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from __future__ import annotations

import statistics
from collections import Counter

from airflow.api_fastapi.core_api.datamodels.ui.dag_runs import DurationStats


def compute_duration_stats(durations: list[float]) -> DurationStats | None:
    """
    Compute duration statistics from a list of completed DAG run durations (in seconds).

    Returns None when the list is empty (no completed runs exist yet).
    Mode is computed on second-rounded values to avoid float precision noise; returns None
    when every run has a unique duration.
    Percentiles use linear interpolation between adjacent sorted values.
    """
    if not durations:
        return None

    # On Postgres 14+, DagRun.duration comes back as Decimal (EXTRACT(epoch ...) returns
    # numeric); coerce to float so the percentile interpolation below (Decimal * float) works.
    sorted_d = sorted(float(d) for d in durations)

    counts = Counter(round(d) for d in sorted_d)
    max_count = max(counts.values())
    mode_val: float | None = (
        float(min(k for k, v in counts.items() if v == max_count)) if max_count > 1 else None
    )

    def _percentile(p: float) -> float:
        idx = (len(sorted_d) - 1) * p / 100
        lo = int(idx)
        hi = min(lo + 1, len(sorted_d) - 1)
        return sorted_d[lo] + (sorted_d[hi] - sorted_d[lo]) * (idx - lo)

    return DurationStats(
        mean=round(statistics.mean(sorted_d), 3),
        mode=round(mode_val, 3) if mode_val is not None else None,
        p50=round(_percentile(50), 3),
        p90=round(_percentile(90), 3),
        p95=round(_percentile(95), 3),
        p99=round(_percentile(99), 3),
    )
