import torch
import dgl

from dgl.dataloading.dataloader import EdgeCollator
from dgl.dataloading import BlockSampler
from dgl.dataloading.pytorch import _pop_subgraph_storage, _pop_blocks_storage
from dgl.base import DGLError

from functools import partial
import copy
import dgl.function as fn


def _prepare_tensor(g, data, name, is_distributed):
    return torch.tensor(data) if is_distributed else dgl.utils.prepare_tensor(g, data, name)


class TemporalSampler(BlockSampler):
    """ Temporal Sampler builds computational and temporal dependency of node representations via
    temporal neighbors selection and screening.

    The sampler expects input node to have same time stamps, in the case of TGN, it should be 
    either positive [src,dst] pair or negative samples. It will first take in-subgraph of seed
    nodes and then screening out edges which happen after that timestamp. Finally it will sample
    a fixed number of neighbor edges using random or topk sampling.

    Parameters
    ----------
    sampler_type : str
        sampler indication string of the final sampler.

        If 'topk' then sample topk most recent nodes

        If 'uniform' then uniform randomly sample k nodes

    k : int
        maximum number of neighors to sampler

        default 10 neighbors as paper stated

    Examples
    ----------
    Please refers to examples/pytorch/tgn/train.py

    """

    def __init__(self, sampler_type='topk', k=10):
        super(TemporalSampler, self).__init__(1, False)
        if sampler_type == 'topk':
            self.sampler = partial(
                dgl.sampling.select_topk, k=k, weight='timestamp')
        elif sampler_type == 'uniform':
            self.sampler = partial(dgl.sampling.sample_neighbors, fanout=k)
        else:
            raise DGLError(
                "Sampler string invalid please use \'topk\' or \'uniform\'")

    def sampler_frontier(self,
                         block_id,
                         g,
                         seed_nodes,
                         timestamp):
        full_neighbor_subgraph = dgl.in_subgraph(g, seed_nodes)
        full_neighbor_subgraph = dgl.add_edges(full_neighbor_subgraph,
                                               seed_nodes, seed_nodes)

        temporal_edge_mask = (full_neighbor_subgraph.edata['timestamp'] < timestamp) + (
            full_neighbor_subgraph.edata['timestamp'] <= 0)
        temporal_subgraph = dgl.edge_subgraph(
            full_neighbor_subgraph, temporal_edge_mask)

        # Map preserve ID
        temp2origin = temporal_subgraph.ndata[dgl.NID]

        # The added new edgge will be preserved hence
        root2sub_dict = dict(
            zip(temp2origin.tolist(), temporal_subgraph.nodes().tolist()))
        temporal_subgraph.ndata[dgl.NID] = g.ndata[dgl.NID][temp2origin]
        seed_nodes = [root2sub_dict[int(n)] for n in seed_nodes]
        final_subgraph = self.sampler(g=temporal_subgraph, nodes=seed_nodes)
        final_subgraph.remove_self_loop()
        return final_subgraph

        # Temporal Subgraph
    def sample_blocks(self,
                      g,
                      seed_nodes,
                      timestamp):
        blocks = []
        frontier = self.sampler_frontier(0, g, seed_nodes, timestamp)
        #block = transform.to_block(frontier,seed_nodes)
        block = frontier
        if self.return_eids:
            self.assign_block_eids(block, frontier)
        blocks.append(block)
        return blocks


class TemporalEdgeCollator(EdgeCollator):
    """ Temporal Edge collator merge the edges specified by eid: items

    Since we cannot keep duplicated nodes on a graph we need to iterate though
    the incoming edges and expand the duplicated node and form a batched block
    graph capture the temporal and computational dependency.

