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

class MultipleNegativesRankingLoss(nn.Module):
    """
        This loss expects as input a batch consisting of sentence pairs (a_1, p_1), (a_2, p_2)..., (a_n, p_n)
        where we assume that (a_i, p_i) are a positive pair and (a_i, p_j) for i!=j a negative pair.

        For each a_i, it uses all other p_j as negative samples, i.e., for a_i, we have 1 positive example (p_i) and
        n-1 negative examples (p_j). It then minimizes the negative log-likehood for softmax normalized scores.

        This loss function works great to train embeddings for retrieval setups where you have positive pairs (e.g. (query, relevant_doc))
        as it will sample in each batch n-1 negative docs randomly.

        The performance usually increases with increasing batch sizes.

        For more information, see: https://arxiv.org/pdf/1705.00652.pdf
        (Efficient Natural Language Response Suggestion for Smart Reply, Section 4.4)

        You can also provide one or multiple hard negatives per anchor-positive pair by structering the data like this:
        (a_1, p_1, n_1), (a_2, p_2, n_2)

        Here, n_1 is a hard negative for (a_1, p_1). The loss will use for the pair (a_i, p_i) all p_j (j!=i) and all n_j as negatives.

        Example::

            from sentence_transformers import SentenceTransformer,  SentencesDataset, LoggingHandler, losses
            from sentence_transformers.readers import InputExample

            model = SentenceTransformer('distilbert-base-nli-mean-tokens')
            train_examples = [InputExample(texts=['Anchor 1', 'Positive 1']),
                InputExample(texts=['Anchor 2', 'Positive 2'])]
            train_dataset = SentencesDataset(train_examples, model)
            train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
            train_loss = losses.MultipleNegativesRankingLoss(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(MultipleNegativesRankingLoss, 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]
        embeddings_a = reps[0]
        embeddings_b = torch.cat(reps[1:])

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

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





