import numpy as np
import torch
import operator
import itertools
import constants
from gloo_wrapper import allgather_sizes, alltoallv_cpu

def get_shuffle_global_nids(rank, world_size, global_nids_ranks, node_data):
    """ 
    For nodes which are not owned by the current rank, whose global_nid <-> shuffle_global-nid mapping
    is not present at the current rank, this function retrieves their shuffle_global_ids from the owner rank

    Parameters: 
    -----------
    rank : integer
        rank of the process
    world_size : integer
        total no. of ranks configured
    global_nids_ranks : list
        list of numpy arrays (of global_nids), index of the list is the rank of the process
                    where global_nid <-> shuffle_global_nid mapping is located. 
    node_data : dictionary
        node_data is a dictionary with keys as column names and values as numpy arrays

    Returns:
    --------
    numpy ndarray
        where the column-0 are global_nids and column-1 are shuffle_global_nids which are retrieved
        from other processes. 
    """
    #build a list of sizes (lengths of lists)
    global_nids_ranks = [torch.from_numpy(x) for x in global_nids_ranks]
    recv_nodes = alltoallv_cpu(rank, world_size, global_nids_ranks)

    # Use node_data to lookup global id to send over.
    send_nodes = []
    for proc_i_nodes in recv_nodes:
        #list of node-ids to lookup
        if proc_i_nodes is not None: 
            global_nids = proc_i_nodes.numpy()
            if(len(global_nids) != 0):
                common, ind1, ind2 = np.intersect1d(node_data[constants.GLOBAL_NID], global_nids, return_indices=True)
                shuffle_global_nids = node_data[constants.SHUFFLE_GLOBAL_NID][ind1]
                send_nodes.append(torch.from_numpy(shuffle_global_nids).type(dtype=torch.int64))
            else:
                send_nodes.append(torch.empty((0), dtype=torch.int64))
        else:
            send_nodes.append(torch.empty((0), dtype=torch.int64))

    #send receive global-ids
    recv_shuffle_global_nids = alltoallv_cpu(rank, world_size, send_nodes)
    shuffle_global_nids = np.concatenate([x.numpy() if x is not None else [] for x in recv_shuffle_global_nids])
    global_nids = np.concatenate([x for x in global_nids_ranks])
    ret_val = np.column_stack([global_nids, shuffle_global_nids])
    return ret_val


def get_shuffle_global_nids_edges(rank, world_size, edge_data, node_part_ids, node_data):
    """
    Edges which are owned by this rank, may have global_nids whose shuffle_global_nids are NOT present locally.
    This function retrieves shuffle_global_nids for such global_nids.

    Parameters: 
    -----------
    rank : integer
        rank of the process
    world_size : integer
        total no. of processes used
    edge_data : numpy ndarray
        edge_data (augmented) as read from the xxx_edges.txt file
    node_part_ids : numpy array 
        list of partition ids indexed by global node ids.
    node_data : dictionary
        node_data, is a dictionary with keys as column_names and values as numpy arrays
    """

    #determine unique node-ids present locally
    global_nids = np.sort(np.unique(np.concatenate((edge_data[constants.GLOBAL_SRC_ID], edge_data[constants.GLOBAL_DST_ID], node_data[constants.GLOBAL_NID]))))

    #determine the rank which owns orig-node-id <-> partition/rank mappings
    part_ids = node_part_ids[global_nids]

    #form list of lists, each list includes global_nids whose mappings (shuffle_global_nids) needs to be retrieved.
    #and rank will be the process which owns mappings of these global_nids
    global_nids_ranks = []
    for i in range(world_size):
        if (i == rank):
            global_nids_ranks.append(np.empty(shape=(0), dtype=np.int64))
            continue

        #not_owned_nodes = part_ids[:,0][part_ids[:,1] == i]
        not_owned_node_ids = np.where(part_ids == i)[0] 
        if not_owned_node_ids.shape[0] == 0: 
            not_owned_nodes = np.empty(shape=(0), dtype=np.int64)
        else: 
            not_owned_nodes = global_nids[not_owned_node_ids]
        global_nids_ranks.append(not_owned_nodes)

    #Retrieve Global-ids for respective node owners
    non_local_nids = get_shuffle_global_nids(rank, world_size, global_nids_ranks, node_data)

