# 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
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import sys
from typing import List, Optional
sys.path.append('./')

import numpy as np
from sklearn.metrics import roc_auc_score
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
import torchvision
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

from dataset import Dataset
from models import eva_clip, coca


import pandas as pd

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(
        "--arch",
        type=str
    )
    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(
        config_file='dinov2/configs/eval/vitl14_pretrain.yaml',
        pretrained_weights='.cache/dinov2/dinov2_vitl14_pretrain.pth',
        arch='',
        train_dataset_str=None,
        val_dataset_str=None,
        test_dataset_strs=None,
        epochs=10,
        batch_size=128,
        num_workers=8,
        epoch_length=1000,
        save_checkpoint_frequency=1000,
        eval_period_iterations=20000,
        learning_rates=[1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2],
        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(),
    )'''
    from dinov2.data.transforms import make_normalize_transform
    transform = make_classification_eval_transform()
    # transform = make_classification_eval_transform_beit3()
    transform.transforms[-1] = make_normalize_transform((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
    test_dataset_str_process = test_dataset_str.split('/')[0]
    test_dataset = Dataset(meta[test_dataset_str_process], 'datasets/' + test_dataset_str, False, 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)

    from dinov2.data.transforms import make_normalize_transform
    train_transform = make_classification_train_transform()
    
    # train_transform = make_classification_train_transform_beit3()
    
    #train_transform.transforms[-1] = make_normalize_transform((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
    #train_transform = make_classification_train_transform()
    '''train_dataset = make_dataset(
        dataset_str=train_dataset_str,
        transform=train_transform,
    )'''
    train_dataset = Dataset(meta[train_dataset_str], 'datasets/' + train_dataset_str, True,train_transform)
    training_num_classes = train_dataset.num_classes
    #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, 2]
    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)

    optimizer = torch.optim.Adam(optim_param_groups, 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):
    if args.arch == 'dinov2':
        model, autocast_dtype = setup_and_build_model(args)
    elif args.arch == 'eva_clip':
        from dinov2.utils.config import setup
        torch.backends.cudnn.benchmark = True
        model = eva_clip('EVA02-CLIP-L-14', 'eva02_clip', '.cache')
        config = setup(args)
        autocast_dtype = torch.float16
    elif args.arch == 'coca':
        from dinov2.utils.config import setup
        torch.backends.cudnn.benchmark = True
        model = coca('coca_ViT-L-14', 'laion2b_s13b_b90k', '.cache')
        # model = create_model_and_transforms('coca_ViT-L-14', 'laion2b_s13b_b90k', cache_dir='.cache')
        config = setup(args)
        autocast_dtype = torch.float16

    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()
    meta = {
        'aircraft': '/data1/dataset/aircraft/fgvc-aircraft-2013b/data/',
        'baijiu': '/data1/dataset/baijiu/chongqing1_round1_train1_20191223/',
        'cub': '/data1/dataset/CUB2011/CUB_200_2011/',
        'deepfashion': '/data1/dataset/deepfashion/',
        'flowers102': '/data1/dataset/flowers102/',
        'food101': '/data1/dataset/food101/',
        'products10k': '/data1/dataset/products10k/',
        'skincon': '/data1/dataset/skincon/',
        'stanfordcar': '/data1/dataset/StanfordCar/',
        'stanforddog': '/data1/dataset/StanfordDog/',
        'vegfru': '/data1/dataset/vegfru/',
        'iNat2021':'/data1/dataset/iNat2021/',
    }
    args.arch='dinov2'
    args.train_dataset_str='aircraft'
    args.batch_size=128
    
    
    args.learning_rates=[1e-3, 2e-3, 5e-3, 1e-2, 2e-2, 5e-2] # 128 *2  64 1  32 2  16 4
    args.learning_rates = [lr for lr in args.learning_rates]
    args.val_dataset_str = args.train_dataset_str

        
    args.output_dir = f'save/cls/{args.arch}/{args.train_dataset_str}_4'

    print(args.train_dataset_str)
    print(args.output_dir)

    
# fintunning   多标签label  
    main(args)
    '''for k in meta.keys():
        args.train_dataset_str = args.val_dataset_str = k
        args.output_dir = 'save/dinov2/' + k
        main(args)'''