# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import argparse
from functools import partial
import json
import logging
import os
import sys
from typing import List, Optional

import numpy as np
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from fvcore.common.checkpoint import Checkpointer, PeriodicCheckpointer

from dinov2.data import SamplerType, make_data_loader, make_dataset
from dinov2.data.transforms import (
    make_classification_eval_transform,
    make_classification_train_transform,
)
import dinov2.distributed as distributed
from dinov2.eval.metrics import MetricType, build_metric
from dinov2.eval.setup import get_args_parser as get_setup_args_parser
from dinov2.eval.setup import setup_and_build_model
from dinov2.eval.utils import ModelWithIntermediateLayers, evaluate
from dinov2.logging import MetricLogger


logger = logging.getLogger("dinov2")


def get_args_parser(
    description: Optional[str] = None,
    parents: Optional[List[argparse.ArgumentParser]] = [],
    add_help: bool = True,
):
    setup_args_parser = get_setup_args_parser(parents=parents, add_help=False)
    parents = [setup_args_parser]
    parser = argparse.ArgumentParser(
        description=description,
        parents=parents,
        add_help=add_help,
    )
    parser.add_argument(
        "--train-dataset",
        dest="train_dataset_str",
        type=str,
        help="Training dataset",
    )
    parser.add_argument(
        "--val-dataset",
        dest="val_dataset_str",
        type=str,
        help="Validation dataset",
    )
    parser.add_argument(
        "--test-datasets",
        dest="test_dataset_strs",
        type=str,
        nargs="+",
        help="Test datasets, none to reuse the validation dataset",
    )
    parser.add_argument(
        "--epochs",
        type=int,
        help="Number of training epochs",
    )
    parser.add_argument(
        "--batch-size",
        type=int,
        help="Batch Size (per GPU)",
    )
    parser.add_argument(
        "--num-workers",
        type=int,
        help="Number de Workers",
    )
    parser.add_argument(
        "--epoch-length",
        type=int,
        help="Length of an epoch in number of iterations",
    )
    parser.add_argument(
        "--save-checkpoint-frequency",
        type=int,
        help="Number of epochs between two named checkpoint saves.",
    )
    parser.add_argument(
        "--eval-period-iterations",
        type=int,
        help="Number of iterations between two evaluations.",
    )
    parser.add_argument(
        "--learning-rates",
        nargs="+",
        type=float,
        help="Learning rates to grid search.",
    )
    parser.add_argument(
        "--no-resume",
        action="store_true",
        help="Whether to not resume from existing checkpoints",
    )
    parser.add_argument(
        "--val-metric-type",
        type=MetricType,
        choices=list(MetricType),
        help="Validation metric",
    )
    parser.add_argument(
        "--test-metric-types",
        type=MetricType,
        choices=list(MetricType),
        nargs="+",
        help="Evaluation metric",
    )
    parser.add_argument(
        "--classifier-fpath",
        type=str,
        help="Path to a file containing pretrained linear classifiers",
    )
    parser.add_argument(
        "--val-class-mapping-fpath",
        type=str,
        help="Path to a file containing a mapping to adjust classifier outputs",
    )
    parser.add_argument(
        "--test-class-mapping-fpaths",
        nargs="+",
        type=str,
        help="Path to a file containing a mapping to adjust classifier outputs",
    )
    parser.set_defaults(
        train_dataset_str="ImageNet:split=TRAIN",
        val_dataset_str="ImageNet:split=VAL",
        test_dataset_strs=None,
        epochs=10,
        batch_size=128,
        num_workers=8,
        epoch_length=1250,
        save_checkpoint_frequency=20,
        eval_period_iterations=1250,
        learning_rates=[
            1e-5,
            2e-5,
            5e-5,
            1e-4,
            2e-4,
            5e-4,
            1e-3,
            2e-3,
            5e-3,
            1e-2,
            2e-2,
            5e-2,
            0.1,
        ],
        val_metric_type=MetricType.MEAN_ACCURACY,
        test_metric_types=None,
        classifier_fpath=None,
        val_class_mapping_fpath=None,
        test_class_mapping_fpaths=[None],
    )
    return parser


def has_ddp_wrapper(m: nn.Module) -> bool:
    return isinstance(m, DistributedDataParallel)


