/*
  ==============================================================================

   This file is part of the JUCE framework.
   Copyright (c) Raw Material Software Limited

   JUCE is an open source framework subject to commercial or open source
   licensing.

   By downloading, installing, or using the JUCE framework, or combining the
   JUCE framework with any other source code, object code, content or any other
   copyrightable work, you agree to the terms of the JUCE End User Licence
   Agreement, and all incorporated terms including the JUCE Privacy Policy and
   the JUCE Website Terms of Service, as applicable, which will bind you. If you
   do not agree to the terms of these agreements, we will not license the JUCE
   framework to you, and you must discontinue the installation or download
   process and cease use of the JUCE framework.

   JUCE End User Licence Agreement: https://juce.com/legal/juce-8-licence/
   JUCE Privacy Policy: https://juce.com/juce-privacy-policy
   JUCE Website Terms of Service: https://juce.com/juce-website-terms-of-service/

   Or:

   You may also use this code under the terms of the AGPLv3:
   https://www.gnu.org/licenses/agpl-3.0.en.html

   THE JUCE FRAMEWORK IS PROVIDED "AS IS" WITHOUT ANY WARRANTY, AND ALL
   WARRANTIES, WHETHER EXPRESSED OR IMPLIED, INCLUDING WARRANTY OF
   MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, ARE DISCLAIMED.

  ==============================================================================
*/

namespace juce::dsp
{

template <typename Element>
class Queue
{
public:
    explicit Queue (int size)
        : fifo (size), storage (static_cast<size_t> (size)) {}

    bool push (Element& element) noexcept
    {
        if (fifo.getFreeSpace() == 0)
            return false;

        const auto writer = fifo.write (1);

        if (writer.blockSize1 != 0)
            storage[static_cast<size_t> (writer.startIndex1)] = std::move (element);
        else if (writer.blockSize2 != 0)
            storage[static_cast<size_t> (writer.startIndex2)] = std::move (element);

        return true;
    }

    template <typename Fn>
    void pop (Fn&& fn) { popN (1, std::forward<Fn> (fn)); }

    template <typename Fn>
    void popAll (Fn&& fn) { popN (fifo.getNumReady(), std::forward<Fn> (fn)); }

    bool hasPendingMessages() const noexcept { return fifo.getNumReady() > 0; }

private:
    template <typename Fn>
    void popN (int n, Fn&& fn)
    {
        fifo.read (n).forEach ([&] (int index)
                               {
                                   fn (storage[static_cast<size_t> (index)]);
                               });
    }

    AbstractFifo fifo;
    std::vector<Element> storage;
};

class BackgroundMessageQueue : private Thread
{
public:
    explicit BackgroundMessageQueue (int entries)
        : Thread ("Convolution background loader"), queue (entries)
    {}

    using IncomingCommand = FixedSizeFunction<400, void()>;

    // Push functions here, and they'll be called later on a background thread.
    // This function is wait-free.
    // This function is only safe to call from a single thread at a time.
    bool push (IncomingCommand& command) { return queue.push (command); }

    void popAll()
    {
        const ScopedLock lock (popMutex);
        queue.popAll ([] (IncomingCommand& command) { command(); command = nullptr; });
    }

    using Thread::startThread;
    using Thread::stopThread;

private:
    void run() override
    {
        while (! threadShouldExit())
        {
            const auto tryPop = [&]
            {
                const ScopedLock lock (popMutex);

                if (! queue.hasPendingMessages())
                    return false;

                queue.pop ([] (IncomingCommand& command) { command(); command = nullptr;});
                return true;
            };

            if (! tryPop())
                sleep (10);
        }
    }

    CriticalSection popMutex;
    Queue<IncomingCommand> queue;

    JUCE_DECLARE_NON_COPYABLE_WITH_LEAK_DETECTOR (BackgroundMessageQueue)
};

struct ConvolutionMessageQueue::Impl  : public BackgroundMessageQueue
{
    using BackgroundMessageQueue::BackgroundMessageQueue;
};

ConvolutionMessageQueue::ConvolutionMessageQueue()
    : ConvolutionMessageQueue (1000)
{}

ConvolutionMessageQueue::ConvolutionMessageQueue (int entries)
    : pimpl (std::make_unique<Impl> (entries))
{
    pimpl->startThread();
}

ConvolutionMessageQueue::~ConvolutionMessageQueue() noexcept
{
    pimpl->stopThread (-1);
}

ConvolutionMessageQueue::ConvolutionMessageQueue (ConvolutionMessageQueue&&) noexcept = default;
ConvolutionMessageQueue& ConvolutionMessageQueue::operator= (ConvolutionMessageQueue&&) noexcept = default;

//==============================================================================
struct ConvolutionEngine
{
    ConvolutionEngine (const float* samples,
                       size_t numSamples,
                       size_t maxBlockSize)
        : blockSize ((size_t) nextPowerOfTwo ((int) maxBlockSize)),
          fftSize (blockSize > 128 ? 2 * blockSize : 4 * blockSize),
          fftObject (std::make_unique<FFT> (roundToInt (std::log2 (fftSize)))),
          numSegments (numSamples / (fftSize - blockSize) + 1u),
          numInputSegments ((blockSize > 128 ? numSegments : 3 * numSegments)),
          bufferInput      (1, static_cast<int> (fftSize)),
          bufferOutput     (1, static_cast<int> (fftSize * 2)),
          bufferTempOutput (1, static_cast<int> (fftSize * 2)),
          bufferOverlap    (1, static_cast<int> (fftSize))
    {
        bufferOutput.clear();

