# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

import tvm
import numpy as np
from tvm import relay
from tvm.contrib import graph_runtime

roundings = ["UPWARD", "TONEAREST"]

def verify(mod, goldens):
    with relay.build_config(opt_level=3):
        graph, lib, params = relay.build(mod, "llvm", params=None)
        golden_data, golden_output = goldens
        rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
        rt_mod.set_input("quantized_data",golden_data)
        rt_mod.set_input(**params)
        rt_mod.run()
        res = rt_mod.get_output(0).asnumpy()
        np.testing.assert_equal(res, golden_output)

def get_mod(data_shape, data_dtype, out_dtype, input_scale, output_scale,
        input_zero_point=0, output_zero_point=0, rounding="TONEAREST"):
    quantized_data = relay.var("quantized_data", shape=data_shape,
            dtype=data_dtype)
    mod = relay.qnn.op.requantize(
            quantized_data,
            input_scale=input_scale,
            input_zero_point=input_zero_point,
            output_scale=output_scale,
            output_zero_point=output_zero_point,
            rounding=rounding,
            out_dtype=out_dtype)

    mod = relay.Function(relay.analysis.free_vars(mod), mod)
    mod = relay.Module.from_expr(mod)
    return mod

def test_same_scale():
    # Have same scales, everything within range
    golden_data = np.arange(-100, 100, 1).astype('int32')
    golden_output = golden_data

    for rounding in roundings:
        mod = get_mod(data_shape=(200, ),
                      data_dtype='int32',
                      out_dtype="int8",
                      input_scale=0.5,
                      output_scale=0.5,
                      rounding=rounding)
        assert 'right_shift' not in mod.astext()
        verify(mod, (golden_data, golden_output))

def test_downscale():
    for rounding in roundings:
        mod = get_mod(data_shape=(32, ),
                      data_dtype='int32',
                      out_dtype='int8',
                      input_scale=1,
                      output_scale=16,
                      rounding=rounding)

        # Try positive values
        # 8 corresponds to 0.5, resulting in 1
        golden_data = np.arange(0, 32, 1).astype('int32')
        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
        verify(mod, (golden_data, golden_output))

        # Try negative values
        # -8 corresponds to -0.5. For UPWARD, this is 0
        golden_data = np.arange(0, -32, -1).astype('int32')
        if rounding == "UPWARD":
            golden_output = np.repeat([0, -1, -2], [9, 16, 7])
        else:
            golden_output = np.repeat([0, -1, -2], [8, 16, 8])
        verify(mod, (golden_data, golden_output))

        # Try a different scale
        mod = get_mod(data_shape=(32, ),
                      data_dtype='int32',
                      out_dtype="int8",
                      input_scale=1,
                      output_scale=4,
                      rounding=rounding)

        # Try positive values
        # 2I corresponds to 0.5, resulting in 1
        golden_data = np.arange(0, 32, 1).astype('int32')
        golden_output = np.repeat([0, 1, 2, 3, 4, 5, 6, 7, 8],
                                  [2, 4, 4, 4, 4, 4, 4, 4, 2])
        verify(mod, (golden_data, golden_output))

        # Try negative values
        # -8 corresponds to -0.5. For UPWARD, this is 0
        golden_data = np.arange(0, -32, -1).astype('int32')
        if rounding == "UPWARD":
            golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8],
                                      [3, 4, 4, 4, 4, 4, 4, 4, 1])
        else:
            golden_output = np.repeat([0, -1, -2, -3, -4, -5, -6, -7, -8],
                                      [2, 4, 4, 4, 4, 4, 4, 4, 2])
        verify(mod, (golden_data, golden_output))

        # Try uint8 out_dtype
        mod = get_mod(data_shape=(32, ),
                      data_dtype='int32',
                      out_dtype='uint8',
                      input_scale=1,
                      output_scale=16,
                      rounding=rounding)

        # Try positive values
        # 8 corresponds to 0.5, resulting in 1
        golden_data = np.arange(0, 32, 1).astype('int32')
        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
        verify(mod, (golden_data, golden_output))

