# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import ast
from PIL import Image
import numpy as np
from tools.infer.utility import draw_ocr_box_txt, init_args as infer_args


def init_args():
    parser = infer_args()

    # params for output
    parser.add_argument("--output", type=str, default='./output')
    # params for table structure
    parser.add_argument("--table_max_len", type=int, default=488)
    parser.add_argument("--table_model_dir", type=str)
    parser.add_argument("--table_char_type", type=str, default='en')
    parser.add_argument(
        "--table_char_dict_path",
        type=str,
        default="../ppocr/utils/dict/table_structure_dict.txt")
    parser.add_argument(
        "--layout_path_model",
        type=str,
        default="lp://PubLayNet/ppyolov2_r50vd_dcn_365e_publaynet/config")
    parser.add_argument(
        "--layout_label_map",
        type=ast.literal_eval,
        default=None,
        help='label map according to ppstructure/layout/README_ch.md')
    # params for ser
    parser.add_argument("--model_name_or_path", type=str)
    parser.add_argument("--max_seq_length", type=int, default=512)
    parser.add_argument(
        "--label_map_path", type=str, default='./vqa/labels/labels_ser.txt')

    parser.add_argument(
        "--mode",
        type=str,
        default='structure',
        help='structure and vqa is supported')
    return parser


def parse_args():
    parser = init_args()
    return parser.parse_args()


def draw_structure_result(image, result, font_path):
    if isinstance(image, np.ndarray):
        image = Image.fromarray(image)
    boxes, txts, scores = [], [], []
    for region in result:
        if region['type'] == 'Table':
            pass
        else:
            for box, rec_res in zip(region['res'][0], region['res'][1]):
                boxes.append(np.array(box).reshape(-1, 2))
                txts.append(rec_res[0])
                scores.append(rec_res[1])
    im_show = draw_ocr_box_txt(
        image, boxes, txts, scores, font_path=font_path, drop_score=0)
    return im_show