        auto updateSegmentsIfNecessary = [this] (size_t numSegmentsToUpdate,
                                                 std::vector<AudioBuffer<float>>& segments)
        {
            if (numSegmentsToUpdate == 0
                || numSegmentsToUpdate != (size_t) segments.size()
                || (size_t) segments[0].getNumSamples() != fftSize * 2)
            {
                segments.clear();

                for (size_t i = 0; i < numSegmentsToUpdate; ++i)
                    segments.push_back ({ 1, static_cast<int> (fftSize * 2) });
            }
        };

        updateSegmentsIfNecessary (numInputSegments, buffersInputSegments);
        updateSegmentsIfNecessary (numSegments,      buffersImpulseSegments);

        auto FFTTempObject = std::make_unique<FFT> (roundToInt (std::log2 (fftSize)));
        size_t currentPtr = 0;

        for (auto& buf : buffersImpulseSegments)
        {
            buf.clear();

            auto* impulseResponse = buf.getWritePointer (0);

            if (&buf == &buffersImpulseSegments.front())
                impulseResponse[0] = 1.0f;

            FloatVectorOperations::copy (impulseResponse,
                                         samples + currentPtr,
                                         static_cast<int> (jmin (fftSize - blockSize, numSamples - currentPtr)));

            FFTTempObject->performRealOnlyForwardTransform (impulseResponse);
            prepareForConvolution (impulseResponse);

            currentPtr += (fftSize - blockSize);
        }

        reset();
    }

    void reset()
    {
        bufferInput.clear();
        bufferOverlap.clear();
        bufferTempOutput.clear();
        bufferOutput.clear();

        for (auto& buf : buffersInputSegments)
            buf.clear();

        currentSegment = 0;
        inputDataPos = 0;
    }

    void processSamples (const float* input, float* output, size_t numSamples)
    {
        // Overlap-add, zero latency convolution algorithm with uniform partitioning
        size_t numSamplesProcessed = 0;

        auto indexStep = numInputSegments / numSegments;

        auto* inputData      = bufferInput.getWritePointer (0);
        auto* outputTempData = bufferTempOutput.getWritePointer (0);
        auto* outputData     = bufferOutput.getWritePointer (0);
        auto* overlapData    = bufferOverlap.getWritePointer (0);

        while (numSamplesProcessed < numSamples)
        {
            const bool inputDataWasEmpty = (inputDataPos == 0);
            auto numSamplesToProcess = jmin (numSamples - numSamplesProcessed, blockSize - inputDataPos);

            FloatVectorOperations::copy (inputData + inputDataPos, input + numSamplesProcessed, static_cast<int> (numSamplesToProcess));

            auto* inputSegmentData = buffersInputSegments[currentSegment].getWritePointer (0);
            FloatVectorOperations::copy (inputSegmentData, inputData, static_cast<int> (fftSize));

            fftObject->performRealOnlyForwardTransform (inputSegmentData);
            prepareForConvolution (inputSegmentData);

            // Complex multiplication
            if (inputDataWasEmpty)
            {
                FloatVectorOperations::fill (outputTempData, 0, static_cast<int> (fftSize + 1));

                auto index = currentSegment;

                for (size_t i = 1; i < numSegments; ++i)
                {
                    index += indexStep;

                    if (index >= numInputSegments)
                        index -= numInputSegments;

                    convolutionProcessingAndAccumulate (buffersInputSegments[index].getWritePointer (0),
                                                        buffersImpulseSegments[i].getWritePointer (0),
                                                        outputTempData);
                }
            }

            FloatVectorOperations::copy (outputData, outputTempData, static_cast<int> (fftSize + 1));

            convolutionProcessingAndAccumulate (inputSegmentData,
                                                buffersImpulseSegments.front().getWritePointer (0),
                                                outputData);

            updateSymmetricFrequencyDomainData (outputData);
            fftObject->performRealOnlyInverseTransform (outputData);

            // Add overlap
            FloatVectorOperations::add (&output[numSamplesProcessed], &outputData[inputDataPos], &overlapData[inputDataPos], (int) numSamplesToProcess);

            // Input buffer full => Next block
            inputDataPos += numSamplesToProcess;

            if (inputDataPos == blockSize)
            {
                // Input buffer is empty again now
                FloatVectorOperations::fill (inputData, 0.0f, static_cast<int> (fftSize));

                inputDataPos = 0;

                // Extra step for segSize > blockSize
                FloatVectorOperations::add (&(outputData[blockSize]), &(overlapData[blockSize]), static_cast<int> (fftSize - 2 * blockSize));

                // Save the overlap
                FloatVectorOperations::copy (overlapData, &(outputData[blockSize]), static_cast<int> (fftSize - blockSize));

                currentSegment = (currentSegment > 0) ? (currentSegment - 1) : (numInputSegments - 1);
            }

            numSamplesProcessed += numSamplesToProcess;
        }
    }

    void processSamplesWithAddedLatency (const float* input, float* output, size_t numSamples)
    {
        // Overlap-add, zero latency convolution algorithm with uniform partitioning
        size_t numSamplesProcessed = 0;

        auto indexStep = numInputSegments / numSegments;

        auto* inputData      = bufferInput.getWritePointer (0);
        auto* outputTempData = bufferTempOutput.getWritePointer (0);
        auto* outputData     = bufferOutput.getWritePointer (0);
        auto* overlapData    = bufferOverlap.getWritePointer (0);

        while (numSamplesProcessed < numSamples)
        {
            auto numSamplesToProcess = jmin (numSamples - numSamplesProcessed, blockSize - inputDataPos);

            FloatVectorOperations::copy (inputData + inputDataPos, input + numSamplesProcessed, static_cast<int> (numSamplesToProcess));

            FloatVectorOperations::copy (output + numSamplesProcessed, outputData + inputDataPos, static_cast<int> (numSamplesToProcess));

            numSamplesProcessed += numSamplesToProcess;
            inputDataPos += numSamplesToProcess;

