# Copyright (c) 2022 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 numpy as np
import fastdeploy as fd
import cv2
import os
import re
from tqdm import trange


def topk_accuracy(topk_list, label_list):
    match_array = np.logical_or.reduce(topk_list == label_list, axis=1)
    topk_acc_score = match_array.sum() / match_array.shape[0]
    return topk_acc_score


def eval_classify(model, image_file_path, label_file_path, topk=5):
    result_list = []
    label_list = []
    image_label_dict = {}
    assert os.path.isdir(
        image_file_path), "The image_file_path:{} is not a directory.".format(
            image_file_path)
    assert os.path.isfile(
        label_file_path), "The label_file_path:{} is not a file.".format(
            label_file_path)
    assert isinstance(topk, int), "The tok:{} is not int type".format(topk)
    with open(label_file_path, 'r') as file:
        lines = file.readlines()
        for line in lines:
            items = line.strip().split()
            image_name = items[0]
            label = items[1]
            image_label_dict[image_name] = int(label)
    images_num = len(image_label_dict)
    for (image, label), i in zip(image_label_dict.items(),
                                 trange(
                                     images_num, desc='Inference Progress')):
        label_list.append([label])
        image_path = os.path.join(image_file_path, image)
        im = cv2.imread(image_path)
        result = model.predict(im, topk)
        result_list.append(result.label_ids)

    topk_acc_score = topk_accuracy(np.array(result_list), np.array(label_list))
    return topk_acc_score