# 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 os
import sys

import numpy as np
import paddle
from paddle import inference
from scipy.special import softmax

from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import AutoTokenizer
from paddlenlp.utils.log import logger

sys.path.append(".")

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", type=str, required=True, help="The directory to static model.")
parser.add_argument("--input_file", type=str, required=True, help="The test set file.")
parser.add_argument("--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument('--device', choices=['cpu', 'gpu', 'xpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument('--use_tensorrt', default=False, type=eval, choices=[True, False], help='Enable to use tensorrt to speed up.')
parser.add_argument("--precision", default="fp32", type=str, choices=["fp32", "fp16", "int8"], help='The tensorrt precision.')
parser.add_argument('--cpu_threads', default=10, type=int, help='Number of threads to predict when using cpu.')
parser.add_argument('--enable_mkldnn', default=False, type=eval, choices=[True, False], help='Enable to use mkldnn to speed up when using cpu.')
parser.add_argument("--benchmark", type=eval, default=False, help="To log some information about environment and running.")
parser.add_argument("--save_log_path", type=str, default="./log_output/", help="The file path to save log.")
parser.add_argument('--model_name_or_path', default="rocketqa-base-cross-encoder", help="The pretrained model used for training")
args = parser.parse_args()
# yapf: enable


def read_text_pair(data_path):
    """Reads data."""
    with open(data_path, "r", encoding="utf-8") as f:
        for line in f:
            data = line.rstrip().split("\t")
            if len(data) != 3:
                continue
            yield {"query": data[0], "title": data[1]}


def convert_example(example, tokenizer, max_seq_length=512, is_test=False):

    query, title = example["query"], example["title"]

    encoded_inputs = tokenizer(text=query, text_pair=title, max_seq_len=max_seq_length)

    input_ids = encoded_inputs["input_ids"]
    token_type_ids = encoded_inputs["token_type_ids"]

    if not is_test:
        label = np.array([example["label"]], dtype="int64")
        return input_ids, token_type_ids, label
    else:
        return input_ids, token_type_ids


class Predictor(object):
    def __init__(
        self,
        model_dir,
        device="gpu",
        max_seq_length=128,
        batch_size=32,
        use_tensorrt=False,
        precision="fp32",
        cpu_threads=10,
        enable_mkldnn=False,
    ):
        self.max_seq_length = max_seq_length
        self.batch_size = batch_size

        model_file = model_dir + "/inference.pdmodel"
        params_file = model_dir + "/inference.pdiparams"
        if not os.path.exists(model_file):
            raise ValueError("not find model file path {}".format(model_file))
        if not os.path.exists(params_file):
            raise ValueError("not find params file path {}".format(params_file))
        config = paddle.inference.Config(model_file, params_file)

        if device == "gpu":
            # set GPU configs accordingly
            # such as initialize the gpu memory, enable tensorrt
            config.enable_use_gpu(100, 0)
            precision_map = {
                "fp16": inference.PrecisionType.Half,
                "fp32": inference.PrecisionType.Float32,
                "int8": inference.PrecisionType.Int8,
            }
            precision_mode = precision_map[precision]

            if args.use_tensorrt:
                config.enable_tensorrt_engine(
                    max_batch_size=batch_size, min_subgraph_size=30, precision_mode=precision_mode
                )
        elif device == "cpu":
            # set CPU configs accordingly,
            # such as enable_mkldnn, set_cpu_math_library_num_threads
            config.disable_gpu()
            if args.enable_mkldnn:
                # cache 10 different shapes for mkldnn to avoid memory leak
                config.set_mkldnn_cache_capacity(10)
                config.enable_mkldnn()
            config.set_cpu_math_library_num_threads(args.cpu_threads)
        elif device == "xpu":
            # set XPU configs accordingly
            config.enable_xpu(100)

        config.switch_use_feed_fetch_ops(False)
        self.predictor = paddle.inference.create_predictor(config)
        self.input_handles = [self.predictor.get_input_handle(name) for name in self.predictor.get_input_names()]
        self.output_handle = self.predictor.get_output_handle(self.predictor.get_output_names()[0])

        if args.benchmark:
            import auto_log

            pid = os.getpid()
            self.autolog = auto_log.AutoLogger(
                model_name=args.model_name_or_path,
                model_precision=precision,
                batch_size=self.batch_size,
                data_shape="dynamic",
                save_path=args.save_log_path,
                inference_config=config,
                pids=pid,
                process_name=None,
                gpu_ids=0,
                time_keys=["preprocess_time", "inference_time", "postprocess_time"],
                warmup=0,
                logger=logger,
            )

    def predict(self, data, tokenizer):
        """
        Predicts the data labels.

        Args:
            data (obj:`List(str)`): The batch data whose each element is a raw text.
            tokenizer(obj:`PretrainedTokenizer`): This tokenizer inherits from :class:`~paddlenlp.transformers.PretrainedTokenizer`
                which contains most of the methods. Users should refer to the superclass for more information regarding methods.
            label_map(obj:`dict`): The label id (key) to label str (value) map.

        Returns:
            results(obj:`dict`): All the predictions labels.
        """
        if args.benchmark:
            self.autolog.times.start()

        examples = []
        for text in data:
            input_ids, segment_ids = convert_example(text, tokenizer, max_seq_length=self.max_seq_length, is_test=True)
            examples.append((input_ids, segment_ids))

        batchify_fn = lambda samples, fn=Tuple(
            Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # input
            Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # segment
        ): fn(samples)

        if args.benchmark:
            self.autolog.times.stamp()

        input_ids, segment_ids = batchify_fn(examples)
        self.input_handles[0].copy_from_cpu(input_ids)
        self.input_handles[1].copy_from_cpu(segment_ids)
        self.predictor.run()
        sim_score = self.output_handle.copy_to_cpu()
        if args.benchmark:
            self.autolog.times.stamp()
        sim_score = softmax(sim_score)[:, 1]

        if args.benchmark:
            self.autolog.times.end(stamp=True)

        return sim_score


if __name__ == "__main__":
    # Define predictor to do prediction.
    predictor = Predictor(
        args.model_dir,
        args.device,
        args.max_seq_length,
        args.batch_size,
        args.use_tensorrt,
        args.precision,
        args.cpu_threads,
        args.enable_mkldnn,
    )

    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)

    test_ds = load_dataset(read_text_pair, data_path=args.input_file, lazy=False)

    data = [{"query": d["query"], "title": d["title"]} for d in test_ds]

    batches = [data[idx : idx + args.batch_size] for idx in range(0, len(data), args.batch_size)]

    results = []
    for batch_data in batches:
        results.extend(predictor.predict(batch_data, tokenizer))
    for idx, text in enumerate(data):
        print("Data: {} \t prob: {}".format(text, results[idx]))
    if args.benchmark:
        predictor.autolog.report()
