local TNN = nerv.class("nerv.TNN", "nerv.Layer")
local DAGLayer = TNN

local function parse_id(str)
    --used to parse layerid[portid],time
    local id, port, time, _
    _, _, id, port, time = string.find(str, "([a-zA-Z0-9_]+)%[([0-9]+)%][,]*([0-9]*)")
    if id == nil or port == nil then
        _, _, id, port, time = string.find(str, "(.+)%[([0-9]+)%][,]*([0-9]*)")
        if not (id == "<input>" or id == "<output>") then
            nerv.error("wrong format of connection id")
        end
    end
    --print(str, id, port, time)
    port = tonumber(port)
    if (time == nil) then
        time = 0
    else
        time = tonumber(time)
    end
    --now time don't need to be parsed
    return id, port
end

local function discover(id, layers, layer_repo)
    local ref = layers[id]
    if id == "<input>" or id == "<output>" then
        return nil
    end
    if ref == nil then
        local layer = layer_repo:get_layer(id)
        local dim_in, dim_out = layer:get_dim()
        ref = {
            layer = layer,
            inputs_m = {}, --storage for computation, inputs_m[port][time]
            outputs_m = {},
            err_inputs_m = {},
            err_outputs_m = {},
            conns_i = {}, --list of inputing connections
            conns_o = {}, --list of outputing connections
            dim_in = dim_in, --list of dimensions of ports
            dim_out = dim_out,
        }
        layers[id] = ref
    end
    return ref
end

nerv.TNN.FC = {} --flag const
nerv.TNN.FC.SEQ_START = 4
nerv.TNN.FC.SEQ_END = 8 
nerv.TNN.FC.HAS_INPUT = 1
nerv.TNN.FC.HAS_LABEL = 2
nerv.TNN.FC.SEQ_NORM = bit.bor(nerv.TNN.FC.HAS_INPUT, nerv.TNN.FC.HAS_LABEL) --This instance have both input and label

function DAGLayer.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_c)
    --Return a table of matrix storage from time (1-chunk_size)..(2*chunk_size)
    if (type(st) ~= "table") then
        nerv.error("st should be a table")
    end
    for i = 1 - chunk_size, chunk_size * 2 do
        if (st[i] == nil) then
            st[i] = {}
        end
        st[i][p] = global_conf.cumat_type(batch_size, dim)
        st[i][p]:fill(0)
        if (st_c ~= nil) then
            if (st_c[i] == nil) then
                st_c[i] = {}
            end
            st_c[i][p_c] = st[i][p]
        end
    end
end

function DAGLayer:__init(id, global_conf, layer_conf)
    local layers = {}
    local inputs_p = {} --map:port of the TDAGLayer to layer ref and port
    local outputs_p = {}
    local dim_in = layer_conf.dim_in
    local dim_out = layer_conf.dim_out
    local parsed_conns = {}
    local _

    for _, ll in pairs(layer_conf.connections) do
        local id_from, port_from = parse_id(ll[1])
        local id_to, port_to = parse_id(ll[2])
        local time_to = ll[3] 

        print(id_from, id_to, time_to)

        local ref_from = discover(id_from, layers, layer_conf.sub_layers)
        local ref_to = discover(id_to, layers, layer_conf.sub_layers)
        
        if (id_from == "<input>") then
            if (dim_in[port_from] ~= ref_to.dim_in[port_to] or time_to ~= 0) then
                nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3])
            end
            inputs_p[port_from] = {["ref"] = ref_to, ["port"] = port_to}          
            ref_to.inputs_m[port_to] = {} --just a place holder
        elseif (id_to == "<output>") then
            if (dim_out[port_to] ~= ref_from.dim_out[port_from] or time_to ~= 0) then
                nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3])
            end
            outputs_p[port_to] = {["ref"] = ref_from, ["port"] = port_from}
            ref_from.outputs_m[port_from] = {} --just a place holder
        else
            conn_now = { 
                ["src"] = {["ref"] = ref_from, ["port"] = port_from},
                ["dst"] = {["ref"] = ref_to, ["port"] = port_to},
                ["time"] = time_to
            }
            if (ref_to.dim_in[port_to] ~= ref_from.dim_out[port_from]) then
                nerv.error("mismatch dimension or wrong time %s,%s,%d", ll[1], ll[2], ll[3])
            end
            table.insert(parsed_conns, conn_now)
            table.insert(ref_to.conns_i, conn_now)
            table.insert(ref_from.conns_o, conn_now)
        end
    end

    self.layers = layers
    self.inputs_p = inputs_p
    self.outputs_p = outputs_p
    self.id = id
    self.dim_in = dim_in
    self.dim_out = dim_out
    self.parsed_conns = parsed_conns
    self.gconf = global_conf
end

function DAGLayer:init(batch_size, chunk_size)
    for i, conn in ipairs(self.parsed_conns) do --init storage for connections inside the NN
        local _, output_dim
        local ref_from, port_from, ref_to, port_to
        ref_from, port_from = conn.src.ref, conn.src.port
        ref_to, port_to = conn.dst.ref, conn.dst.port
        local dim = ref_from.dim_out[port_from]
        if (dim == 0) then
            nerv.error("layer %s has a zero dim port", ref_from.layer.id)
        end

