import copy
import json

from tools.rag.build_chunks import TOKEN_COUNTER
from tools.rag.llm_context import compact_rag_payload


def response(text="Unikátny dôkaz o práci osoby. "):
    return {
        "status": "ok",
        "query": "Práca?",
        "engine": "test",
        "strategies": ["test"],
        "complete": None,
        "truncated": False,
        "truncation_reasons": [],
        "index_version": {"version": "test"},
        "instructions": ["Cituj iba dodané zdroje."],
        "answer_format": {},
        "sources": [
            {
                "source_id": "S1",
                "document_path": "pages/eva/README.md",
                "title": "Eva",
                "source_url": "https://example.test/eva",
                "text": text,
                "query_evidence": {"huge_diagnostic": text * 10},
                "retrieval": {"rank": 1},
            }
        ],
        "context": text,
    }


def test_compact_payload_contains_evidence_once_and_preserves_sources():
    original = response()
    before = copy.deepcopy(original)
    compact = compact_rag_payload(original)
    assert original == before
    assert json.dumps(compact, ensure_ascii=False).count(original["sources"][0]["text"]) == 1
    assert "text" not in compact["sources"][0] and "query_evidence" not in compact["sources"][0]
    assert compact["sources"][0]["source_url"] == "https://example.test/eva"
    assert compact["index_version"] == original["index_version"]
    assert compact_rag_payload(compact) == compact


def test_entire_tool_json_is_bounded_and_marks_omitted_evidence():
    original = response("Veľmi dlhý dôkaz 🧑🏽‍💻. " * 2000)
    original["sources"].append({**original["sources"][0], "source_id": "S2", "text": "Ďalší dôkaz"})
    compact = compact_rag_payload(original, max_tokens=700)
    used = TOKEN_COUNTER.count(json.dumps(compact, ensure_ascii=False, separators=(",", ":")))
    assert used <= 700 and compact["context_budget"]["used_tokens"] == used
    assert compact["complete"] is False and compact["truncated"] is True
    assert "llm_context_budget" in compact["truncation_reasons"]
    assert "KONIEC ZDROJA S1" in compact["context"]
    assert compact["source_count"] == 1 and "[OBSAH ZDROJA OREZANÝ]" in compact["context"]
    assert json.loads(compact["context"].splitlines()[1])["truncated"] is True


def test_diagnostics_are_explicit_and_never_forwarded_by_projection():
    verbose = compact_rag_payload(response(), include_diagnostics=True)
    assert verbose["diagnostics"]["sources"][0]["query_evidence"]
    projected = compact_rag_payload(verbose)
    assert "diagnostics" not in projected
    assert "huge_diagnostic" not in json.dumps(projected)


def test_http_serialization_stays_within_budget_and_diagnostics_are_opt_in(monkeypatch):
    from fastapi.testclient import TestClient
    import app.main as main
    import app.routes as routes

    original = response("Dlhý dôkaz 🐈 " * 1800)
    monkeypatch.setattr(main, "embed_query", lambda _: None)
    monkeypatch.setattr(routes, "build_rag_context", lambda *a, **kw: original)
    monkeypatch.setenv("RAG_LLM_MAX_TOKENS", "700")
    with TestClient(main.app) as client:
        headers = {"X-API-Key": "a" * 64}
        compact = client.post("/rag", json={"query": "Práca?"}, headers=headers)
        assert compact.status_code == 200
        assert TOKEN_COUNTER.count(compact.text) <= 700
        assert "diagnostics" not in compact.json()
        assert "text" not in compact.json()["sources"][0]
        verbose = client.post(
            "/rag", json={"query": "Práca?", "include_diagnostics": True}, headers=headers
        )
        assert verbose.status_code == 200 and verbose.json()["diagnostics"] == original