# ################################################################
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under both the BSD-style license (found in the
# LICENSE file in the root directory of this source tree) and the GPLv2 (found
# in the COPYING file in the root directory of this source tree).
# You may select, at your option, one of the above-listed licenses.
# ##########################################################################

import argparse
import glob
import json
import os
import pickle as pk
import subprocess
import time
import urllib.request

GITHUB_API_PR_URL = "https://api.github.com/repos/facebook/zstd/pulls?state=open"
GITHUB_URL_TEMPLATE = "https://github.com/{}/zstd"
RELEASE_BUILD = {"user": "facebook", "branch": "dev", "hash": None}

# check to see if there are any new PRs every minute
DEFAULT_MAX_API_CALL_FREQUENCY_SEC = 60
PREVIOUS_PRS_FILENAME = "prev_prs.pk"

# Not sure what the threshold for triggering alarms should be
# 1% regression sounds like a little too sensitive but the desktop
# that I'm running it on is pretty stable so I think this is fine
CSPEED_REGRESSION_TOLERANCE = 0.01
DSPEED_REGRESSION_TOLERANCE = 0.01


def get_new_open_pr_builds(prev_state=True):
    prev_prs = None
    if os.path.exists(PREVIOUS_PRS_FILENAME):
        with open(PREVIOUS_PRS_FILENAME, "rb") as f:
            prev_prs = pk.load(f)
    data = json.loads(urllib.request.urlopen(GITHUB_API_PR_URL).read().decode("utf-8"))
    prs = {
        d["url"]: {
            "user": d["user"]["login"],
            "branch": d["head"]["ref"],
            "hash": d["head"]["sha"].strip(),
        }
        for d in data
    }
    with open(PREVIOUS_PRS_FILENAME, "wb") as f:
        pk.dump(prs, f)
    if not prev_state or prev_prs == None:
        return list(prs.values())
    return [pr for url, pr in prs.items() if url not in prev_prs or prev_prs[url] != pr]


def get_latest_hashes():
    tmp = subprocess.run(["git", "log", "-1"], stdout=subprocess.PIPE).stdout.decode("utf-8")
    sha1 = tmp.split("\n")[0].split(" ")[1]
    tmp = subprocess.run(["git", "show", f"{sha1}^1"], stdout=subprocess.PIPE).stdout.decode(
        "utf-8"
    )
    sha2 = tmp.split("\n")[0].split(" ")[1]
    tmp = subprocess.run(["git", "show", f"{sha1}^2"], stdout=subprocess.PIPE).stdout.decode(
        "utf-8"
    )
    sha3 = "" if len(tmp) == 0 else tmp.split("\n")[0].split(" ")[1]
    return [sha1.strip(), sha2.strip(), sha3.strip()]


def get_builds_for_latest_hash():
    hashes = get_latest_hashes()
    for b in get_new_open_pr_builds(False):
        if b["hash"] in hashes:
            return [b]
    return []


def clone_and_build(build):
    if build["user"] != None:
        github_url = GITHUB_URL_TEMPLATE.format(build["user"])
        os.system(
            """
            rm -rf zstd-{user}-{sha} &&
            git clone {github_url} zstd-{user}-{sha} &&
            cd zstd-{user}-{sha} &&
            {checkout_command}
            make -j &&
            cd ../
        """.format(
                user=build["user"],
                github_url=github_url,
                sha=build["hash"],
                checkout_command="git checkout {} &&".format(build["hash"])
                if build["hash"] != None
                else "",
            )
        )
        return "zstd-{user}-{sha}/zstd".format(user=build["user"], sha=build["hash"])
    else:
        os.system("cd ../ && make -j && cd tests")
        return "../zstd"


def parse_benchmark_output(output):
    idx = [i for i, d in enumerate(output) if d == "MB/s"]
    return [float(output[idx[0] - 1]), float(output[idx[1] - 1])]


def benchmark_single(executable, level, filename):
    return parse_benchmark_output(
        subprocess.run(
            [executable, f"-qb{level}", filename],
            stdout=subprocess.PIPE,
            stderr=subprocess.STDOUT,
        )
        .stdout.decode("utf-8")
        .split(" ")
    )