        print("TNN initing storage", ref_from.layer.id, "->", ref_to.layer.id)
        self.makeInitialStore(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, global_conf, ref_to.inputs_m, port_to)
        self.makeInitialStore(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, global_conf, ref_to.err_outputs_m, port_to)
        
    end

    self.outputs_m = {}
    self.err_inputs_m = {}
    for i = 1, #self.dim_out do --Init storage for output ports 
        local ref = self.outputs_p[i].ref
        local p = self.outputs_p[i].port
        self.makeInitialStore(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i)
        self.makeInitialStore(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i)
    end 

    self.inputs_m = {}
    self.err_outputs_m = {}
    for i = 1, #self.dim_in do --Init storage for input ports 
        local ref = self.inputs_p[i].ref
        local p = self.inputs_p[i].port
        self.makeInitialStore(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i)
        self.makeInitialStore(ref.err_outputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.err_outputs_m, i)
    end 

    for id, ref in pairs(self.layers) do --Calling init for child layers
        for i = 1, #ref.dim_in do
            if (ref.inputs_m[i] == nil or ref.err_outputs_m[i] == nil) then
                nerv.error("dangling input port %d of layer %s", i, id)
            end
        end
        for i = 1, #ref.dim_out do
            if (ref.outputs_m[i] == nil or ref.err_inputs_m[i] == nil) then
                nerv.error("dangling output port %d of layer %s", i, id)
            end
        end
        -- initialize sub layers
        ref.layer:init(batch_size)
    end

    local flags_now = {}
    for i = 1, chunk_size do
        flags_now[i] = {}
    end

    self.feeds_now = {} --feeds is for the reader to fill
    self.feeds_now.inputs_m = self.inputs_m
    self.feeds_now.flags_now = flags_now
end

--[[
function DAGLayer:batch_resize(batch_size)
    self.gconf.batch_size = batch_size

    for i, conn in ipairs(self.parsed_conn) do
        local _, output_dim
        local ref_from, port_from, ref_to, port_to
        ref_from, port_from = unpack(conn[1])
        ref_to, port_to = unpack(conn[2])
        _, output_dim = ref_from.layer:get_dim()

        if ref_from.outputs[port_from]:nrow() ~= batch_size and output_dim[port_from] > 0 then
            local mid = self.gconf.cumat_type(batch_size, output_dim[port_from])
            local err_mid = mid:create()

            ref_from.outputs[port_from] = mid
            ref_to.inputs[port_to] = mid

            ref_from.err_inputs[port_from] = err_mid
            ref_to.err_outputs[port_to] = err_mid
        end
    end
    for id, ref in pairs(self.layers) do
        ref.layer:batch_resize(batch_size)
    end
    collectgarbage("collect")
end
]]--

--reader: some reader
--Returns: bool, whether has new feed
--Returns: feeds, a table that will be filled with the reader's feeds
function DAGLayer:getFeedFromReader(reader)
    local feeds = self.feeds_now
    local got_new = reader:get_batch(feeds)
    return got_new, feeds
end

function DAGLayer:update(bp_err, input, output)
    self:set_err_inputs(bp_err)
    self:set_inputs(input)
    self:set_outputs(output)
    -- print("update")
    for id, ref in pairs(self.queue) do
        -- print(ref.layer.id)
        ref.layer:update(ref.err_inputs, ref.inputs, ref.outputs)
    end
end

function DAGLayer:propagate(input, output)
    self:set_inputs(input)
    self:set_outputs(output)
    local ret = false
    for i = 1, #self.queue do
        local ref = self.queue[i]
        -- print(ref.layer.id)
        ret = ref.layer:propagate(ref.inputs, ref.outputs)
    end
    return ret
end

function DAGLayer:back_propagate(bp_err, next_bp_err, input, output)
    self:set_err_outputs(next_bp_err)
    self:set_err_inputs(bp_err)
    self:set_inputs(input)
    self:set_outputs(output)
    for i = #self.queue, 1, -1 do
        local ref = self.queue[i]
        -- print(ref.layer.id)
        ref.layer:back_propagate(ref.err_inputs, ref.err_outputs, ref.inputs, ref.outputs)
    end
end

--Return: nerv.ParamRepo
function DAGLayer:get_params()
    local param_repos = {}
    for id, ref in pairs(self.queue) do
        table.insert(param_repos, ref.layer:get_params())
    end
    return nerv.ParamRepo.merge(param_repos)
end

DAGLayer.PORT_TYPES = {
    INPUT = {},
    OUTPUT = {},
    ERR_INPUT = {},
    ERR_OUTPUT = {}
}

function DAGLayer:get_intermediate(id, port_type)
    if id == "<input>" or id == "<output>" then
        nerv.error("an actual real layer id is expected")
    end
    local layer = self.layers[id]
    if layer == nil then
        nerv.error("layer id %s not found", id)
    end
    if port_type == DAGLayer.PORT_TYPES.INPUT then
        return layer.inputs
    elseif port_type == DAGLayer.PORT_TYPES.OUTPUT then
        return layer.outputs
    elseif port_type == DAGLayer.PORT_TYPES.ERR_INPUT then
        return layer.err_inputs
    elseif port_type == DAGLayer.PORT_TYPES.ERR_OUTPUT then
        return layer.err_outputs
    end
    nerv.error("unrecognized port type")
end