require 'kaldi_io'
require 'kaldi_seq'
gconf = {lrate = 0.00001, wcost = 0, momentum = 0.0,
        cumat_type = nerv.CuMatrixFloat,
        mmat_type = nerv.MMatrixFloat,
        frm_ext = 5,
        tr_scp = "ark,o:/slfs6/users/ymz09/kaldi/src/featbin/copy-feats scp:/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_mmi/train.scp ark:- |",
        initialized_param = {"/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_init_mmi.nerv",
        "/slfs6/users/ymz09/nerv-project/nerv/nerv-speech/kaldi_seq/test/chime3_global_transf_mmi.nerv"},
        debug = false}

function make_layer_repo(param_repo)
    local layer_repo = nerv.LayerRepo(
    {
        -- global transf
        ["nerv.BiasLayer"] =
        {
            blayer1 = {{bias = "bias1"}, {dim_in = {440}, dim_out = {440}}},
            blayer2 = {{bias = "bias2"}, {dim_in = {440}, dim_out = {440}}}
        },
        ["nerv.WindowLayer"] =
        {
            wlayer1 = {{window = "window1"}, {dim_in = {440}, dim_out = {440}}},
            wlayer2 = {{window = "window2"}, {dim_in = {440}, dim_out = {440}}}
        },
        -- biased linearity
        ["nerv.AffineLayer"] =
        {
            affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
            {dim_in = {440}, dim_out = {2048}}},
            affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
            {dim_in = {2048}, dim_out = {2048}}},
            affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
            {dim_in = {2048}, dim_out = {2048}}},
            affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
            {dim_in = {2048}, dim_out = {2048}}},
            affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
            {dim_in = {2048}, dim_out = {2048}}},
            affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
            {dim_in = {2048}, dim_out = {2048}}},
            affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
            {dim_in = {2048}, dim_out = {2048}}},
            affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"},
            {dim_in = {2048}, dim_out = {2011}}}
        },
        ["nerv.SigmoidLayer"] =
        {
            sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}},
            sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}},
            sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}},
            sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}},
            sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}},
            sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
            sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
        },
        ["nerv.MMILayer"] =
        {
            mmi_crit = {{}, {dim_in = {2011, -1}, dim_out = {1},
                        cmd = {
                            arg = "--class-frame-counts=/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced/ali_train_pdf.counts --acoustic-scale=0.1 --lm-scale=1.0 --learn-rate=0.00001 --drop-frames=true --verbose=1",
                            mdl = "/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_ali/final.mdl",
                            lat = "scp:/slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_denlats/lat.scp",
                            ali = "ark:gunzip -c /slfs5/users/ymz09/chime/baseline/ASR/exp/tri4a_dnn_tr05_multi_enhanced_ali/ali.*.gz |"
                        }
                    }
                }
        },
        ["nerv.SoftmaxLayer"] = -- softmax for decode output
        {
            softmax = {{}, {dim_in = {2011}, dim_out = {2011}}}
        }
    }, param_repo, gconf)

    layer_repo:add_layers(
    {
        ["nerv.DAGLayer"] =
        {
            global_transf = {{}, {
                dim_in = {440}, dim_out = {440},
                sub_layers = layer_repo,
                connections = {
                    ["<input>[1]"] = "blayer1[1]",
                    ["blayer1[1]"] = "wlayer1[1]",
                    ["wlayer1[1]"] = "blayer2[1]",
                    ["blayer2[1]"] = "wlayer2[1]",
                    ["wlayer2[1]"] = "<output>[1]"
                }
            }},
            main = {{}, {
                dim_in = {440}, dim_out = {2011},
                sub_layers = layer_repo,
                connections = {
                    ["<input>[1]"] = "affine0[1]",
                    ["affine0[1]"] = "sigmoid0[1]",
                    ["sigmoid0[1]"] = "affine1[1]",
                    ["affine1[1]"] = "sigmoid1[1]",
                    ["sigmoid1[1]"] = "affine2[1]",
                    ["affine2[1]"] = "sigmoid2[1]",
                    ["sigmoid2[1]"] = "affine3[1]",
                    ["affine3[1]"] = "sigmoid3[1]",
                    ["sigmoid3[1]"] = "affine4[1]",
                    ["affine4[1]"] = "sigmoid4[1]",
                    ["sigmoid4[1]"] = "affine5[1]",
                    ["affine5[1]"] = "sigmoid5[1]",
                    ["sigmoid5[1]"] = "affine6[1]",
                    ["affine6[1]"] = "sigmoid6[1]",
                    ["sigmoid6[1]"] = "affine7[1]",
                    ["affine7[1]"] = "<output>[1]"
                }
            }}
        }
    }, param_repo, gconf)

    layer_repo:add_layers(
    {
        ["nerv.DAGLayer"] =
        {
            mmi_output = {{}, {
                dim_in = {440, -1}, dim_out = {1},
                sub_layers = layer_repo,
                connections = {
                    ["<input>[1]"] = "main[1]",
                    ["main[1]"] = "mmi_crit[1]",
                    ["<input>[2]"] = "mmi_crit[2]",
                    ["mmi_crit[1]"] = "<output>[1]"
                }
            }},
            softmax_output = {{}, {
                dim_in = {440}, dim_out = {2011},
                sub_layers = layer_repo,
                connections = {
                    ["<input>[1]"] = "main[1]",
                    ["main[1]"] = "softmax[1]",
                    ["softmax[1]"] = "<output>[1]"
                }
            }}
        }
    }, param_repo, gconf)

    return layer_repo
end

function get_network(layer_repo)
    return layer_repo:get_layer("mmi_output")
end

function get_decode_network(layer_repo)
    return layer_repo:get_layer("softmax_output")
end

function get_global_transf(layer_repo)
    return layer_repo:get_layer("global_transf")
end

function make_readers(feature_rspecifier, layer_repo)
    return {
                {reader = nerv.KaldiReader(gconf,
                    {
                        id = "main_scp",
                        feature_rspecifier = feature_rspecifier,
                        frm_ext = gconf.frm_ext,
                        global_transf = layer_repo:get_layer("global_transf"),
                        mlfs = {}
                    })
                }
            }
end

function get_input_order()
    return {{id = "main_scp", global_transf = true},
            {id = "key"}}
end

function get_accuracy(layer_repo)
    return 0
end

function print_stat(layer_repo)
    local mmi_crit = layer_repo:get_layer("mmi_crit")
    nerv.info("*** training stat begin ***")
    nerv.printf("frames:\t\t\t%d\n", mmi_crit.total_frames)
    nerv.info("*** training stat end ***")
end