            // processing itself when needed (with latency)
            if (inputDataPos == blockSize)
            {
                // Copy input data in input segment
                auto* inputSegmentData = buffersInputSegments[currentSegment].getWritePointer (0);
                FloatVectorOperations::copy (inputSegmentData, inputData, static_cast<int> (fftSize));

                fftObject->performRealOnlyForwardTransform (inputSegmentData);
                prepareForConvolution (inputSegmentData);

                // Complex multiplication
                FloatVectorOperations::fill (outputTempData, 0, static_cast<int> (fftSize + 1));

                auto index = currentSegment;

                for (size_t i = 1; i < numSegments; ++i)
                {
                    index += indexStep;

                    if (index >= numInputSegments)
                        index -= numInputSegments;

                    convolutionProcessingAndAccumulate (buffersInputSegments[index].getWritePointer (0),
                                                        buffersImpulseSegments[i].getWritePointer (0),
                                                        outputTempData);
                }

                FloatVectorOperations::copy (outputData, outputTempData, static_cast<int> (fftSize + 1));

                convolutionProcessingAndAccumulate (inputSegmentData,
                                                    buffersImpulseSegments.front().getWritePointer (0),
                                                    outputData);

                updateSymmetricFrequencyDomainData (outputData);
                fftObject->performRealOnlyInverseTransform (outputData);

                // Add overlap
                FloatVectorOperations::add (outputData, overlapData, static_cast<int> (blockSize));

                // Input buffer is empty again now
                FloatVectorOperations::fill (inputData, 0.0f, static_cast<int> (fftSize));

                // Extra step for segSize > blockSize
                FloatVectorOperations::add (&(outputData[blockSize]), &(overlapData[blockSize]), static_cast<int> (fftSize - 2 * blockSize));

                // Save the overlap
                FloatVectorOperations::copy (overlapData, &(outputData[blockSize]), static_cast<int> (fftSize - blockSize));

                currentSegment = (currentSegment > 0) ? (currentSegment - 1) : (numInputSegments - 1);

                inputDataPos = 0;
            }
        }
    }

    // After each FFT, this function is called to allow convolution to be performed with only 4 SIMD functions calls.
    void prepareForConvolution (float *samples) noexcept
    {
        auto FFTSizeDiv2 = fftSize / 2;

        for (size_t i = 0; i < FFTSizeDiv2; i++)
            samples[i] = samples[i << 1];

        samples[FFTSizeDiv2] = 0;

        for (size_t i = 1; i < FFTSizeDiv2; i++)
            samples[i + FFTSizeDiv2] = -samples[((fftSize - i) << 1) + 1];
    }

    // Does the convolution operation itself only on half of the frequency domain samples.
    void convolutionProcessingAndAccumulate (const float *input, const float *impulse, float *output)
    {
        auto FFTSizeDiv2 = fftSize / 2;

        FloatVectorOperations::addWithMultiply      (output, input, impulse, static_cast<int> (FFTSizeDiv2));
        FloatVectorOperations::subtractWithMultiply (output, &(input[FFTSizeDiv2]), &(impulse[FFTSizeDiv2]), static_cast<int> (FFTSizeDiv2));

        FloatVectorOperations::addWithMultiply      (&(output[FFTSizeDiv2]), input, &(impulse[FFTSizeDiv2]), static_cast<int> (FFTSizeDiv2));
        FloatVectorOperations::addWithMultiply      (&(output[FFTSizeDiv2]), &(input[FFTSizeDiv2]), impulse, static_cast<int> (FFTSizeDiv2));

        output[fftSize] += input[fftSize] * impulse[fftSize];
    }

    // Undoes the re-organization of samples from the function prepareForConvolution.
    // Then takes the conjugate of the frequency domain first half of samples to fill the
    // second half, so that the inverse transform will return real samples in the time domain.
    void updateSymmetricFrequencyDomainData (float* samples) noexcept
    {
        auto FFTSizeDiv2 = fftSize / 2;

        for (size_t i = 1; i < FFTSizeDiv2; i++)
        {
            samples[(fftSize - i) << 1] = samples[i];
            samples[((fftSize - i) << 1) + 1] = -samples[FFTSizeDiv2 + i];
        }

        samples[1] = 0.f;

        for (size_t i = 1; i < FFTSizeDiv2; i++)
        {
            samples[i << 1] = samples[(fftSize - i) << 1];
            samples[(i << 1) + 1] = -samples[((fftSize - i) << 1) + 1];
        }
    }

    //==============================================================================
    const size_t blockSize;
    const size_t fftSize;
    const std::unique_ptr<FFT> fftObject;
    const size_t numSegments;
    const size_t numInputSegments;
    size_t currentSegment = 0, inputDataPos = 0;

    AudioBuffer<float> bufferInput, bufferOutput, bufferTempOutput, bufferOverlap;
    std::vector<AudioBuffer<float>> buffersInputSegments, buffersImpulseSegments;
};

