# Filename: cider.py

#

# Description: Describes the class to compute the CIDEr (Consensus-Based Image Description Evaluation) Metric 

#               by Vedantam, Zitnick, and Parikh (http://arxiv.org/abs/1411.5726)

#

# Creation Date: Sun Feb  8 14:16:54 2015

#

# Authors: Ramakrishna Vedantam <vrama91@vt.edu> and Tsung-Yi Lin <tl483@cornell.edu>

from .cider_scorer import CiderScorer

import pdb

class Cider:

    """

    Main Class to compute the CIDEr metric 

    """

    def __init__(self, test=None, refs=None, n=4, sigma=6.0):

        # set cider to sum over 1 to 4-grams

        self._n = n

        # set the standard deviation parameter for gaussian penalty

        self._sigma = sigma

    def compute_score(self, gts, res):

        """

        Main function to compute CIDEr score

        :param  hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence>

                ref_for_image (dict)  : dictionary with key <image> and value <tokenized reference sentence>

        :return: cider (float) : computed CIDEr score for the corpus 

        """

        assert(gts.keys() == res.keys())

        imgIds = gts.keys()

        cider_scorer = CiderScorer(n=self._n, sigma=self._sigma)

        for id in imgIds:

            hypo = res[id]

            ref = gts[id]

            # Sanity check.

            assert(type(hypo) is list)

            assert(len(hypo) == 1)

            assert(type(ref) is list)

            assert(len(ref) > 0)

            cider_scorer += (hypo[0], ref)

        (score, scores) = cider_scorer.compute_score()

        return score, scores

    def method(self):

        return "CIDEr"