import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
from .. import util

class MultipleNegativesSymmetricRankingLoss(nn.Module):
    """
        This loss is an adaptation of MultipleNegativesRankingLoss. MultipleNegativesRankingLoss computes the following loss:
        For a given anchor and a list of candidates, find the positive candidate.

        In MultipleNegativesSymmetricRankingLoss, we add another loss term: Given the positive and a list of all anchors,
        find the correct (matching) anchor.

        For the example of question-answering: You have (question, answer)-pairs. MultipleNegativesRankingLoss just computes
        the loss to find the answer for a given question. MultipleNegativesSymmetricRankingLoss additionally computes the
        loss to find the question for a given answer.

        Note: If you pass triplets, the negative entry will be ignored. A anchor is just searched for the positive.

        Example::

            from sentence_transformers import SentenceTransformer, losses, InputExample
            from torch.utils.data import DataLoader

            model = SentenceTransformer('distilbert-base-uncased')
            train_examples = [InputExample(texts=['Anchor 1', 'Positive 1']),
                InputExample(texts=['Anchor 2', 'Positive 2'])]
            train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=32)
            train_loss = losses.MultipleNegativesSymmetricRankingLoss(model=model)
    """
    def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct = util.cos_sim):
        """
        :param model: SentenceTransformer model
        :param scale: Output of similarity function is multiplied by scale value
        :param similarity_fct: similarity function between sentence embeddings. By default, cos_sim. Can also be set to dot product (and then set scale to 1)
        """
        super(MultipleNegativesSymmetricRankingLoss, self).__init__()
        self.model = model
        self.scale = scale
        self.similarity_fct = similarity_fct
        self.cross_entropy_loss = nn.CrossEntropyLoss()


    def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor):
        reps = [self.model(sentence_feature)['sentence_embedding'] for sentence_feature in sentence_features]
        anchor = reps[0]
        candidates = torch.cat(reps[1:])

        scores = self.similarity_fct(anchor, candidates) * self.scale
        labels = torch.tensor(range(len(scores)), dtype=torch.long, device=scores.device)  # Example a[i] should match with b[i]

        anchor_positive_scores = scores[:, 0:len(reps[1])]
        forward_loss = self.cross_entropy_loss(scores, labels)
        backward_loss = self.cross_entropy_loss(anchor_positive_scores.transpose(0, 1), labels)
        return (forward_loss + backward_loss) / 2

    def get_config_dict(self):
        return {'scale': self.scale, 'similarity_fct': self.similarity_fct.__name__}