    Parameters
    ----------

    g : DGLGraph
        The graph from which the edges are iterated in minibatches and the subgraphs
        are generated.

    eids : Tensor or dict[etype, Tensor]
        The edge set in graph :attr:`g` to compute outputs.

    block_sampler : dgl.dataloading.BlockSampler
        The neighborhood sampler.

    g_sampling : DGLGraph, optional
        The graph where neighborhood sampling and message passing is performed.
        Note that this is not necessarily the same as :attr:`g`.
        If None, assume to be the same as :attr:`g`.

    exclude : str, optional
        Whether and how to exclude dependencies related to the sampled edges in the
        minibatch.  Possible values are

        * None, which excludes nothing.

        * ``'reverse_id'``, which excludes the reverse edges of the sampled edges.  The said
          reverse edges have the same edge type as the sampled edges.  Only works
          on edge types whose source node type is the same as its destination node type.

        * ``'reverse_types'``, which excludes the reverse edges of the sampled edges.  The
          said reverse edges have different edge types from the sampled edges.

        If ``g_sampling`` is given, ``exclude`` is ignored and will be always ``None``.

    reverse_eids : Tensor or dict[etype, Tensor], optional
        The mapping from original edge ID to its reverse edge ID.
        Required and only used when ``exclude`` is set to ``reverse_id``.
        For heterogeneous graph this will be a dict of edge type and edge IDs.  Note that
        only the edge types whose source node type is the same as destination node type
        are needed.

    reverse_etypes : dict[etype, etype], optional
        The mapping from the edge type to its reverse edge type.
        Required and only used when ``exclude`` is set to ``reverse_types``.

    negative_sampler : callable, optional
        The negative sampler.  Can be omitted if no negative sampling is needed.
        The negative sampler must be a callable that takes in the following arguments:

        * The original (heterogeneous) graph.

        * The ID array of sampled edges in the minibatch, or the dictionary of edge
          types and ID array of sampled edges in the minibatch if the graph is
          heterogeneous.

        It should return

        * A pair of source and destination node ID arrays as negative samples,
          or a dictionary of edge types and such pairs if the graph is heterogenenous.

        A set of builtin negative samplers are provided in
        :ref:`the negative sampling module <api-dataloading-negative-sampling>`.

    example
    ----------
    Please refers to examples/pytorch/tgn/train.py

    """

    def _collate_with_negative_sampling(self, items):
        items = _prepare_tensor(self.g_sampling, items, 'items', False)
        # Here node id will not change
        pair_graph = self.g.edge_subgraph(items, relabel_nodes=False)
        induced_edges = pair_graph.edata[dgl.EID]

        neg_srcdst_raw = self.negative_sampler(self.g, items)
        neg_srcdst = {self.g.canonical_etypes[0]: neg_srcdst_raw}
        dtype = list(neg_srcdst.values())[0][0].dtype
        neg_edges = {
            etype: neg_srcdst.get(etype, (torch.tensor(
                [], dtype=dtype), torch.tensor([], dtype=dtype)))
            for etype in self.g.canonical_etypes}

        neg_pair_graph = dgl.heterograph(
            neg_edges, {ntype: self.g.number_of_nodes(ntype) for ntype in self.g.ntypes})
        pair_graph, neg_pair_graph = dgl.transform.compact_graphs(
            [pair_graph, neg_pair_graph])
        # Need to remap id
        pair_graph.ndata[dgl.NID] = self.g.nodes()[pair_graph.ndata[dgl.NID]]
        neg_pair_graph.ndata[dgl.NID] = self.g.nodes()[
            neg_pair_graph.ndata[dgl.NID]]

        pair_graph.edata[dgl.EID] = induced_edges

        batch_graphs = []
        nodes_id = []
        timestamps = []

