"""Decomposed reward function.

Two stages:
1. **Per-step reward** ``compute_step_reward``: shapes behaviour with small
   incentives (progress, evidence quality, valid prerequisites) and
   penalties (rule violations, repeated work, wasted resources).
2. **Terminal reward** ``compute_terminal_reward``: graded only when the
   agent submits a discovery claim or runs out of resources. Compares the
   submitted claim against the hidden ``LatentParticle`` truth.

The terminal reward is intentionally dominant so the policy must care about
the *correct* discovery, not just looking busy.
"""

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Dict, List, Optional

import numpy as np

from models import (
    ActionType,
    DiscoveryClaim,
    ExperimentAction,
    IntermediateOutput,
)

from server.rules.engine import RuleResult, ViolationCode
from server.simulator.latent_state import FullLatentState


# ── Configuration ────────────────────────────────────────────────────────


@dataclass
class RewardWeights:
    # ── per-step shaping ────────────────────────────────────────
    valid_action: float = 0.05
    progress_milestone: float = 0.25
    evidence_quality: float = 0.20
    tool_fit: float = 0.10
    soft_violation: float = -0.05
    hard_violation: float = -0.50
    redundancy: float = -0.10
    resource_overspend: float = -0.30
    failure: float = -0.30

    # ── terminal grading ────────────────────────────────────────
    terminal_scale: float = 5.0   # multiplied with the convex sum below

    mass_calibration: float = 0.30
    significance_quality: float = 0.20
    channel_correctness: float = 0.20
    spin_correctness: float = 0.10
    width_calibration: float = 0.05
    confidence_calibration: float = 0.10
    efficiency_bonus: float = 0.05

    overconfident_wrong_penalty: float = 4.0  # subtracted from terminal


# ── Outputs ──────────────────────────────────────────────────────────────


@dataclass
class RewardBreakdown:
    components: Dict[str, float] = field(default_factory=dict)
    total: float = 0.0

    def add(self, key: str, value: float) -> None:
        self.components[key] = self.components.get(key, 0.0) + value
        self.total += value


@dataclass
class StepReward:
    reward: float
    breakdown: RewardBreakdown


@dataclass
class TerminalReward:
    reward: float
    breakdown: RewardBreakdown
    discovered: bool
    correct_mass: bool
    correct_channel: bool
    correct_spin: bool


# ── Per-step ─────────────────────────────────────────────────────────────


_PROGRESS_FLAGS = [
    "beam_configured",
    "luminosity_allocated",
    "trigger_set",
    "collisions_collected",
    "channel_selected",
    "tracks_reconstructed",
    "detector_calibrated",
    "invariant_mass_built",
    "background_subtracted",
    "resonance_fitted",
    "significance_estimated",
]


def _milestone_progress(state_before: FullLatentState, state_after: FullLatentState) -> int:
    """Number of new progress milestones unlocked this step."""
    delta = 0
    for flag in _PROGRESS_FLAGS:
        was = getattr(state_before.progress, flag)
        now = getattr(state_after.progress, flag)
        if now and not was:
            delta += 1
    return delta


def compute_step_reward(
    *,
    action: ExperimentAction,
    output: IntermediateOutput,
    state_before: FullLatentState,
    state_after: FullLatentState,
    rule_result: RuleResult,
    weights: RewardWeights = RewardWeights(),
) -> StepReward:
    breakdown = RewardBreakdown()

    if rule_result.allowed and output.success:
        breakdown.add("valid_action", weights.valid_action)
    if not output.success:
        breakdown.add("failure", weights.failure)

    # progress
    new_milestones = _milestone_progress(state_before, state_after)
    if new_milestones > 0:
        breakdown.add("progress", weights.progress_milestone * new_milestones)

    # evidence quality
    if output.success:
        breakdown.add("evidence_quality", weights.evidence_quality * float(output.quality_score))

    # tool fit (named method exists in the recommended toolset)
    if action.method:
        breakdown.add("tool_fit", weights.tool_fit * 0.5)

    # rule penalties
    if rule_result.violations:
        breakdown.add("hard_violation", weights.hard_violation * len(rule_result.violations))
    if rule_result.soft_violations:
        soft_redundant = sum(1 for v in rule_result.soft_violations if v == ViolationCode.REDUNDANT)
        soft_other = len(rule_result.soft_violations) - soft_redundant
        if soft_redundant:
            breakdown.add("redundancy", weights.redundancy * soft_redundant)
        if soft_other:
            breakdown.add("soft_violation", weights.soft_violation * soft_other)

    # resource overspend
    res = state_after.resources
    if res.budget_used_musd > res.budget_total_musd:
        breakdown.add("budget_overspend", weights.resource_overspend)
    if res.luminosity_used_fb > res.luminosity_total_fb:
        breakdown.add("lumi_overspend", weights.resource_overspend)
    if res.time_used_days > res.time_limit_days:
        breakdown.add("time_overspend", weights.resource_overspend)

    return StepReward(reward=float(breakdown.total), breakdown=breakdown)


