from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import cv2
import tarfile
import numpy as np
from PIL import Image
from os import path
from paddle.dataset.image import load_image
import paddle

SOS = 0
EOS = 1
NUM_CLASSES = 95
DATA_SHAPE = [1, 48, 512]

DATA_MD5 = "7256b1d5420d8c3e74815196e58cdad5"
DATA_URL = "http://paddle-ocr-data.bj.bcebos.com/data.tar.gz"
CACHE_DIR_NAME = "ctc_data"
SAVED_FILE_NAME = "data.tar.gz"
DATA_DIR_NAME = "data"
TRAIN_DATA_DIR_NAME = "train_images"
TEST_DATA_DIR_NAME = "test_images"
TRAIN_LIST_FILE_NAME = "train.list"
TEST_LIST_FILE_NAME = "test.list"


class DataGenerator(object):
    def __init__(self, model="crnn_ctc"):
        self.model = model

    def train_reader(self,
                     img_root_dir,
                     img_label_list,
                     batchsize,
                     cycle,
                     max_length,
                     shuffle=True):
        '''
        Reader interface for training.

        :param img_root_dir: The root path of the image for training.
        :type img_root_dir: str

        :param img_label_list: The path of the <image_name, label> file for training.
        :type img_label_list: str

        :param cycle: If number of iterations is greater than dataset_size / batch_size
        it reiterates dataset over as many times as necessary.
        :type cycle: bool

        '''

        img_label_lines = []
        to_file = "tmp.txt"
        if not shuffle:
            cmd = "cat " + img_label_list + " | awk '{print $1,$2,$3,$4;}' > " + to_file
        elif batchsize == 1:
            cmd = "cat " + img_label_list + " | awk '{print $1,$2,$3,$4;}' | shuf > " + to_file
        else:
            #cmd1: partial shuffle
            cmd = "cat " + img_label_list + " | awk '{printf(\"%04d%.4f %s\\n\", $1, rand(), $0)}' | sort | sed 1,$((1 + RANDOM % 100))d | "
            #cmd2: batch merge and shuffle
            cmd += "awk '{printf $2\" \"$3\" \"$4\" \"$5\" \"; if(NR % " + str(
                batchsize) + " == 0) print \"\";}' | shuf | "
            #cmd3: batch split
            cmd += "awk '{if(NF == " + str(
                batchsize
            ) + " * 4) {for(i = 0; i < " + str(
                batchsize
            ) + "; i++) print $(4*i+1)\" \"$(4*i+2)\" \"$(4*i+3)\" \"$(4*i+4);}}' > " + to_file
        os.system(cmd)
        print("finish batch shuffle")
        img_label_lines = open(to_file, 'r').readlines()

        def reader():
            sizes = len(img_label_lines) // batchsize
            if sizes == 0:
                raise ValueError('batchsize is bigger than the dataset size.')
            while True:
                for i in range(sizes):
                    result = []
                    sz = [0, 0]
                    max_len = 0
                    for k in range(batchsize):
                        line = img_label_lines[i * batchsize + k]
                        items = line.split(' ')
                        label = [int(c) for c in items[-1].split(',')]
                        max_len = max(max_len, len(label))

                    #print( "max len", max_len, i)
                    max_length = max_len

                    #mask = np.zeros( (batchsize, max_length)).astype('float32')

                    for j in range(batchsize):
                        line = img_label_lines[i * batchsize + j]
                        items = line.split(' ')
                        label = [int(c) for c in items[-1].split(',')]

                        mask = np.zeros((max_len)).astype('float32')
                        mask[:len(label) + 1] = 1.0
                        #mask[ j, :len(label) + 1] = 1.0
                        if max_length > len(label) + 1:
                            extend_label = [EOS] * (max_length - len(label) - 1)
                            label.extend(extend_label)
                        else:
                            label = label[0:max_length - 1]
                        img = Image.open(os.path.join(img_root_dir, items[
                            2])).convert('L')
                        if j == 0:
                            sz = img.size
                        img = img.resize((sz[0], sz[1]))
                        img = np.array(img) - 127.5
                        img = img[np.newaxis, ...]
                        result.append([img, [SOS] + label, label + [EOS], mask])
                    yield result
                if not cycle:
                    break

        return reader

    def test_reader(self, img_root_dir, img_label_list):
        '''
        Reader interface for inference.