        for i, edge in enumerate(zip(self.g.edges()[0][items], self.g.edges()[1][items])):
            ts = pair_graph.edata['timestamp'][i]
            timestamps.append(ts)
            subg = self.block_sampler.sample_blocks(self.g_sampling,
                                                    list(edge),
                                                    timestamp=ts)[0]
            subg.ndata['timestamp'] = ts.repeat(subg.num_nodes())
            nodes_id.append(subg.srcdata[dgl.NID])
            batch_graphs.append(subg)
        timestamps = torch.tensor(timestamps).repeat_interleave(
            self.negative_sampler.k)
        for i, neg_edge in enumerate(zip(neg_srcdst_raw[0].tolist(), neg_srcdst_raw[1].tolist())):
            ts = timestamps[i]
            subg = self.block_sampler.sample_blocks(self.g_sampling,
                                                    [neg_edge[1]],
                                                    timestamp=ts)[0]
            subg.ndata['timestamp'] = ts.repeat(subg.num_nodes())
            batch_graphs.append(subg)
        blocks = [dgl.batch(batch_graphs)]
        input_nodes = torch.cat(nodes_id)
        return input_nodes, pair_graph, neg_pair_graph, blocks

    def collator(self, items):
        """
        The interface of collator, input items is edge id of the attached graph
        """
        result = super().collate(items)
        # Copy the feature from parent graph
        _pop_subgraph_storage(result[1], self.g)
        _pop_subgraph_storage(result[2], self.g)
        _pop_blocks_storage(result[-1], self.g_sampling)
        return result


class TemporalEdgeDataLoader(dgl.dataloading.EdgeDataLoader):
    """ TemporalEdgeDataLoader is an iteratable object to generate blocks for temporal embedding
    as well as pos and neg pair graph for memory update.

    The batch generated will follow temporal order

    Parameters
    ----------
    g : dgl.Heterograph
        graph for batching the temporal edge id as well as generate negative subgraph

    eids : torch.tensor() or numpy array
        eids range which to be batched, it is useful to split training validation test dataset

    block_sampler : dgl.dataloading.BlockSampler
        temporal neighbor sampler which sample temporal and computationally depend blocks for computation

    device : str
        'cpu' means load dataset on cpu
        'cuda' means load dataset on gpu

    collator : dgl.dataloading.EdgeCollator
        Merge input eid from pytorch dataloader to graph

    Example
    ----------
    Please refers to examples/pytorch/tgn/train.py

    """

    def __init__(self, g, eids, block_sampler, device='cpu', collator=TemporalEdgeCollator, **kwargs):
        collator_kwargs = {}
        dataloader_kwargs = {}
        for k, v in kwargs.items():
            if k in self.collator_arglist:
                collator_kwargs[k] = v
            else:
                dataloader_kwargs[k] = v
        self.collator = collator(g, eids, block_sampler, **collator_kwargs)

        assert not isinstance(g, dgl.distributed.DistGraph), \
            'EdgeDataLoader does not support DistGraph for now. ' \
            + 'Please use DistDataLoader directly.'
        self.dataloader = torch.utils.data.DataLoader(
            self.collator.dataset, collate_fn=self.collator.collate, **dataloader_kwargs)
        self.device = device

        # Precompute the CSR and CSC representations so each subprocess does not
        # duplicate.
        if dataloader_kwargs.get('num_workers', 0) > 0:
            g.create_formats_()

# ====== Fast Mode ======

# Part of code in reservoir sampling comes from PyG library
# https://github.com/rusty1s/pytorch_geometric/nn/models/tgn.py


class FastTemporalSampler(BlockSampler):
    """Temporal Sampler which implemented with a fast query lookup table. Sample
    temporal and computationally depending subgraph.

    The sampler maintains a lookup table of most current k neighbors of each node
    each time, the sampler need to be updated with new edges from incoming batch to
    update the lookup table.

