import json

from pycocoevalcap.bleu.bleu import Bleu
from pycocoevalcap.cider.cider import Cider
from pycocoevalcap.meteor.meteor import Meteor
from pycocoevalcap.rouge.rouge import Rouge
from pycocoevalcap.spice.spice import Spice
from pycocoevalcap.tokenizer.ptbtokenizer import PTBTokenizer
# from open_clip import tokenize
from tqdm.auto import tqdm

# from open_clip.tokenizer import _tokenizer
from core.vision_encoder.tokenizer import _tokenizer, tokenize

"""
Code adapted from https://github.com/salaniz/pycocoevalcap/blob/master/eval.py
Thanks to @salaniz for the code!
"""


class COCOEvalCap:
    def __init__(self, results):
        self.evalImgs = []
        self.eval = {}
        self.imgToEval = {}
        self.results = results

    def evaluate(self):
        gts = {}
        res = {}
        for imgId, r in enumerate(self.results):
            gts[imgId] = r["true"]
            res[imgId] = r["gen"]
        # =================================================
        # Set up scorers
        # =================================================
        print("tokenization...")
        tokenizer = PTBTokenizer()
        gts = tokenizer.tokenize(gts)
        res = tokenizer.tokenize(res)

        # =================================================
        # Set up scorers
        # =================================================
        print("setting up scorers...")
        scorers = [
            (Bleu(4), ["Bleu_1", "Bleu_2", "Bleu_3", "Bleu_4"]),
            (Meteor(), "METEOR"),
            (Rouge(), "ROUGE_L"),
            (Cider(), "CIDEr"),
            (Spice(), "SPICE"),
        ]

        # =================================================
        # Compute scores
        # =================================================
        for scorer, method in scorers:
            print("computing %s score..." % (scorer.method()))
            score, scores = scorer.compute_score(gts, res)
            if type(method) == list:
                for sc, scs, m in zip(score, scores, method):
                    self.setEval(sc, m)
                    self.setImgToEvalImgs(scs, gts.keys(), m)
                    print("%s: %0.3f" % (m, sc))
            else:
                self.setEval(score, method)
                self.setImgToEvalImgs(scores, gts.keys(), method)
                print("%s: %0.3f" % (method, score))
        self.setEvalImgs()

    def setEval(self, score, method):
        self.eval[method] = score

    def setImgToEvalImgs(self, scores, imgIds, method):
        for imgId, score in zip(imgIds, scores):
            if not imgId in self.imgToEval:
                self.imgToEval[imgId] = {}
                self.imgToEval[imgId]["image_id"] = imgId
            self.imgToEval[imgId][method] = score

    def setEvalImgs(self):
        self.evalImgs = [eval for imgId, eval in self.imgToEval.items()]


def evaluate(
    model,
    dataloader,
    batch_size,
    device,
    transform,
    train_dataloader=None,
    num_workers=None,
    amp=True,
    verbose=False,
):
    results = []
    image_id = 0
    gt = []
    for idx, (img, captions) in enumerate(tqdm(dataloader)):
        out = model.generate(img.to(device))
        decoded = [
            _tokenizer.decode(i)
            .split("<end_of_text>")[0]
            .replace("<start_of_text>", "")
            .strip()
            for i in out.cpu().numpy()
        ]
        for pred, true in zip(decoded, captions):
            true = [{"caption": t} for t in true]
            pred = [{"caption": pred}]
            results.append({"image_id": image_id, "gen": pred, "true": true})
            image_id += 1
    coco_eval = COCOEvalCap(results)
    coco_eval.evaluate()
    metrics = coco_eval.eval
    # print output evaluation scores
    for metric, score in metrics.items():
        print(f"{metric}: {score:.3f}")
    return metrics