"""
L1 graders — run live on every query.

Metrics:
  pii_leakage        — regex scan for PII patterns in response
  token_budget       — response within allowed token ceiling
  answer_relevancy   — cosine similarity between query and response embeddings
  faithfulness       — NLI cross-encoder: entailment score per (chunk, claim) pair
  chain_terminology  — deterministic: client-specific terms used (via RosettaStone)
"""

import logging
import re
from dataclasses import dataclass, field
from typing import Any

import numpy as np
from config import EMBEDDER_MODEL
from rosetta import check_terminology
from sentence_transformers import CrossEncoder, SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

log = logging.getLogger(__name__)

_embedder: SentenceTransformer | None = None
_nli_model: CrossEncoder | None = None

# cross-encoder/nli-deberta-v3-small: 3-class NLI, columns = [contradiction, entailment, neutral]
NLI_MODEL = "cross-encoder/nli-deberta-v3-small"
_NLI_ENTAILMENT_IDX = 1


def get_embedder() -> SentenceTransformer:
    """Return the shared sentence-transformer instance, loading it on first call."""
    global _embedder
    if _embedder is None:
        _embedder = SentenceTransformer(EMBEDDER_MODEL)
    return _embedder


def get_nli_model() -> CrossEncoder:
    """Return the shared NLI cross-encoder, loading it on first call."""
    global _nli_model
    if _nli_model is None:
        _nli_model = CrossEncoder(NLI_MODEL)
    return _nli_model


@dataclass(slots=True)
class GradeResult:
    metric: str
    passed: bool
    score: float
    detail: str = ""
    metadata: dict[str, Any] = field(default_factory=dict)


@dataclass(slots=True)
class GradeReport:
    client: str
    query: str
    results: list[GradeResult] = field(default_factory=list)

    @property
    def overall(self) -> bool:
        return all(r.passed for r in self.results)

    @property
    def summary(self) -> dict[str, Any]:
        return {
            "overall_pass": self.overall,
            "metrics": {
                r.metric: {"passed": r.passed, "score": round(r.score, 3), "detail": r.detail}
                for r in self.results
            },
        }


_SENTENCE_SPLIT = re.compile(r"(?<=[.!?])\s+")

_PII_PATTERNS = [
    (r"\b\d{3}-\d{2}-\d{4}\b", "SSN"),
    (r"\b\d{16}\b", "credit card"),
    (r"\b[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}\b", "email"),
    (r"\b\d{3}[\s.\-]?\d{3}[\s.\-]?\d{4}\b", "phone"),
]

TOKEN_BUDGET = 512
RELEVANCY_THRESHOLD = 0.45
FAITHFULNESS_THRESHOLD = 0.35

_SENTINEL = "NOT IN DOCUMENTS"

# Fallback patterns for responses that predate the sentinel instruction or
# where the model ignores the sentinel format.
_REFUSAL_FALLBACK = re.compile(
    r"(i (don't|do not|cannot|can't|'m not able to) (have|find|provide|answer)|"
    r"not enough (information|context)|"
    r"the (context|provided) (does not|doesn't) (contain|include|mention))",
    re.IGNORECASE,
)


def _is_refusal(response: str) -> bool:
    if _SENTINEL in response.upper():
        lines = response.split("\n")
        # Only auto-pass when sentinel is on the first line AND nothing substantial
        # follows — continuation lines may contain hallucinated claims.
        has_continuation = any(len(ln.split()) >= 3 for ln in lines[1:])
        return _SENTINEL in lines[0].upper() and not has_continuation
    return bool(_REFUSAL_FALLBACK.search(response))


def grade_pii_leakage(response: str) -> GradeResult:
    """Scan response for PII patterns; fail on any match."""
    found = [label for pattern, label in _PII_PATTERNS if re.search(pattern, response)]
    return GradeResult(
        metric="pii_leakage",
        passed=not found,
        score=0.0 if found else 1.0,
        detail=f"Detected: {', '.join(found)}" if found else "Clean",
    )


def grade_token_budget(response: str, budget: int = TOKEN_BUDGET) -> GradeResult:
    """Fail if estimated token count exceeds budget."""
    approx_tokens = len(response) // 4
    passed = approx_tokens <= budget
    return GradeResult(
        metric="token_budget",
        passed=passed,
        score=1.0 if passed else round(budget / approx_tokens, 3),
        detail=f"~{approx_tokens} tokens (budget: {budget})",
        metadata={"approx_tokens": approx_tokens, "budget": budget},
    )


def grade_answer_relevancy(query: str, response: str) -> GradeResult:
    """Score semantic similarity between query and response via cosine distance."""
    embedder = get_embedder()
    q_vec = embedder.encode([query])
    r_vec = embedder.encode([response])
    score = float(cosine_similarity(q_vec, r_vec)[0][0])
    return GradeResult(
        metric="answer_relevancy",
        passed=score >= RELEVANCY_THRESHOLD,
        score=score,
        detail=f"Cosine {score:.3f} (threshold: {RELEVANCY_THRESHOLD})",
    )


def _strip_chunk_title(chunk: str) -> str:
    """Remove [Title] prefix added by _build_context before NLI scoring."""
    if chunk.startswith("[") and "]\n" in chunk:
        return chunk.split("]\n", 1)[1].strip()
    return chunk


def decompose_claims(response: str) -> list[str]:
    """Split response into atomic claim sentences (≥3 words each)."""
    sentences = _SENTENCE_SPLIT.split(response.strip())
    return [s.strip() for s in sentences if len(s.split()) >= 3]


def _context_sentences(chunks: list[str]) -> list[str]:
    """Flatten context chunks into individual sentences for sentence-level NLI scoring.

