// Copyright (C) 2018-2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#pragma once

#include <gtest/gtest.h>
#include <fstream>
#include "inference_engine.hpp"

class TestsCommonFunc {

    InferenceEngine::Blob::Ptr readBMP(std::string path, unsigned batch) {

        std::ifstream input(path, std::ios::binary);
        if (!input) return nullptr;

        unsigned char bmpFileHeader[14];
        input.read((char*)bmpFileHeader, sizeof(bmpFileHeader));
        if(bmpFileHeader[0]!='B' || bmpFileHeader[1]!='M') return nullptr;
        if(bmpFileHeader[11]!=0  || bmpFileHeader[12]!=0 || bmpFileHeader[13]!=0 ) return nullptr;

        unsigned char bmpInfoHeader[40];
        input.read((char*)bmpInfoHeader, sizeof(bmpInfoHeader));
        if(bmpInfoHeader[14]!=24) return nullptr; // bits per pixel
        if(bmpInfoHeader[16]!=0) return nullptr; // compression is not supported

        bool  rowsReversed = (*(int32_t*)(bmpInfoHeader + 8)) < 0;
        uint32_t width  = *(int32_t*)(bmpInfoHeader + 4);
        uint32_t height = abs(*(int32_t*)(bmpInfoHeader + 8));

        size_t padSize = width & 3;
        char pad[3];

        InferenceEngine::Blob::Ptr blob(new InferenceEngine::TBlob<float>( InferenceEngine::Precision::FP32, InferenceEngine::NCHW, {batch, 3, width, height} ));
        blob->allocate();
        float *blob_ptr = (float*)(void*)blob->buffer();

        unsigned int offset = *(unsigned int *)(bmpFileHeader + 10);
        for (int b = 0; b < batch; b++) {
            int b_off = 3*width*height*b;
            input.seekg(offset, std::ios::beg);
            //reading by rows in invert vertically
            for (uint32_t i = 0; i < height; i++) {
                int storeAt = rowsReversed ? i : height - 1 - i;

                for (uint32_t j = 0; j < width; j++) {
                    unsigned char RGBA[3];
                    input.read((char *) RGBA, sizeof(RGBA));

                    blob_ptr[b_off + j + storeAt * width] = RGBA[0];
                    blob_ptr[b_off + j + storeAt * width + height * width * 1] = RGBA[1];
                    blob_ptr[b_off + j + storeAt * width + height * width * 2] = RGBA[2];
                }
                input.read(pad, padSize);
            }
        }

        return blob;
    }

    inline void bswap_32(char* ptr, size_t size) {
        char* end = ptr + size;
        char tmp;
        for (; ptr<end; ptr+=4) {
            tmp = ptr[0]; ptr[0] = ptr[3]; ptr[3] = tmp;
            tmp = ptr[1]; ptr[1] = ptr[2]; ptr[2] = tmp;
        }
    }

    InferenceEngine::Blob::Ptr readUbyte(std::string path, unsigned batch) {

        std::ifstream input(path, std::ios::binary);
        struct {
            uint32_t magic_number;
            uint32_t n_images;
            uint32_t n_rows;
            uint32_t n_cols;
        } hdr;

        input.read((char *) &hdr, sizeof(hdr));
        bswap_32((char *) &hdr, sizeof(hdr));
        if (hdr.magic_number != 2051) return nullptr; // Invalid MNIST image file

        InferenceEngine::Blob::Ptr blob(new InferenceEngine::TBlob<float>(InferenceEngine::Precision::FP32, InferenceEngine::NCHW,
                {batch, hdr.n_images, hdr.n_cols, hdr.n_rows }));
        blob->allocate();
        float *blob_ptr = (float*)(void*)blob->buffer();
        for (int b = 0; b < batch; b++) {
            input.seekg(sizeof(hdr), std::ios::beg);
            int b_off = b*hdr.n_images*hdr.n_rows*hdr.n_cols;
            for (uint32_t i = 0; i < hdr.n_images; ++i) {
                for (uint32_t r = 0; r < hdr.n_rows; ++r) {
                    for (uint32_t c = 0; c < hdr.n_cols; ++c) {
                        unsigned char temp = 0;
                        input.read((char *) &temp, sizeof(temp));
                        blob_ptr[b_off + i * hdr.n_rows * hdr.n_cols + r * hdr.n_cols + c] = temp;
                    }
                }
            }
        }
        return blob;
    }

protected:

