from __future__ import annotations

import json
import os
import statistics
import time
import urllib.request

import cv2
import numpy as np

DEMO_URL = "https://raw.githubusercontent.com/open-mmlab/mmpose/v1.3.2/tests/data/coco/000000000785.jpg"


def percentile(values: list[float], p: float) -> float:
    values = sorted(values)
    return values[min(len(values) - 1, max(0, int(np.ceil(len(values) * p)) - 1))]


def run_offline_benchmark(models, threshold: float, max_people: int) -> None:
    path = "/logs/offline-benchmark.json"
    if os.path.exists(path):
        return
    try:
        encoded = np.frombuffer(urllib.request.urlopen(DEMO_URL, timeout=30).read(), dtype=np.uint8)
        one = cv2.imdecode(encoded, cv2.IMREAD_COLOR)
        if one is None:
            raise RuntimeError("official demo image decode failed")
        images = {1: one, 2: np.concatenate([one, one], axis=1),
                  4: np.concatenate([np.concatenate([one, one], axis=1)] * 2, axis=0)}
        for _ in range(50):
            models.infer(one, threshold, max_people)
        report = {"source_url": DEMO_URL, "warmup_iterations": 50, "measured_iterations": 60,
                  "image_retained": False, "profiles": {}}
        for requested, image in images.items():
            samples = []; counts = []
            for _ in range(60):
                started = time.perf_counter_ns()
                people, det_ms, pose_ms = models.infer(image, threshold, max_people)
                samples.append((time.perf_counter_ns() - started) / 1e6); counts.append(len(people))
            report["profiles"][str(requested)] = {
                "requested_people": requested, "detected_people_min": min(counts), "detected_people_max": max(counts),
                "p50_ms": round(percentile(samples, .50), 3), "p95_ms": round(percentile(samples, .95), 3),
                "p99_ms": round(percentile(samples, .99), 3), "fps_from_p50": round(1000 / percentile(samples, .50), 3)}
        with open(path, "x", encoding="utf-8") as fh:
            json.dump(report, fh, indent=2)
    except Exception as exc:
        with open(path, "w", encoding="utf-8") as fh:
            json.dump({"error": f"{type(exc).__name__}: {str(exc)[:240]}", "source_url": DEMO_URL,
                       "image_retained": False}, fh, indent=2)