# 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 argparse
import sys
import time
import math
import unittest
import contextlib
import numpy as np
import six
import paddle.fluid as fluid
import paddle

import utils


def parse_args():
    parser = argparse.ArgumentParser("gru4rec benchmark.")
    parser.add_argument(
        '--test_dir', type=str, default='test_data', help='test file address')
    parser.add_argument(
        '--start_index', type=int, default='1', help='start index')
    parser.add_argument(
        '--last_index', type=int, default='10', help='end index')
    parser.add_argument(
        '--model_dir', type=str, default='model_recall20', help='model dir')
    parser.add_argument(
        '--use_cuda', type=int, default='0', help='whether use cuda')
    parser.add_argument(
        '--batch_size', type=int, default='5', help='batch_size')
    parser.add_argument(
        '--vocab_path', type=str, default='vocab.txt', help='vocab file')
    args = parser.parse_args()
    return args


def infer(test_reader, use_cuda, model_path):
    """ inference function """
    place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
    exe = fluid.Executor(place)

    with fluid.scope_guard(fluid.Scope()):
        infer_program, feed_target_names, fetch_vars = fluid.io.load_inference_model(
            model_path, exe)
        accum_num_recall = 0.0
        accum_num_sum = 0.0
        t0 = time.time()
        step_id = 0
        for data in test_reader():
            step_id += 1
            src_wordseq = utils.to_lodtensor([dat[0] for dat in data], place)
            label_data = [dat[1] for dat in data]
            dst_wordseq = utils.to_lodtensor(label_data, place)
            para = exe.run(
                infer_program,
                feed={"src_wordseq": src_wordseq,
                      "dst_wordseq": dst_wordseq},
                fetch_list=fetch_vars,
                return_numpy=False)

            acc_ = para[1]._get_float_element(0)
            data_length = len(
                np.concatenate(
                    label_data, axis=0).astype("int64"))
            accum_num_sum += (data_length)
            accum_num_recall += (data_length * acc_)
            if step_id % 1 == 0:
                print("step:%d  recall@20:%.4f" %
                      (step_id, accum_num_recall / accum_num_sum))
        t1 = time.time()
        print("model:%s recall@20:%.3f time_cost(s):%.2f" %
              (model_path, accum_num_recall / accum_num_sum, t1 - t0))


if __name__ == "__main__":
    utils.check_version()
    args = parse_args()
    start_index = args.start_index
    last_index = args.last_index
    test_dir = args.test_dir
    model_dir = args.model_dir
    batch_size = args.batch_size
    vocab_path = args.vocab_path
    use_cuda = True if args.use_cuda else False
    print("start index: ", start_index, " last_index:", last_index)
    vocab_size, test_reader = utils.prepare_data(
        test_dir,
        vocab_path,
        batch_size=batch_size,
        buffer_size=1000,
        word_freq_threshold=0,
        is_train=False)

    for epoch in range(start_index, last_index + 1):
        epoch_path = model_dir + "/epoch_" + str(epoch)
        infer(
            test_reader=test_reader, use_cuda=use_cuda, model_path=epoch_path)