"""run inference of NLVR2 (single GPU only)"""
import argparse
import json
import os
from os.path import exists
from time import time

import torch
from torch.utils.data import DataLoader
from apex import amp

from data import (DetectFeatLmdb, TxtTokLmdb,
                  PrefetchLoader, TokenBucketSampler,
                  Nlvr2PairedEvalDataset, Nlvr2TripletEvalDataset,
                  nlvr2_paired_eval_collate, nlvr2_triplet_eval_collate)
from model.model import UniterConfig
from model.nlvr2 import (UniterForNlvr2Paired, UniterForNlvr2Triplet,
                         UniterForNlvr2PairedAttn)

from utils.misc import Struct
from utils.const import IMG_DIM, BUCKET_SIZE


def main(opts):
    device = torch.device("cuda")  # support single GPU only
    train_opts = Struct(json.load(open(f'{opts.train_dir}/log/hps.json')))

    if 'paired' in train_opts.model:
        EvalDatasetCls = Nlvr2PairedEvalDataset
        eval_collate_fn = nlvr2_paired_eval_collate
        if train_opts.model == 'paired':
            ModelCls = UniterForNlvr2Paired
        elif train_opts.model == 'paired-attn':
            ModelCls = UniterForNlvr2PairedAttn
        else:
            raise ValueError('unrecognized model type')
    elif train_opts.model == 'triplet':
        EvalDatasetCls = Nlvr2TripletEvalDataset
        ModelCls = UniterForNlvr2Triplet
        eval_collate_fn = nlvr2_triplet_eval_collate
    else:
        raise ValueError('unrecognized model type')

    img_db = DetectFeatLmdb(opts.img_db,
                            train_opts.conf_th, train_opts.max_bb,
                            train_opts.min_bb, train_opts.num_bb,
                            opts.compressed_db)
    txt_db = TxtTokLmdb(opts.txt_db, -1)
    dset = EvalDatasetCls(txt_db, img_db, train_opts.use_img_type)
    batch_size = (train_opts.val_batch_size if opts.batch_size is None
                  else opts.batch_size)
    sampler = TokenBucketSampler(dset.lens, bucket_size=BUCKET_SIZE,
                                 batch_size=batch_size, droplast=False)
    eval_dataloader = DataLoader(dset, batch_sampler=sampler,
                                 num_workers=opts.n_workers,
                                 pin_memory=opts.pin_mem,
                                 collate_fn=eval_collate_fn)
    eval_dataloader = PrefetchLoader(eval_dataloader)

    # Prepare model
    ckpt_file = f'{opts.train_dir}/ckpt/model_step_{opts.ckpt}.pt'
    checkpoint = torch.load(ckpt_file)
    model_config = UniterConfig.from_json_file(
        f'{opts.train_dir}/log/model.json')
    model = ModelCls(model_config, img_dim=IMG_DIM)
    model.init_type_embedding()
    model.load_state_dict(checkpoint, strict=False)
    model.to(device)
    model = amp.initialize(model, enabled=opts.fp16, opt_level='O2')

    results = evaluate(model, eval_dataloader, device)
    # write results
    if not exists(opts.output_dir):
        os.makedirs(opts.output_dir)
    with open(f'{opts.output_dir}/results.csv', 'w') as f:
        for id_, ans in results:
            f.write(f'{id_},{ans}\n')
    print(f'all results written')


@torch.no_grad()
def evaluate(model, eval_loader, device):
    print("start running evaluation...")
    model.eval()
    n_ex = 0
    st = time()
    results = []
    for i, batch in enumerate(eval_loader):
        qids = batch['qids']
        del batch['targets']
        del batch['qids']
        scores = model(**batch, targets=None, compute_loss=False)
        answers = ['True' if i == 1 else 'False'
                   for i in scores.max(dim=-1, keepdim=False
                                       )[1].cpu().tolist()]
        results.extend(zip(qids, answers))
        n_results = len(results)
        print(f'{n_results}/{len(eval_loader.dataset)} answers predicted')
        n_ex += len(qids)
    tot_time = time()-st
    model.train()
    print(f"evaluation finished in {int(tot_time)} seconds "
          f"at {int(n_ex/tot_time)} examples per second")
    return results


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    # Required parameters
    parser.add_argument("--txt_db",
                        type=str, required=True,
                        help="The input train corpus.")
    parser.add_argument("--img_db",
                        type=str, required=True,
                        help="The input train images.")

    parser.add_argument('--compressed_db', action='store_true',
                        help='use compressed LMDB')
    parser.add_argument("--batch_size", type=int,
                        help="batch size for evaluation")
    parser.add_argument('--n_workers', type=int, default=4,
                        help="number of data workers")
    parser.add_argument('--pin_mem', action='store_true',
                        help="pin memory")
    parser.add_argument('--fp16', action='store_true',
                        help="fp16 inference")

    parser.add_argument("--train_dir", type=str, required=True,
                        help="The directory storing NLVR2 finetuning output")
    parser.add_argument("--ckpt", type=int, required=True,
                        help="specify the checkpoint to run inference")
    parser.add_argument("--output_dir", type=str, required=True,
                        help="The output directory where the prediction "
                             "results will be written.")
    args = parser.parse_args()

    main(args)