        # Try uint8 in_dtyope and uint8 out_dtype
        mod = get_mod(data_shape=(32, ),
                      data_dtype='uint8',
                      out_dtype='uint8',
                      input_scale=1,
                      output_scale=16,
                      rounding=rounding)

        # Try positive values
        # 8 corresponds to 0.5, resulting in 1
        golden_data = np.arange(0, 32, 1).astype('int32')
        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
        verify(mod, (golden_data, golden_output))

def test_upscale():
    for rounding in roundings:
        mod = get_mod(data_shape=(32, ),
                      data_dtype='int32',
                      out_dtype="int8",
                      input_scale=2,
                      output_scale=1,
                      rounding=rounding)

        # Try positive values
        # 8 corresponds to 0.5, resulting in 1
        golden_data = np.arange(0, 32, 1).astype('int32')
        golden_output = np.multiply(2, golden_data)
        verify(mod, (golden_data, golden_output))

        # Try negative values
        # -8 corresponds to -0.5. For UPWARD, this is 0
        golden_data = np.arange(0, -32, -1).astype('int32')
        golden_output = np.multiply(2, golden_data)
        verify(mod, (golden_data, golden_output))

def test_saturation():
    for rounding in roundings:
        mod = get_mod(data_shape=(16, ),
                      data_dtype='int32',
                      out_dtype="int8",
                      input_scale=0.5,
                      output_scale=0.5,
                      rounding=rounding)
        golden_data = np.arange(0, 16, 1).astype('int32')
        golden_data = np.add(120, golden_data)
        output = np.array([120, 121, 122, 123, 124, 125, 126, 127,
                           127, 127, 127, 127, 127, 127, 127, 127])
        golden_output = output
        verify(mod, (golden_data, golden_output))

        # Try negative numbers
        golden_data = np.arange(0, -16, -1).astype('int32')
        golden_data = np.add(-120, golden_data)
        output = np.array([-120, -121, -122, -123, -124, -125, -126, -127,
                           -128, -128, -128, -128, -128, -128, -128, -128])
        golden_output = output
        verify(mod, (golden_data, golden_output))

def test_zero_point():
    # Output zero point
    for rounding in roundings:
        mod = get_mod(data_shape=(32, ),
                      data_dtype='int32',
                      out_dtype='int8',
                      input_scale=1,
                      output_scale=16,
                      output_zero_point=1,
                      rounding=rounding)

        # Try positive values
        # 8 corresponds to 0.5, resulting in 1
        golden_data = np.arange(0, 32, 1).astype('int32')
        golden_output = np.repeat([0, 1, 2], [8, 16, 8])
        golden_output = np.add(1, golden_output)
        verify(mod, (golden_data, golden_output))

        # Try negative values
        # -8 corresponds to -0.5. For UPWARD, this is 0
        golden_data = np.arange(-32, -64, -1).astype('int32')
        if rounding == "UPWARD":
            golden_output = np.repeat([-2, -3, -4], [9, 16, 7])
        else:
            golden_output = np.repeat([-2, -3, -4], [8, 16, 8])
        golden_output = np.add(1, golden_output)
        verify(mod, (golden_data, golden_output))

    # Input zero point
    for rounding in roundings:
        mod = get_mod(data_shape=(32, ),
                      data_dtype='int32',
                      out_dtype='int8',
                      input_scale=1,
                      output_scale=16,
                      input_zero_point=16,
                      rounding=rounding)

        # Try positive values
        golden_data = np.arange(32, 64, 1).astype('int32')
        golden_output = np.repeat([2, 3, 4], [8, 16, 8])
        golden_output = np.subtract(golden_output, 1)
        verify(mod, (golden_data, golden_output))

        # Try negative values
        golden_data = np.arange(-32, -64, -1).astype('int32')
        if rounding == "UPWARD":
            golden_output = np.repeat([-2, -3, -4], [9, 16, 7])
        else:
            golden_output = np.repeat([-2, -3, -4], [8, 16, 8])
        golden_output = np.subtract(golden_output, 1)
        verify(mod, (golden_data, golden_output))

if __name__ == "__main__":
    test_same_scale()
    test_downscale()
    test_upscale()
    test_saturation()
    test_zero_point()
