import torch
from torch.nn import Embedding
from torch.utils.data import DataLoader
from torch_sparse import SparseTensor
from sklearn.linear_model import LogisticRegression

from torch_geometric.utils.num_nodes import maybe_num_nodes

try:
    import torch_cluster  # noqa
    random_walk = torch.ops.torch_cluster.random_walk
except ImportError:
    random_walk = None

EPS = 1e-15


class Node2Vec(torch.nn. Module):
    r"""The Node2Vec model from the
    `"node2vec: Scalable Feature Learning for Networks"
    <https://arxiv.org/abs/1607.00653>`_ paper where random walks of
    length :obj:`walk_length` are sampled in a given graph, and node embeddings
    are learned via negative sampling optimization. 

    .. note::

        For an example of using Node2Vec, see `examples/node2vec.py
        <https://github.com/rusty1s/pytorch_geometric/blob/master/examples/
        node2vec.py>`_. 

    Args:
        edge_index (LongTensor): The edge indices. 
        embedding_dim (int): The size of each embedding vector. 
        walk_length (int): The walk length. 
        context_size (int): The actual context size which is considered for
            positive samples.  This parameter increases the effective sampling
            rate by reusing samples across different source nodes. 
        walks_per_node (int, optional): The number of walks to sample for each
            node. ( default: :obj:`1`)
        p (float, optional): Likelihood of immediately revisiting a node in the
            walk. ( default: :obj:`1`)
        q (float, optional): Control parameter to interpolate between
            breadth-first strategy and depth-first strategy (default: :obj:`1`)
        num_negative_samples (int, optional): The number of negative samples to
            use for each positive sample. ( default: :obj:`1`)
        num_nodes (int, optional): The number of nodes. ( default: :obj:`None`)
        sparse (bool, optional): If set to :obj:`True`, gradients w.r.t.  to the
            weight matrix will be sparse. ( default: :obj:`False`)
    """
    def __init__(self, edge_index, embedding_dim, walk_length, context_size,
                 walks_per_node=1, p=1, q=1, num_negative_samples=1,
                 num_nodes=None, sparse=False):
        super(Node2Vec, self).__init__()

        if random_walk is None:
            raise ImportError('`Node2Vec` requires `torch-cluster`.') 

        N = maybe_num_nodes(edge_index, num_nodes)
        row, col = edge_index
        self.adj = SparseTensor(row=row, col=col, sparse_sizes=(N, N))
        self.adj = self.adj.to('cpu')

        assert walk_length >= context_size

        self.embedding_dim = embedding_dim
        self.walk_length = walk_length - 1
        self.context_size = context_size
        self.walks_per_node = walks_per_node
        self.p = p
        self.q = q
        self.num_negative_samples = num_negative_samples

        self.embedding = Embedding(N, embedding_dim, sparse=sparse)

        self.reset_parameters()

    def reset_parameters(self):
        self.embedding.reset_parameters()

    def forward(self, batch=None):
        """Returns the embeddings for the nodes in :obj:`batch`.""" 
        emb = self.embedding.weight
        return emb if batch is None else emb[batch]

    def loader(self, **kwargs):
        return DataLoader(range(self.adj.sparse_size(0)),
                          collate_fn=self.sample, **kwargs)

    def pos_sample(self, batch):
        batch = batch.repeat(self.walks_per_node)
        rowptr, col, _ = self.adj.csr()
        rw = random_walk(rowptr, col, batch, self.walk_length, self.p, self.q)

        walks = []
        num_walks_per_rw = 1 + self.walk_length + 1 - self.context_size
        for j in range(num_walks_per_rw):
            walks.append(rw[:, j:j + self.context_size])
        return torch.cat(walks, dim=0)

    def neg_sample(self, batch):
        batch = batch.repeat(self.walks_per_node * self.num_negative_samples)

        rw = torch.randint(self.adj.sparse_size(0),
                           (batch.size(0), self.walk_length))
        rw = torch.cat([batch.view(-1, 1), rw], dim=-1)

        walks = []
        num_walks_per_rw = 1 + self.walk_length + 1 - self.context_size
        for j in range(num_walks_per_rw):
            walks.append(rw[:, j:j + self.context_size])
        return torch.cat(walks, dim=0)

    def sample(self, batch):
        if not isinstance(batch, torch. Tensor):
            batch = torch.tensor(batch)
        return self.pos_sample(batch), self.neg_sample(batch)

    def loss(self, pos_rw, neg_rw):
        r"""Computes the loss given positive and negative random walks.""" 

        # Positive loss. 
        start, rest = pos_rw[:, 0], pos_rw[:, 1:].contiguous()

        h_start = self.embedding(start).view(pos_rw.size(0), 1,
                                             self.embedding_dim)
        h_rest = self.embedding(rest.view(-1)).view(pos_rw.size(0), -1,
                                                    self.embedding_dim)

        out = (h_start * h_rest).sum(dim=-1).view(-1)
        pos_loss = -torch.log(torch.sigmoid(out) + EPS).mean()

        # Negative loss. 
        start, rest = neg_rw[:, 0], neg_rw[:, 1:].contiguous()

        h_start = self.embedding(start).view(neg_rw.size(0), 1,
                                             self.embedding_dim)
        h_rest = self.embedding(rest.view(-1)).view(neg_rw.size(0), -1,
                                                    self.embedding_dim)

        out = (h_start * h_rest).sum(dim=-1).view(-1)
        neg_loss = -torch.log(1 - torch.sigmoid(out) + EPS).mean()

        return pos_loss + neg_loss

    def test(self, train_z, train_y, test_z, test_y, solver='lbfgs',
             multi_class='auto', *args, **kwargs):
        r"""Evaluates latent space quality via a logistic regression downstream
        task.""" 
        clf = LogisticRegression(solver=solver, multi_class=multi_class, *args,
                                 **kwargs).fit(train_z.detach().cpu().numpy(),
                                               train_y.detach().cpu().numpy())
        return clf.score(test_z.detach().cpu().numpy(),
                         test_y.detach().cpu().numpy())

    def __repr__(self):
        return '{}({}, {})'.format(self.__class__.__name__,
                                   self.embedding.weight.size(0),
                                   self.embedding.weight.size(1))