    Parameters
    ----------
    g : dgl.Heterograph
        graph to be sampled here it which only exist to provide feature and data reference

    k : int
        number of neighbors the lookup table is maintaining

    device : str
        indication str which represent where the data will be stored
        'cpu' store the intermediate data on cpu memory
        'cuda' store the intermediate data on gpu memory 

    Example
    ----------
    Please refers to examples/pytorch/tgn/train.py
    """

    def __init__(self, g, k, device='cpu'):
        self.k = k
        self.g = g
        num_nodes = g.num_nodes()
        self.neighbors = torch.empty(
            (num_nodes, k), dtype=torch.long, device=device)
        self.e_id = torch.empty(
            (num_nodes, k), dtype=torch.long, device=device)
        self.__assoc__ = torch.empty(
            num_nodes, dtype=torch.long, device=device)
        self.last_update = torch.zeros(num_nodes, dtype=torch.double)
        self.reset()

    def sample_frontier(self,
                        block_id,
                        g,
                        seed_nodes):
        n_id = seed_nodes
        # Here Assume n_id is the bg nid
        neighbors = self.neighbors[n_id]
        nodes = n_id.view(-1, 1).repeat(1, self.k)
        e_id = self.e_id[n_id]
        mask = e_id >= 0

        neighbors[~mask] = nodes[~mask]
        # Screen out orphan node

        orphans = nodes[~mask].unique()
        nodes = nodes[mask]
        neighbors = neighbors[mask]

        e_id = e_id[mask]
        neighbors = neighbors.flatten()
        nodes = nodes.flatten()
        n_id = torch.cat([nodes, neighbors]).unique()
        self.__assoc__[n_id] = torch.arange(n_id.size(0), device=n_id.device)

        neighbors, nodes = self.__assoc__[neighbors], self.__assoc__[nodes]
        subg = dgl.graph((nodes, neighbors))

        # New node to complement orphans which haven't created
        subg.add_nodes(len(orphans))

        # Copy the seed node feature to subgraph
        subg.edata['timestamp'] = torch.zeros(subg.num_edges()).double()
        subg.edata['timestamp'] = self.g.edata['timestamp'][e_id]

        n_id = torch.cat([n_id, orphans])
        subg.ndata['timestamp'] = self.last_update[n_id]
        subg.edata['feats'] = torch.zeros(
            (subg.num_edges(), self.g.edata['feats'].shape[1])).float()
        subg.edata['feats'] = self.g.edata['feats'][e_id]
        subg = dgl.add_self_loop(subg)
        subg.ndata[dgl.NID] = n_id
        return subg

    def sample_blocks(self,
                      g,
                      seed_nodes):
        blocks = []
        frontier = self.sample_frontier(0, g, seed_nodes)
        block = frontier
        blocks.append(block)
        return blocks

    def add_edges(self, src, dst):
        """
        Add incoming batch edge info to the lookup table

        Parameters
        ----------
        src : torch.Tensor
            src node of incoming batch of it should be consistent with self.g

        dst : torch.Tensor
            src node of incoming batch of it should be consistent with self.g
        """
        neighbors = torch.cat([src, dst], dim=0)
        nodes = torch.cat([dst, src], dim=0)
        e_id = torch.arange(self.cur_e_id, self.cur_e_id + src.size(0),
                            device=src.device).repeat(2)
        self.cur_e_id += src.numel()

        # Convert newly encountered interaction ids so that they point to
        # locations of a "dense" format of shape [num_nodes, size].
        nodes, perm = nodes.sort()
        neighbors, e_id = neighbors[perm], e_id[perm]

        n_id = nodes.unique()
        self.__assoc__[n_id] = torch.arange(n_id.numel(), device=n_id.device)

        dense_id = torch.arange(nodes.size(0), device=nodes.device) % self.k
        dense_id += self.__assoc__[nodes].mul_(self.k)

        dense_e_id = e_id.new_full((n_id.numel() * self.k, ), -1)
        dense_e_id[dense_id] = e_id
        dense_e_id = dense_e_id.view(-1, self.k)

        dense_neighbors = e_id.new_empty(n_id.numel() * self.k)
        dense_neighbors[dense_id] = neighbors
        dense_neighbors = dense_neighbors.view(-1, self.k)

