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import pytest

import numpy as np
from onnx import TensorProto, helper
from qonnx.core.datatype import DataType
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.custom_op.general.multithreshold import multithreshold
from qonnx.custom_op.registry import getCustomOp
from qonnx.transformation.general import GiveUniqueNodeNames
from qonnx.util.basic import gen_finn_dt_tensor

import finn.core.onnx_exec as oxe
from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode


def _infer_sparse_weight_tensor(W_conv, k_h, k_w, channels):
    W_sparse = np.zeros((channels, channels, k_h, k_w), dtype=np.float32)
    for ch in range(channels):
        W_sparse[ch][ch] = W_conv[ch][0]
    W_conv = W_sparse.astype(np.float32)
    W_matmul = W_conv.transpose(0, 2, 3, 1)
    W_matmul = W_matmul.reshape(channels, channels * k_h * k_w)
    W_matmul = W_matmul.T

    return W_matmul


def _calculate_dot_prod_range(dt_a, dt_b, len):
    """Returns the (min,max) values a dot product between two (un)signed vectors of
    types dt_a and dt_b of len elements can take."""
    min_prod = 2**30
    max_prod = -(2**30)
    for a_val in [dt_a.min(), dt_a.max()]:
        for b_val in [dt_b.min(), dt_b.max()]:
            prod = a_val * b_val * len
            if prod < min_prod:
                min_prod = prod
            if prod > max_prod:
                max_prod = prod
    return (min_prod, max_prod)


def _make_single_vvau_modelwrapper(
    W,
    pe,
    k_h,
    k_w,
    channels,
    dim_h,
    dim_w,
    wdt,
    idt,
    odt,
    T=None,
    tdt=None,
    mem_mode="const",
):
    in_shape = [1, dim_h, dim_w, k_h * k_w * channels]  # [N, H, W, K*K*CH]
    out_shape = [
        1,
        dim_h,
        dim_w,
        channels,
    ]  # [N, H, W, OFM_CH] (OFM_CH=IFM_CH because depthwise convolution)

    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, in_shape)
    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, out_shape)

    if T is not None:
        no_act = 0
        node_inp_list = ["inp", "weights", "thresh"]
        actval = odt.min()
    else:
        no_act = 1
        node_inp_list = ["inp", "weights"]
        actval = 0

    VVAU_node = helper.make_node(
        "VectorVectorActivation",
        node_inp_list,
        ["outp"],
        domain="finn.custom_op.fpgadataflow",
        backend="fpgadataflow",
        PE=pe,
        Dim=[dim_h, dim_w],
        Channels=channels,
        Kernel=[k_h, k_w],
        resType="lut",
        ActVal=actval,
        inputDataType=idt.name,
        weightDataType=wdt.name,
        outputDataType=odt.name,
        noActivation=no_act,
        mem_mode=mem_mode,
    )

    graph = helper.make_graph(
        nodes=[VVAU_node], name="vvau_graph", inputs=[inp], outputs=[outp]
    )

    model = helper.make_model(graph, producer_name="vvau-model")
    model = ModelWrapper(model)

    model.set_tensor_datatype("inp", idt)
    model.set_tensor_datatype("outp", odt)
    model.set_tensor_datatype("weights", wdt)

    model.set_initializer("weights", W)
    model.set_tensor_shape("weights", (channels, 1, k_h, k_w))

    if T is not None:
        model.set_tensor_datatype("thresh", tdt)
        model.set_initializer("thresh", T)

