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# Program Listing for File ptq.h

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    /*
     * Copyright (c) NVIDIA Corporation.
     * All rights reserved.
     *
     * This library is licensed under the BSD-style license found in the
     * LICENSE file in the root directory of this source tree.
     */
    #pragma once

    #include <string>
    #include <vector>
    #include <memory>
    #include <iostream>
    #include <fstream>
    #include <iterator>
    #include <sstream>

    #include "torch/torch.h"
    #include "trtorch/logging.h"
    #include "NvInfer.h"

    #ifndef DOXYGEN_SHOULD_SKIP_THIS
    namespace nvinfer1 {
    class IInt8Calibrator;
    class IInt8EntropyCalibrator2;
    }

    namespace trtorch {
    namespace ptq {
    bool get_batch_impl(void* bindings[], const char* names[], int nbBindings, torch::Tensor& data);
    }
    }
    #endif //DOXYGEN_SHOULD_SKIP_THIS

    namespace trtorch {
    namespace ptq {

    template<typename Algorithm, typename DataLoaderUniquePtr>
    class Int8Calibrator : Algorithm {
        using DataLoader = typename DataLoaderUniquePtr::element_type;
        using Batch = typename DataLoader::super::BatchType;
    public:
        Int8Calibrator(DataLoaderUniquePtr dataloader, const std::string& cache_file_path, bool use_cache)
          : dataloader_(dataloader.get()), cache_file_path_(cache_file_path), use_cache_(use_cache) {
              for (auto batch : *dataloader_) {
                batched_data_.push_back(batch.data);
              }
              it_ = batched_data_.begin();
          }

        int getBatchSize() const override {
            // HACK: TRTorch only uses explict batch sizing, INT8 Calibrator does not
            // work when reporting the batch size here and having explicity batching.
            // So we just report batch size 1 (warnings will still be printed out).
            return 1;
            //return static_cast<int>(dataloader_->options().batch_size);
        }

        bool getBatch(void* bindings[], const char* names[], int nbBindings) override {
            if (it_ != batched_data_.end()) {
                auto status = get_batch_impl(bindings, names, nbBindings, *it_);
                it_ = ++it_;
                return status;
            } else {
                // Reset iterator if incase calibrator is going to be used again
                it_ = batched_data_.begin();
                return false;
            }
        }

        const void* readCalibrationCache(size_t& length) override {
            if (use_cache_) {
                std::stringstream ss;
                ss << "Reading Calibration Cache from " << cache_file_path_;
                logging::log(logging::Level::kINFO, ss.str());

                cache_.clear();
                std::ifstream input(cache_file_path_, std::ios::binary);
                input >> std::noskipws;
                if (input.good()) {
                    std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(),
                        std::back_inserter(cache_));
                    logging::log(logging::Level::kDEBUG, "Cache read");
                }
                length = cache_.size();
                return length ? cache_.data() : nullptr;
            }
            return nullptr;
        }

        void writeCalibrationCache(const void* cache, size_t length) override {
            std::ofstream cache_file(cache_file_path_, std::ios::binary);
            cache_file.write(reinterpret_cast<const char*>(cache), length);
            std::stringstream ss;
            ss << "Saved Calibration Cache to " << cache_file_path_;
            logging::log(logging::Level::kINFO, ss.str());
        }

        operator nvinfer1::IInt8Calibrator* () {
            return reinterpret_cast<nvinfer1::IInt8Calibrator*>(this);
        }

    private:
        DataLoader* dataloader_;
        const std::string& cache_file_path_;
        size_t cache_size_ = 0;
        bool use_cache_;
        std::vector<char> cache_;
        std::vector<torch::Tensor> batched_data_;
        std::vector<torch::Tensor>::iterator it_;

    };

    template<typename Algorithm>
    class Int8CacheCalibrator : Algorithm {
    public:
        Int8CacheCalibrator(const std::string& cache_file_path)
          : cache_file_path_(cache_file_path) {}

        int getBatchSize() const override {
            // HACK: TRTorch only uses explict batch sizing, INT8 Calibrator does not
            // work when reporting the batch size here and having explicity batching.
            // So we just report batch size 1 (warnings will still be printed out).
            return 1;
        }

        bool getBatch(void* bindings[], const char* names[], int nbBindings) override {
            return false;
        }

        const void* readCalibrationCache(size_t& length) override {
            std::stringstream ss;
            ss << "Reading Calibration Cache from " << cache_file_path_;
            logging::log(logging::Level::kINFO, ss.str());

            cache_.clear();
            std::ifstream input(cache_file_path_, std::ios::binary);
            input >> std::noskipws;
            if (input.good()) {
                std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(),
                    std::back_inserter(cache_));
                logging::log(logging::Level::kDEBUG, "Cache read");
            }
            length = cache_.size();
            return length ? cache_.data() : nullptr;
        }

        void writeCalibrationCache(const void* cache, size_t length) override {
            std::ofstream cache_file(cache_file_path_, std::ios::binary);
            cache_file.write(reinterpret_cast<const char*>(cache), length);
            std::stringstream ss;
            ss << "Saved Calibration Cache to " << cache_file_path_;
            logging::log(logging::Level::kINFO, ss.str());
        }

        operator nvinfer1::IInt8Calibrator* () {
            return reinterpret_cast<nvinfer1::IInt8Calibrator*>(this);
        }

    private:
        const std::string& cache_file_path_;
        size_t cache_size_ = 0;
        std::vector<char> cache_;
    };

    template<typename Algorithm = nvinfer1::IInt8EntropyCalibrator2, typename DataLoader>
    TRTORCH_API inline Int8Calibrator<Algorithm, DataLoader> make_int8_calibrator(DataLoader dataloader, const std::string& cache_file_path, bool use_cache) {
        return Int8Calibrator<Algorithm, DataLoader>(std::move(dataloader), cache_file_path, use_cache);
    }

    template<typename Algorithm = nvinfer1::IInt8EntropyCalibrator2>
    TRTORCH_API inline Int8CacheCalibrator<Algorithm> make_int8_cache_calibrator(const std::string& cache_file_path) {
        return Int8CacheCalibrator<Algorithm>(cache_file_path);
    }

    } // namespace ptq
    } // namespace trtorch