        # Collect new and old interactions...
        e_id = torch.cat([self.e_id[n_id, :self.k], dense_e_id], dim=-1)
        neighbors = torch.cat(
            [self.neighbors[n_id, :self.k], dense_neighbors], dim=-1)

        # And sort them based on `e_id`.
        e_id, perm = e_id.topk(self.k, dim=-1)
        self.e_id[n_id] = e_id
        self.neighbors[n_id] = torch.gather(neighbors, 1, perm)

    def reset(self):
        """
        Clean up the lookup table
        """
        self.cur_e_id = 0
        self.e_id.fill_(-1)

    def attach_last_update(self, last_t):
        """
        Attach current last timestamp a node has been updated

        Parameters:
        ----------
        last_t : torch.Tensor
            last timestamp a node has been updated its size need to be consistent with self.g

        """
        self.last_update = last_t

    def sync(self, sampler):
        """
        Copy the lookup table information from another sampler

        This method is useful run the test dataset with new node,
        when test new node dataset the lookup table's state should
        be restored from the sampler just after validation 

        Parameters
        ----------
        sampler : FastTemporalSampler
            The sampler from which current sampler get the lookup table info
        """
        self.cur_e_id = sampler.cur_e_id
        self.neighbors = copy.deepcopy(sampler.neighbors)
        self.e_id = copy.deepcopy(sampler.e_id)
        self.__assoc__ = copy.deepcopy(sampler.__assoc__)


class FastTemporalEdgeCollator(EdgeCollator):
    """ Temporal Edge collator merge the edges specified by eid: items

    Since we cannot keep duplicated nodes on a graph we need to iterate though
    the incoming edges and expand the duplicated node and form a batched block
    graph capture the temporal and computational dependency.

    Parameters
    ----------

    g : DGLGraph
        The graph from which the edges are iterated in minibatches and the subgraphs
        are generated.

    eids : Tensor or dict[etype, Tensor]
        The edge set in graph :attr:`g` to compute outputs.

    block_sampler : dgl.dataloading.BlockSampler
        The neighborhood sampler.

    g_sampling : DGLGraph, optional
        The graph where neighborhood sampling and message passing is performed.
        Note that this is not necessarily the same as :attr:`g`.
        If None, assume to be the same as :attr:`g`.

    exclude : str, optional
        Whether and how to exclude dependencies related to the sampled edges in the
        minibatch.  Possible values are

        * None, which excludes nothing.

        * ``'reverse_id'``, which excludes the reverse edges of the sampled edges.  The said
          reverse edges have the same edge type as the sampled edges.  Only works
          on edge types whose source node type is the same as its destination node type.

        * ``'reverse_types'``, which excludes the reverse edges of the sampled edges.  The
          said reverse edges have different edge types from the sampled edges.

        If ``g_sampling`` is given, ``exclude`` is ignored and will be always ``None``.

    reverse_eids : Tensor or dict[etype, Tensor], optional
        The mapping from original edge ID to its reverse edge ID.
        Required and only used when ``exclude`` is set to ``reverse_id``.
        For heterogeneous graph this will be a dict of edge type and edge IDs.  Note that
        only the edge types whose source node type is the same as destination node type
        are needed.

    reverse_etypes : dict[etype, etype], optional
        The mapping from the edge type to its reverse edge type.
        Required and only used when ``exclude`` is set to ``reverse_types``.

    negative_sampler : callable, optional
        The negative sampler.  Can be omitted if no negative sampling is needed.
        The negative sampler must be a callable that takes in the following arguments:

        * The original (heterogeneous) graph.

        * The ID array of sampled edges in the minibatch, or the dictionary of edge
          types and ID array of sampled edges in the minibatch if the graph is
          heterogeneous.

        It should return

        * A pair of source and destination node ID arrays as negative samples,
          or a dictionary of edge types and such pairs if the graph is heterogenenous.

