# This file is part of Xpra.
# Copyright (C) 2011 Serviware (Arthur Huillet, <ahuillet@serviware.com>)
# Copyright (C) 2010-2024 Antoine Martin <antoine@xpra.org>
# Copyright (C) 2008 Nathaniel Smith <njs@pobox.com>
# Xpra is released under the terms of the GNU GPL v2, or, at your option, any
# later version. See the file COPYING for details.

from math import sqrt
from time import monotonic
from typing import Any
from collections import deque
from collections.abc import Sequence

from xpra.server.cystats import (
    logp, calculate_time_weighted_average, calculate_size_weighted_average,
    calculate_for_target, time_weighted_average, queue_inspect,
)
from xpra.util.stats import get_list_stats
from xpra.log import Logger

log = Logger("network", "stats")

NRECS = 500


def safeint(v, default: int = 0) -> int:
    try:
        return int(v)
    except ValueError:
        return default


class GlobalPerformanceStatistics:
    """
    Statistics which are shared by all WindowSources
    """

    def __init__(self):
        self.reset()

    # assume 100ms until we get some data to compute the real values
    DEFAULT_LATENCY = 0.1

    def reset(self, maxlen=NRECS):
        def d(maxlen=maxlen) -> deque:
            return deque(maxlen=maxlen)

        # mmap state:
        self.mmap_size = 0
        self.mmap_bytes_sent = 0
        self.mmap_free_size = 0  # how much of the mmap space is left (it may be negative if we failed to write the last chunk)
        # queue statistics:
        self.compression_work_qsizes = d()  # size of the compression_work_queue before we add a new record to it
        # (event_time, size)
        self.packet_qsizes = d()  # size of the packet_queue before we add a new packet to it
        # (event_time, size)
        self.damage_packet_qpixels = d()  # number of pixels waiting in the packet_queue for a specific window,
        # before we add a new packet to it
        # (event_time, wid, size)
        self.damage_last_events = d()  # records the x11 damage requests as they are received:
        # (wid, event time, no of pixels)
        self.client_decode_time = d()  # records how long it took the client to decode frames:
        # (wid, event_time, no of pixels, decoding_time*1000*1000)
        self.client_latency = d()  # how long it took for a packet to get to the client and get the echo back.
        # (wid, event_time, no of pixels, client_latency)
        self.client_ping_latency = d()  # time it took to get a ping_echo back from the client:
        # (event_time, elapsed_time_in_seconds)
        self.server_ping_latency = d()  # time it took for the client to get a ping_echo back from us:
        # (event_time, elapsed_time_in_seconds)
        self.congestion_send_speed = d(NRECS // 4)  # when we are being throttled, record what speed we are sending at
        # last NRECS: (event_time, lateness_pct, duration)
        self.bytes_sent = d(NRECS // 4)  # how much bandwidth we are using
        # last NRECS: (sample_time, bytes)
        self.quality = d()  # quality used for sending updates:
        # (event_time, no of pixels, quality)
        self.speed = d()  # speed used for sending updates:
        # (event_time, no of pixels, speed)
        self.frame_total_latency = d()  # how long it takes from the time we get a damage event
        # until we get the ack back from the client
        # (wid, event_time, no_of_pixels, latency)
        self.client_load = None
        self.last_congestion_time = 0
        self.congestion_value = 0
        self.damage_events_count = 0
        self.packet_count = 0
        self.decode_errors = 0
        # these values are calculated from the values above (see update_averages)
        self.min_client_latency = self.DEFAULT_LATENCY
        self.avg_client_latency = self.DEFAULT_LATENCY
        self.recent_client_latency = self.DEFAULT_LATENCY
        self.min_client_ping_latency = self.DEFAULT_LATENCY
        self.avg_client_ping_latency = self.DEFAULT_LATENCY
        self.recent_client_ping_latency = self.DEFAULT_LATENCY
        self.min_server_ping_latency = self.DEFAULT_LATENCY
        self.avg_server_ping_latency = self.DEFAULT_LATENCY
        self.recent_server_ping_latency = self.DEFAULT_LATENCY
        self.avg_congestion_send_speed = 0
        self.avg_frame_total_latency = 0