//==============================================================================
class MultichannelEngine
{
public:
    MultichannelEngine (const AudioBuffer<float>& buf,
                        int maxBlockSize,
                        int maxBufferSize,
                        Convolution::NonUniform headSizeIn,
                        bool isZeroDelayIn)
        : tailBuffer (1, maxBlockSize),
          latency (isZeroDelayIn ? 0 : maxBufferSize),
          irSize (buf.getNumSamples()),
          blockSize (maxBlockSize),
          isZeroDelay (isZeroDelayIn)
    {
        constexpr auto numChannels = 2;

        const auto makeEngine = [&] (int channel, int offset, int length, uint32 thisBlockSize)
        {
            return std::make_unique<ConvolutionEngine> (buf.getReadPointer (jmin (buf.getNumChannels() - 1, channel), offset),
                                                        length,
                                                        static_cast<size_t> (thisBlockSize));
        };

        if (headSizeIn.headSizeInSamples == 0)
        {
            for (int i = 0; i < numChannels; ++i)
                head.emplace_back (makeEngine (i, 0, buf.getNumSamples(), static_cast<uint32> (maxBufferSize)));
        }
        else
        {
            const auto size = jmin (buf.getNumSamples(), headSizeIn.headSizeInSamples);

            for (int i = 0; i < numChannels; ++i)
                head.emplace_back (makeEngine (i, 0, size, static_cast<uint32> (maxBufferSize)));

            const auto tailBufferSize = static_cast<uint32> (headSizeIn.headSizeInSamples + (isZeroDelay ? 0 : maxBufferSize));

            if (size != buf.getNumSamples())
                for (int i = 0; i < numChannels; ++i)
                    tail.emplace_back (makeEngine (i, size, buf.getNumSamples() - size, tailBufferSize));
        }
    }

    void reset()
    {
        for (const auto& e : head)
            e->reset();

        for (const auto& e : tail)
            e->reset();
    }

    void processSamples (const AudioBlock<const float>& input, AudioBlock<float>& output)
    {
        const auto numChannels = jmin (head.size(), input.getNumChannels(), output.getNumChannels());
        const auto numSamples  = jmin (input.getNumSamples(), output.getNumSamples());

        const AudioBlock<float> fullTailBlock (tailBuffer);
        const auto tailBlock = fullTailBlock.getSubBlock (0, (size_t) numSamples);

        const auto isUniform = tail.empty();

        for (size_t channel = 0; channel < numChannels; ++channel)
        {
            if (! isUniform)
                tail[channel]->processSamplesWithAddedLatency (input.getChannelPointer (channel),
                                                               tailBlock.getChannelPointer (0),
                                                               numSamples);

            if (isZeroDelay)
                head[channel]->processSamples (input.getChannelPointer (channel),
                                               output.getChannelPointer (channel),
                                               numSamples);
            else
                head[channel]->processSamplesWithAddedLatency (input.getChannelPointer (channel),
                                                               output.getChannelPointer (channel),
                                                               numSamples);

            if (! isUniform)
                output.getSingleChannelBlock (channel) += tailBlock;
        }

        const auto numOutputChannels = output.getNumChannels();

        for (auto i = numChannels; i < numOutputChannels; ++i)
            output.getSingleChannelBlock (i).copyFrom (output.getSingleChannelBlock (0));
    }

    int getIRSize() const noexcept     { return irSize; }
    int getLatency() const noexcept    { return latency; }
    int getBlockSize() const noexcept  { return blockSize; }

private:
    std::vector<std::unique_ptr<ConvolutionEngine>> head, tail;
    AudioBuffer<float> tailBuffer;

    const int latency;
    const int irSize;
    const int blockSize;
    const bool isZeroDelay;
};

static AudioBuffer<float> fixNumChannels (const AudioBuffer<float>& buf, Convolution::Stereo stereo)
{
    const auto numChannels = jmin (buf.getNumChannels(), stereo == Convolution::Stereo::yes ? 2 : 1);
    const auto numSamples = buf.getNumSamples();

    AudioBuffer<float> result (numChannels, buf.getNumSamples());

    for (auto channel = 0; channel != numChannels; ++channel)
        result.copyFrom (channel, 0, buf.getReadPointer (channel), numSamples);

    if (result.getNumSamples() == 0 || result.getNumChannels() == 0)
    {
        result.setSize (1, 1);
        result.setSample (0, 0, 1.0f);
    }

    return result;
}

static AudioBuffer<float> trimImpulseResponse (const AudioBuffer<float>& buf)
{
    const auto thresholdTrim = Decibels::decibelsToGain (-80.0f);

    const auto numChannels = buf.getNumChannels();
    const auto numSamples = buf.getNumSamples();

    std::ptrdiff_t offsetBegin = numSamples;
    std::ptrdiff_t offsetEnd   = numSamples;

    for (auto channel = 0; channel < numChannels; ++channel)
    {
        const auto indexAboveThreshold = [&] (auto begin, auto end)
        {
            return std::distance (begin, std::find_if (begin, end, [&] (float sample)
            {
                return std::abs (sample) >= thresholdTrim;
            }));
        };

        const auto channelBegin = buf.getReadPointer (channel);
        const auto channelEnd = channelBegin + numSamples;
        const auto itStart = indexAboveThreshold (channelBegin, channelEnd);
        const auto itEnd = indexAboveThreshold (std::make_reverse_iterator (channelEnd),
                                                std::make_reverse_iterator (channelBegin));

        offsetBegin = jmin (offsetBegin, itStart);
        offsetEnd   = jmin (offsetEnd,   itEnd);
    }

    if (offsetBegin == numSamples)
    {
        auto result = AudioBuffer<float> (numChannels, 1);
        result.clear();
        return result;
    }

    const auto newLength = jmax (1, numSamples - static_cast<int> (offsetBegin + offsetEnd));