    return model


def prepare_inputs(input_tensor):
    return {"inp": input_tensor}


# input datatype
@pytest.mark.parametrize("idt", [DataType["UINT4"], DataType["UINT8"]])
# weight datatype
@pytest.mark.parametrize("wdt", [DataType["INT4"]])
# activation: None or DataType
@pytest.mark.parametrize("act", [DataType["UINT4"], None])
# PE
@pytest.mark.parametrize("pe", [1, "channels"])
# Input image shape
@pytest.mark.parametrize("dim_h", [10])
@pytest.mark.parametrize("dim_w", [10, 1])
# Kernel shape
@pytest.mark.parametrize("k_h", [3])
@pytest.mark.parametrize("k_w", [3, 1])
# Number of input and output channels
@pytest.mark.parametrize("channels", [3, 4])
# memory mode
@pytest.mark.parametrize("mem_mode", ["const", "decoupled"])
# execution mode
@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
@pytest.mark.fpgadataflow
@pytest.mark.slow
@pytest.mark.vivado
def test_fpgadataflow_vvau(
    idt, wdt, act, pe, dim_h, dim_w, k_h, k_w, channels, mem_mode, exec_mode
):
    if pe == "channels":
        pe = channels

    if dim_w == 1 and k_w != 1:
        pytest.skip("1D image requires 1D kernel, skipping.")

    if channels % pe != 0:
        pytest.skip("Requirement Channels divisable by PE is violated.")

    # Generate weights in expected shape for ONNX and HLS node
    W = gen_finn_dt_tensor(wdt, (channels, 1, k_h, k_w))  # shape: [channels, 1, k, k]
    W_onnx = _infer_sparse_weight_tensor(
        W, k_h, k_w, channels
    )  # shape: [k*k*channels, channels]

    # Generate inputs in expected format for ONNX and HLS node
    x = gen_finn_dt_tensor(idt, (1, dim_h, dim_w, k_h * k_w * channels))
    x_vvau = x.reshape(1, dim_h, dim_w, k_h * k_w, channels // pe, pe)
    x_vvau = x_vvau.transpose(0, 1, 2, 4, 3, 5)
    x_vvau = x_vvau.reshape(1, dim_h, dim_w, channels * k_h * k_w)

    if act is None:
        T = None
        tdt = None
        odt = DataType["INT32"]
    else:
        odt = act
        (min_v, max_v) = _calculate_dot_prod_range(idt, wdt, k_h * k_w * channels)
        n_steps = act.get_num_possible_values() - 1
        T = np.random.randint(min_v, max_v - 1, (channels, n_steps)).astype(np.float32)
        T = np.sort(T, axis=1)
        tdt = DataType["INT32"]

    model = _make_single_vvau_modelwrapper(
        W, pe, k_h, k_w, channels, dim_h, dim_w, wdt, idt, odt, T, tdt, mem_mode
    )

    if exec_mode == "cppsim":
        model = model.transform(SetExecMode("cppsim"))
        model = model.transform(PrepareCppSim())
        model = model.transform(CompileCppSim())
    elif exec_mode == "rtlsim":
        model = model.transform(SetExecMode("rtlsim"))
        model = model.transform(GiveUniqueNodeNames())
        model = model.transform(PrepareIP("xc7z020clg400-1", 5))
        model = model.transform(HLSSynthIP())
        model = model.transform(PrepareRTLSim())
    else:
        raise Exception("Unknown exec_mode in test_fpgadataflow_vvau")

    input_dict = prepare_inputs(x_vvau)

    # Calculate output
    y_expected = np.matmul(x, W_onnx)  # Y is in [N, H, W, C] format
    if T is not None:
        # Reshape Y, as multithreshold expects Y to be in [N, C, H, W] format
        y_expected = np.transpose(y_expected, (0, 3, 1, 2))
        y_expected = multithreshold(y_expected, T)
        y_expected = np.transpose(y_expected, (0, 2, 3, 1))
        # signed offset
        y_expected += act.min()

    y_produced = oxe.execute_onnx(model, input_dict, return_full_exec_context=False)[
        "outp"
    ]

    assert (y_produced == y_expected).all(), "cppsim failed"

    if exec_mode == "rtlsim":
        node = model.get_nodes_by_op_type("VectorVectorActivation")[0]
        inst = getCustomOp(node)
        cycles_rtlsim = inst.get_nodeattr("cycles_rtlsim")
        exp_cycles_dict = model.analysis(exp_cycles_per_layer)
        exp_cycles = exp_cycles_dict[node.name]
        assert np.isclose(exp_cycles, cycles_rtlsim, atol=10)
        assert exp_cycles != 0