from __future__ import annotations

import math
import os
import random
from collections.abc import Mapping
from dataclasses import dataclass

ENABLED_ENV = "APEX_HUMANIZE_ENABLED"
TAP_RADIUS_ENV = "APEX_HUMANIZE_TAP_RADIUS_PX"
SWIPE_CURVATURE_ENV = "APEX_HUMANIZE_SWIPE_CURVATURE"
SWIPE_WAYPOINTS_ENV = "APEX_HUMANIZE_SWIPE_WAYPOINTS"
DURATION_SPREAD_ENV = "APEX_HUMANIZE_DURATION_SPREAD"

DEFAULT_TAP_RADIUS_PX = 5.0
DEFAULT_SWIPE_CURVATURE = 0.15
DEFAULT_SWIPE_WAYPOINTS = 8
DEFAULT_DURATION_SPREAD = 0.15


@dataclass(frozen=True)
class HumanizeConfig:
    enabled: bool = True
    tap_radius_px: float = DEFAULT_TAP_RADIUS_PX
    swipe_curvature: float = DEFAULT_SWIPE_CURVATURE
    swipe_waypoints: int = DEFAULT_SWIPE_WAYPOINTS
    duration_spread: float = DEFAULT_DURATION_SPREAD


def load_humanize_config(env: Mapping[str, str] | None = None) -> HumanizeConfig:
    if env is None:
        values: Mapping[str, str] = os.environ
    else:
        values = env
    return HumanizeConfig(
        enabled=_parse_bool(values.get(ENABLED_ENV), default=True),
        tap_radius_px=_parse_float(values.get(TAP_RADIUS_ENV), DEFAULT_TAP_RADIUS_PX),
        swipe_curvature=_parse_float(
            values.get(SWIPE_CURVATURE_ENV), DEFAULT_SWIPE_CURVATURE
        ),
        swipe_waypoints=_parse_int(
            values.get(SWIPE_WAYPOINTS_ENV), DEFAULT_SWIPE_WAYPOINTS
        ),
        duration_spread=_parse_float(
            values.get(DURATION_SPREAD_ENV), DEFAULT_DURATION_SPREAD
        ),
    )


def _parse_bool(value: str | None, *, default: bool) -> bool:
    if value is None:
        return default
    return value.strip().lower() in {"1", "true", "yes", "on", "enabled"}


def _parse_float(value: str | None, default: float) -> float:
    if value is None:
        return default
    try:
        return float(value)
    except ValueError:
        return default


def _parse_int(value: str | None, default: int) -> int:
    if value is None:
        return default
    try:
        return int(value)
    except ValueError:
        return default


_rng: random.Random | None = None


def get_rng() -> random.Random:
    global _rng
    if _rng is None:
        _rng = random.Random()
    return _rng


def set_rng(rng: random.Random | None) -> None:
    """Inject a seeded rng (tests). Pass ``None`` to reset to the default."""
    global _rng
    _rng = rng


def jitter_point(
    x: float, y: float, *, radius: float, rng: random.Random
) -> tuple[float, float]:
    """Polar-Gaussian offset around the target.

    Magnitude is drawn from a folded Gaussian with sigma=radius/2, then
    capped at ``radius``. The angle is uniform on [0, 2*pi). This concentrates
    jittered points near the target rather than uniformly on the circle edge,
    matching how humans tap close to (but not exactly on) a button center.
    """
    r = abs(rng.gauss(0.0, radius / 2.0))
    r = min(r, radius)
    angle = rng.uniform(0, 2 * math.pi)
    dx = r * math.cos(angle)
    dy = r * math.sin(angle)
    # Guard against cos/sin FP drift pushing distance slightly past radius.
    fd = math.hypot(dx, dy)
    if fd > radius:
        scale = radius / fd
        dx *= scale
        dy *= scale
    # Final clamp: hypot of the returned offset may overshoot by ulps; tighten
    # to ``radius - 1e-10`` to keep ``<= radius`` after subtraction/hypot.
    fx = x + dx
    fy = y + dy
    fd_final = math.hypot(fx - x, fy - y)
    if fd_final > radius:
        scale = (radius - 1e-10) / fd_final
        fx = x + dx * scale
        fy = y + dy * scale
    return (fx, fy)


def jitter_duration(
    value_ms: int, *, spread: float, rng: random.Random
) -> int:
    """Uniform jitter within ``value_ms * spread``; floored at 1ms."""
    delta = value_ms * spread
    return max(1, int(value_ms + rng.uniform(-delta, delta)))


def swipe_waypoints(
    start: tuple[float, float],
    end: tuple[float, float],
    *,
    curvature: float,
    n: int,
    rng: random.Random,
) -> list[tuple[float, float]]:
    """Return start + ``n`` interior + end points. Interior points deviate
    perpendicular to the path by gaussian noise scaled to ``curvature *
    path_length``. Degenerate (zero-length) path returns ``[start, end]``."""
    sx, sy = start
    ex, ey = end
    dx = ex - sx
    dy = ey - sy
    length = math.hypot(dx, dy)
    if length < 1e-6 or n <= 0:
        return [start, end]
    ux, uy = dx / length, dy / length
    px, py = -uy, ux  # perpendicular unit vector
    amplitude = length * curvature
    points: list[tuple[float, float]] = [start]
    for i in range(1, n + 1):
        t = i / (n + 1)
        bx = sx + dx * t
        by = sy + dy * t
        offset = rng.gauss(0.0, amplitude / 2.0)
        points.append((bx + px * offset, by + py * offset))
    points.append(end)
    return points