from enum import Enum
from typing import Iterable, Dict

import torch.nn.functional as F
from torch import nn, Tensor

from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel


class TripletDistanceMetric(Enum):
    """
    The metric for the triplet loss
    """
    COSINE = lambda x, y: 1 - F.cosine_similarity(x, y)
    EUCLIDEAN = lambda x, y: F.pairwise_distance(x, y, p=2)
    MANHATTAN = lambda x, y: F.pairwise_distance(x, y, p=1)


class TripletLoss(nn.Module):
    def __init__(self, model: SiameseTransQuestModel, distance_metric=TripletDistanceMetric.EUCLIDEAN,
                 triplet_margin=1):
        super(TripletLoss, self).__init__()
        self.model = model
        self.distance_metric = distance_metric
        self.triplet_margin = triplet_margin

    def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor):
        reps = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]

        rep_anchor, rep_pos, rep_neg = reps
        distance_pos = self.distance_metric(rep_anchor, rep_pos)
        distance_neg = self.distance_metric(rep_anchor, rep_neg)

        losses = F.relu(distance_pos - distance_neg + self.triplet_margin)
        return losses.mean()