    AudioBuffer<float> result (numChannels, newLength);

    for (auto channel = 0; channel < numChannels; ++channel)
    {
        result.copyFrom (channel,
                         0,
                         buf.getReadPointer (channel, static_cast<int> (offsetBegin)),
                         result.getNumSamples());
    }

    return result;
}

static float calculateNormalisationFactor (float sumSquaredMagnitude)
{
    if (sumSquaredMagnitude < 1e-8f)
        return 1.0f;

    return 0.125f / std::sqrt (sumSquaredMagnitude);
}

static void normaliseImpulseResponse (AudioBuffer<float>& buf)
{
    const auto numChannels = buf.getNumChannels();
    const auto numSamples  = buf.getNumSamples();
    const auto channelPtrs = buf.getArrayOfWritePointers();

    const auto maxSumSquaredMag = std::accumulate (channelPtrs, channelPtrs + numChannels, 0.0f, [numSamples] (auto max, auto* channel)
    {
        return jmax (max, std::accumulate (channel, channel + numSamples, 0.0f, [] (auto sum, auto samp)
        {
            return sum + (samp * samp);
        }));
    });

    const auto normalisationFactor = calculateNormalisationFactor (maxSumSquaredMag);

    std::for_each (channelPtrs, channelPtrs + numChannels, [normalisationFactor, numSamples] (auto* channel)
    {
        FloatVectorOperations::multiply (channel, normalisationFactor, numSamples);
    });
}

static AudioBuffer<float> resampleImpulseResponse (const AudioBuffer<float>& buf,
                                                   const double srcSampleRate,
                                                   const double destSampleRate)
{
    if (approximatelyEqual (srcSampleRate, destSampleRate))
        return buf;

    const auto factorReading = srcSampleRate / destSampleRate;

    AudioBuffer<float> original = buf;
    MemoryAudioSource memorySource (original, false);
    ResamplingAudioSource resamplingSource (&memorySource, false, buf.getNumChannels());

    const auto finalSize = roundToInt (jmax (1.0, buf.getNumSamples() / factorReading));
    resamplingSource.setResamplingRatio (factorReading);
    resamplingSource.prepareToPlay (finalSize, srcSampleRate);

    AudioBuffer<float> result (buf.getNumChannels(), finalSize);
    resamplingSource.getNextAudioBlock ({ &result, 0, result.getNumSamples() });

    return result;
}

//==============================================================================
template <typename Element>
class TryLockedPtr
{
public:
    void set (std::unique_ptr<Element> p)
    {
        const SpinLock::ScopedLockType lock (mutex);
        ptr = std::move (p);
    }

    std::unique_ptr<MultichannelEngine> get()
    {
        const SpinLock::ScopedTryLockType lock (mutex);
        return lock.isLocked() ? std::move (ptr) : nullptr;
    }

private:
    std::unique_ptr<Element> ptr;
    SpinLock mutex;
};

struct BufferWithSampleRate
{
    BufferWithSampleRate() = default;

    BufferWithSampleRate (AudioBuffer<float>&& bufferIn, double sampleRateIn)
        : buffer (std::move (bufferIn)), sampleRate (sampleRateIn) {}

    AudioBuffer<float> buffer;
    double sampleRate = 0.0;
};

static BufferWithSampleRate loadStreamToBuffer (std::unique_ptr<InputStream> stream, size_t maxLength)
{
    AudioFormatManager manager;
    manager.registerBasicFormats();
    std::unique_ptr<AudioFormatReader> formatReader (manager.createReaderFor (std::move (stream)));

    if (formatReader == nullptr)
        return {};

    const auto fileLength = static_cast<size_t> (formatReader->lengthInSamples);
    const auto lengthToLoad = maxLength == 0 ? fileLength : jmin (maxLength, fileLength);

    BufferWithSampleRate result { { jlimit (1, 2, static_cast<int> (formatReader->numChannels)),
                                    static_cast<int> (lengthToLoad) },
                                  formatReader->sampleRate };

    formatReader->read (result.buffer.getArrayOfWritePointers(),
                        result.buffer.getNumChannels(),
                        0,
                        result.buffer.getNumSamples());

    return result;
}

// This class caches the data required to build a new convolution engine
// (in particular, impulse response data and a ProcessSpec).
// Calls to `setProcessSpec` and `setImpulseResponse` construct a
// new engine, which can be retrieved by calling `getEngine`.
class ConvolutionEngineFactory
{
public:
    ConvolutionEngineFactory (Convolution::Latency requiredLatency,
                              Convolution::NonUniform requiredHeadSize)
        : latency  { (requiredLatency.latencyInSamples   <= 0) ? 0 : jmax (64, nextPowerOfTwo (requiredLatency.latencyInSamples)) },
          headSize { (requiredHeadSize.headSizeInSamples <= 0) ? 0 : jmax (64, nextPowerOfTwo (requiredHeadSize.headSizeInSamples)) },
          shouldBeZeroLatency (requiredLatency.latencyInSamples == 0)
    {}

    // It is safe to call this method simultaneously with other public
    // member functions.
    void setProcessSpec (const ProcessSpec& spec)
    {
        const std::lock_guard<std::mutex> lock (mutex);
        processSpec = spec;

        engine.set (makeEngine());
    }

    // It is safe to call this method simultaneously with other public
    // member functions.
    void setImpulseResponse (BufferWithSampleRate&& buf,
                             Convolution::Stereo stereo,
                             Convolution::Trim trim,
                             Convolution::Normalise normalise)
    {
        const std::lock_guard<std::mutex> lock (mutex);
        wantsNormalise = normalise;
        originalSampleRate = buf.sampleRate;

        impulseResponse = [&]
        {
            auto corrected = fixNumChannels (buf.buffer, stereo);
            return trim == Convolution::Trim::yes ? trimImpulseResponse (corrected) : corrected;
        }();

        engine.set (makeEngine());
    }

    // Returns the most recently-created engine, or nullptr
    // if there is no pending engine, or if the engine is currently
    // being updated by one of the setter methods.
    // It is safe to call this simultaneously with other public
    // member functions.
    std::unique_ptr<MultichannelEngine> getEngine() { return engine.get(); }

