReduceLabels:
    def __call__(self, image, target):
        if not isinstance(target, np.ndarray):
            target = np.array(target).astype(np.uint8)
        # avoid using underflow conversion
        target[target == 0] = 255
        target = target - 1
        target[target == 254] = 255

        target = Image.fromarray(target)
        return image, target


def parse_args():
    parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to a pretrained model or model identifier from huggingface.co/models.",
        default="nvidia/mit-b0",
    )
    parser.add_argument(
        "--dataset_name",
        type=str,
        help="Name of the dataset on the hub.",
        default="segments/sidewalk-semantic",
    )
    parser.add_argument(
        "--reduce_labels",
        action="store_true",
        help="Whether or not to reduce all labels by 1 and replace background by 255.",
    )
    parser.add_argument(
        "--train_val_split",
        type=float,
        default=0.15,
        help="Fraction of the dataset to be used for validation.",
    )
    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(
        "--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="polynomial",
        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(
        "--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 load in all available experiment trackers from the environment and use them for logging.",
    )
    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()

    # 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 pick up all supported trackers in the environment
    accelerator = Accelerator(log_with="all", logging_dir=args.output_dir) if args.with_tracking else Accelerator()
    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:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            repo = Repository(args.output_dir, clone_from=repo_name)

            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.
    # TODO support datasets from local folders
    dataset = load_dataset(args.dataset_name, cache_dir=args.cache_dir)

    # Rename column names to standardized names (only "image" and "label" need to be present)
    if "pixel_values" in dataset["train"].column_names:
        dataset = dataset.rename_columns({"pixel_values": "image"})
    if "annotation" in dataset["train"].column_names:
        dataset = dataset.rename_columns({"annotation": "label"})

    # 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)
        dataset["train"] = split["train"]
        dataset["validation"] = split["test"]

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    if args.dataset_name == "scene_parse_150":
        repo_id = "datasets/huggingface/label-files"
        filename = "ade20k-id2label.json"
    else:
        repo_id = f"datasets/{args.dataset_name}"
        filename = "id2label.json"
    id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
    id2label = {int(k): v for k, v in id2label.items()}
    label2id = {v: k for k, v in id2label.items()}

    # Load pretrained model and feature extractor
    config = AutoConfig.from_pretrained(args.model_name_or_path, id2label=id2label, label2id=label2id)
    feature_extractor = AutoFeatureExtractor.from_pretrained(args.model_name_or_path)
    model = AutoModelForSemanticSegmentation.from_pretrained(args.model_name_or_path, config=config)

    # Preprocessing the datasets
    # Define torchvision transforms to be applied to each image + target.
    # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
    # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
    train_transforms = Compose(
        [
            ReduceLabels() if args.reduce_labels else Identity(),
            RandomCrop(size=feature_extractor.size),
            RandomHorizontalFlip(flip_prob=0.5),
            PILToTensor(),
            ConvertImageDtype(torch.float),
            Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
        ]
    )
    # Define torchvision transform to be applied to each image.
    # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
    val_transforms = Compose(
        [
            ReduceLabels() if args.reduce_labels else Identity(),
            Resize(size=(feature_extractor.size, feature_extractor.size)),
            PILToTensor(),
            ConvertImageDtype(torch.float),
            Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std),
        ]
    )

    def preprocess_train(example_batch):
        pixel_values = []
        labels = []
        for image, target in zip(example_batch["image"], example_batch["label"]):
            image, target = train_transforms(image.convert("RGB"), target)
            pixel_values.append(image)
            labels.append(target)

        encoding = dict()
        encoding["pixel_values"] = torch.stack(pixel_values)
        encoding["labels"] = torch.stack(labels)

        return encoding

    def preprocess_val(example_batch):
        pixel_values = []
        labels = []
        for image, target in zip(example_batch["image"], example_batch["label"]):
            image, target = val_transforms(image.convert("RGB"), target)
            pixel_values.append(image)
            labels.append(target)

        encoding = dict()
        encoding["pixel_values"] = torch.stack(pixel_values)
        encoding["labels"] = torch.stack(labels)

        return encoding

    with accelerator.main_process_first():
        train_dataset = dataset["train"].with_transform(preprocess_train)
        eval_dataset = dataset["validation"].with_transform(preprocess_val)

    train_dataloader = DataLoader(
        train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
    )
    eval_dataloader = DataLoader(
        eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
    )