        A set of builtin negative samplers are provided in
        :ref:`the negative sampling module <api-dataloading-negative-sampling>`.

    example
    ----------
    Please refers to examples/pytorch/tgn/train.py

    """

    def _collate_with_negative_sampling(self, items):
        items = _prepare_tensor(self.g_sampling, items, 'items', False)
        # Here node id will not change
        pair_graph = self.g.edge_subgraph(items, relabel_nodes=False)
        induced_edges = pair_graph.edata[dgl.EID]

        neg_srcdst_raw = self.negative_sampler(self.g, items)
        neg_srcdst = {self.g.canonical_etypes[0]: neg_srcdst_raw}
        dtype = list(neg_srcdst.values())[0][0].dtype
        neg_edges = {
            etype: neg_srcdst.get(etype, (torch.tensor(
                [], dtype=dtype), torch.tensor([], dtype=dtype)))
            for etype in self.g.canonical_etypes}

        neg_pair_graph = dgl.heterograph(
            neg_edges, {ntype: self.g.number_of_nodes(ntype) for ntype in self.g.ntypes})
        pair_graph, neg_pair_graph = dgl.transform.compact_graphs(
            [pair_graph, neg_pair_graph])
        # Need to remap id

        pair_graph.ndata[dgl.NID] = self.g.nodes()[pair_graph.ndata[dgl.NID]]
        neg_pair_graph.ndata[dgl.NID] = self.g.nodes()[
            neg_pair_graph.ndata[dgl.NID]]

        pair_graph.edata[dgl.EID] = induced_edges

        seed_nodes = pair_graph.ndata[dgl.NID]
        blocks = self.block_sampler.sample_blocks(self.g_sampling, seed_nodes)
        blocks[0].ndata['timestamp'] = torch.zeros(
            blocks[0].num_nodes()).double()
        input_nodes = blocks[0].edges()[1]

        # update sampler
        _src = self.g.nodes()[self.g.edges()[0][items]]
        _dst = self.g.nodes()[self.g.edges()[1][items]]
        self.block_sampler.add_edges(_src, _dst)
        return input_nodes, pair_graph, neg_pair_graph, blocks

    def collator(self, items):
        result = super().collate(items)
        # Copy the feature from parent graph
        _pop_subgraph_storage(result[1], self.g)
        _pop_subgraph_storage(result[2], self.g)
        _pop_blocks_storage(result[-1], self.g_sampling)
        return result


# ====== Simple Mode ======

# Part of code comes from paper
# "APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding"
# that will be appeared in SIGMOD 21, code repo https://github.com/WangXuhongCN/APAN

class SimpleTemporalSampler(dgl.dataloading.BlockSampler):
    '''
    Simple Temporal Sampler just choose the edges that happen before the current timestamp, to build the subgraph of the corresponding nodes. 
    And then the sampler uses the simplest static graph neighborhood sampling methods.

    Parameters
    ----------

    fanouts : [int, ..., int] int list
        The neighbors sampling strategy 

    '''

    def __init__(self, g, fanouts, return_eids=False):
        super().__init__(len(fanouts), return_eids)

        self.fanouts = fanouts  
        self.ts = 0
        self.frontiers = [None for _ in range(len(fanouts))]

    def sample_frontier(self, block_id, g, seed_nodes):
        '''
        Deleting the the edges that happen after the current timestamp, then use a simple topk edge sampling by timestamp.
        '''
        fanout = self.fanouts[block_id]
        # List of neighbors to sample per edge type for each GNN layer, starting from the first layer.
        g = dgl.in_subgraph(g, seed_nodes)  
        g.remove_edges(torch.where(g.edata['timestamp'] > self.ts)[0])  # Deleting the the edges that happen after the current timestamp

        if fanout is None:  # full neighborhood sampling
            frontier = g
        else:
            frontier = dgl.sampling.select_topk(g, fanout, 'timestamp', seed_nodes)  # most recent timestamp edge sampling
        self.frontiers[block_id] = frontier  # save frontier
        return frontier


class SimpleTemporalEdgeCollator(dgl.dataloading.EdgeCollator):
    '''
    Temporal Edge collator merge the edges specified by eid: items