private:
    std::unique_ptr<MultichannelEngine> makeEngine()
    {
        auto resampled = resampleImpulseResponse (impulseResponse, originalSampleRate, processSpec.sampleRate);

        if (wantsNormalise == Convolution::Normalise::yes)
            normaliseImpulseResponse (resampled);
        else
            resampled.applyGain ((float) (originalSampleRate / processSpec.sampleRate));

        const auto currentLatency = jmax (processSpec.maximumBlockSize, (uint32) latency.latencyInSamples);
        const auto maxBufferSize = shouldBeZeroLatency ? static_cast<int> (processSpec.maximumBlockSize)
                                                       : nextPowerOfTwo (static_cast<int> (currentLatency));

        return std::make_unique<MultichannelEngine> (resampled,
                                                     processSpec.maximumBlockSize,
                                                     maxBufferSize,
                                                     headSize,
                                                     shouldBeZeroLatency);
    }

    static AudioBuffer<float> makeImpulseBuffer()
    {
        AudioBuffer<float> result (1, 1);
        result.setSample (0, 0, 1.0f);
        return result;
    }

    ProcessSpec processSpec { 44100.0, 128, 2 };
    AudioBuffer<float> impulseResponse = makeImpulseBuffer();
    double originalSampleRate = processSpec.sampleRate;
    Convolution::Normalise wantsNormalise = Convolution::Normalise::no;
    const Convolution::Latency latency;
    const Convolution::NonUniform headSize;
    const bool shouldBeZeroLatency;

    TryLockedPtr<MultichannelEngine> engine;

    mutable std::mutex mutex;
};

static void setImpulseResponse (ConvolutionEngineFactory& factory,
                                const void* sourceData,
                                size_t sourceDataSize,
                                Convolution::Stereo stereo,
                                Convolution::Trim trim,
                                size_t size,
                                Convolution::Normalise normalise)
{
    factory.setImpulseResponse (loadStreamToBuffer (std::make_unique<MemoryInputStream> (sourceData, sourceDataSize, false), size),
                                stereo, trim, normalise);
}

static void setImpulseResponse (ConvolutionEngineFactory& factory,
                                const File& fileImpulseResponse,
                                Convolution::Stereo stereo,
                                Convolution::Trim trim,
                                size_t size,
                                Convolution::Normalise normalise)
{
    factory.setImpulseResponse (loadStreamToBuffer (std::make_unique<FileInputStream> (fileImpulseResponse), size),
                                stereo, trim, normalise);
}

// This class acts as a destination for convolution engines which are loaded on
// a background thread.

// Deriving from `enable_shared_from_this` allows us to capture a reference to
// this object when adding commands to the background message queue.
// That way, we can avoid dangling references in the background thread in the case
// that a Convolution instance is deleted before the background message queue.
class ConvolutionEngineQueue final : public std::enable_shared_from_this<ConvolutionEngineQueue>
{
public:
    ConvolutionEngineQueue (BackgroundMessageQueue& queue,
                            Convolution::Latency latencyIn,
                            Convolution::NonUniform headSizeIn)
        : messageQueue (queue), factory (latencyIn, headSizeIn) {}

    void loadImpulseResponse (AudioBuffer<float>&& buffer,
                              double sr,
                              Convolution::Stereo stereo,
                              Convolution::Trim trim,
                              Convolution::Normalise normalise)
    {
        callLater ([b = std::move (buffer), sr, stereo, trim, normalise] (ConvolutionEngineFactory& f) mutable
        {
            f.setImpulseResponse ({ std::move (b), sr }, stereo, trim, normalise);
        });
    }

    void loadImpulseResponse (const void* sourceData,
                              size_t sourceDataSize,
                              Convolution::Stereo stereo,
                              Convolution::Trim trim,
                              size_t size,
                              Convolution::Normalise normalise)
    {
        callLater ([sourceData, sourceDataSize, stereo, trim, size, normalise] (ConvolutionEngineFactory& f) mutable
        {
            setImpulseResponse (f, sourceData, sourceDataSize, stereo, trim, size, normalise);
        });
    }

    void loadImpulseResponse (const File& fileImpulseResponse,
                              Convolution::Stereo stereo,
                              Convolution::Trim trim,
                              size_t size,
                              Convolution::Normalise normalise)
    {
        callLater ([fileImpulseResponse, stereo, trim, size, normalise] (ConvolutionEngineFactory& f) mutable
        {
            setImpulseResponse (f, fileImpulseResponse, stereo, trim, size, normalise);
        });
    }

    void prepare (const ProcessSpec& spec)
    {
        factory.setProcessSpec (spec);
    }

    // Call this regularly to try to resend any pending message.
    // This allows us to always apply the most recently requested
    // state (eventually), even if the message queue fills up.
    void postPendingCommand()
    {
        if (pendingCommand == nullptr)
            return;

        if (messageQueue.push (pendingCommand))
            pendingCommand = nullptr;
    }

    std::unique_ptr<MultichannelEngine> getEngine() { return factory.getEngine(); }

private:
    template <typename Fn>
    void callLater (Fn&& fn)
    {
        // If there was already a pending command (because the queue was full) we'll end up deleting it here.
        // Not much we can do about that!
        pendingCommand = [weak = weakFromThis(), callback = std::forward<Fn> (fn)]() mutable
        {
            if (auto t = weak.lock())
                callback (t->factory);
        };

        postPendingCommand();
    }

    std::weak_ptr<ConvolutionEngineQueue> weakFromThis() { return shared_from_this(); }