        :param img_root_dir: The root path of the images for training.
        :type img_root_dir: str

        :param img_label_list: The path of the <image_name, label> file for testing.
        :type img_label_list: str
        '''

        def reader():
            for line in open(img_label_list):
                # h, w, img_name, labels
                items = line.split(' ')

                label = [int(c) for c in items[-1].split(',')]
                img = Image.open(os.path.join(img_root_dir, items[2])).convert(
                    'L')
                img = np.array(img) - 127.5
                img = img[np.newaxis, ...]
                if self.model == "crnn_ctc":
                    yield img, label
                else:
                    yield img, [SOS] + label, label + [EOS]

        return reader

    def infer_reader(self, img_root_dir=None, img_label_list=None, cycle=False):
        '''A reader interface for inference.

        :param img_root_dir: The root path of the images for training.
        :type img_root_dir: str

        :param img_label_list: The path of the <image_name, label> file for
        inference. It should be the path of <image_path> file if img_root_dir
        was None. If img_label_list was set to None, it will read image path
        from stdin.
        :type img_root_dir: str

        :param cycle: If number of iterations is greater than dataset_size /
        batch_size it reiterates dataset over as many times as necessary.
        :type cycle: bool
        '''

        def reader():
            def yield_img_and_label(lines):
                for line in lines:
                    if img_root_dir is not None:
                        # h, w, img_name, labels
                        img_name = line.split(' ')[2]
                        img_path = os.path.join(img_root_dir, img_name)
                    else:
                        img_path = line.strip("\t\n\r")
                    img = Image.open(img_path).convert('L')
                    img = np.array(img) - 127.5
                    img = img[np.newaxis, ...]
                    label = [int(c) for c in line.split(' ')[3].split(',')]
                    yield img, label

            if img_label_list is not None:
                lines = []
                with open(img_label_list) as f:
                    lines = f.readlines()
                for img, label in yield_img_and_label(lines):
                    yield img, label
                while cycle:
                    for img, label in yield_img_and_label(lines):
                        yield img, label
            else:
                while True:
                    img_path = input("Please input the path of image: ")
                    img = Image.open(img_path).convert('L')
                    img = np.array(img) - 127.5
                    img = img[np.newaxis, ...]
                    yield img, [[0]]

        return reader


def num_classes():
    '''Get classes number of this dataset.
    '''
    return NUM_CLASSES


def data_shape():
    '''Get image shape of this dataset. It is a dummy shape for this dataset.
    '''
    return DATA_SHAPE


def train(batch_size,
          max_length,
          train_images_dir=None,
          train_list_file=None,
          cycle=False,
          shuffle=False,
          model="crnn_ctc"):
    generator = DataGenerator(model)
    if train_images_dir is None:
        data_dir = download_data()
        train_images_dir = path.join(data_dir, TRAIN_DATA_DIR_NAME)
    if train_list_file is None:
        train_list_file = path.join(data_dir, TRAIN_LIST_FILE_NAME)
    return generator.train_reader(
        train_images_dir,
        train_list_file,
        batch_size,
        cycle,
        max_length,
        shuffle=shuffle)


def test(batch_size=1,
         test_images_dir=None,
         test_list_file=None,
         model="crnn_ctc"):
    generator = DataGenerator(model)
    if test_images_dir is None:
        data_dir = download_data()
        test_images_dir = path.join(data_dir, TEST_DATA_DIR_NAME)
    if test_list_file is None:
        test_list_file = path.join(data_dir, TEST_LIST_FILE_NAME)
    return paddle.batch(
        generator.test_reader(test_images_dir, test_list_file), batch_size)


def inference(batch_size=1,
              infer_images_dir=None,
              infer_list_file=None,
              cycle=False,
              model="crnn_ctc"):
    generator = DataGenerator(model)
    return paddle.batch(
        generator.infer_reader(infer_images_dir, infer_list_file, cycle),
        batch_size)


def download_data():
    '''Download train and test data.
    '''
    tar_file = paddle.dataset.common.download(
        DATA_URL, CACHE_DIR_NAME, DATA_MD5, save_name=SAVED_FILE_NAME)
    data_dir = path.join(path.dirname(tar_file), DATA_DIR_NAME)
    if not path.isdir(data_dir):
        t = tarfile.open(tar_file, "r:gz")
        t.extractall(path=path.dirname(tar_file))
        t.close()
    return data_dir