    Cross-encoder NLI degrades on multi-sentence inputs — performance is calibrated
    on single-sentence (premise, hypothesis) pairs matching the SNLI/MNLI training format.
    """
    sentences = []
    for chunk in chunks:
        for s in _SENTENCE_SPLIT.split(chunk.strip()):
            if len(s.split()) >= 3:
                sentences.append(s.strip())
    return sentences


def grade_faithfulness(response: str, context: str) -> GradeResult:
    """Whole-response faithfulness: max entailment score across all context chunks."""
    if _is_refusal(response):
        return GradeResult(
            metric="faithfulness", passed=True, score=1.0,
            detail="Refusal — no factual claims to verify",
        )
    model = get_nli_model()
    raw_chunks = [c.strip() for c in context.split("\n\n") if c.strip()]
    if not raw_chunks:
        return GradeResult(metric="faithfulness", passed=False, score=0.0, detail="No context")
    chunks = [_strip_chunk_title(c) for c in raw_chunks]
    sentences = _context_sentences(chunks)
    pairs = [(s, response) for s in sentences]
    scores_matrix: np.ndarray = model.predict(pairs, apply_softmax=True)
    entailment: np.ndarray = scores_matrix[:, _NLI_ENTAILMENT_IDX]
    log.info("NLI entailment scores: %s", [round(float(s), 3) for s in entailment])
    score = float(entailment.max())
    return GradeResult(
        metric="faithfulness",
        passed=score >= FAITHFULNESS_THRESHOLD,
        score=score,
        detail=f"Faithfulness {score:.3f} (threshold: {FAITHFULNESS_THRESHOLD})",
    )


def grade_faithfulness_decomposed(response: str, context: str) -> GradeResult:
    """Claim-level faithfulness: each sentence verified independently against context.

    Supported claims / total claims — catches partial hallucinations missed by whole-response NLI.
    """
    if _is_refusal(response):
        return GradeResult(
            metric="faithfulness", passed=True, score=1.0,
            detail="Refusal — no factual claims to verify",
        )
    raw_chunks = [c.strip() for c in context.split("\n\n") if c.strip()]
    if not raw_chunks:
        return GradeResult(metric="faithfulness", passed=False, score=0.0, detail="No context")

    chunks = [_strip_chunk_title(c) for c in raw_chunks]
    claims = decompose_claims(response)
    if not claims:
        return GradeResult(metric="faithfulness", passed=False, score=0.0, detail="No claims extracted")

    sentences = _context_sentences(chunks)
    model = get_nli_model()
    claim_results: list[dict[str, Any]] = []

    for claim in claims:
        pairs = [(s, claim) for s in sentences]
        scores_matrix: np.ndarray = model.predict(pairs, apply_softmax=True)
        entailment: np.ndarray = scores_matrix[:, _NLI_ENTAILMENT_IDX]
        best = float(entailment.max())
        claim_results.append({"claim": claim, "score": round(best, 3), "supported": best >= FAITHFULNESS_THRESHOLD})

    supported = sum(1 for c in claim_results if c["supported"])
    score = supported / len(claim_results)
    log.info("Claim decomposition: %d/%d supported (score=%.3f)", supported, len(claim_results), score)

    return GradeResult(
        metric="faithfulness",
        passed=score >= FAITHFULNESS_THRESHOLD,
        score=score,
        detail=f"{supported}/{len(claim_results)} claims supported (threshold: {FAITHFULNESS_THRESHOLD})",
        metadata={"claims": claim_results},
    )


def grade_chain_terminology(response: str, client: str) -> GradeResult:
    """Check that the response uses client-specific terms, not rival terminology."""
    result = check_terminology(response, client)
    violations = result["violations"]
    checked = result["checked"]
    score = 1.0 - (len(violations) / checked) if checked else 1.0
    detail = (
        f"{len(violations)} violation(s): " +
        ", ".join(f"{v['found']!r} → should be {v['expected']!r}" for v in violations)
        if violations else f"All {checked} terms correct"
    )
    return GradeResult(
        metric="chain_terminology",
        passed=result["pass"],
        score=score,
        detail=detail,
        metadata={"violations": violations},
    )


def grade(
    query: str,
    response: str,
    context: str,
    client: str,
    token_budget: int = TOKEN_BUDGET,
) -> GradeReport:
    """Run all L1 graders and return a consolidated report."""
    report = GradeReport(client=client, query=query)
    report.results = [
        grade_pii_leakage(response),
        grade_token_budget(response, token_budget),
        grade_answer_relevancy(query, response),
        grade_faithfulness_decomposed(response, context),
        grade_chain_terminology(response, client),
    ]
    return report