    def record_latency(self, wid: int, damage_packet_sequence: int, decode_time: int,
                       queued_at: float, pixels: int, bytecount: int, latency: int) -> None:
        now = monotonic()
        total = now - queued_at  # from the moment we queue the packet until we process the ack here
        dt = decode_time / 1000.0 / 1000.0  # decode_time is given in microseconds, convert to seconds
        log("latency: %6.1fms for %9i pixels :      %6.1f send     + %6.1f decoding            %6iKB,  sequence %5i",
            total * 1000, pixels, queued_at * 1000, dt * 1000,
            bytecount // 1024, damage_packet_sequence)
        net_total_latency = max(0.0, total - dt)
        if self.min_client_latency is None or self.min_client_latency > net_total_latency:
            self.min_client_latency = net_total_latency
        self.client_latency.append((wid, now, pixels, net_total_latency))
        self.frame_total_latency.append((wid, now, pixels, latency))

    def get_damage_pixels(self, wid: int) -> Sequence[tuple[float, int]]:
        """ returns the tuple of (event_time, pixelcount) for the given window id """
        return tuple(
            (event_time, value) for event_time, dwid, value in tuple(self.damage_packet_qpixels) if dwid == wid)

    def update_averages(self) -> None:
        def latency_averages(values):
            avg, recent = calculate_time_weighted_average(values)
            return max(0.001, avg), max(0.001, recent)

        client_latency = tuple(self.client_latency)
        if client_latency:
            data = tuple((when, latency) for _, when, _, latency in client_latency)
            self.min_client_latency = min(x for _, x in data)
            self.avg_client_latency, self.recent_client_latency = latency_averages(data)
        # client ping latency: from ping packets
        client_ping_latency = tuple(self.client_ping_latency)
        if client_ping_latency:
            self.min_client_ping_latency = min(x for _, x in client_ping_latency)
            self.avg_client_ping_latency, self.recent_client_ping_latency = latency_averages(client_ping_latency)
        # server ping latency: from ping packets
        server_ping_latency = tuple(self.server_ping_latency)
        if server_ping_latency:
            self.min_server_ping_latency = min(x for _, x in server_ping_latency)
            self.avg_server_ping_latency, self.recent_server_ping_latency = latency_averages(server_ping_latency)
        # set to 0 if we have less than 2 events in the last 60 seconds:
        now = monotonic()
        min_time = now - 60
        css = tuple(x for x in tuple(self.congestion_send_speed) if x[0] > min_time)
        acss = 0
        if len(css) >= 2:
            # weighted average of the send speed over the last minute:
            acss = safeint(calculate_size_weighted_average(css)[0])
            latest_ctime = self.congestion_send_speed[-1][0]
            elapsed = now - latest_ctime
            # require at least one recent event:
            if elapsed < 30:
                # as the last event recedes in the past, increase limit:
                acss *= 1 + elapsed
        self.avg_congestion_send_speed = int(acss)
        # how often we get congestion events:
        # first chunk it into second intervals
        cst = tuple(x[0] for x in css)
        cps = []
        for t in range(10):
            etime = now - t
            matches = tuple(1 for x in cst if etime - 1 < x <= etime) or (0,)
            cps.append((etime, sum(matches)))
        # log("cps(%s)=%s (now=%s)", cst, cps, now)
        self.congestion_value = time_weighted_average(cps)
        ftl = tuple(self.frame_total_latency)
        if ftl:
            edata = tuple((event_time, pixels, latency) for _, event_time, pixels, latency in ftl)
            # (wid, event_time, no_of_pixels, latency)
            self.avg_frame_total_latency = safeint(calculate_size_weighted_average(edata)[1])

    def get_factors(self, pixel_count: int) -> list[tuple[str, dict, float, float]]:
        factors = []

        def mayaddfac(metric: str, info: dict, factor: float, weight: float):
            if weight > 0.01:
                factors.append((metric, info, factor, weight))