    

    Parameters
    ----------

    g : DGLGraph
        The graph from which the edges are iterated in minibatches and the subgraphs
        are generated.

    eids : Tensor or dict[etype, Tensor]
        The edge set in graph :attr:`g` to compute outputs.

    block_sampler : dgl.dataloading.BlockSampler
        The neighborhood sampler.

    g_sampling : DGLGraph, optional
        The graph where neighborhood sampling and message passing is performed.
        Note that this is not necessarily the same as :attr:`g`.
        If None, assume to be the same as :attr:`g`.

    exclude : str, optional
        Whether and how to exclude dependencies related to the sampled edges in the
        minibatch.  Possible values are

        * None, which excludes nothing.

        * ``'reverse_id'``, which excludes the reverse edges of the sampled edges.  The said
          reverse edges have the same edge type as the sampled edges.  Only works
          on edge types whose source node type is the same as its destination node type.

        * ``'reverse_types'``, which excludes the reverse edges of the sampled edges.  The
          said reverse edges have different edge types from the sampled edges.

        If ``g_sampling`` is given, ``exclude`` is ignored and will be always ``None``.

    reverse_eids : Tensor or dict[etype, Tensor], optional
        The mapping from original edge ID to its reverse edge ID.
        Required and only used when ``exclude`` is set to ``reverse_id``.
        For heterogeneous graph this will be a dict of edge type and edge IDs.  Note that
        only the edge types whose source node type is the same as destination node type
        are needed.

    reverse_etypes : dict[etype, etype], optional
        The mapping from the edge type to its reverse edge type.
        Required and only used when ``exclude`` is set to ``reverse_types``.

    negative_sampler : callable, optional
        The negative sampler.  Can be omitted if no negative sampling is needed.
        The negative sampler must be a callable that takes in the following arguments:

        * The original (heterogeneous) graph.

        * The ID array of sampled edges in the minibatch, or the dictionary of edge
          types and ID array of sampled edges in the minibatch if the graph is
          heterogeneous.

        It should return

        * A pair of source and destination node ID arrays as negative samples,
          or a dictionary of edge types and such pairs if the graph is heterogenenous.

        A set of builtin negative samplers are provided in
        :ref:`the negative sampling module <api-dataloading-negative-sampling>`.
    '''
    def __init__(self, g, eids, block_sampler, g_sampling=None, exclude=None,
                reverse_eids=None, reverse_etypes=None, negative_sampler=None):
        super(SimpleTemporalEdgeCollator,self).__init__(g,eids,block_sampler,
                                                 g_sampling,exclude,reverse_eids,reverse_etypes,negative_sampler)
        self.n_layer = len(self.block_sampler.fanouts)

    def collate(self,items): 
        '''
        items: edge id in graph g.
        We sample iteratively k-times and batch them into one single subgraph.
        '''
        current_ts = self.g.edata['timestamp'][items[0]]     #only sample edges before current timestamp
        self.block_sampler.ts = current_ts    # restore the current timestamp to the graph sampler.

        # if link prefiction, we use a negative_sampler to generate neg-graph for loss computing.
        if self.negative_sampler is None:
            neg_pair_graph = None
            input_nodes, pair_graph, blocks = self._collate(items)
        else:
            input_nodes, pair_graph, neg_pair_graph, blocks = self._collate_with_negative_sampling(items)

        # we sampling k-hop subgraph and batch them into one graph
        for i in range(self.n_layer-1):
            self.block_sampler.frontiers[0].add_edges(*self.block_sampler.frontiers[i+1].edges())
        frontier = self.block_sampler.frontiers[0]
        # computing node last-update timestamp
        frontier.update_all(fn.copy_e('timestamp','ts'), fn.max('ts','timestamp'))
    
        return input_nodes, pair_graph, neg_pair_graph, [frontier]




