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#
#   http://www.apache.org/licenses/LICENSE-2.0
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import tvm
import numpy as np
from tvm import relay
from tvm.contrib import graph_runtime

def test_dequantize_op():

    def quantize_test_driver(in_dtype, quant_args, in_data, verify_output_data):
        shape = in_data.shape
        input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
        input_zero_point = quant_args['in_zero_point']
        input_scale = quant_args['in_scale']
        quantized_output = relay.qnn.op.dequantize(input_data, input_scale=input_scale,
                                                   input_zero_point=input_zero_point)
        mod = relay.Function(relay.analysis.free_vars(quantized_output), quantized_output)
        mod = relay.Module.from_expr(mod)
        with relay.build_config(opt_level=3):
            graph, lib, params = relay.build(mod, "llvm", params=None)
            rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
            rt_mod.set_input(input_data=in_data)
            rt_mod.set_input(**params)
            rt_mod.run()
            res = rt_mod.get_output(0).asnumpy()
            np.testing.assert_equal(res, verify_output_data)
            assert res.dtype == np.float32

    def test_uint8_to_float32():
        data = np.array([0, 1, 2, 3, 4, 251, 252, 253, 254, 255]) \
            .astype('uint8') \
            .reshape((2, 5))
        output = np.array([-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64]) \
            .astype('float32') \
            .reshape((2, 5))
        quant_args = {"in_zero_point":127, "in_scale":0.5}
        quantize_test_driver(in_dtype='uint8', quant_args=quant_args, in_data=data,
                             verify_output_data=output)

    def test_int8_to_float32():
        data = np.array([-128, -127, -126, -125, -124, 123, 124, 125, 126, 127]) \
            .astype('int8') \
            .reshape((2, 5))
        output = np.array([-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64]) \
            .astype('float32') \
            .reshape((2, 5))
        quant_args = {"in_zero_point": -1, "in_scale": 0.5}
        quantize_test_driver(in_dtype='int8', quant_args=quant_args, in_data=data,
                             verify_output_data=output)

    def test_int32_to_float32():
        data = np.array([113, 29, -1052]).astype('int32')
        output = np.array([0.6550452, 0.16810896, -6.098297]).astype('float32')
        quant_args = {"in_zero_point": 0, "in_scale": 0.0057968604}
        quantize_test_driver(in_dtype='int32', quant_args=quant_args, in_data=data,
                             verify_output_data=output)

    test_uint8_to_float32()
    test_int8_to_float32()
    test_int32_to_float32()


if __name__ == "__main__":
    test_dequantize_op()