# ── Terminal grading ─────────────────────────────────────────────────────


def _mass_score(true_mass: float, claim_mass: Optional[float], unc: Optional[float]) -> float:
    """1.0 within 1σ, smoothly decays to 0 by 5 GeV (or 5σ, whichever larger)."""
    if claim_mass is None or true_mass <= 0:
        return 0.0
    err = abs(claim_mass - true_mass)
    # Tolerance: max(1.0 GeV, 1% of true mass, claimed unc)
    tol = max(1.0, 0.01 * true_mass)
    if unc is not None and unc > 0:
        tol = max(tol, float(unc))
    if err <= tol:
        return 1.0
    if err >= 5 * tol:
        return 0.0
    return float(np.clip(1.0 - (err - tol) / (4 * tol), 0.0, 1.0))


def _significance_score(state: FullLatentState, claim_sigma: Optional[float]) -> float:
    """High score when claimed σ matches measured σ and is ≥ 5."""
    measured = state.progress.best_significance_sigma or 0.0
    if claim_sigma is None:
        return 0.0
    over_claim = max(0.0, claim_sigma - measured)
    base = float(np.clip(measured / 5.0, 0.0, 1.0))
    penalty = float(np.clip(over_claim / 3.0, 0.0, 1.0))
    return float(np.clip(base - 0.5 * penalty, 0.0, 1.0))


def _confidence_calibration(claim_conf: float, mass_score: float, channel_correct: bool) -> float:
    """Reward agents whose confidence tracks their actual accuracy."""
    actual = 0.5 * mass_score + 0.5 * (1.0 if channel_correct else 0.0)
    err = abs(actual - claim_conf)
    return float(np.clip(1.0 - err, 0.0, 1.0))


def _efficiency_bonus(state: FullLatentState) -> float:
    """Reward leftover budget (encourages succinct experiments)."""
    res = state.resources
    score = 0.0
    score += np.clip(res.budget_remaining / res.budget_total_musd, 0.0, 1.0)
    score += np.clip(res.luminosity_remaining / res.luminosity_total_fb, 0.0, 1.0)
    score += np.clip(res.time_remaining / res.time_limit_days, 0.0, 1.0)
    return float(score / 3.0)


def compute_terminal_reward(
    *,
    state: FullLatentState,
    claim: DiscoveryClaim,
    weights: RewardWeights = RewardWeights(),
) -> TerminalReward:
    breakdown = RewardBreakdown()
    truth = state.particle

    mass_score = _mass_score(truth.mass_gev, claim.mass_estimate_gev, claim.mass_uncertainty_gev)
    breakdown.add("mass_calibration", weights.mass_calibration * mass_score)

    sig_score = _significance_score(state, claim.significance_sigma)
    breakdown.add("significance_quality", weights.significance_quality * sig_score)

    channel_ok = claim.decay_channel == truth.primary_channel
    breakdown.add("channel_correctness", weights.channel_correctness * (1.0 if channel_ok else 0.0))

    spin_ok = claim.spin_hypothesis is not None and claim.spin_hypothesis == truth.spin
    breakdown.add("spin_correctness", weights.spin_correctness * (1.0 if spin_ok else 0.0))

    width_score = 0.0
    if claim.width_estimate_gev is not None and truth.width_gev > 0:
        rel = abs(claim.width_estimate_gev - truth.width_gev) / max(truth.width_gev, 1e-3)
        width_score = float(np.clip(1.0 - rel, 0.0, 1.0))
    breakdown.add("width_calibration", weights.width_calibration * width_score)

    conf_score = _confidence_calibration(claim.confidence, mass_score, channel_ok)
    breakdown.add("confidence_calibration", weights.confidence_calibration * conf_score)

    eff_score = _efficiency_bonus(state)
    breakdown.add("efficiency_bonus", weights.efficiency_bonus * eff_score)

    discovered = (
        mass_score >= 0.5
        and channel_ok
        and (claim.significance_sigma or 0.0) >= 4.5
    )

    raw = breakdown.total * weights.terminal_scale

    # Overconfident-wrong penalty: high confidence but wrong channel & far mass
    if claim.confidence >= 0.8 and (mass_score < 0.2 or not channel_ok):
        raw -= weights.overconfident_wrong_penalty
        breakdown.add("overconfident_wrong", -weights.overconfident_wrong_penalty)

    return TerminalReward(
        reward=float(raw),
        breakdown=breakdown,
        discovered=discovered,
        correct_mass=mass_score >= 0.5,
        correct_channel=channel_ok,
        correct_spin=spin_ok,
    )


__all__ = [
    "RewardBreakdown",
    "RewardWeights",
    "StepReward",
    "TerminalReward",
    "compute_step_reward",
    "compute_terminal_reward",
]