"""Profiling utilities for integration test endpoint latency measurement.

Records per-endpoint timing data and produces summary reports with
P50/P95/P99 percentiles. Flags endpoints exceeding 500ms as slow.

Usage as a pytest fixture (add to conftest.py):
    @pytest.fixture
    def profiler():
        p = EndpointProfiler()
        yield p
        p.print_summary()

Usage as a context manager around httpx calls:
    async with profiler.track("GET /api/companies"):
        resp = await client.get("/api/companies")
"""

from __future__ import annotations

import json
import statistics
import time
from collections import defaultdict
from contextlib import asynccontextmanager
from dataclasses import dataclass, field
from pathlib import Path
from typing import AsyncIterator

SLOW_THRESHOLD_MS = 500.0


@dataclass
class EndpointProfiler:
    """Collects per-endpoint latency samples and produces summary reports."""

    _timings: dict[str, list[float]] = field(
        default_factory=lambda: defaultdict(list)
    )

    @asynccontextmanager
    async def track(self, endpoint: str) -> AsyncIterator[None]:
        """Context manager that records wall-clock time for an endpoint call.

        Uses ``time.monotonic()`` for accurate, monotonically increasing
        measurements unaffected by system clock adjustments.
        """
        start = time.monotonic()
        try:
            yield
        finally:
            elapsed_ms = (time.monotonic() - start) * 1000
            self._timings[endpoint].append(elapsed_ms)

    def record(self, endpoint: str, elapsed_ms: float) -> None:
        """Manually record a timing sample for *endpoint*."""
        self._timings[endpoint].append(elapsed_ms)

    # ------------------------------------------------------------------
    # Percentile helpers
    # ------------------------------------------------------------------

    @staticmethod
    def percentile(values: list[float], pct: float) -> float:
        """Compute the *pct*-th percentile from *values*.

        Uses the same interpolation method as ``statistics.quantiles``
        (exclusive / Method 6) but works for any list length ≥ 1.
        """
        if not values:
            return 0.0
        sorted_vals = sorted(values)
        n = len(sorted_vals)
        if n == 1:
            return sorted_vals[0]
        # Use statistics.quantiles when we have enough data points
        # quantiles(n=100) gives 99 cut points; index pct-1 is the pct-th
        # percentile.  For very small samples we fall back to simple
        # nearest-rank.
        if n >= 2:
            try:
                quantile_cuts = statistics.quantiles(sorted_vals, n=100)
                idx = max(0, min(int(pct) - 1, len(quantile_cuts) - 1))
                return quantile_cuts[idx]
            except statistics.StatisticsError:
                pass
        # Fallback: nearest-rank
        rank = (pct / 100) * (n - 1)
        lower = int(rank)
        upper = min(lower + 1, n - 1)
        weight = rank - lower
        return sorted_vals[lower] * (1 - weight) + sorted_vals[upper] * weight

    # ------------------------------------------------------------------
    # Summary / reporting
    # ------------------------------------------------------------------

    def summary(self) -> dict:
        """Return a dict with per-endpoint stats and slow endpoint list.

        The returned structure matches the JSON contract from the design
        doc::

            {
                "endpoints": {
                    "GET /api/companies": {
                        "p50_ms": 12,
                        "p95_ms": 25,
                        "p99_ms": 45,
                        "count": 5,
                        "mean_ms": 18
                    },
                    ...
                },
                "slow_endpoints": ["POST /evaluate"],
                "total_requests": 150,
                "total_duration_ms": 4500.0
            }
        """
        endpoints: dict[str, dict] = {}
        slow_endpoints: list[str] = []
        total_requests = 0
        total_duration_ms = 0.0

        for endpoint, timings in sorted(self._timings.items()):
            count = len(timings)
            mean_ms = statistics.mean(timings) if timings else 0.0
            p50 = self.percentile(timings, 50)
            p95 = self.percentile(timings, 95)
            p99 = self.percentile(timings, 99)

            endpoints[endpoint] = {
                "p50_ms": round(p50, 2),
                "p95_ms": round(p95, 2),
                "p99_ms": round(p99, 2),
                "count": count,
                "mean_ms": round(mean_ms, 2),
            }

            if p99 > SLOW_THRESHOLD_MS:
                slow_endpoints.append(endpoint)

            total_requests += count
            total_duration_ms += sum(timings)

        return {
            "endpoints": endpoints,
            "slow_endpoints": slow_endpoints,
            "total_requests": total_requests,
            "total_duration_ms": round(total_duration_ms, 2),
        }

    def print_summary(self) -> None:
        """Print a human-readable summary table to stdout."""
        data = self.summary()
        endpoints = data["endpoints"]

        if not endpoints:
            print("No profiling data recorded.")
            return

        # Header
        header = (
            f"{'Endpoint':<40} {'Count':>5} {'P50':>7} {'P95':>7} "
            f"{'P99':>7} {'Slow?':>8}"
        )
        separator = "\u2500" * len(header)

        print()
        print(header)
        print(separator)

        for name, stats in endpoints.items():
            slow_marker = "\u26a0 SLOW" if name in data["slow_endpoints"] else ""
            print(
                f"{name:<40} {stats['count']:>5} "
                f"{stats['p50_ms']:>5.0f}ms "
                f"{stats['p95_ms']:>5.0f}ms "
                f"{stats['p99_ms']:>5.0f}ms "
                f"{slow_marker:>8}"
            )

        print(separator)
        print(
            f"Total requests: {data['total_requests']}  "
            f"Total duration: {data['total_duration_ms']:.0f}ms"
        )
        if data["slow_endpoints"]:
            print(
                f"\u26a0  Slow endpoints (P99 > {SLOW_THRESHOLD_MS:.0f}ms): "
                + ", ".join(data["slow_endpoints"])
            )
        print()

    def write_json(self, path: str | Path) -> None:
        """Write the summary as JSON to *path*."""
        dest = Path(path)
        dest.parent.mkdir(parents=True, exist_ok=True)
        dest.write_text(json.dumps(self.summary(), indent=2) + "\n")