/*
 * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
 * SPDX-License-Identifier: Apache-2.0
 *
 * Licensed 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.
 */
#include "AdProcessing.hpp"

#include "AdModel.hpp"

namespace arm {
namespace app {

AdPreProcess::AdPreProcess(TfLiteTensor* inputTensor,
                           uint32_t melSpectrogramFrameLen,
                           uint32_t melSpectrogramFrameStride,
                           float adModelTrainingMean):
       m_validInstance{false},
       m_melSpectrogramFrameLen{melSpectrogramFrameLen},
       m_melSpectrogramFrameStride{melSpectrogramFrameStride},
        /**< Model is trained on features downsampled 2x */
       m_inputResizeScale{2},
        /**< We are choosing to move by 20 frames across the audio for each inference. */
       m_numMelSpecVectorsInAudioStride{20},
       m_audioDataStride{m_numMelSpecVectorsInAudioStride * melSpectrogramFrameStride},
       m_melSpec{melSpectrogramFrameLen}
{
    UNUSED(this->m_melSpectrogramFrameStride);

    if (!inputTensor) {
        printf_err("Invalid input tensor provided to pre-process\n");
        return;
    }

    TfLiteIntArray* inputShape = inputTensor->dims;

    if (!inputShape) {
        printf_err("Invalid input tensor dims\n");
        return;
    }

    const uint32_t kNumRows = inputShape->data[AdModel::ms_inputRowsIdx];
    const uint32_t kNumCols = inputShape->data[AdModel::ms_inputColsIdx];

    /* Deduce the data length required for 1 inference from the network parameters. */
    this->m_audioDataWindowSize = (((this->m_inputResizeScale * kNumCols) - 1) *
                                    melSpectrogramFrameStride) +
                                    melSpectrogramFrameLen;
    this->m_numReusedFeatureVectors = kNumRows -
                                      (this->m_numMelSpecVectorsInAudioStride /
                                       this->m_inputResizeScale);
    this->m_melSpec.Init();

    /* Creating a Mel Spectrogram sliding window for the data required for 1 inference.
     * "resizing" done here by multiplying stride by resize scale. */
    this->m_melWindowSlider = audio::SlidingWindow<const int16_t>(
            nullptr, /* to be populated later. */
            this->m_audioDataWindowSize,
            melSpectrogramFrameLen,
            melSpectrogramFrameStride * this->m_inputResizeScale);

    /* Construct feature calculation function. */
    this->m_featureCalc = GetFeatureCalculator(this->m_melSpec, inputTensor,
                                               this->m_numReusedFeatureVectors,
                                               adModelTrainingMean);
    this->m_validInstance = true;
}

bool AdPreProcess::DoPreProcess(const void* input, size_t inputSize)
{
    /* Check that we have a valid instance. */
    if (!this->m_validInstance) {
        printf_err("Invalid pre-processor instance\n");
        return false;
    }

    /* We expect that we can traverse the size with which the MEL spectrogram
     * sliding window was initialised with. */
    if (!input || inputSize < this->m_audioDataWindowSize) {
        printf_err("Invalid input provided for pre-processing\n");
        return false;
    }

    /* We moved to the next window - set the features sliding to the new address. */
    this->m_melWindowSlider.Reset(static_cast<const int16_t*>(input));

    /* The first window does not have cache ready. */
    const bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedFeatureVectors > 0;

    /* Start calculating features inside one audio sliding window. */
    while (this->m_melWindowSlider.HasNext()) {
        const int16_t* melSpecWindow = this->m_melWindowSlider.Next();
        std::vector<int16_t> melSpecAudioData = std::vector<int16_t>(
                melSpecWindow,
                melSpecWindow + this->m_melSpectrogramFrameLen);

        /* Compute features for this window and write them to input tensor. */
        this->m_featureCalc(melSpecAudioData,
                            this->m_melWindowSlider.Index(),
                            useCache,
                            this->m_numMelSpecVectorsInAudioStride,
                            this->m_inputResizeScale);
    }

    return true;
}

uint32_t AdPreProcess::GetAudioWindowSize()
{
    return this->m_audioDataWindowSize;
}

uint32_t AdPreProcess::GetAudioDataStride()
{
    return this->m_audioDataStride;
}

void AdPreProcess::SetAudioWindowIndex(uint32_t idx)
{
    this->m_audioWindowIndex = idx;
}

AdPostProcess::AdPostProcess(TfLiteTensor* outputTensor) :
    m_outputTensor {outputTensor}
{}

bool AdPostProcess::DoPostProcess()
{
    switch (this->m_outputTensor->type) {
        case kTfLiteInt8:
            this->Dequantize<int8_t>();
            break;
        default:
            printf_err("Unsupported tensor type");
            return false;
    }

    math::MathUtils::SoftmaxF32(this->m_dequantizedOutputVec);
    return true;
}

float AdPostProcess::GetOutputValue(uint32_t index)
{
    if (index < this->m_dequantizedOutputVec.size()) {
        return this->m_dequantizedOutputVec[index];
    }
    printf_err("Invalid index for output\n");
    return 0.0;
}

std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)>
GetFeatureCalculator(audio::AdMelSpectrogram& melSpec,
                     TfLiteTensor* inputTensor,
                     size_t cacheSize,
                     float trainingMean)
{
    std::function<void (std::vector<int16_t>&, size_t, bool, size_t, size_t)> melSpecFeatureCalc = nullptr;

    TfLiteQuantization quant = inputTensor->quantization;

    if (kTfLiteAffineQuantization == quant.type) {

        auto* quantParams = static_cast<TfLiteAffineQuantization*>(quant.params);
        const float quantScale = quantParams->scale->data[0];
        const int quantOffset = quantParams->zero_point->data[0];

        switch (inputTensor->type) {
            case kTfLiteInt8: {
                melSpecFeatureCalc = FeatureCalc<int8_t>(
                        inputTensor,
                        cacheSize,
                        [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
                            return melSpec.MelSpecComputeQuant<int8_t>(
                                    audioDataWindow,
                                    quantScale,
                                    quantOffset,
                                    trainingMean);
                        }
                );
                break;
            }
            default:
            printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
        }
    } else {
        melSpecFeatureCalc = FeatureCalc<float>(
                inputTensor,
                cacheSize,
                [=, &melSpec](
                        std::vector<int16_t>& audioDataWindow) {
                    return melSpec.ComputeMelSpec(
                            audioDataWindow,
                            trainingMean);
                });
    }
    return melSpecFeatureCalc;
}

} /* namespace app */
} /* namespace arm */