# Copyright (c) 2021 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 argparse

import numpy as np

parser = argparse.ArgumentParser()
parser.add_argument("--similar_text_pair", type=str, default="", help="The full path of similat pair file")
parser.add_argument("--recall_result_file", type=str, default="", help="The full path of recall result file")
parser.add_argument(
    "--recall_num", type=int, default=10, help="Most similair number of doc recalled from corpus per query"
)
args = parser.parse_args()


def recall(rs, N=10):
    """
    Ratio of recalled Ground Truth at topN Recalled Docs
    >>> rs = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]
    >>> recall(rs, N=1)
    0.333333
    >>> recall(rs, N=2)
    >>> 0.6666667
    >>> recall(rs, N=3)
    >>> 1.0
    Args:
        rs: Iterator of recalled flag()
    Returns:
        Recall@N
    """

    recall_flags = [np.sum(r[0:N]) for r in rs]
    return np.mean(recall_flags)


if __name__ == "__main__":
    text2similar = {}
    with open(args.similar_text_pair, "r", encoding="utf-8") as f:
        for line in f:
            text, similar_text = line.rstrip().split("\t")
            text2similar[text] = similar_text

    rs = []

    with open(args.recall_result_file, "r", encoding="utf-8") as f:
        relevance_labels = []
        for index, line in enumerate(f):

            if index % args.recall_num == 0 and index != 0:
                rs.append(relevance_labels)
                relevance_labels = []

            text, recalled_text, cosine_sim = line.rstrip().split("\t")
            if text == recalled_text:
                continue
            if text2similar[text] == recalled_text:
                relevance_labels.append(1)
            else:
                relevance_labels.append(0)

    recall_N = []
    for topN in (10, 50):
        R = round(100 * recall(rs, N=topN), 3)
        recall_N.append(str(R))
    print("\t".join(recall_N))