        if self.client_latency:
            # client latency: (we want to keep client latency as low as can be)
            metric = "client-latency"
            l = 0.005 + self.min_client_latency
            wm = logp(l / 0.020)
            mayaddfac(*calculate_for_target(metric, l, self.avg_client_latency, self.recent_client_latency,
                                            aim=0.8, slope=0.005, smoothing=sqrt, weight_multiplier=wm))
        if self.client_ping_latency:
            metric = "client-ping-latency"
            l = 0.005 + self.min_client_ping_latency
            wm = logp(l / 0.050)
            mayaddfac(*calculate_for_target(metric, l, self.avg_client_ping_latency, self.recent_client_ping_latency,
                                            aim=0.95, slope=0.005, smoothing=sqrt, weight_multiplier=wm))
        if self.server_ping_latency:
            metric = "server-ping-latency"
            l = 0.005 + self.min_server_ping_latency
            wm = logp(l / 0.050)
            mayaddfac(*calculate_for_target(metric, l, self.avg_server_ping_latency, self.recent_server_ping_latency,
                                            aim=0.95, slope=0.005, smoothing=sqrt, weight_multiplier=wm))
        # packet queue size: (includes packets from all windows)
        mayaddfac(*queue_inspect("packet-queue-size", self.packet_qsizes, smoothing=sqrt))
        # packet queue pixels (global):
        qpix_time_values = tuple((event_time, value) for event_time, _, value in tuple(self.damage_packet_qpixels))
        mayaddfac(*queue_inspect("packet-queue-pixels", qpix_time_values, div=pixel_count, smoothing=sqrt))
        # compression data queue: (This is an important metric
        # since each item will consume a fair amount of memory
        # and each will later on go through the other queues.)
        mayaddfac(*queue_inspect("compression-work-queue", self.compression_work_qsizes))
        if self.mmap_size > 0:
            # full: effective range is 0.0 to ~1.2
            full = 1.0 - self.mmap_free_size / self.mmap_size
            # aim for ~33%
            mayaddfac("mmap-area", {"full-pct": int(100 * full)}, logp(3 * full), (3 * full) ** 2)
        if self.congestion_value > 0:
            mayaddfac("congestion", {}, 1 + self.congestion_value, self.congestion_value * 10)
        return factors

    def get_connection_info(self) -> dict[str, Any]:
        latencies = tuple(int(x * 1000) for (_, _, _, x) in tuple(self.client_latency))
        now = monotonic()
        info = {
            "mmap_bytecount": self.mmap_bytes_sent,
            "latency": get_list_stats(latencies),
            "server": {
                "ping_latency": get_list_stats(int(1000 * x[1]) for x in tuple(self.server_ping_latency)),
            },
            "client": {
                "ping_latency": get_list_stats(int(1000 * x[1]) for x in tuple(self.client_ping_latency)),
            },
            "congestion": {
                "avg-send-speed": self.avg_congestion_send_speed,
                "elapsed-time": int(now - self.last_congestion_time),
            },
        }
        if self.min_client_latency is not None:
            info["latency"] = {"absmin": int(self.min_client_latency * 1000)}
        return info

    def get_info(self) -> dict[str, Any]:
        cwqsizes = tuple(x[1] for x in tuple(self.compression_work_qsizes))
        pqsizes = tuple(x[1] for x in tuple(self.packet_qsizes))
        now = monotonic()
        time_limit = now - 60  # ignore old records (60s)
        avg_ping = round((self.avg_client_ping_latency + self.avg_server_ping_latency) / 2)
        client_latency = max(0, self.avg_frame_total_latency - avg_ping)
        info: dict[str, Any] = {
            "damage": {
                "events": self.damage_events_count,
                "packets_sent": self.packet_count,
                "data_queue": {
                    "size": get_list_stats(cwqsizes),
                },
                "packet_queue": {
                    "size": get_list_stats(pqsizes),
                },
                "frame-total-latency": self.avg_frame_total_latency,
                "client-latency": client_latency,
            },
            "connection": self.get_connection_info(),
        }
        einfo: dict[str, Any] = {
            "decode_errors": self.decode_errors,
        }
        info["encoding"] = einfo
        if self.quality:
            ql = tuple(quality for _, _, quality in tuple(self.quality))
            einfo["quality"] = get_list_stats(ql)
        if self.speed:
            sl = tuple(speed for _, _, speed in tuple(self.speed))
            einfo["speed"] = get_list_stats(sl)
        # client pixels per second:
        # pixels per second: decode time and overall
        total_pixels = 0  # total number of pixels processed
        total_time = 0  # total decoding time
        start_time = None  # when we start counting from (oldest record)
        region_sizes = []
        for _, event_time, pixels, decode_time in tuple(self.client_decode_time):
            # time filter and ignore failed decoding (decode_time==0)
            if event_time < time_limit or decode_time <= 0:
                continue
            if start_time is None or start_time > event_time:
                start_time = event_time
            total_pixels += pixels
            total_time += decode_time
            region_sizes.append(pixels)
        log("total_time=%s, total_pixels=%s", total_time, total_pixels)
        if total_time > 0:
            pixels_decoded_per_second = int(total_pixels * 1000 * 1000 / total_time)
            einfo["pixels_decoded_per_second"] = pixels_decoded_per_second
        if start_time:
            elapsed = now - start_time
            pixels_per_second = int(total_pixels / elapsed)
            einfo.update({
                "pixels_per_second": pixels_per_second,
                "regions_per_second": int(len(region_sizes) / elapsed),
                "average_region_size": int(total_pixels / len(region_sizes)),
            })
        return info
