"""Per-agent inference tests: momentum (#114), overdue-task (#115), recent-patterns (#116),
and focus-area (#113) preferred_areas wiring."""
from __future__ import annotations

import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", ".."))

from datetime import datetime, timezone
import pytest

from ml.agents.inference.history import FeedbackEvent, TaskCompletion, UserHistory
from ml.agents.inference.framework import run_inference
from ml.agents.momentum import MomentumAgent, MANIFEST as MOMENTUM_MANIFEST
from ml.agents.overdue_task import OverdueTaskAgent, MANIFEST as OVERDUE_MANIFEST
from ml.agents.recent_patterns import RecentPatternsAgent, MANIFEST as RECENT_MANIFEST
from ml.agents.focus_area import FocusAreaAgent
from ml.agents.base import AgentInput

_NOW = datetime(2026, 5, 8, 14, 0, 0, tzinfo=timezone.utc)


def _inp(**kwargs) -> AgentInput:
    defaults = dict(user_id="u1", tasks=[], profile={}, now=_NOW, agent_prefs={})
    defaults.update(kwargs)
    return AgentInput(**defaults)


def _event(action: str, days_ago: float = 1.0) -> FeedbackEvent:
    from datetime import timedelta
    ts = (_NOW - timedelta(days=days_ago)).isoformat()
    dwell = 60_000 if action == "done" else 500
    return FeedbackEvent(action=action, dwell_ms=dwell, created_at=ts)


def _history(*events: FeedbackEvent, completions: list[TaskCompletion] | None = None) -> UserHistory:
    return UserHistory(user_id="u1", events=list(events), task_completions=completions or [])


def _completion(project_id: str | None, lateness_days: float) -> TaskCompletion:
    """Build a TaskCompletion where completed_at is lateness_days after due_at."""
    from datetime import timedelta
    due = _NOW - timedelta(days=30)
    completed = due + timedelta(days=lateness_days)
    return TaskCompletion(
        project_id=project_id,
        completed_at=completed.isoformat(),
        due_at=due.isoformat(),
    )


# ── momentum: engagement_trend ───────────────────────────────────────────────

class TestMomentumInference:
    def test_cold_start_below_min_history(self):
        history = _history(*[_event("done", days_ago=i) for i in range(5)])
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "stable"  # cold_start_default

    def test_trend_up_when_recent_done_rate_higher(self):
        # 8 done in last 7 days, 1 done in prior 7 days → trending up
        recent = [_event("done", days_ago=i) for i in range(1, 9)]
        older = [_event("dismiss", days_ago=i) for i in range(8, 15)]
        older[0] = _event("done", days_ago=8)  # one done in older window
        history = _history(*recent, *older)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "up"

    def test_trend_down_when_recent_done_rate_lower(self):
        recent = [_event("dismiss", days_ago=i) for i in range(1, 8)]
        older = [_event("done", days_ago=i) for i in range(8, 15)]
        history = _history(*recent, *older)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "down"

    def test_trend_stable_when_similar(self):
        events = [_event("done" if i % 2 == 0 else "dismiss", days_ago=i) for i in range(1, 15)]
        history = _history(*events)
        result = run_inference(MOMENTUM_MANIFEST, history)
        assert result["engagement_trend"] == "stable"

    def test_agent_uses_trend_in_snippet(self):
        out = MomentumAgent().compute(_inp(agent_prefs={"engagement_trend": "up"}))
        assert "trending up" in out.prompt_text

    def test_agent_uses_down_trend_in_snippet(self):
        out = MomentumAgent().compute(_inp(agent_prefs={"engagement_trend": "down"}))
        assert "trending down" in out.prompt_text

    def test_snapshot_includes_trend(self):
        out = MomentumAgent().compute(_inp(agent_prefs={"engagement_trend": "stable"}))
        assert "engagement_trend" in out.signals_snapshot

    def test_version_bumped(self):
        assert MOMENTUM_MANIFEST.version == "1.1.0"


# ── overdue-task: lateness_tolerance_days + project_realness (#115) ──────────

class TestOverdueTaskInference:
    # -- lateness_tolerance_days inference --

    def test_cold_start_returns_zero_when_few_completions(self):
        # Below min_history=10 task completions → cold start
        cs = [_completion("p1", 2.0) for _ in range(5)]
        history = _history(*[_event("done")] * 5, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == 0.0

    def test_punctual_user_zero_tolerance(self):
        # User always finishes early or on time (negative lateness) → tolerance 0
        cs = [_completion("p1", -1.0) for _ in range(12)]
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == 0.0

    def test_chronic_late_user_positive_tolerance(self):
        # User consistently finishes 5 days late → p50 = 5
        cs = [_completion("p1", 5.0) for _ in range(12)]
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == pytest.approx(5.0)

    def test_mixed_lateness_uses_median(self):
        # 6 tasks at +1d, 6 tasks at +3d → median = 2
        cs = [_completion("p1", 1.0)] * 6 + [_completion("p1", 3.0)] * 6
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["lateness_tolerance_days"] == pytest.approx(2.0)