def remove_ddp_wrapper(m: nn.Module) -> nn.Module:
    return m.module if has_ddp_wrapper(m) else m


def _pad_and_collate(batch):
    maxlen = max(len(targets) for image, targets in batch)
    padded_batch = [
        (image, np.pad(targets, (0, maxlen - len(targets)), constant_values=-1))
        for image, targets in batch
    ]
    return torch.utils.data.default_collate(padded_batch)


def create_linear_input(x_tokens_list, use_n_blocks, use_avgpool):
    intermediate_output = x_tokens_list[-use_n_blocks:]
    output = torch.cat([class_token for _, class_token in intermediate_output], dim=-1)
    if use_avgpool:
        output = torch.cat(
            (
                output,
                torch.mean(intermediate_output[-1][0], dim=1),  # patch tokens
            ),
            dim=-1,
        )
        output = output.reshape(output.shape[0], -1)
    return output.float()


class LinearClassifier(nn.Module):
    """Linear layer to train on top of frozen features"""

    def __init__(self, out_dim, use_n_blocks, use_avgpool, num_classes=1000):
        super().__init__()
        self.out_dim = out_dim
        self.use_n_blocks = use_n_blocks
        self.use_avgpool = use_avgpool
        self.num_classes = num_classes
        self.linear = nn.Linear(out_dim, num_classes)
        self.linear.weight.data.normal_(mean=0.0, std=0.01)
        self.linear.bias.data.zero_()

    def forward(self, x_tokens_list):
        output = create_linear_input(x_tokens_list, self.use_n_blocks, self.use_avgpool)
        return self.linear(output)


class AllClassifiers(nn.Module):
    def __init__(self, classifiers_dict):
        super().__init__()
        self.classifiers_dict = nn.ModuleDict()
        self.classifiers_dict.update(classifiers_dict)

    def forward(self, inputs):
        return {k: v.forward(inputs) for k, v in self.classifiers_dict.items()}

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


class LinearPostprocessor(nn.Module):
    def __init__(self, linear_classifier, class_mapping=None):
        super().__init__()
        self.linear_classifier = linear_classifier
        self.register_buffer(
            "class_mapping",
            None if class_mapping is None else torch.LongTensor(class_mapping),
        )

    def forward(self, samples, targets):
        preds = self.linear_classifier(samples)
        return {
            "preds": (
                preds[:, self.class_mapping]
                if self.class_mapping is not None
                else preds
            ),
            "target": targets,
        }


def scale_lr(learning_rates, batch_size):
    return learning_rates * (batch_size * distributed.get_global_size()) / 256.0


def setup_linear_classifiers(
    sample_output, n_last_blocks_list, learning_rates, batch_size, num_classes=1000
):
    linear_classifiers_dict = nn.ModuleDict()
    optim_param_groups = []
    for n in n_last_blocks_list:
        for avgpool in [False, True]:
            for _lr in learning_rates:
                lr = scale_lr(_lr, batch_size)
                out_dim = create_linear_input(
                    sample_output, use_n_blocks=n, use_avgpool=avgpool
                ).shape[1]
                linear_classifier = LinearClassifier(
                    out_dim,
                    use_n_blocks=n,
                    use_avgpool=avgpool,
                    num_classes=num_classes,
                )
                linear_classifier = linear_classifier.cuda()
                linear_classifiers_dict[
                    f"classifier_{n}_blocks_avgpool_{avgpool}_lr_{lr:.5f}".replace(
                        ".", "_"
                    )
                ] = linear_classifier
                optim_param_groups.append(
                    {"params": linear_classifier.parameters(), "lr": lr}
                )

    linear_classifiers = AllClassifiers(linear_classifiers_dict)
    if distributed.is_enabled():
        linear_classifiers = nn.parallel.DistributedDataParallel(linear_classifiers)

    return linear_classifiers, optim_param_groups


@torch.no_grad()
def evaluate_linear_classifiers(
    feature_model,
    linear_classifiers,
    data_loader,
    metric_type,
    metrics_file_path,
    training_num_classes,
    iteration,
    prefixstring="",
    class_mapping=None,
    best_classifier_on_val=None,
):
    logger.info("running validation !")

