"""Certification Capability Hypotheses — capability-centric library + empirical confidence.

Pins the reuse design (one capability, many supporting certs -> ~40-60 hypotheses, not ~300), the
automatic multi-certification merge, the empirical (computed) confidence loop, and the Welt-1 guarantee
that capabilities NO cert suggests (SBOM, signed updates, CVD) are never inferred -> they stay in the
delta and get asked. Then the Advisor consumes the resolved library end-to-end.
"""

from __future__ import annotations

import os

import yaml

from compliance.onboarding import (
    CapabilityHypothesis,
    Observation,
    ObservationType,
    OnboardingInput,
    advisor_start,
    empirical_confidence,
    empirical_distribution,
    inferred_hypotheses,
    resolve_for_certifications,
)
from compliance.transition_reasoning import TargetRequirement

_DIR = os.path.dirname(__file__)
_LIB = [CapabilityHypothesis(**h) for h in yaml.safe_load(
    open(os.path.join(_DIR, "..", "knowledge", "certification_hypotheses", "hypotheses.yaml"), encoding="utf-8"))["hypotheses"]]


def test_library_is_capability_centric_and_reuses_certs():
    # the shared core is small (reuse, not 30-per-cert) and document control is supported by many certs
    doc = next(h for h in _LIB if h.capability == "document_and_change_control")
    assert len(doc.supported_by) >= 4
    assert len(_LIB) <= 60  # whole library, not ~300


def test_multi_certification_merges_automatically():
    # a company with ISO9001 + ISO14001 + TISAX gets the UNION of their hypotheses, deduped
    merged = inferred_hypotheses(["ISO9001", "ISO14001", "TISAX"], _LIB)
    caps = {h.capability for h in merged}
    assert "document_and_change_control" in caps          # ISO9001 + TISAX
    assert "information_security_management" in caps        # TISAX
    assert "environmental_management_documentation" in caps  # ISO14001
    # SBOM / signed updates are suggested by NO certificate -> never inferred
    assert "sbom_creation" not in caps and "secure_signed_update_distribution" not in caps


def test_observations_are_richer_than_binary_and_review_gated():
    # the learning unit is the QUESTION; an answer can be partial with a scope note, not just yes/no
    raw = [Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.CONFIRMED)]
    assert empirical_confidence(raw) is None                # unreviewed -> does NOT calibrate (review gate)
    obs = [
        Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.CONFIRMED, reviewed=True),
        Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.PARTIAL,
                    scope_note="nur kritische Lieferanten", reviewed=True),
        Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.REFUTED, reviewed=True),
        Observation(hypothesis_id="HYP-supplier", observation_type=ObservationType.NOT_APPLICABLE, reviewed=True),
    ]
    dist = empirical_distribution(obs)                      # a DISTRIBUTION, not a single percentage
    assert dist["confirmed"] == 1 and dist["partial"] == 1 and dist["refuted"] == 1 and dist["not_applicable"] == 1
    # confidence = (confirmed + 0.5*partial) / (confirmed+partial+refuted); n.a. excluded from the base
    assert empirical_confidence(obs) == 0.5


def test_resolve_adapts_to_advisor_input():
    res = resolve_for_certifications(["ISO27001", "ISO9001"], _LIB)
    assert "incident_management" in res["ISO27001"]
    assert "document_and_change_control" in res["ISO9001"]


def test_advisor_consumes_the_library_end_to_end():
    cra = yaml.safe_load(open(os.path.join(_DIR, "..", "knowledge", "transition_patterns",
                          "transition_pattern_iso27001_to_cra_maschinenvo_v1.yaml"), encoding="utf-8"))
    req = [TargetRequirement(capability_id=a["capability"]) for a in cra["likely_covered"]]
    req += [TargetRequirement(capability_id=d["capability"], expected_evidence=d.get("expected_evidence", []))
            for d in cra["delta_requirements"]]
    inp = OnboardingInput(company="x", certifications=["ISO27001", "TISAX", "ISO9001", "ISO14001"], target=["CRA"])
    hyp = resolve_for_certifications(inp.certifications, _LIB)        # library -> advisor input
    res = advisor_start(inp, hyp, req, target_id="CRA", corpus_status={"CRA": "validated"})
    assert res.inferred_assumptions and res.next_best_questions
    assert any(r.certification == "ISO14001" for r in res.rejected_assumptions)  # not relevant to CRA