    BackgroundMessageQueue& messageQueue;
    ConvolutionEngineFactory factory;
    BackgroundMessageQueue::IncomingCommand pendingCommand;
};

class CrossoverMixer
{
public:
    void reset()
    {
        smoother.setCurrentAndTargetValue (1.0f);
    }

    void prepare (const ProcessSpec& spec)
    {
        smoother.reset (spec.sampleRate, 0.05);
        smootherBuffer.setSize (1, static_cast<int> (spec.maximumBlockSize));
        mixBuffer.setSize (static_cast<int> (spec.numChannels), static_cast<int> (spec.maximumBlockSize));
        reset();
    }

    template <typename ProcessCurrent, typename ProcessPrevious, typename NotifyDone>
    void processSamples (const AudioBlock<const float>& input,
                         AudioBlock<float>& output,
                         ProcessCurrent&& current,
                         ProcessPrevious&& previous,
                         NotifyDone&& notifyDone)
    {
        if (smoother.isSmoothing())
        {
            const auto numSamples = static_cast<int> (input.getNumSamples());

            for (auto sample = 0; sample != numSamples; ++sample)
                smootherBuffer.setSample (0, sample, smoother.getNextValue());

            AudioBlock<float> mixBlock (mixBuffer);
            mixBlock.clear();
            previous (input, mixBlock);

            for (size_t channel = 0; channel != output.getNumChannels(); ++channel)
            {
                FloatVectorOperations::multiply (mixBlock.getChannelPointer (channel),
                                                 smootherBuffer.getReadPointer (0),
                                                 numSamples);
            }

            FloatVectorOperations::multiply (smootherBuffer.getWritePointer (0), -1.0f, numSamples);
            FloatVectorOperations::add (smootherBuffer.getWritePointer (0), 1.0f, numSamples);

            current (input, output);

            for (size_t channel = 0; channel != output.getNumChannels(); ++channel)
            {
                FloatVectorOperations::multiply (output.getChannelPointer (channel),
                                                 smootherBuffer.getReadPointer (0),
                                                 numSamples);
                FloatVectorOperations::add (output.getChannelPointer (channel),
                                            mixBlock.getChannelPointer (channel),
                                            numSamples);
            }

            if (! smoother.isSmoothing())
                notifyDone();
        }
        else
        {
            current (input, output);
        }
    }

    void beginTransition()
    {
        smoother.setCurrentAndTargetValue (1.0f);
        smoother.setTargetValue (0.0f);
    }

private:
    LinearSmoothedValue<float> smoother;
    AudioBuffer<float> smootherBuffer;
    AudioBuffer<float> mixBuffer;
};

using OptionalQueue = OptionalScopedPointer<ConvolutionMessageQueue>;

class Convolution::Impl
{
public:
    Impl (Latency requiredLatency,
          NonUniform requiredHeadSize,
          OptionalQueue&& queue)
        : messageQueue (std::move (queue)),
          engineQueue (std::make_shared<ConvolutionEngineQueue> (*messageQueue->pimpl,
                                                                 requiredLatency,
                                                                 requiredHeadSize))
    {}

    void reset()
    {
        mixer.reset();

        if (currentEngine != nullptr)
            currentEngine->reset();

        destroyPreviousEngine();
    }

    void prepare (const ProcessSpec& spec)
    {
        messageQueue->pimpl->popAll();
        mixer.prepare (spec);
        engineQueue->prepare (spec);

        if (auto newEngine = engineQueue->getEngine())
            currentEngine = std::move (newEngine);

        previousEngine = nullptr;
        jassert (currentEngine != nullptr);
    }

    void processSamples (const AudioBlock<const float>& input, AudioBlock<float>& output)
    {
        engineQueue->postPendingCommand();

        if (previousEngine == nullptr)
            installPendingEngine();

        mixer.processSamples (input,
                              output,
                              [this] (const AudioBlock<const float>& in, AudioBlock<float>& out)
                              {
                                  currentEngine->processSamples (in, out);
                              },
                              [this] (const AudioBlock<const float>& in, AudioBlock<float>& out)
                              {
                                  if (previousEngine != nullptr)
                                      previousEngine->processSamples (in, out);
                                  else
                                      out.copyFrom (in);
                              },
                              [this] { destroyPreviousEngine(); });
    }

    int getCurrentIRSize() const { return currentEngine != nullptr ? currentEngine->getIRSize() : 0; }

    int getLatency() const { return currentEngine != nullptr ? currentEngine->getLatency() : 0; }

    void loadImpulseResponse (AudioBuffer<float>&& buffer,
                              double originalSampleRate,
                              Stereo stereo,
                              Trim trim,
                              Normalise normalise)
    {
        engineQueue->loadImpulseResponse (std::move (buffer), originalSampleRate, stereo, trim, normalise);
    }

    void loadImpulseResponse (const void* sourceData,
                              size_t sourceDataSize,
                              Stereo stereo,
                              Trim trim,
                              size_t size,
                              Normalise normalise)
    {
        engineQueue->loadImpulseResponse (sourceData, sourceDataSize, stereo, trim, size, normalise);
    }

    void loadImpulseResponse (const File& fileImpulseResponse,
                              Stereo stereo,
                              Trim trim,
                              size_t size,
                              Normalise normalise)
    {
        engineQueue->loadImpulseResponse (fileImpulseResponse, stereo, trim, size, normalise);
    }

private:
    void destroyPreviousEngine()
    {
        // If the queue is full, we'll destroy this straight away
        BackgroundMessageQueue::IncomingCommand command = [p = std::move (previousEngine)]() mutable { p = nullptr; };
        messageQueue->pimpl->push (command);
    }

    void installNewEngine (std::unique_ptr<MultichannelEngine> newEngine)
    {
        destroyPreviousEngine();
        previousEngine = std::move (currentEngine);
        currentEngine = std::move (newEngine);
        mixer.beginTransition();
    }

    void installPendingEngine()
    {
        if (auto newEngine = engineQueue->getEngine())
            installNewEngine (std::move (newEngine));
    }