    # -- project_realness inference --

    def test_project_realness_cold_start_empty(self):
        cs = [_completion("p1", 1.0) for _ in range(5)]  # below min_history
        history = _history(*[_event("done")] * 5, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["project_realness"] == {}

    def test_project_realness_punctual_project_scores_high(self):
        # p1 always on time (0d late), p2 always 10d late → p1 should be realness ≈ 1
        cs = [_completion("p1", 0.0)] * 6 + [_completion("p2", 10.0)] * 6
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        assert result["project_realness"]["p1"] > result["project_realness"]["p2"]

    def test_project_realness_values_clipped_01(self):
        cs = [_completion("p1", 0.0)] * 6 + [_completion("p2", 100.0)] * 6
        history = _history(*[_event("done")] * 12, completions=cs)
        result = run_inference(OVERDUE_MANIFEST, history)
        for v in result["project_realness"].values():
            assert 0.0 <= v <= 1.0

    # -- compute() reads inferred prefs --

    def test_tolerance_filters_tasks(self):
        tasks = [
            {"content": "Fresh overdue", "is_overdue": True, "task_age_days": 0.5},
            {"content": "Old overdue", "is_overdue": True, "task_age_days": 3.0},
        ]
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs={"lateness_tolerance_days": 2}))
        assert "1 overdue task" in out.prompt_text
        assert "Old overdue" in out.prompt_text

    def test_low_realness_softens_language(self):
        tasks = [{"content": "Wishlist", "is_overdue": True, "task_age_days": 3.0,
                  "project_id": "aspirational"}]
        prefs = {"lateness_tolerance_days": 0, "project_realness": {"aspirational": 0.2}}
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs=prefs))
        assert "target date" in out.prompt_text

    def test_high_realness_uses_overdue_language(self):
        tasks = [{"content": "Critical", "is_overdue": True, "task_age_days": 3.0,
                  "project_id": "work"}]
        prefs = {"lateness_tolerance_days": 0, "project_realness": {"work": 0.9}}
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs=prefs))
        assert "overdue" in out.prompt_text

    def test_snapshot_includes_realness(self):
        tasks = [{"content": "T", "is_overdue": True, "task_age_days": 1.0, "project_id": "p1"}]
        prefs = {"lateness_tolerance_days": 0, "project_realness": {"p1": 0.8}}
        out = OverdueTaskAgent().compute(_inp(tasks=tasks, agent_prefs=prefs))
        assert "realness" in out.signals_snapshot["top_overdue"][0]

    def test_version_bumped(self):
        assert OVERDUE_MANIFEST.version == "1.2.0"


# ── recent-patterns: window_days ─────────────────────────────────────────────

class TestRecentPatternsInference:
    def test_cold_start_default_7(self):
        history = _history(*[_event("done") for _ in range(3)])  # below min_history=5
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 7  # cold_start_default

    def test_sparse_history_widens_window(self):
        history = _history(*[_event("done") for _ in range(5)])  # 5 events, n < 7 → 30 days
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 30

    def test_moderate_history_14_days(self):
        history = _history(*[_event("done") for _ in range(10)])  # 7 ≤ n < 14 → 14 days
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 14

    def test_dense_history_stays_7(self):
        history = _history(*[_event("done") for _ in range(20)])  # 20+ → 7 days
        result = run_inference(RECENT_MANIFEST, history)
        assert result["window_days"] == 7

    def test_agent_uses_window_days_pref(self):
        from datetime import timedelta
        # 5 feedback events, all within 14 days but older than 7 days
        feedback = [
            {"action": "done", "dwell_ms": 60000,
             "created_at": (_NOW - timedelta(days=10)).isoformat()}
        ] * 5
        # With window_days=7 → 0 events seen; with window_days=14 → 5 events
        out_narrow = RecentPatternsAgent().compute(
            _inp(feedback_history=feedback, agent_prefs={"window_days": 7})
        )
        out_wide = RecentPatternsAgent().compute(
            _inp(feedback_history=feedback, agent_prefs={"window_days": 14})
        )
        assert "No tip reactions" in out_narrow.prompt_text
        assert "5 tip reactions" in out_wide.prompt_text

    def test_snapshot_includes_window_days(self):
        out = RecentPatternsAgent().compute(_inp(agent_prefs={"window_days": 14}))
        assert out.signals_snapshot["window_days"] == 14

    def test_version_bumped(self):
        assert RECENT_MANIFEST.version == "1.1.0"


# ── focus-area: preferred_areas wiring ───────────────────────────────────────

class TestFocusAreaPreferredAreas:
    agent = FocusAreaAgent()

    def _task(self, content: str, project_id: str, is_overdue: bool = False) -> dict:
        return {"id": "t1", "content": content, "is_overdue": is_overdue,
                "task_age_days": 2.0, "priority": 1, "project_id": project_id}

    def test_preferred_area_wins_tie(self):
        tasks = [
            self._task("Work thing", "work"),
            self._task("Home thing", "home"),
        ]
        out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
        assert "work" in out.prompt_text
        assert "matches the user's stated focus preferences" in out.prompt_text

    def test_no_preferred_areas_uses_congestion_score(self):
        tasks = [
            self._task("W1", "work"),
            self._task("H1", "home"),
            self._task("H2", "home"),
        ]
        out = self.agent.compute(_inp(tasks=tasks))
        # home has more tasks → wins without any preference
        assert "home" in out.prompt_text

    def test_snapshot_includes_preferred_areas(self):
        tasks = [self._task("T", "work")]
        out = self.agent.compute(_inp(tasks=tasks, agent_prefs={"preferred_areas": ["work"]}))
        assert out.signals_snapshot["preferred_areas"] == ["work"]

    def test_version_bumped(self):
        from ml.agents.focus_area import MANIFEST as FA_MANIFEST
        assert FA_MANIFEST.version == "1.1.0"