    num_classes = (
        len(class_mapping) if class_mapping is not None else training_num_classes
    )
    metric = build_metric(metric_type, num_classes=num_classes)
    postprocessors = {
        k: LinearPostprocessor(v, class_mapping)
        for k, v in linear_classifiers.classifiers_dict.items()
    }
    metrics = {k: metric.clone() for k in linear_classifiers.classifiers_dict}

    _, results_dict_temp = evaluate(
        feature_model,
        data_loader,
        postprocessors,
        metrics,
        torch.cuda.current_device(),
    )

    logger.info("")
    results_dict = {}
    max_accuracy = 0
    best_classifier = ""
    for i, (classifier_string, metric) in enumerate(results_dict_temp.items()):
        logger.info(f"{prefixstring} -- Classifier: {classifier_string} * {metric}")
        if (
            best_classifier_on_val is None and metric["top-1"].item() > max_accuracy
        ) or classifier_string == best_classifier_on_val:
            max_accuracy = metric["top-1"].item()
            best_classifier = classifier_string

    results_dict["best_classifier"] = {
        "name": best_classifier,
        "accuracy": max_accuracy,
    }

    logger.info(f"best classifier: {results_dict['best_classifier']}")

    if distributed.is_main_process():
        with open(metrics_file_path, "a") as f:
            f.write(f"iter: {iteration}\n")
            for k, v in results_dict.items():
                f.write(json.dumps({k: v}) + "\n")
            f.write("\n")

    return results_dict


def eval_linear(
    *,
    feature_model,
    linear_classifiers,
    train_data_loader,
    val_data_loader,
    metrics_file_path,
    optimizer,
    scheduler,
    output_dir,
    max_iter,
    checkpoint_period,  # In number of iter, creates a new file every period
    running_checkpoint_period,  # Period to update main checkpoint file
    eval_period,
    metric_type,
    training_num_classes,
    resume=True,
    classifier_fpath=None,
    val_class_mapping=None,
):
    checkpointer = Checkpointer(
        linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler
    )
    start_iter = (
        checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get(
            "iteration", -1
        )
        + 1
    )

    periodic_checkpointer = PeriodicCheckpointer(
        checkpointer, checkpoint_period, max_iter=max_iter
    )
    iteration = start_iter
    logger.info("Starting training from iteration {}".format(start_iter))
    metric_logger = MetricLogger(delimiter="  ")
    header = "Training"

    for data, labels in metric_logger.log_every(
        train_data_loader,
        10,
        header,
        max_iter,
        start_iter,
    ):
        data = data.cuda(non_blocking=True)
        labels = labels.cuda(non_blocking=True)

        features = feature_model(data)
        outputs = linear_classifiers(features)

        losses = {
            f"loss_{k}": nn.CrossEntropyLoss()(v, labels) for k, v in outputs.items()
        }
        loss = sum(losses.values())

        # compute the gradients
        optimizer.zero_grad()
        loss.backward()

        # step
        optimizer.step()
        scheduler.step()

        # log
        if iteration % 10 == 0:
            torch.cuda.synchronize()
            metric_logger.update(loss=loss.item())
            metric_logger.update(lr=optimizer.param_groups[0]["lr"])
            print("lr", optimizer.param_groups[0]["lr"])

        if iteration - start_iter > 5:
            if iteration % running_checkpoint_period == 0:
                torch.cuda.synchronize()
                if distributed.is_main_process():
                    logger.info("Checkpointing running_checkpoint")
                    periodic_checkpointer.save(
                        "running_checkpoint_linear_eval", iteration=iteration
                    )
                torch.cuda.synchronize()
        periodic_checkpointer.step(iteration)

        if (
            eval_period > 0
            and (iteration + 1) % eval_period == 0
            and iteration != max_iter - 1
        ):
            _ = evaluate_linear_classifiers(
                feature_model=feature_model,
                linear_classifiers=remove_ddp_wrapper(linear_classifiers),
                data_loader=val_data_loader,
                metrics_file_path=metrics_file_path,
                prefixstring=f"ITER: {iteration}",
                metric_type=metric_type,
                training_num_classes=training_num_classes,
                iteration=iteration,
                class_mapping=val_class_mapping,
            )
            torch.cuda.synchronize()