def benchmark_n(executable, level, filename, n):
    speeds_arr = [benchmark_single(executable, level, filename) for _ in range(n)]
    cspeed, dspeed = max(b[0] for b in speeds_arr), max(b[1] for b in speeds_arr)
    print(
        f"Bench (executable={os.path.basename(executable)} level={level} filename={os.path.basename(filename)}, iterations={n}):\n\t[cspeed: {cspeed} MB/s, dspeed: {dspeed} MB/s]"
    )
    return (cspeed, dspeed)


def benchmark(build, filenames, levels, iterations):
    executable = clone_and_build(build)
    return [[benchmark_n(executable, l, f, iterations) for f in filenames] for l in levels]


def benchmark_dictionary_single(
    executable, filenames_directory, dictionary_filename, level, iterations
):
    cspeeds, dspeeds = [], []
    for _ in range(iterations):
        output = (
            subprocess.run(
                [executable, f"-qb{level}", "-D", dictionary_filename, "-r", filenames_directory],
                stdout=subprocess.PIPE,
            )
            .stdout.decode("utf-8")
            .split(" ")
        )
        cspeed, dspeed = parse_benchmark_output(output)
        cspeeds.append(cspeed)
        dspeeds.append(dspeed)
    max_cspeed, max_dspeed = max(cspeeds), max(dspeeds)
    print(
        f"Bench (executable={os.path.basename(executable)} level={level} filenames_directory={os.path.basename(filenames_directory)}, dictionary_filename={os.path.basename(dictionary_filename)}, iterations={iterations}):\n\t[cspeed: {max_cspeed} MB/s, dspeed: {max_dspeed} MB/s]"
    )
    return (max_cspeed, max_dspeed)


def benchmark_dictionary(build, filenames_directory, dictionary_filename, levels, iterations):
    executable = clone_and_build(build)
    return [
        benchmark_dictionary_single(
            executable, filenames_directory, dictionary_filename, l, iterations
        )
        for l in levels
    ]


def parse_regressions_and_labels(
    old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build
):
    cspeed_reg = (old_cspeed - new_cspeed) / old_cspeed
    dspeed_reg = (old_dspeed - new_dspeed) / old_dspeed
    baseline_label = "{}:{} ({})".format(
        baseline_build["user"], baseline_build["branch"], baseline_build["hash"]
    )
    test_label = "{}:{} ({})".format(test_build["user"], test_build["branch"], test_build["hash"])
    return cspeed_reg, dspeed_reg, baseline_label, test_label


def get_regressions(baseline_build, test_build, iterations, filenames, levels):
    old = benchmark(baseline_build, filenames, levels, iterations)
    new = benchmark(test_build, filenames, levels, iterations)
    regressions = []
    for j, level in enumerate(levels):
        for k, filename in enumerate(filenames):
            old_cspeed, old_dspeed = old[j][k]
            new_cspeed, new_dspeed = new[j][k]
            cspeed_reg, dspeed_reg, baseline_label, test_label = parse_regressions_and_labels(
                old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build
            )
            if cspeed_reg > CSPEED_REGRESSION_TOLERANCE:
                regressions.append(
                    f"[COMPRESSION REGRESSION] (level={level} filename={filename})\n\t{baseline_label} -> {test_label}\n\t{old_cspeed} -> {new_cspeed} ({cspeed_reg * 100.0:0.2f}%)"
                )
            if dspeed_reg > DSPEED_REGRESSION_TOLERANCE:
                regressions.append(
                    f"[DECOMPRESSION REGRESSION] (level={level} filename={filename})\n\t{baseline_label} -> {test_label}\n\t{old_dspeed} -> {new_dspeed} ({dspeed_reg * 100.0:0.2f}%)"
                )
    return regressions


