/****************************************************************************
*
*    Copyright (c) 2021 Vivante Corporation
*
*    Permission is hereby granted, free of charge, to any person obtaining a
*    copy of this software and associated documentation files (the "Software"),
*    to deal in the Software without restriction, including without limitation
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*    and/or sell copies of the Software, and to permit persons to whom the
*    Software is furnished to do so, subject to the following conditions:
*
*    The above copyright notice and this permission notice shall be included in
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*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/activations.h"

#include "gtest/gtest.h"
#include "src/tim/vx/test_utils.h"

TEST(Linear, shape_5_1_fp32) {
    auto ctx = tim::vx::Context::Create();
    auto graph = ctx->CreateGraph();

    tim::vx::ShapeType io_shape({5, 1});
    tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
                            io_shape, tim::vx::TensorAttribute::INPUT);
    tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
                            io_shape, tim::vx::TensorAttribute::OUTPUT);

    auto input_tensor = graph->CreateTensor(input_spec);
    auto output_tensor = graph->CreateTensor(output_spec);

    std::vector<float> in_data = { -2.5, -0.1, 0, 0.55, std::numeric_limits<float>::infinity() };
    std::vector<float> golden = {-0.5, 1.9, 2, 2.55, std::numeric_limits<float>::infinity() };

    EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

    auto op = graph->CreateOperation<tim::vx::ops::Linear>(1, 2);
    (*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

    EXPECT_TRUE(graph->Compile());
    EXPECT_TRUE(graph->Run());
    std::vector<float> output(5, 0);
    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
    EXPECT_EQ(golden, output);
}

TEST(Linear, shape_5_1_fp32_omit_b) {
    auto ctx = tim::vx::Context::Create();
    auto graph = ctx->CreateGraph();

    tim::vx::ShapeType io_shape({5, 1});
    tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
                            io_shape, tim::vx::TensorAttribute::INPUT);
    tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
                            io_shape, tim::vx::TensorAttribute::OUTPUT);

    auto input_tensor = graph->CreateTensor(input_spec);
    auto output_tensor = graph->CreateTensor(output_spec);

    std::vector<float> in_data = { -2.5, -0.1, 0, 0.55, std::numeric_limits<float>::infinity() };
    std::vector<float> golden = {-5.0, -0.2, 0, 1.1, std::numeric_limits<float>::infinity() };

    EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));

    auto op = graph->CreateOperation<tim::vx::ops::Linear>(2);
    (*op).BindInputs({input_tensor}).BindOutputs({output_tensor});

    EXPECT_TRUE(graph->Compile());
    EXPECT_TRUE(graph->Run());
    std::vector<float> output(5, 0);
    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
    EXPECT_EQ(golden, output);
}

TEST(Gelu, shape_5_1_fp32) {
    auto ctx = tim::vx::Context::Create();
    auto graph = ctx->CreateGraph();

    tim::vx::ShapeType in_shape({5, 1});
    tim::vx::ShapeType out_shape({5, 1});
    tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32,
                    in_shape, tim::vx::TensorAttribute::INPUT);
    tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32,
                            out_shape, tim::vx::TensorAttribute::OUTPUT);

    auto in_tensor = graph->CreateTensor(in_spec);
    auto out_tensor = graph->CreateTensor(out_spec);

    std::vector<float> in_data = {
        -3, -1, 0, 1, 3
    };
    std::vector<float> golden = {
        -0.00404951, -0.15865529, 0, 0.8413447, 2.9959507
    };

    EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
    auto op = graph->CreateOperation<tim::vx::ops::Gelu>(false);
    (*op).BindInput(in_tensor).BindOutput(out_tensor);

    EXPECT_TRUE(graph->Compile());
    EXPECT_TRUE(graph->Run());

    std::vector<float> output(golden.size());
    EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
    EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}

TEST(Gelu, shape_5_1_fp32_approximate) {
    auto ctx = tim::vx::Context::Create();
    auto graph = ctx->CreateGraph();

    tim::vx::ShapeType in_shape({5, 1});
    tim::vx::ShapeType out_shape({5, 1});
    tim::vx::TensorSpec in_spec(tim::vx::DataType::FLOAT32,
                    in_shape, tim::vx::TensorAttribute::INPUT);
    tim::vx::TensorSpec out_spec(tim::vx::DataType::FLOAT32,
                            out_shape, tim::vx::TensorAttribute::OUTPUT);

    auto in_tensor = graph->CreateTensor(in_spec);
    auto out_tensor = graph->CreateTensor(out_spec);

    std::vector<float> in_data = {
        -3, -1, 0, 1, 3
    };
    std::vector<float> golden = {
        -0.00363752, -0.15880796, 0, 0.841192, 2.9963627
    };

    EXPECT_TRUE(in_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
    auto op = graph->CreateOperation<tim::vx::ops::Gelu>(true);
    (*op).BindInput(in_tensor).BindOutput(out_tensor);

    EXPECT_TRUE(graph->Compile());
    EXPECT_TRUE(graph->Run());

    std::vector<float> output(golden.size());
    EXPECT_TRUE(out_tensor->CopyDataFromTensor(output.data()));
    EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}

TEST(Gelu, shape_5_1_uint8_Quantized) {
    auto ctx = tim::vx::Context::Create();
    auto graph = ctx->CreateGraph();

    tim::vx::ShapeType in_shape({5, 1});
    tim::vx::ShapeType out_shape({5, 1});

    const float InputMin = -127, InputMax = 128, OutputMin = -127, OutputMax = 128;

    std::pair<float, int32_t> scalesAndZp;

    scalesAndZp = QuantizationParams<uint8_t>(InputMin, InputMax);
    std::vector<float> scalesInput = {scalesAndZp.first};   //scale
    std::vector<int32_t> zeroPointsInput = {scalesAndZp.second}; //zero point

    scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
    std::vector<float> scalesOutput = {scalesAndZp.first};
    std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};


    tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 1,
                                   scalesInput, zeroPointsInput);
    tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 1,
                                    scalesOutput, zeroPointsOutput);

    tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, in_shape,
                                 tim::vx::TensorAttribute::INPUT, quantInput);

    tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, out_shape,
                                tim::vx::TensorAttribute::OUTPUT, quantOutput);

    auto input_tensor = graph->CreateTensor(input_spec);
    auto output_tensor = graph->CreateTensor(output_spec);

    std::vector<float> in_float_data = {
        -3, -1, 0, 1, 3
    };
    std::vector<float> golden_float = {
        -0.00404951, -0.15865529, 0, 0.8413447, 2.9959507
    };

    std::vector<uint8_t> input_data =
      Quantize<uint8_t>(in_float_data, scalesInput[0], zeroPointsInput[0]);   //Quantification process
    std::vector<uint8_t> golden =
      Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);

    EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()*4));
    auto op = graph->CreateOperation<tim::vx::ops::Gelu>(false);
    (*op).BindInput(input_tensor).BindOutput(output_tensor);

    EXPECT_TRUE(graph->Compile());
    EXPECT_TRUE(graph->Run());
    std::vector<uint8_t> output(golden.size());

    EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
    EXPECT_TRUE(ArraysMatch(golden, output, (uint8_t)1));
}