    OptionalQueue messageQueue;
    std::shared_ptr<ConvolutionEngineQueue> engineQueue;
    std::unique_ptr<MultichannelEngine> previousEngine, currentEngine;
    CrossoverMixer mixer;
};

//==============================================================================
void Convolution::Mixer::prepare (const ProcessSpec& spec)
{
    for (auto& dry : volumeDry)
        dry.reset (spec.sampleRate, 0.05);

    for (auto& wet : volumeWet)
        wet.reset (spec.sampleRate, 0.05);

    sampleRate = spec.sampleRate;

    dryBlock = AudioBlock<float> (dryBlockStorage,
                                  jmin (spec.numChannels, 2u),
                                  spec.maximumBlockSize);

}

template <typename ProcessWet>
void Convolution::Mixer::processSamples (const AudioBlock<const float>& input,
                                         AudioBlock<float>& output,
                                         bool isBypassed,
                                         ProcessWet&& processWet) noexcept
{
    const auto numChannels = jmin (input.getNumChannels(), volumeDry.size());
    const auto numSamples  = jmin (input.getNumSamples(), output.getNumSamples());

    auto dry = dryBlock.getSubsetChannelBlock (0, numChannels);

    if (volumeDry[0].isSmoothing())
    {
        dry.copyFrom (input);

        for (size_t channel = 0; channel < numChannels; ++channel)
            volumeDry[channel].applyGain (dry.getChannelPointer (channel), (int) numSamples);

        processWet (input, output);

        for (size_t channel = 0; channel < numChannels; ++channel)
            volumeWet[channel].applyGain (output.getChannelPointer (channel), (int) numSamples);

        output += dry;
    }
    else
    {
        if (! currentIsBypassed)
            processWet (input, output);

        if (isBypassed != currentIsBypassed)
        {
            currentIsBypassed = isBypassed;

            for (size_t channel = 0; channel < numChannels; ++channel)
            {
                volumeDry[channel].setTargetValue (isBypassed ? 0.0f : 1.0f);
                volumeDry[channel].reset (sampleRate, 0.05);
                volumeDry[channel].setTargetValue (isBypassed ? 1.0f : 0.0f);

                volumeWet[channel].setTargetValue (isBypassed ? 1.0f : 0.0f);
                volumeWet[channel].reset (sampleRate, 0.05);
                volumeWet[channel].setTargetValue (isBypassed ? 0.0f : 1.0f);
            }
        }
    }
}

void Convolution::Mixer::reset() { dryBlock.clear(); }

//==============================================================================
Convolution::Convolution()
    : Convolution (Latency { 0 })
{}

Convolution::Convolution (ConvolutionMessageQueue& queue)
    : Convolution (Latency { 0 }, queue)
{}

Convolution::Convolution (const Latency& requiredLatency)
    : Convolution (requiredLatency,
                   {},
                   OptionalQueue { std::make_unique<ConvolutionMessageQueue>() })
{}

Convolution::Convolution (const NonUniform& nonUniform)
    : Convolution ({},
                   nonUniform,
                   OptionalQueue { std::make_unique<ConvolutionMessageQueue>() })
{}

Convolution::Convolution (const Latency& requiredLatency, ConvolutionMessageQueue& queue)
    : Convolution (requiredLatency, {}, OptionalQueue { queue })
{}

Convolution::Convolution (const NonUniform& nonUniform, ConvolutionMessageQueue& queue)
    : Convolution ({}, nonUniform, OptionalQueue { queue })
{}

Convolution::Convolution (const Latency& latency,
                          const NonUniform& nonUniform,
                          OptionalQueue&& queue)
    : pimpl (std::make_unique<Impl> (latency, nonUniform, std::move (queue)))
{}

Convolution::~Convolution() noexcept = default;

void Convolution::loadImpulseResponse (const void* sourceData,
                                       size_t sourceDataSize,
                                       Stereo stereo,
                                       Trim trim,
                                       size_t size,
                                       Normalise normalise)
{
    pimpl->loadImpulseResponse (sourceData, sourceDataSize, stereo, trim, size, normalise);
}

void Convolution::loadImpulseResponse (const File& fileImpulseResponse,
                                       Stereo stereo,
                                       Trim trim,
                                       size_t size,
                                       Normalise normalise)
{
    pimpl->loadImpulseResponse (fileImpulseResponse, stereo, trim, size, normalise);
}

void Convolution::loadImpulseResponse (AudioBuffer<float>&& buffer,
                                       double originalSampleRate,
                                       Stereo stereo,
                                       Trim trim,
                                       Normalise normalise)
{
    pimpl->loadImpulseResponse (std::move (buffer), originalSampleRate, stereo, trim, normalise);
}

void Convolution::prepare (const ProcessSpec& spec)
{
    mixer.prepare (spec);
    pimpl->prepare (spec);
    isActive = true;
}

void Convolution::reset() noexcept
{
    mixer.reset();
    pimpl->reset();
}

void Convolution::processSamples (const AudioBlock<const float>& input,
                                  AudioBlock<float>& output,
                                  bool isBypassed) noexcept
{
    if (! isActive)
        return;

    jassert (input.getNumChannels() == output.getNumChannels());
    jassert (isPositiveAndBelow (input.getNumChannels(), static_cast<size_t> (3))); // only mono and stereo is supported

    mixer.processSamples (input, output, isBypassed, [this] (const auto& in, auto& out)
    {
        pimpl->processSamples (in, out);
    });
}

int Convolution::getCurrentIRSize() const { return pimpl->getCurrentIRSize(); }

int Convolution::getLatency() const { return pimpl->getLatency(); }

} // namespace juce::dsp
