from agent.loop import _humanize, _parse_action, answer_agentic
from agent.schemas import Answer


def test_parse_action_extracts_json_from_noise():
    assert _parse_action('sure! {"action":"search_docs","query":"sgd"} ')["query"] == "sgd"
    assert _parse_action("no json here")["action"] == "answer"
    assert _parse_action('{"action":"bogus"}')["action"] == "answer"


def test_humanize_renders_each_step_and_falls_back():
    # a search step → query + the last segment of each heading path, capped at 4
    line = "search_docs('sgd momentum') → ['torch.optim > SGD', 'SGD > Per-parameter options']"
    out = _humanize(line)
    assert "🔍" in out and "sgd momentum" in out
    assert "SGD" in out and "Per-parameter options" in out
    assert "torch.optim >" not in out  # only the leaf heading is shown
    assert _humanize("read_page(http://x) → full page added").startswith("📖")
    assert _humanize("ask_source → 2 referral links").startswith("🔗")
    # an unrecognised shape degrades to the raw line, never crashes
    assert "weird step" in _humanize("weird step")


def test_loop_streams_a_progress_trace(monkeypatch):
    scripted = ScriptedAgent(
        ['{"action":"search_docs","query":"dataloader"}', '{"action":"answer"}'], None
    )
    monkeypatch.setattr("agent.llm._raw_completion", scripted.plan)
    monkeypatch.setattr(
        "agent.tools.search_docs",
        lambda q, library=None, kind=None, k=8: {"query": q, "sections": [], "titles": ["H"]},
    )
    monkeypatch.setattr(
        "agent.grounded.answer_from_sections",
        lambda q, s, referrals=None, provider=None, client=None: Answer(answer_md="ok"),
    )
    seen: list[str] = []
    answer_agentic("how do I load data?", progress=seen.append)
    # seed search + planner search are both surfaced, and the final writing step
    assert sum(1 for line in seen if line.startswith("🔍")) == 2
    assert seen[-1].startswith("✍️")


class ScriptedAgent:
    """Drives the loop: a queue of planner actions, then a final Answer JSON."""

    def __init__(self, plan_actions, final_answer):
        self._plans = list(plan_actions)
        self._final = final_answer

    def plan(self, prompt, system, provider=None, client=None):
        return self._plans.pop(0)

    def final(self, *a, **k):
        return self._final


def test_loop_decomposes_and_answers(monkeypatch):
    # planner asks for two searches, then answers
    scripted = ScriptedAgent(
        ['{"action":"search_docs","query":"cnn image classification"}',
         '{"action":"search_docs","query":"dataloader images"}',
         '{"action":"answer"}'],
        None,
    )
    calls = {"search": 0}

    def fake_search(query, library=None, kind=None, k=8):
        calls["search"] += 1
        return {"query": query, "sections": [{"url": f"u{calls['search']}", "anchor": "",
                "heading_path": "H", "content": "text"}], "titles": ["H"]}

    monkeypatch.setattr("agent.llm._raw_completion", scripted.plan)
    monkeypatch.setattr("agent.tools.search_docs", fake_search)

    captured = {}

    def fake_answer(question, sections, referrals=None, provider=None, client=None):
        captured["sections"] = sections
        captured["referrals"] = referrals
        return Answer(answer_md="done")

    monkeypatch.setattr("agent.grounded.answer_from_sections", fake_answer)

    result = answer_agentic("how do I build a CNN for images?")
    assert result.answer_md == "done"
    # 1 seed search + 2 planner-driven searches, all distinct results accumulated
    assert calls["search"] == 3
    assert len(captured["sections"]) == 3


def test_loop_source_question_adds_referrals(monkeypatch):
    scripted = ScriptedAgent(
        ['{"action":"ask_source","question":"conv2d internals"}', '{"action":"answer"}'],
        None,
    )
    monkeypatch.setattr("agent.llm._raw_completion", scripted.plan)
    # seed search runs first — stub it so the test stays offline
    monkeypatch.setattr(
        "agent.tools.search_docs",
        lambda q, library=None, kind=None, k=8: {"query": q, "sections": [], "titles": []},
    )

    captured = {}

    def fake_answer(question, sections, referrals=None, provider=None, client=None):
        captured["referrals"] = referrals
        return Answer(answer_md="see source")

    monkeypatch.setattr("agent.grounded.answer_from_sections", fake_answer)
    answer_agentic("how is conv2d implemented?")
    assert captured["referrals"]  # ask_source contributed referral links
    assert any("deepwiki" in r.url for r in captured["referrals"])


def test_loop_stops_at_budget(monkeypatch):
    # planner always wants to search; budget must cap it
    scripted = ScriptedAgent(
        ['{"action":"search_docs","query":"x"}'] * 20, None
    )
    monkeypatch.setattr("agent.llm._raw_completion", scripted.plan)
    monkeypatch.setattr(
        "agent.tools.search_docs",
        lambda q, library=None, kind=None, k=8: {"query": q, "sections": [], "titles": []},
    )
    captured = {}
    monkeypatch.setattr(
        "agent.grounded.answer_from_sections",
        lambda q, s, referrals=None, provider=None, client=None: captured.setdefault("hit", True)
        or Answer(answer_md="x"),
    )
    answer_agentic("q")  # must terminate (MAX_STEPS / budget), not loop forever
    assert captured["hit"]


def test_planner_kind_reaches_search_docs(monkeypatch):
    # the planner decides the content space; its kind must reach the tool
    scripted = ScriptedAgent(
        ['{"action":"search_docs","query":"cross entropy loss","kind":"api"}',
         '{"action":"answer"}'],
        None,
    )
    seen = []

    def fake_search(query, library=None, kind=None, k=8):
        seen.append((query, kind))
        return {"query": query, "sections": [], "titles": []}

    monkeypatch.setattr("agent.llm._raw_completion", scripted.plan)
    monkeypatch.setattr("agent.tools.search_docs", fake_search)
    monkeypatch.setattr(
        "agent.grounded.answer_from_sections",
        lambda q, s, referrals=None, provider=None, client=None: Answer(answer_md="ok"),
    )
    answer_agentic("what loss functions exist for classification?")
    # seed search first (no kind), then the planner's api-scoped search
    assert seen[0] == ("what loss functions exist for classification?", None)
    assert seen[1] == ("cross entropy loss", "api")