        iteration = iteration + 1

    val_results_dict = evaluate_linear_classifiers(
        feature_model=feature_model,
        linear_classifiers=remove_ddp_wrapper(linear_classifiers),
        data_loader=val_data_loader,
        metrics_file_path=metrics_file_path,
        metric_type=metric_type,
        training_num_classes=training_num_classes,
        iteration=iteration,
        class_mapping=val_class_mapping,
    )
    return val_results_dict, feature_model, linear_classifiers, iteration


def make_eval_data_loader(test_dataset_str, batch_size, num_workers, metric_type):
    test_dataset = make_dataset(
        dataset_str=test_dataset_str,
        transform=make_classification_eval_transform(),
    )
    test_data_loader = make_data_loader(
        dataset=test_dataset,
        batch_size=batch_size,
        num_workers=num_workers,
        sampler_type=SamplerType.DISTRIBUTED,
        drop_last=False,
        shuffle=False,
        persistent_workers=False,
        collate_fn=(
            _pad_and_collate
            if metric_type == MetricType.IMAGENET_REAL_ACCURACY
            else None
        ),
    )
    return test_data_loader


def test_on_datasets(
    feature_model,
    linear_classifiers,
    test_dataset_strs,
    batch_size,
    num_workers,
    test_metric_types,
    metrics_file_path,
    training_num_classes,
    iteration,
    best_classifier_on_val,
    prefixstring="",
    test_class_mappings=[None],
):
    results_dict = {}
    for test_dataset_str, class_mapping, metric_type in zip(
        test_dataset_strs, test_class_mappings, test_metric_types
    ):
        logger.info(f"Testing on {test_dataset_str}")
        test_data_loader = make_eval_data_loader(
            test_dataset_str, batch_size, num_workers, metric_type
        )
        dataset_results_dict = evaluate_linear_classifiers(
            feature_model,
            remove_ddp_wrapper(linear_classifiers),
            test_data_loader,
            metric_type,
            metrics_file_path,
            training_num_classes,
            iteration,
            prefixstring="",
            class_mapping=class_mapping,
            best_classifier_on_val=best_classifier_on_val,
        )
        results_dict[f"{test_dataset_str}_accuracy"] = (
            100.0 * dataset_results_dict["best_classifier"]["accuracy"]
        )
    return results_dict


def run_eval_linear(
    model,
    output_dir,
    train_dataset_str,
    val_dataset_str,
    batch_size,
    epochs,
    epoch_length,
    num_workers,
    save_checkpoint_frequency,
    eval_period_iterations,
    learning_rates,
    autocast_dtype,
    test_dataset_strs=None,
    resume=True,
    classifier_fpath=None,
    val_class_mapping_fpath=None,
    test_class_mapping_fpaths=[None],
    val_metric_type=MetricType.MEAN_ACCURACY,
    test_metric_types=None,
):
    seed = 0

    if test_dataset_strs is None:
        test_dataset_strs = [val_dataset_str]
    if test_metric_types is None:
        test_metric_types = [val_metric_type] * len(test_dataset_strs)
    else:
        assert len(test_metric_types) == len(test_dataset_strs)
    assert len(test_dataset_strs) == len(test_class_mapping_fpaths)

    train_transform = make_classification_train_transform()
    train_dataset = make_dataset(
        dataset_str=train_dataset_str,
        transform=train_transform,
    )
    training_num_classes = len(
        torch.unique(torch.Tensor(train_dataset.get_targets().astype(int)))
    )
    sampler_type = SamplerType.SHARDED_INFINITE
    # sampler_type = SamplerType.INFINITE

    n_last_blocks_list = [1, 4]
    n_last_blocks = max(n_last_blocks_list)
    autocast_ctx = partial(torch.cuda.amp.autocast, enabled=True, dtype=autocast_dtype)
    feature_model = ModelWithIntermediateLayers(model, n_last_blocks, autocast_ctx)
    sample_output = feature_model(train_dataset[0][0].unsqueeze(0).cuda())

    linear_classifiers, optim_param_groups = setup_linear_classifiers(
        sample_output,
        n_last_blocks_list,
        learning_rates,
        batch_size,
        training_num_classes,
    )

