/*
 * MIT License
 *
 * Copyright (c) 2019 Alexey Edelev <semlanik@gmail.com>
 *
 * This file is part of NeuralNetwork project https://git.semlanik.org/semlanik/NeuralNetwork
 *
 * 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 the rights to use, copy, modify,
 * merge, publish, distribute, sublicense, 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 all copies
 * or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
 * INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
 * PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
 * FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
 * OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
 * DEALINGS IN THE SOFTWARE.
 */

package training

import (
	"encoding/binary"
	"fmt"
	"io"
	"log"
	"os"

	mat "gonum.org/v1/gonum/mat"
)

type MNISTReader struct {
	file                      *os.File
	resultsFile               *os.File
	fileValidation            *os.File
	resultsFileValidation     *os.File
	size                      int
	imageSize                 int
	buffered                  *mat.Dense
	resultsBuffered           *mat.Dense
	bufferedValidation        *mat.Dense
	resultsBufferedValidation *mat.Dense
}

func NewMNISTReader(dataFilename string, resultsFilename string) (r *MNISTReader) {
	r = &MNISTReader{}

	var err error
	r.file, err = os.Open(dataFilename)
	if err != nil {
		return nil
	}

	r.resultsFile, err = os.Open(resultsFilename)
	if err != nil {
		return nil
	}

	buffer := make([]byte, 16)
	r.file.Read(buffer)
	header := binary.BigEndian.Uint32(buffer[:4])
	if header != 0x00000803 {
		return nil
	}
	r.size = int(binary.BigEndian.Uint32(buffer[4:8]))
	r.imageSize = int(binary.BigEndian.Uint32(buffer[8:12])) * int(binary.BigEndian.Uint32(buffer[12:16]))
	fmt.Printf("Image size: %v\n", r.imageSize)
	buffer = make([]byte, 8)
	r.resultsFile.Read(buffer)
	header = binary.BigEndian.Uint32(buffer[0:4])
	if header != 0x00000801 {
		return nil
	}
	resultsSize := int(binary.BigEndian.Uint32(buffer[4:8]))
	if resultsSize != r.size {
		return nil
	}

	//Separation validation part
	r.fileValidation, err = os.Open(dataFilename)
	if err != nil {
		return nil
	}

	r.resultsFileValidation, err = os.Open(resultsFilename)
	if err != nil {
		return nil
	}

	r.Reset()
	return
}

func (r *MNISTReader) GetData() (*mat.Dense, *mat.Dense) {
	return r.buffered, r.resultsBuffered
}

func (r *MNISTReader) NextData() bool {
	r.buffered, r.resultsBuffered = r.readNextData(r.fileValidation, r.resultsFileValidation)
	if r.buffered != nil && r.resultsBuffered != nil {
		return true
	}
	r.Reset()
	return false
}

func (r *MNISTReader) Reset() {
	r.file.Seek(16, 0)
	r.resultsFile.Seek(8, 0)

	r.fileValidation.Seek(16, 0)
	r.resultsFileValidation.Seek(8, 0)
}

func (r *MNISTReader) GetValidator() (*mat.Dense, *mat.Dense) {
	return r.bufferedValidation, r.resultsBufferedValidation
}

func (r *MNISTReader) NextValidator() bool {
	r.bufferedValidation, r.resultsBufferedValidation = r.readNextData(r.fileValidation, r.resultsFileValidation)
	if r.bufferedValidation != nil && r.resultsBufferedValidation != nil {
		return true
	}
	r.Reset()
	return false
}

func (r *MNISTReader) readNextData(file *os.File, resultsFile *os.File) (buffered, resultsBuffered *mat.Dense) {
	buffer := make([]byte, r.imageSize)
	_, err := file.Read(buffer)

	if err == io.EOF {
		return nil, nil
	} else if err != nil {
		log.Fatal("File read error\n")
	}

	values := make([]float64, r.imageSize)
	for i, v := range buffer {
		values[i] = float64(v) / 255.0
	}

	buffered = mat.NewDense(r.imageSize, 1, values)

	buffer = make([]byte, 1)
	_, err = resultsFile.Read(buffer)
	if err != nil {
		log.Fatal("Result file read error\n")
	}

	num := int(buffer[0])

	resultsBuffered = mat.NewDense(10, 1, nil)
	resultsBuffered.Set(num, 0, 1.0)

	return buffered, resultsBuffered
}

func (r *MNISTReader) GetDataByIndex(i int) (*mat.Dense, *mat.Dense) {
	file, err := os.Open(r.file.Name())
	if err != nil {
		return nil, nil
	}
	defer file.Close()

	resultsFile, err := os.Open(r.resultsFile.Name())
	if err != nil {
		return nil, nil
	}
	defer resultsFile.Close()

	file.Seek(16+int64(r.imageSize*i), 0)
	resultsFile.Seek(8+int64(i), 0)

	return r.readNextData(file, resultsFile)
}

func (r *MNISTReader) GetDataCount() int {
	return r.size
}