    #Add global_nid <-> shuffle_global_nid mappings to the received data
    for i in range(world_size):
        if (i == rank):
            own_node_ids = np.where(part_ids == i)[0]
            own_global_nids = global_nids[own_node_ids]
            common, ind1, ind2 = np.intersect1d(node_data[constants.GLOBAL_NID], own_global_nids, return_indices=True)
            my_shuffle_global_nids = node_data[constants.SHUFFLE_GLOBAL_NID][ind1]
            local_mappings = np.column_stack((own_global_nids, my_shuffle_global_nids))
            resolved_global_nids = np.concatenate((non_local_nids, local_mappings))

    #form a dictionary of mappings between orig-node-ids and global-ids
    resolved_mappings = dict(zip(resolved_global_nids[:,0], resolved_global_nids[:,1]))

    #determine global-ids for the orig-src-id and orig-dst-id
    shuffle_global_src_id = [resolved_mappings[x] for x in edge_data[constants.GLOBAL_SRC_ID]]
    shuffle_global_dst_id = [resolved_mappings[x] for x in edge_data[constants.GLOBAL_DST_ID]]
    edge_data[constants.SHUFFLE_GLOBAL_SRC_ID] = np.array(shuffle_global_src_id, dtype=np.int64)
    edge_data[constants.SHUFFLE_GLOBAL_DST_ID] = np.array(shuffle_global_dst_id, dtype=np.int64)

def assign_shuffle_global_nids_nodes(rank, world_size, node_data):
    """
    Utility function to assign shuffle global ids to nodes at a given rank
    node_data gets converted from [ntype, global_type_nid, global_nid]
    to [shuffle_global_nid, ntype, global_type_nid, global_nid, part_local_type_nid]
    where shuffle_global_nid : global id of the node after data shuffle
            ntype : node-type as read from xxx_nodes.txt
            global_type_nid : node-type-id as read from xxx_nodes.txt
            global_nid : node-id as read from xxx_nodes.txt, implicitly 
                            this is the line no. in the file
            part_local_type_nid : type_nid assigned by the current rank within its scope
            
    Parameters:
    -----------
    rank : integer
        rank of the process
    world_size : integer
        total number of processes used in the process group
    ntype_counts: list of tuples
        list of tuples (x,y), where x=ntype and y=no. of nodes whose shuffle_global_nids are needed
    node_data : dictionary
        node_data is a dictionary with keys as column names and values as numpy arrays
    """
    # Compute prefix sum to determine node-id offsets
    prefix_sum_nodes = allgather_sizes([node_data[constants.GLOBAL_NID].shape[0]], world_size)

    # assigning node-ids from localNodeStartId to (localNodeEndId - 1)
    # Assuming here that the nodeDataArr is sorted based on the nodeType.
    shuffle_global_nid_start = prefix_sum_nodes[rank]
    shuffle_global_nid_end = prefix_sum_nodes[rank + 1]

    # add a column with global-ids (after data shuffle)
    shuffle_global_nids = np.arange(shuffle_global_nid_start, shuffle_global_nid_end, dtype=np.int64)
    node_data[constants.SHUFFLE_GLOBAL_NID] = shuffle_global_nids


def assign_shuffle_global_nids_edges(rank, world_size, edge_data):
    """
    Utility function to assign shuffle_global_eids to edges
    edge_data gets converted from [global_src_nid, global_dst_nid, global_type_eid, etype]
    to [shuffle_global_src_nid, shuffle_global_dst_nid, global_src_nid, global_dst_nid, global_type_eid, etype]

    Parameters:
    -----------
    rank : integer
        rank of the current process
    world_size : integer
        total count of processes in execution
    etype_counts : list of tuples
        list of tuples (x,y), x = rank, y = no. of edges
    edge_data : numpy ndarray
        edge data as read from xxx_edges.txt file

    Returns:
    --------
    integer
        shuffle_global_eid_start, which indicates the starting value from which shuffle_global-ids are assigned to edges
        on this rank
    """
    #get prefix sum of edge counts per rank to locate the starting point
    #from which global-ids to edges are assigned in the current rank
    prefix_sum_edges = allgather_sizes([edge_data[constants.GLOBAL_SRC_ID].shape[0]], world_size)
    shuffle_global_eid_start = prefix_sum_edges[rank]
    shuffle_global_eid_end = prefix_sum_edges[rank + 1]

    # assigning edge-ids from localEdgeStart to (localEdgeEndId - 1)
    # Assuming here that the edge_data is sorted by edge_type
    shuffle_global_eids = np.arange(shuffle_global_eid_start, shuffle_global_eid_end, dtype=np.int64)
    edge_data[constants.SHUFFLE_GLOBAL_EID] = shuffle_global_eids
    return shuffle_global_eid_start
