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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
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import tvm
from tvm import te
import numpy as np
from tvm import relay
from tvm.contrib import graph_runtime

def quantize_test_driver(in_dtype, quant_args, axis, out_dtype, in_data, verify_output_data):
    shape = in_data.shape
    input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
    output_zero_point = relay.const(quant_args['out_zero_point'])
    output_scale = relay.const(quant_args['out_scale'])
    quantized_output = relay.qnn.op.quantize(input_data, output_scale=output_scale,
                                             output_zero_point=output_zero_point,
                                             axis=axis,
                                             out_dtype=out_dtype)
    mod = relay.Function(relay.analysis.free_vars(quantized_output), quantized_output)
    mod = tvm.IRModule.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 == out_dtype

def test_float32_to_uint8():
    data = np.array([-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64]) \
        .astype('float32') \
        .reshape((2,5))
    output = np.array([0, 1, 2, 3, 4, 251, 252, 253, 254, 255]) \
        .astype('uint8') \
        .reshape((2,5))
    quant_args = {"out_zero_point":np.int32(127), "out_scale": np.float32(0.5)}
    quantize_test_driver(in_dtype='float32', quant_args=quant_args, axis=-1, out_dtype='uint8',
                         in_data=data, verify_output_data=output)

def test_float32_to_int8():
    data = np.array([-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64]) \
        .astype('float32') \
        .reshape((2,5))
    output = np.array([-128, -127, -126, -125, -124, 123, 124, 125, 126, 127]) \
        .astype('int8') \
        .reshape((2,5))
    quant_args = {"out_zero_point":np.int32(-1), "out_scale":np.float32(0.5)}
    quantize_test_driver(in_dtype='float32', quant_args=quant_args, axis=-1, out_dtype='int8',
                         in_data=data, verify_output_data=output)

def test_channelwise_axis_0():
    data = np.array([-63.5, -63, -62.5, -62, -61.5, 30, 31, 31.5, 31.75, 32]) \
        .astype('float32') \
        .reshape((2,5))
    output = np.array([0, 1, 2, 3, 4, 243, 247, 249, 250, 251]) \
        .astype('uint8') \
        .reshape((2,5))
    quant_args = {"out_zero_point" : np.array([127, 123]).astype('int32'),
                  "out_scale"      : np.array([0.5, 0.25]).astype('float32')}

    quantize_test_driver(in_dtype='float32', quant_args=quant_args, axis=0, out_dtype='uint8',
                         in_data=data, verify_output_data=output)

def test_channelwise_axis_1():
    data = np.transpose(np.array([-63.5, -63, -62.5, -62, -61.5, 30, 31, 31.5, 31.75, 32]) \
                        .astype('float32').reshape((2,5)))
    output = np.transpose(np.array([0, 1, 2, 3, 4, 243, 247, 249, 250, 251]) \
                          .astype('uint8').reshape((2,5)))
    quant_args = {"out_zero_point" : np.array([127, 123]).astype('int32'),
                  "out_scale"      : np.array([0.5, 0.25]).astype('float32')}

    quantize_test_driver(in_dtype='float32', quant_args=quant_args, axis=1, out_dtype='uint8',
                         in_data=data, verify_output_data=output)


if __name__ == "__main__":
    test_float32_to_uint8()
    test_float32_to_int8()
    test_channelwise_axis_0()
    test_channelwise_axis_1()
