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import tvm
import numpy as np
from tvm import relay


def test_tflite_same_io_qnn_params():
    data_dtype = 'uint8'

    x = relay.var("x", shape=(1, 4), dtype=data_dtype)
    y = relay.var("y", shape=(1, 4), dtype=data_dtype)
    z = relay.qnn.op.add(lhs=x, rhs=y,
                         lhs_scale=relay.const(0.00784314, 'float32'),
                         lhs_zero_point=relay.const(127, 'int32'),
                         rhs_scale=relay.const(0.00784314, 'float32'),
                         rhs_zero_point=relay.const(127, 'int32'),
                         output_scale=relay.const(0.00784314, 'float32'),
                         output_zero_point=relay.const(127, 'int32'))

    func = relay.Function([x, y], z)
    mod = tvm.IRModule.from_expr(func)
    mod = relay.qnn.transform.CanonicalizeOps()(mod)
    func = mod["main"]

    x_datas = [np.array((140, 153, 165, 178)).reshape((1, 4)),
               np.array((25, 153, 178, 216)).reshape((1, 4)),
               np.array((25, 153, 216, 165)).reshape((1, 4))]
    y_datas = [np.array((204, 178, 165, 140)).reshape((1, 4)),
               np.array((204, 178, 191, 25)).reshape((1, 4)),
               np.array((204, 178, 25, 191)).reshape((1, 4))]
    golden_outputs = [np.array((217, 204, 203, 191)).reshape((1, 4)),
                      np.array((102, 204, 242, 114)).reshape((1, 4)),
                      np.array((102, 204, 114, 229)).reshape((1, 4))]

    for i in range(0, 3):
        x_data = x_datas[i]
        y_data = y_datas[i]
        golden_output = golden_outputs[i]

        intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
        op_res = intrp.evaluate(func)(x_data, y_data)
        np.testing.assert_equal(op_res.asnumpy(), golden_output)


def test_tflite_different_io_qnn_params():
    data_dtype = 'uint8'

    x = relay.var("x", shape=(1, 4), dtype=data_dtype)
    y = relay.var("y", shape=(1, 4), dtype=data_dtype)
    z = relay.qnn.op.add(lhs=x, rhs=y,
                         lhs_scale=relay.const(0.0156863, 'float32'),
                         lhs_zero_point=relay.const(127, 'int32'),
                         rhs_scale=relay.const(0.0117647, 'float32'),
                         rhs_zero_point=relay.const(85, 'int32'),
                         output_scale=relay.const(0.0235294, 'float32'),
                         output_zero_point=relay.const(128, 'int32'))

    func = relay.Function([x, y], z)
    mod = tvm.IRModule.from_expr(func)
    mod = relay.qnn.transform.CanonicalizeOps()(mod)
    func = mod["main"]

    x_datas = [np.array((76, 140, 153, 172)).reshape((1, 4)),
               np.array((133, 140, 146, 153)).reshape((1, 4)),
               np.array((76, 140, 172, 146)).reshape((1, 4))]
    y_datas = [np.array((136, 119, 128, 17)).reshape((1, 4)),
               np.array((136, 119, 111, 94)).reshape((1, 4)),
               np.array((136, 119, 17, 128)).reshape((1, 4))]
    golden_outputs = [np.array((120, 154, 167, 124)).reshape((1, 4)),
                      np.array((158, 154, 154, 150)).reshape((1, 4)),
                      np.array((120, 154, 124, 163)).reshape((1, 4))]

    for i in range(0, 3):
        x_data = x_datas[i]
        y_data = y_datas[i]
        golden_output = golden_outputs[i]

        intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
        op_res = intrp.evaluate(func)(x_data, y_data)
        np.testing.assert_equal(op_res.asnumpy(), golden_output)


def test_saturation():
    # Same params
    data_dtype = 'uint8'
    x = relay.var("x", shape=(1, 4), dtype=data_dtype)
    y = relay.var("y", shape=(1, 4), dtype=data_dtype)
    z = relay.qnn.op.add(lhs=x, rhs=y,
                         lhs_scale=relay.const(0.125, 'float32'),
                         lhs_zero_point=relay.const(0, 'int32'),
                         rhs_scale=relay.const(0.125, 'float32'),
                         rhs_zero_point=relay.const(0, 'int32'),
                         output_scale=relay.const(0.125, 'float32'),
                         output_zero_point=relay.const(0, 'int32'))

