import argparse
import csv

import pandas as pd
from tqdm import tqdm

from marcai.predict import predict_onnx
from marcai.process import multiprocess_pairs
from marcai.utils import load_config
from marcai.utils.parsing import load_records, record_dict


def args_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument("-i", "--inputs", nargs="+", help="MARC files", required=True)
    parser.add_argument(
        "-p",
        "--pair-indices",
        help="File containing indices of comparisons",
        required=True,
    )
    parser.add_argument("-C", "--chunksize", help="Chunk size", type=int, default=50000)
    parser.add_argument(
        "-P", "--processes", help="Number of processes", type=int, default=1
    )
    parser.add_argument(
        "-m",
        "--model-dir",
        help="Directory containing model ONNX and YAML files",
        required=True,
    )
    parser.add_argument("-o", "--output", help="Output file", required=True)
    parser.add_argument("-t", "--threshold", help="Threshold for matching", type=float)

    return parser


def main(args):
    config_path = f"{args.model_dir}/config.yaml"
    model_onnx = f"{args.model_dir}/model.onnx"

    config = load_config(config_path)

    # Load records
    print("Loading records...")
    records = []
    for path in args.inputs:
        records.extend([record_dict(r) for r in load_records(path)])

    records_df = pd.DataFrame(records)

    print(f"Loaded {len(records)} records.")

    print("Processing and comparing records...")
    written = False
    with open(args.pair_indices, "r") as indices_file:
        reader = csv.reader(indices_file)
        # Process records
        for df in tqdm(
            multiprocess_pairs(records_df, reader, args.chunksize, args.processes)
        ):
            input_df = df[config["model"]["features"]]
            prediction = predict_onnx(model_onnx, input_df)
            df.loc[:, "prediction"] = prediction.squeeze()

            df = df[df["prediction"] >= args.threshold]

            if not df.empty:
                if not written:
                    df.to_csv(args.output, index=False)
                    written = True
                else:
                    df.to_csv(args.output, index=False, mode="a", header=False)


if __name__ == "__main__":
    args = args_parser().parse_args()
    main(args)