def get_regressions_dictionary(
    baseline_build, test_build, filenames_directory, dictionary_filename, levels, iterations
):
    old = benchmark_dictionary(
        baseline_build, filenames_directory, dictionary_filename, levels, iterations
    )
    new = benchmark_dictionary(
        test_build, filenames_directory, dictionary_filename, levels, iterations
    )
    regressions = []
    for j, level in enumerate(levels):
        old_cspeed, old_dspeed = old[j]
        new_cspeed, new_dspeed = new[j]
        cspeed_reg, dspeed_reg, baesline_label, test_label = parse_regressions_and_labels(
            old_cspeed, new_cspeed, old_dspeed, new_dspeed, baseline_build, test_build
        )
        if cspeed_reg > CSPEED_REGRESSION_TOLERANCE:
            regressions.append(
                f"[COMPRESSION REGRESSION] (level={level} filenames_directory={filenames_directory} dictionary_filename={dictionary_filename})\n\t{baseline_label} -> {test_label}\n\t{old_cspeed} -> {new_cspeed} ({cspeed_reg * 100.0:0.2f}%)"
            )
        if dspeed_reg > DSPEED_REGRESSION_TOLERANCE:
            regressions.append(
                f"[DECOMPRESSION REGRESSION] (level={level} filenames_directory={filenames_directory} dictionary_filename={dictionary_filename})\n\t{baseline_label} -> {test_label}\n\t{old_dspeed} -> {new_dspeed} ({dspeed_reg * 100.0:0.2f}%)"
            )
        return regressions


def main(
    filenames,
    levels,
    iterations,
    builds=None,
    emails=None,
    continuous=False,
    frequency=DEFAULT_MAX_API_CALL_FREQUENCY_SEC,
    dictionary_filename=None,
):
    if builds == None:
        builds = get_new_open_pr_builds()
    while True:
        for test_build in builds:
            if dictionary_filename == None:
                regressions = get_regressions(
                    RELEASE_BUILD, test_build, iterations, filenames, levels
                )
            else:
                regressions = get_regressions_dictionary(
                    RELEASE_BUILD, test_build, filenames, dictionary_filename, levels, iterations
                )
            body = "\n".join(regressions)
            if len(regressions) > 0:
                if emails != None:
                    os.system(
                        f"""
                        echo "{body}" | mutt -s "[zstd regression] caused by new pr" {emails}
                    """
                    )
                    print(f"Emails sent to {emails}")
                print(body)
        if not continuous:
            break
        time.sleep(frequency)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--directory", help="directory with files to benchmark", default="golden-compression"
    )
    parser.add_argument("--levels", help="levels to test e.g. ('1,2,3')", default="1")
    parser.add_argument("--iterations", help="number of benchmark iterations to run", default="1")
    parser.add_argument(
        "--emails",
        help="email addresses of people who will be alerted upon regression. Only for continuous mode",
        default=None,
    )
    parser.add_argument(
        "--frequency",
        help="specifies the number of seconds to wait before each successive check for new PRs in continuous mode",
        default=DEFAULT_MAX_API_CALL_FREQUENCY_SEC,
    )
    parser.add_argument(
        "--mode",
        help="'fastmode', 'onetime', 'current', or 'continuous' (see README.md for details)",
        default="current",
    )
    parser.add_argument(
        "--dict",
        help="filename of dictionary to use (when set, this dictionary will be used to compress the files provided inside --directory)",
        default=None,
    )

    args = parser.parse_args()
    filenames = args.directory
    levels = [int(l) for l in args.levels.split(",")]
    mode = args.mode
    iterations = int(args.iterations)
    emails = args.emails
    frequency = int(args.frequency)
    dictionary_filename = args.dict

    if dictionary_filename == None:
        filenames = glob.glob(f"{filenames}/**")

    if len(filenames) == 0:
        print("0 files found")
        quit()

    if mode == "onetime":
        main(
            filenames,
            levels,
            iterations,
            frequency=frequenc,
            dictionary_filename=dictionary_filename,
        )
    elif mode == "current":
        builds = [{"user": None, "branch": "None", "hash": None}]
        main(
            filenames,
            levels,
            iterations,
            builds,
            frequency=frequency,
            dictionary_filename=dictionary_filename,
        )
    elif mode == "fastmode":
        builds = [{"user": "facebook", "branch": "release", "hash": None}]
        main(
            filenames,
            levels,
            iterations,
            builds,
            frequency=frequency,
            dictionary_filename=dictionary_filename,
        )
    else:
        main(
            filenames,
            levels,
            iterations,
            None,
            emails,
            True,
            frequency=frequency,
            dictionary_filename=dictionary_filename,
        )
