from rag.answer import GroundedAnswer
from rag.conversation import DeterministicSummariser, InMemoryConversationStore
from rag.conversational import (
    SMALLTALK_REPLY,
    ConversationalLoopService,
)
from rag.models import (
    Evidence,
    EvidenceDecision,
    QueryIntent,
    RetrievalResult,
    SubjectScope,
)
from rag.reasoning import ClarifyReason
from rag.routing import CatalogDrugResolver
from rag.sections import SectionResolver


def _grounded(answer, evidence_text):
    ev = Evidence("e1", "e1", "prose", evidence_text, 1.0, (), False, False)
    result = RetrievalResult(
        EvidenceDecision.ANSWERABLE, "grounded_evidence_available",
        (ev,), "metformin", "resolved",
    )
    return GroundedAnswer(result, answer, (), False)


class FakeAnswers:
    def __init__(self, answer_text, evidence_text):
        self._a = answer_text
        self._e = evidence_text
        self.calls = []

    def answer(self, query, subject_scope, intent, drug_id=None):
        self.calls.append((query, drug_id))
        return _grounded(self._a, self._e)


def _service(answers):
    return ConversationalLoopService(
        answers=answers,
        resolver=CatalogDrugResolver({"metformin": {"metformin"}}),
        section_resolver=SectionResolver(),
        store=InMemoryConversationStore(),
        summariser=DeterministicSummariser(),
    )


def test_smalltalk_answers_socially_without_calling_engine():
    answers = FakeAnswers("x", "x")
    svc = _service(answers)
    out = svc.answer("c1", "chào bạn", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP)
    assert out.smalltalk is True
    assert out.answer == SMALLTALK_REPLY
    assert answers.calls == []  # a greeting is not a drug lookup


def test_medical_turn_returns_grounded_answer():
    answers = FakeAnswers("Quá mẫn với metformin.", "Quá mẫn với metformin.")
    svc = _service(answers)
    out = svc.answer(
        "c2", "chống chỉ định metformin", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP
    )
    assert out.smalltalk is False
    assert out.answer == "Quá mẫn với metformin."
    assert out.grounded is not None


def test_followup_inherits_drug_and_passes_it_resolved():
    answers = FakeAnswers(
        "Ở trẻ em điều chỉnh theo cân nặng.",
        "Ở trẻ em, liều metformin điều chỉnh theo cân nặng.",
    )
    svc = _service(answers)
    svc.answer("c3", "chống chỉ định metformin", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP)
    out = svc.answer("c3", "còn trẻ em thì sao?", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP)
    assert out.inherited_drug == "metformin"
    assert out.answer.startswith("Về Metformin:")
    # The inherited drug is passed already-resolved (not re-resolved from the
    # rewritten turn text), and the raw follow-up drives section routing.
    last_query, last_drug_id = answers.calls[-1]
    assert last_drug_id == "metformin"
    assert last_query == "còn trẻ em thì sao?"
    # State carried the drug forward.
    assert svc._store.load("c3").focus.drug_id == "metformin"


def test_confirmation_is_not_fuzzy_matched_to_a_drug():
    """'đúng' must not be fuzzy-matched to terbinafin/tretinoin (the did-you-mean
    loop the reviewer hit); it asks which drug instead."""
    answers = FakeAnswers("x", "x")
    svc = _service(answers)
    out = svc.answer("cc", "đúng", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP)
    assert out.clarification is not None
    assert out.clarification.reason == "confirm_without_context"
    assert answers.calls == []


def test_a_long_sentence_that_names_no_drug_is_not_offered_did_you_mean():
    """A full question ('EPO điều trị thiếu máu...') that resolves no drug is
    answered honestly, not with garbage suggestions from fuzzing the sentence."""
    answers = FakeAnswers("x", "x")
    svc = _service(answers)  # catalog holds only metformin
    out = svc.answer(
        "cl", "EPO điều trị thiếu máu do hóa trị ung thư liều khởi đầu bao nhiêu",
        SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP,
    )
    assert out.clarification is not None
    assert out.clarification.reason == "drug_not_supported"
    assert answers.calls == []


def test_no_close_drug_reports_not_supported():
    answers = FakeAnswers("x", "x")
    svc = _service(answers)  # catalog holds only metformin
    out = svc.answer("c4", "cái này thế nào?", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP)
    assert out.answer is None
    assert out.clarification is not None
    # Nothing close to a real drug: honest "not in the formulary", not a guess.
    assert out.clarification.reason == "drug_not_supported"
    assert answers.calls == []


def test_typo_offers_did_you_mean_not_silent_resolution():
    answers = FakeAnswers("x", "x")
    svc = _service(answers)  # catalog holds only metformin
    out = svc.answer("c5", "metformim", SubjectScope.HUMAN, QueryIntent.FACT_LOOKUP)
    # A near-miss is asked about, never auto-resolved on a similarity threshold.
    assert out.answer is None
    assert out.clarification is not None
    assert out.clarification.reason == "did_you_mean"
    assert "Metformin" in out.clarification.options
    assert answers.calls == []