# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 logging
import os
import imghdr
import cv2
import random
import numpy as np
import paddle


def print_dict(d, logger, delimiter=0):
    """
    Recursively visualize a dict and
    indenting acrrording by the relationship of keys.
    """
    for k, v in sorted(d.items()):
        if isinstance(v, dict):
            logger.info("{}{} : ".format(delimiter * " ", str(k)))
            print_dict(v, logger, delimiter + 4)
        elif isinstance(v, list) and len(v) >= 1 and isinstance(v[0], dict):
            logger.info("{}{} : ".format(delimiter * " ", str(k)))
            for value in v:
                print_dict(value, logger, delimiter + 4)
        else:
            logger.info("{}{} : {}".format(delimiter * " ", k, v))


def get_check_global_params(mode):
    check_params = ['use_gpu', 'max_text_length', 'image_shape', \
                    'image_shape', 'character_type', 'loss_type']
    if mode == "train_eval":
        check_params = check_params + [ \
            'train_batch_size_per_card', 'test_batch_size_per_card']
    elif mode == "test":
        check_params = check_params + ['test_batch_size_per_card']
    return check_params


def get_image_file_list(img_file):
    imgs_lists = []
    if img_file is None or not os.path.exists(img_file):
        raise Exception("not found any img file in {}".format(img_file))

    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif', 'GIF'}
    if os.path.isfile(img_file) and imghdr.what(img_file) in img_end:
        imgs_lists.append(img_file)
    elif os.path.isdir(img_file):
        for single_file in os.listdir(img_file):
            file_path = os.path.join(img_file, single_file)
            if os.path.isfile(file_path) and imghdr.what(file_path) in img_end:
                imgs_lists.append(file_path)
    if len(imgs_lists) == 0:
        raise Exception("not found any img file in {}".format(img_file))
    imgs_lists = sorted(imgs_lists)
    return imgs_lists


def check_and_read_gif(img_path):
    if os.path.basename(img_path)[-3:] in ['gif', 'GIF']:
        gif = cv2.VideoCapture(img_path)
        ret, frame = gif.read()
        if not ret:
            logger = logging.getLogger('ppocr')
            logger.info("Cannot read {}. This gif image maybe corrupted.")
            return None, False
        if len(frame.shape) == 2 or frame.shape[-1] == 1:
            frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
        imgvalue = frame[:, :, ::-1]
        return imgvalue, True
    return None, False


def load_vqa_bio_label_maps(label_map_path):
    with open(label_map_path, "r", encoding='utf-8') as fin:
        lines = fin.readlines()
    lines = [line.strip() for line in lines]
    if "O" not in lines:
        lines.insert(0, "O")
    labels = []
    for line in lines:
        if line == "O":
            labels.append("O")
        else:
            labels.append("B-" + line)
            labels.append("I-" + line)
    label2id_map = {label: idx for idx, label in enumerate(labels)}
    id2label_map = {idx: label for idx, label in enumerate(labels)}
    return label2id_map, id2label_map


def set_seed(seed=1024):
    random.seed(seed)
    np.random.seed(seed)
    paddle.seed(seed)


class AverageMeter:
    def __init__(self):
        self.reset()

    def reset(self):
        """reset"""
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        """update"""
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
