#!/usr/bin/env python
# Tsung-Yi Lin <tl483@cornell.edu>
# Ramakrishna Vedantam <vrama91@vt.edu>

# ReinforceCIDErScorer is an alternative implementation of CIDEr Scorer according to my personal
# input output format "tastes" and needs --- Jia Cheng

import copy
from collections import defaultdict
import numpy as np
import pdb
import math


def precook(s, n=4, out=False):
    """
    Takes a string as input and returns an object that can be given to
    either cook_refs or cook_test. This is optional: cook_refs and cook_test
    can take string arguments as well.
    :param s: string : sentence to be converted into ngrams
    :param n: int    : number of ngrams for which representation is calculated
    :return: term frequency vector for occuring ngrams
    """
    words = s.split()
    counts = defaultdict(int)
    for k in range(1, n + 1):
        for i in range(len(words) - k + 1):
            ngram = tuple(words[i:i + k])
            counts[ngram] += 1
    return counts


def cook_refs(refs, n=4):  ## lhuang: oracle will call with "average"
    '''Takes a list of reference sentences for a single segment
    and returns an object that encapsulates everything that BLEU
    needs to know about them.
    :param refs: list of string : reference sentences for some image
    :param n: int : number of ngrams for which (ngram) representation is calculated
    :return: result (list of dict)
    '''
    return [precook(ref, n) for ref in refs]


def cook_test(test, n=4):
    '''Takes a test sentence and returns an object that
    encapsulates everything that BLEU needs to know about it.
    :param test: list of string : hypothesis sentence for some image
    :param n: int : number of ngrams for which (ngram) representation is calculated
    :return: result (dict)
    '''
    return precook(test, n, True)


class ReinforceCiderScorer(object):
    """CIDEr scorer.
    """

    def __init__(self, corpus_crefs, n=4, sigma=6.0):
        ''' singular instance '''
        self.n = n
        self.sigma = sigma

        df_crefs = []
        for refs in corpus_crefs:
            df_crefs.append(cook_refs(refs))

        self.document_frequency = self.compute_doc_freq(df_crefs)
        self.corpus_ref_len = np.log(float(len(df_crefs)))


    def compute_doc_freq(self, df_crefs):
        '''
        Compute term frequency for reference data.
        This will be used to compute idf (inverse document frequency later)
        The term frequency is stored in the object
        :return: None
        '''
        document_frequency = defaultdict(float)
        for refs in df_crefs:
            # refs, k ref captions of one image
            for ngram in set([ngram for ref in refs for (ngram, count) in ref.items()]):
                document_frequency[ngram] += 1
            # maxcounts[ngram] = max(maxcounts.get(ngram,0), count)
        return document_frequency

    def compute_cider(self, refs, tests):

        ctest = []
        crefs = []
        for idx in range(len(tests)):
            test = tests[idx]
            ref = refs[idx]
            ctest.append(cook_test(test))
            crefs.append(cook_refs(ref))

        def counts2vec(cnts):
            """
            Function maps counts of ngram to vector of tfidf weights.
            The function returns vec, an array of dictionary that store mapping of n-gram and tf-idf weights.
            The n-th entry of array denotes length of n-grams.
            :param cnts:
            :param ref_len:
            :return: vec (array of dict), norm (array of float), length (int)
            """
            vec = [defaultdict(float) for _ in range(self.n)]
            length = 0
            norm = [0.0 for _ in range(self.n)]
            for (ngram, term_freq) in cnts.items():
                # give word count 1 if it doesn't appear in reference corpus
                df = np.log(max(1.0, self.document_frequency[ngram]))
                # ngram index
                n = len(ngram) - 1
                # tf (term_freq) * idf (precomputed idf) for n-grams
                vec[n][ngram] = float(term_freq) * (self.corpus_ref_len - df)
                # compute norm for the vector.  the norm will be used for computing similarity
                norm[n] += pow(vec[n][ngram], 2)

                if n == 1:
                    length += term_freq
            norm = [np.sqrt(n) for n in norm]
            return vec, norm, length

        def sim(vec_hyp, vec_ref, norm_hyp, norm_ref, length_hyp, length_ref):
            '''
            Compute the cosine similarity of two vectors.
            :param vec_hyp: array of dictionary for vector corresponding to hypothesis
            :param vec_ref: array of dictionary for vector corresponding to reference
            :param norm_hyp: array of float for vector corresponding to hypothesis
            :param norm_ref: array of float for vector corresponding to reference
            :param length_hyp: int containing length of hypothesis
            :param length_ref: int containing length of reference
            :return: array of score for each n-grams cosine similarity
            '''
            delta = float(length_hyp - length_ref)
            # measure consine similarity
            val = np.array([0.0 for _ in range(self.n)])
            for n in range(self.n):
                # ngram
                for (ngram, count) in vec_hyp[n].items():
                    # vrama91 : added clipping
                    val[n] += min(vec_hyp[n][ngram], vec_ref[n][ngram]) * vec_ref[n][ngram]

                if (norm_hyp[n] != 0) and (norm_ref[n] != 0):
                    val[n] /= (norm_hyp[n] * norm_ref[n])

                assert (not math.isnan(val[n]))
                # vrama91: added a length based gaussian penalty
                val[n] *= np.e ** (-(delta ** 2) / (2 * self.sigma ** 2))
            return val

        # compute log reference length

        scores = []
        for test, refs in zip(ctest, crefs):
            # compute vector for test captions
            vec, norm, length = counts2vec(test)
            # compute vector for ref captions
            score = np.array([0.0 for _ in range(self.n)])
            for ref in refs:
                vec_ref, norm_ref, length_ref = counts2vec(ref)
                score += sim(vec, vec_ref, norm, norm_ref, length, length_ref)
            # change by vrama91 - mean of ngram scores, instead of sum
            score_avg = np.mean(score)
            # divide by number of references
            score_avg /= len(refs)
            # multiply score by 10
            score_avg *= 10.0
            # append score of an image to the score list
            scores.append(score_avg)
        return scores

    """
    refs must be contained in the dataset for document frequency
    """
    def compute_score(self, refs, tests, option=None, verbose=0):
        # compute idf
        # self.compute_doc_freq()
        # assert to check document frequency
        # assert(len(self.ctest) >= max(self.document_frequency.values()))
        # compute cider score
        score = self.compute_cider(refs, tests)
        # debug
        # print score
        return np.mean(np.array(score)), np.array(score)