    InferenceEngine::Blob::Ptr readInput(std::string path, int batch = 1) {
        if ( path.substr(path.rfind('.') + 1) == "bmp" ) return readBMP(path, batch);
        if ( path.substr(path.rfind('-') + 1) == "ubyte" ) return readUbyte(path, batch);
        return nullptr;
    }

    bool compareTop(InferenceEngine::Blob& blob, std::vector<std::pair<int, float>> &ref_top, int batch_to_compare = 0, float threshold = 0.005f) {
        if (blob.dims()[0] == 7)
            return compareTopLikeObjDetection(blob, ref_top, batch_to_compare);
        else
            return compareTopLikeClassification(blob, ref_top, batch_to_compare, threshold);
    }

    bool compareTopLikeObjDetection (InferenceEngine::Blob& blob, std::vector<std::pair<int, float>> &ref_top,
                int batch_to_compare = 0) {
        assert(blob.dims()[0] == 7);

        const int box_info_size = 7;

        int top_num = (int)ref_top.size();
        float *data_ptr = blob.buffer().as<float*>();
        const int data_size = blob.size();
        if (data_size/box_info_size < top_num) {
            EXPECT_TRUE(data_size/box_info_size >= top_num) << "Dst blob contains less data then expected";
            return false;
        }

        for (int i=0; i<top_num; i++) {
            int lable = data_ptr[i*box_info_size + 1];
            float confidence = data_ptr[i*box_info_size + 2];

            if (lable != ref_top[i].first) {
                EXPECT_EQ(lable , ref_top[i].first) << "Label mismatch";
                return false;
            }

            if (fabs(confidence - ref_top[i].second)/ref_top[i].second > 0.005) {
                EXPECT_NEAR(confidence, ref_top[i].second, ref_top[i].second * 0.005);
                return false;
            }
        }

        return true;
    }

    bool compareTopLikeClassification (InferenceEngine::Blob& blob, std::vector<std::pair<int, float>> &ref_top,
                     int batch_to_compare = 0, float threshold = 0.005f) {
        int top_num = (int)ref_top.size();

        size_t data_size = blob.size();
        float *data_ptr = (float*)(void*)blob.buffer();

        int batch_size = blob.dims()[blob.dims().size() - 1];
        assert(batch_size > batch_to_compare);
        data_size /= batch_size;
        data_ptr += data_size*batch_to_compare;

        std::vector<int> top(data_size);

        for (size_t i = 0; i < data_size; i++) top[i] = (int)i;
        std::partial_sort (top.begin(), top.begin()+top_num, top.end(),
                           [&](int l, int r) -> bool { return data_ptr[l] > data_ptr[r]; } );

        for (int i = 0 ; i < top_num; i++) {
            if (top[i] != ref_top[i].first) {
                EXPECT_EQ(top[i] , ref_top[i].first);
                return false;
            }

            if (fabs(data_ptr[top[i]] - ref_top[i].second)/ref_top[i].second > threshold) {
                EXPECT_NEAR(data_ptr[top[i]] , ref_top[i].second , ref_top[i].second * threshold);
                return false;
            }
        }
        return true;
    }

    void zeroing (InferenceEngine::Blob& blob) {
        size_t data_size = blob.size();
        float *data_ptr = (float *) (void *) blob.buffer();

        int zero;
        *(float*)&zero = 0.0f;
        memset(data_ptr, zero, data_size * sizeof(float));
    }

    bool isZeroFilled (InferenceEngine::Blob& blob, int batch_to_compare = 0) {
        size_t data_size = blob.size();
        float *data_ptr = (float*)(void*)blob.buffer();

        int batch_size = blob.dims()[blob.dims().size() - 1];
        assert(batch_size > batch_to_compare);
        data_size /= batch_size;
        data_ptr += data_size*batch_to_compare;

        for (int i = 0 ; i < data_size; i++) {
            EXPECT_FLOAT_EQ(data_ptr[i] , 0.0f);
        }
        return true;
    }

};
