import sys
import timeit
import logging
import argparse
import subprocess

import tqdm
import tabulate

import capa.main
import capa.perf
import capa.rules
import capa.engine
import capa.helpers
import capa.features
import capa.features.common
import capa.features.freeze

logger = logging.getLogger("capa.profile")


def main(argv=None):
    if argv is None:
        argv = sys.argv[1:]

    label = subprocess.run(
        "git show --pretty=oneline --abbrev-commit | head -n 1", shell=True, capture_output=True, text=True
    ).stdout.strip()
    is_dirty = (
        subprocess.run(
            "git status | grep 'modified: ' | grep -v 'rules' | grep -v 'tests/data'",
            shell=True,
            capture_output=True,
            text=True,
        ).stdout
        != ""
    )

    if is_dirty:
        label += " (dirty)"

    parser = argparse.ArgumentParser(description="Profile capa performance")
    capa.main.install_common_args(parser, wanted={"format", "sample", "signatures", "rules"})

    parser.add_argument("--number", type=int, default=3, help="batch size of profile collection")
    parser.add_argument("--repeat", type=int, default=30, help="batch count of profile collection")
    parser.add_argument("--label", type=str, default=label, help="description of the profile collection")

    args = parser.parse_args(args=argv)
    capa.main.handle_common_args(args)

    try:
        taste = capa.helpers.get_file_taste(args.sample)
    except IOError as e:
        logger.error("%s", str(e))
        return -1

    try:
        with capa.main.timing("load rules"):
            rules = capa.rules.RuleSet(capa.main.get_rules(args.rules, disable_progress=True))
    except (IOError) as e:
        logger.error("%s", str(e))
        return -1

    try:
        sig_paths = capa.main.get_signatures(args.signatures)
    except (IOError) as e:
        logger.error("%s", str(e))
        return -1

    if (args.format == "freeze") or (args.format == "auto" and capa.features.freeze.is_freeze(taste)):
        with open(args.sample, "rb") as f:
            extractor = capa.features.freeze.load(f.read())
    else:
        extractor = capa.main.get_extractor(
            args.sample, args.format, capa.main.BACKEND_VIV, sig_paths, should_save_workspace=False
        )

    with tqdm.tqdm(total=args.number * args.repeat) as pbar:

        def do_iteration():
            capa.perf.reset()
            capa.main.find_capabilities(rules, extractor, disable_progress=True)
            pbar.update(1)

        samples = timeit.repeat(do_iteration, number=args.number, repeat=args.repeat)

    logger.debug("perf: find capabilities: min: %0.2fs" % (min(samples) / float(args.number)))
    logger.debug("perf: find capabilities: avg: %0.2fs" % (sum(samples) / float(args.repeat) / float(args.number)))
    logger.debug("perf: find capabilities: max: %0.2fs" % (max(samples) / float(args.number)))

    for (counter, count) in capa.perf.counters.most_common():
        logger.debug("perf: counter: {:}: {:,}".format(counter, count))

    print(
        tabulate.tabulate(
            [
                (
                    args.label,
                    "{:,}".format(capa.perf.counters["evaluate.feature"]),
                    "%0.2fs" % (sum(samples) / float(args.repeat) / float(args.number)),
                    "%0.2fs" % (min(samples) / float(args.number)),
                    "%0.2fs" % (max(samples) / float(args.number)),
                )
            ],
            headers=["label", "count(evaluations)", "avg(time)", "min(time)", "max(time)"],
            tablefmt="github",
        )
    )

    return 0


if __name__ == "__main__":
    sys.exit(main())

add py script for profiling time