from __future__ import division, unicode_literals

import torch
import onmt.io


class TranslationBuilder(object):
    """
    Build a word-based translation from the batch output
    of translator and the underlying dictionaries.

    Replacement based on "Addressing the Rare Word
    Problem in Neural Machine Translation" :cite:`Luong2015b`

    Args:
       data (DataSet):
       fields (dict of Fields): data fields
       n_best (int): number of translations produced
       replace_unk (bool): replace unknown words using attention
       has_tgt (bool): will the batch have gold targets
    """
    def __init__(self, data, fields, n_best=1, replace_unk=False,
                 has_tgt=False):
        self.data = data
        self.fields = fields
        self.n_best = n_best
        self.replace_unk = replace_unk
        self.has_tgt = has_tgt

    def _build_target_tokens(self, src, src_vocab, src_raw, pred, attn):
        vocab = self.fields["tgt"].vocab
        tokens = []
        for tok in pred:
            if tok < len(vocab):
                tokens.append(vocab.itos[tok])
            else:
                tokens.append(src_vocab.itos[tok - len(vocab)])
            if tokens[-1] == onmt.io.EOS_WORD:
                tokens = tokens[:-1]
                break
        if self.replace_unk and (attn is not None) and (src is not None):
            for i in range(len(tokens)):
                if tokens[i] == vocab.itos[onmt.io.UNK]:
                    _, maxIndex = attn[i].max(0)
                    tokens[i] = src_raw[maxIndex[0]]
        return tokens

    def from_batch(self, translation_batch):
        batch = translation_batch["batch"]
        assert(len(translation_batch["gold_score"]) ==
               len(translation_batch["predictions"]))
        batch_size = batch.batch_size

        preds, pred_score, attn, gold_score, indices = list(zip(
            *sorted(zip(translation_batch["predictions"],
                        translation_batch["scores"],
                        translation_batch["attention"],
                        translation_batch["gold_score"],
                        batch.indices.data),
                    key=lambda x: x[-1])))

        # Sorting
        inds, perm = torch.sort(batch.indices.data)
        data_type = self.data.data_type
        if data_type == 'text':
            src = batch.src[0].data.index_select(1, perm)
        else:
            src = None

        if self.has_tgt:
            tgt = batch.tgt.data.index_select(1, perm)
        else:
            tgt = None

        translations = []
        for b in range(batch_size):
            if data_type == 'text':
                src_vocab = self.data.src_vocabs[inds[b]] \
                  if self.data.src_vocabs else None
                src_raw = self.data.examples[inds[b]].src
            else:
                src_vocab = None
                src_raw = None
            pred_sents = [self._build_target_tokens(
                src[:, b] if src is not None else None,
                src_vocab, src_raw,
                preds[b][n], attn[b][n])
                          for n in range(self.n_best)]
            gold_sent = None
            if tgt is not None:
                gold_sent = self._build_target_tokens(
                    src[:, b] if src is not None else None,
                    src_vocab, src_raw,
                    tgt[1:, b] if tgt is not None else None, None)

            translation = Translation(src[:, b] if src is not None else None,
                                      src_raw, pred_sents,
                                      attn[b], pred_score[b], gold_sent,
                                      gold_score[b])
            translations.append(translation)

        return translations


class Translation(object):
    """
    Container for a translated sentence.

    Attributes:
        src (`LongTensor`): src word ids
        src_raw ([str]): raw src words

        pred_sents ([[str]]): words from the n-best translations
        pred_scores ([[float]]): log-probs of n-best translations
        attns ([`FloatTensor`]) : attention dist for each translation
        gold_sent ([str]): words from gold translation
        gold_score ([float]): log-prob of gold translation

    """
    def __init__(self, src, src_raw, pred_sents,
                 attn, pred_scores, tgt_sent, gold_score):
        self.src = src
        self.src_raw = src_raw
        self.pred_sents = pred_sents
        self.attns = attn
        #self.pred_scores = pred_scores
        self.pred_scores = [10**s for s in pred_scores]
        self.pred_scores = [x/sum(self.pred_scores) for x in self.pred_scores]
        self.gold_sent = tgt_sent
        self.gold_score = gold_score

    def log(self, sent_number):
        """
        Log translation to stdout.
        """
        output = '\nSENT {}: {}\n'.format(sent_number, self.src_raw)

        best_pred = self.pred_sents[0]
        best_score = self.pred_scores[0]
        pred_sent = ' '.join(best_pred)
        output += 'PRED {}: {}\n'.format(sent_number, pred_sent)
        print("PRED SCORE: {:.4f}".format(best_score))

        if self.gold_sent is not None:
            tgt_sent = ' '.join(self.gold_sent)
            output += 'GOLD {}: {}\n'.format(sent_number, tgt_sent)
            output += ("GOLD SCORE: {:.4f}".format(self.gold_score))

        if len(self.pred_sents) > 1:
            print('\nBEST HYP:')
            for score, sent in zip(self.pred_scores, self.pred_sents):
                output += "[{:.4f}] {}\n".format(score, sent)

        return output