    # 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
    if hasattr(args.checkpointing_steps, "isdigit"):
        checkpointing_steps = args.checkpointing_steps
        if args.checkpointing_steps.isdigit():
            checkpointing_steps = int(args.checkpointing_steps)
    else:
        checkpointing_steps = None

    # Scheduler and math around the number of training steps.
    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
    else:
        args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)

    lr_scheduler = get_scheduler(
        name=args.lr_scheduler_type,
        optimizer=optimizer,
        num_warmup_steps=args.num_warmup_steps,
        num_training_steps=args.max_train_steps,
    )

    # Prepare everything with our `accelerator`.
    model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, eval_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)
    args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch

    # Instantiate metric
    metric = load_metric("mean_iou")

    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("semantic_segmentation_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(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 != "":
            accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}")
            accelerator.load_state(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
        # 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
        else:
            resume_step = int(training_difference.replace("step_", ""))
            starting_epoch = resume_step // len(train_dataloader)
            resume_step -= starting_epoch * len(train_dataloader)

    for epoch in range(starting_epoch, args.num_train_epochs):
        if args.with_tracking:
            total_loss = 0
        model.train()
        for step, batch in enumerate(train_dataloader):
            # We need to skip steps until we reach the resumed step
            if args.resume_from_checkpoint and epoch == starting_epoch:
                if resume_step is not None and step < resume_step:
                    completed_steps += 1
                    continue
            outputs = model(**batch)
            loss = outputs.loss
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            loss = loss / args.gradient_accumulation_steps
            accelerator.backward(loss)
            if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
                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:
                            feature_extractor.save_pretrained(args.output_dir)
                            repo.push_to_hub(
                                commit_message=f"Training in progress {completed_steps} steps",
                                blocking=False,
                                auto_lfs_prune=True,
                            )

            if completed_steps >= args.max_train_steps:
                break

        logger.info("***** Running evaluation *****")
        model.eval()
        samples_seen = 0
        for step, batch in enumerate(tqdm(eval_dataloader, disable=not accelerator.is_local_main_process)):
            with torch.no_grad():
                outputs = model(**batch)

            upsampled_logits = torch.nn.functional.interpolate(
                outputs.logits, size=batch["labels"].shape[-2:], mode="bilinear", align_corners=False
            )
            predictions = upsampled_logits.argmax(dim=1)

            predictions, references = accelerator.gather((predictions, batch["labels"]))

            # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.num_processes > 1:
                if step == len(eval_dataloader) - 1:
                    predictions = predictions[: len(eval_dataloader.dataset) - samples_seen]
                    references = references[: len(eval_dataloader.dataset) - samples_seen]
                else:
                    samples_seen += references.shape[0]

            metric.add_batch(
                predictions=predictions,
                references=references,
            )

        eval_metrics = metric.compute(
            num_labels=len(id2label),
            ignore_index=255,
            reduce_labels=False,  # we've already reduced the labels before
        )
        logger.info(f"epoch {epoch}: {eval_metrics}")

        if args.with_tracking:
            accelerator.log(
                {
                    "mean_iou": eval_metrics["mean_iou"],
                    "mean_accuracy": eval_metrics["mean_accuracy"],
                    "overall_accuracy": eval_metrics["overall_accuracy"],
                    "train_loss": total_loss,
                    "epoch": epoch,
                    "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:
                feature_extractor.save_pretrained(args.output_dir)
                repo.push_to_hub(
                    commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
                )

        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:
            feature_extractor.save_pretrained(args.output_dir)
            if args.push_to_hub:
                repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)

            with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
                json.dump({"eval_overall_accuracy": eval_metrics["overall_accuracy"]}, f)


if __name__ == "__main__":
    main()