    optimizer = torch.optim.SGD(optim_param_groups, momentum=0.9, weight_decay=0)
    max_iter = epochs * epoch_length
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
        optimizer, max_iter, eta_min=0
    )
    checkpointer = Checkpointer(
        linear_classifiers, output_dir, optimizer=optimizer, scheduler=scheduler
    )
    start_iter = (
        checkpointer.resume_or_load(classifier_fpath or "", resume=resume).get(
            "iteration", -1
        )
        + 1
    )
    train_data_loader = make_data_loader(
        dataset=train_dataset,
        batch_size=batch_size,
        num_workers=num_workers,
        shuffle=True,
        seed=seed,
        sampler_type=sampler_type,
        sampler_advance=start_iter,
        drop_last=True,
        persistent_workers=True,
    )
    val_data_loader = make_eval_data_loader(
        val_dataset_str, batch_size, num_workers, val_metric_type
    )

    checkpoint_period = save_checkpoint_frequency * epoch_length

    if val_class_mapping_fpath is not None:
        logger.info(f"Using class mapping from {val_class_mapping_fpath}")
        val_class_mapping = np.load(val_class_mapping_fpath)
    else:
        val_class_mapping = None

    test_class_mappings = []
    for class_mapping_fpath in test_class_mapping_fpaths:
        if class_mapping_fpath is not None and class_mapping_fpath != "None":
            logger.info(f"Using class mapping from {class_mapping_fpath}")
            class_mapping = np.load(class_mapping_fpath)
        else:
            class_mapping = None
        test_class_mappings.append(class_mapping)

    metrics_file_path = os.path.join(output_dir, "results_eval_linear.json")
    val_results_dict, feature_model, linear_classifiers, iteration = eval_linear(
        feature_model=feature_model,
        linear_classifiers=linear_classifiers,
        train_data_loader=train_data_loader,
        val_data_loader=val_data_loader,
        metrics_file_path=metrics_file_path,
        optimizer=optimizer,
        scheduler=scheduler,
        output_dir=output_dir,
        max_iter=max_iter,
        checkpoint_period=checkpoint_period,
        running_checkpoint_period=epoch_length,
        eval_period=eval_period_iterations,
        metric_type=val_metric_type,
        training_num_classes=training_num_classes,
        resume=resume,
        val_class_mapping=val_class_mapping,
        classifier_fpath=classifier_fpath,
    )
    results_dict = {}
    if len(test_dataset_strs) > 1 or test_dataset_strs[0] != val_dataset_str:
        results_dict = test_on_datasets(
            feature_model,
            linear_classifiers,
            test_dataset_strs,
            batch_size,
            0,  # num_workers,
            test_metric_types,
            metrics_file_path,
            training_num_classes,
            iteration,
            val_results_dict["best_classifier"]["name"],
            prefixstring="",
            test_class_mappings=test_class_mappings,
        )
    results_dict["best_classifier"] = val_results_dict["best_classifier"]["name"]
    results_dict[f"{val_dataset_str}_accuracy"] = (
        100.0 * val_results_dict["best_classifier"]["accuracy"]
    )
    logger.info("Test Results Dict " + str(results_dict))

    return results_dict


def main(args):
    model, autocast_dtype = setup_and_build_model(args)
    run_eval_linear(
        model=model,
        output_dir=args.output_dir,
        train_dataset_str=args.train_dataset_str,
        val_dataset_str=args.val_dataset_str,
        test_dataset_strs=args.test_dataset_strs,
        batch_size=args.batch_size,
        epochs=args.epochs,
        epoch_length=args.epoch_length,
        num_workers=args.num_workers,
        save_checkpoint_frequency=args.save_checkpoint_frequency,
        eval_period_iterations=args.eval_period_iterations,
        learning_rates=args.learning_rates,
        autocast_dtype=autocast_dtype,
        resume=not args.no_resume,
        classifier_fpath=args.classifier_fpath,
        val_metric_type=args.val_metric_type,
        test_metric_types=args.test_metric_types,
        val_class_mapping_fpath=args.val_class_mapping_fpath,
        test_class_mapping_fpaths=args.test_class_mapping_fpaths,
    )
    return 0


if __name__ == "__main__":
    description = "DINOv2 linear evaluation"
    args_parser = get_args_parser(description=description)
    args = args_parser.parse_args()
    sys.exit(main(args))