    func = relay.Function([x, y], z)
    mod = tvm.IRModule.from_expr(func)
    mod = relay.qnn.transform.CanonicalizeOps()(mod)
    func = mod["main"]

    x_data = np.array((255, 1, 1, 0)).reshape((1, 4))
    y_data = np.array((255, 255, 128, 0)).reshape((1, 4))
    golden_output = np.array((255, 255, 129, 0)).reshape((1, 4))

    intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
    op_res = intrp.evaluate(func)(x_data, y_data)
    np.testing.assert_equal(op_res.asnumpy(), golden_output)

    # Same params, different scale
    z = relay.qnn.op.add(lhs=x, rhs=y,
                         lhs_scale=relay.const(0.125, 'float32'),
                         lhs_zero_point=relay.const(0, 'int32'),
                         rhs_scale=relay.const(0.125, 'float32'),
                         rhs_zero_point=relay.const(0, 'int32'),
                         output_scale=relay.const(0.25, 'float32'),
                         output_zero_point=relay.const(0, 'int32'))

    func = relay.Function([x, y], z)
    mod = tvm.IRModule.from_expr(func)
    mod = relay.qnn.transform.CanonicalizeOps()(mod)
    func = mod["main"]

    x_data = np.array((255, 1, 1, 0)).reshape((1, 4))
    y_data = np.array((255, 255, 127, 0)).reshape((1, 4))
    golden_output = np.array((255, 129, 65, 0)).reshape((1, 4))

    intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
    op_res = intrp.evaluate(func)(x_data, y_data)
    np.testing.assert_equal(op_res.asnumpy(), golden_output)

    # Same io params, different output scale
    z = relay.qnn.op.add(lhs=x, rhs=y,
                         lhs_scale=relay.const(0.125, 'float32'),
                         lhs_zero_point=relay.const(0, 'int32'),
                         rhs_scale=relay.const(0.125, 'float32'),
                         rhs_zero_point=relay.const(0, 'int32'),
                         output_scale=relay.const(0.25, 'float32'),
                         output_zero_point=relay.const(0, 'int32'))

    func = relay.Function([x, y], z)
    mod = tvm.IRModule.from_expr(func)
    mod = relay.qnn.transform.CanonicalizeOps()(mod)
    func = mod["main"]

    x_data = np.array((255, 1, 1, 0)).reshape((1, 4))
    y_data = np.array((255, 255, 127, 0)).reshape((1, 4))
    golden_output = np.array((255, 129, 65, 0)).reshape((1, 4))

    intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
    op_res = intrp.evaluate(func)(x_data, y_data)
    np.testing.assert_equal(op_res.asnumpy(), golden_output)

    # All params different
    z = relay.qnn.op.add(lhs=x, rhs=y,
                         lhs_scale=relay.const(0.5, 'float32'),
                         lhs_zero_point=relay.const(0, 'int32'),
                         rhs_scale=relay.const(0.25, 'float32'),
                         rhs_zero_point=relay.const(0, 'int32'),
                         output_scale=relay.const(0.125, 'float32'),
                         output_zero_point=relay.const(0, 'int32'))

    func = relay.Function([x, y], z)
    mod = tvm.IRModule.from_expr(func)
    mod = relay.qnn.transform.CanonicalizeOps()(mod)
    func = mod["main"]

    x_data = np.array((255, 0, 1, 0)).reshape((1, 4))
    y_data = np.array((0, 128, 64, 0)).reshape((1, 4))
    golden_output = np.array((255, 255, 132, 0)).reshape((1, 4))

    intrp = relay.create_executor("graph", ctx=tvm.cpu(0), target="llvm")
    op_res = intrp.evaluate(func)(x_data, y_data)
    np.testing.assert_equal(op_res.asnumpy(), golden_output)


if __name__ == '__main__':
    test_tflite_same_io_qnn_params()
    test_tflite_different_io_qnn_params()
    test_saturation()
