import re
import os

from app.core.logging import get_logger

logger = get_logger(__name__)


class GuardClassifier:
    """
    GuardClassifier integrates a fine-tuned DistilBERT instance aiming
    for strictly relevant inputs (>0.70 confidence threshold).
    If the fine-tuned model path does not exist (or torch is not installed),
    it falls back to permissive Regex heuristics.
    """
    def __init__(self, model_path: str = "fine_tuning/guard_classifier/model"):
        self.model_path = model_path
        self._model = None
        self._tokenizer = None

        if os.path.exists(self.model_path) and os.listdir(self.model_path):
            try:
                import torch  # noqa: F811 — lazy import, not installed in CI or prod API
                from transformers import AutoTokenizer, AutoModelForSequenceClassification

                logger.info("Loading GuardClassifier model from %s", self.model_path)
                self._tokenizer = AutoTokenizer.from_pretrained(self.model_path)
                self._model = AutoModelForSequenceClassification.from_pretrained(self.model_path)
                self._model.eval()
            except ImportError:
                logger.warning("torch/transformers not installed, falling back to rule-based guard.")
                self._model = None
            except Exception as e:
                logger.warning("Failed to load DistilBERT Guard model, falling back to rule-based: %s", e)
                self._model = None
        else:
            logger.info("GuardClassifier model path not found, falling back to rule-based.")

    def is_safe_and_relevant(self, query: str) -> bool:
        """Wrapper to maintain existing pipeline signature."""
        safe, score = self.is_in_scope(query)
        return safe

    def is_in_scope(self, text: str) -> tuple[bool, float]:
        """
        Returns (is_in_scope, confidence_score).
        Threshold: 0.70. Below threshold -> out of scope.
        """
        if not self._model or not self._tokenizer:
            result = self._rule_based_check(text)
            return (result, 1.0 if result else 0.0)

        try:
            import torch

            inputs = self._tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
            with torch.no_grad():
                outputs = self._model(**inputs)

            probs = torch.softmax(outputs.logits, dim=-1)[0]
            in_scope_prob = probs[1].item()

            is_in_scope = in_scope_prob >= 0.70
            return (is_in_scope, float(in_scope_prob))

        except Exception as e:
            logger.warning("Inference error, reverting to rules: %s", e)
            result = self._rule_based_check(text)
            return (result, 1.0 if result else 0.0)

    # Compiled once at class load — cheaper than recompiling per call.
    _INJECTION_PATTERNS: list = []

    @classmethod
    def _build_patterns(cls) -> list:
        """Compile and cache all injection-detection regexes."""
        if cls._INJECTION_PATTERNS:
            return cls._INJECTION_PATTERNS

        raw = [
            # ── Classic prompt injection ──────────────────────────────────────
            r"ignore\s+(all\s+)?(previous|prior|above|earlier)\s+(instructions?|prompts?|rules?|context)",
            r"disregard\s+(all\s+)?(previous|prior|above)\s+(instructions?|prompts?|rules?|context)",
            r"forget\s+(everything|all\s+(previous|prior|your))",
            r"override\s+(your\s+)?(instructions?|rules?|directives?|constraints?)",
            r"bypass\s+your\s+(restrictions?|safety|filters?|rules?|instructions?)",
            r"(do\s+not\s+follow|stop\s+following)\s+(your\s+)?(instructions?|rules?|guidelines?)",

            # ── System prompt extraction ──────────────────────────────────────
            r"(repeat|print|output|reveal|show|display|dump|share)\s+(your\s+)?(system\s+)?(prompt|instructions?|rules?|directives?|constraints?|message)",
            r"what\s+(are|were)\s+your\s+(instructions?|rules?|system\s+prompt|directives?)",
            r"(tell|show)\s+me\s+(your\s+)?(system|initial|original|hidden|secret)\s+(prompt|instructions?|message)",
            r"\bsystem\s+message\b",

            # ── Role / persona jailbreaks ─────────────────────────────────────
            r"you\s+are\s+now\s+(a\s+|an\s+)?(?!(darshan|assistant))",
            r"(pretend|act|behave)\s+(like|as\s+if)\s+you\s+(are|have\s+no|don.t\s+have)",
            r"(pretend|imagine|assume|suppose)\s+you\s+(are|were)\s+(a\s+|an\s+)?(?!(darshan))",
            r"roleplay\s+as",
            r"(simulate|impersonate)\s+(a\s+|an\s+)?(different|other|unrestricted|evil|jailbroken)",
            r"(act|respond)\s+as\s+if\s+you\s+(have\s+no|don.t\s+have)\s+(restrictions?|rules?|guidelines?|filters?|safety)",
            r"you\s+(have\s+no|don.t\s+have)\s+(restrictions?|rules?|limits?|filters?)",
            r"\bdan\s+(mode|prompt|jailbreak)\b",
            r"developer\s+mode",
            r"jailbreak\b",
            r"unrestricted\s+(mode|access|version|ai)",
            r"no\s+filter(s|ed)?\s+(mode|version|response)",

            # ── Hypothetical / simulation bypass (meta-instruction targeted only) ─────
            # Note: kept narrow on purpose — Darshan has security/infosec repos and
            # visitors may legitimately ask about prompt injection, exploits, bypass
            # techniques, etc. as topics. These patterns only fire when they are
            # clearly attempts to change the *bot's behaviour*, not discuss a topic.
            r"in\s+a\s+(simulation|hypothetical|imaginary|alternate)\s+(scenario|world|universe).{0,30}(no\s+rules?|no\s+restrictions?|you\s+can)",
            r"(act|respond|behave).{0,20}as\s+if.{0,20}(no\s+restrictions?|no\s+rules?|unrestricted|jailbroken)",

            # ── User private-info extraction ──────────────────────────────────
            r"(what|share|give|show|tell).{0,20}(user.{0,10})?(email|phone|address|password|credit.?card|ssn|date.of.birth|location|ip.?address)",
            r"(collect|store|log|extract|retrieve|access).{0,20}(user|visitor|personal)\s+(data|info|information|details)",
            r"(do\s+you\s+have|can\s+you\s+access).{0,20}(my|the\s+user.s?)\s+(email|phone|data|address|password)",

            # ── Reputation / defamation attacks ──────────────────────────────
            r"(say|write|tell|claim|state)\s+(that\s+)?darshan\s+(is|was|has\s+been).{0,40}(bad|stupid|incompetent|fraud|liar|criminal|terrible|fake|cheat)",
            r"(make|portray|describe)\s+darshan.{0,20}(negatively|badly|unfavorably|as\s+a\s+(fraud|liar|failure))",
            r"write\s+a\s+(negative|bad|false|defamatory|fake).{0,20}(review|statement|claim).{0,20}(about|of)\s+darshan",
            r"(discredit|slander|defame|insult|mock)\s+darshan",

            # ── Instruction injection via delimiters ──────────────────────────
            r"<\|\s*(system|user|assistant|im_start|im_end)\s*\|>",
            r"<<\s*sys\s*>>",
            r"\[\s*inst\s*\]",
            r"---\s*system\s*---",
            r"#+\s*system\s*prompt",
            r"#+\s*new\s+instructions?",

            # ── Training-data poisoning signals ──────────────────────────────
            r"(add|inject|insert|plant|embed)\s+(this|the\s+following|text|instructions?)\s+(into|in)\s+(your\s+)?(training|context|memory|knowledge)",
            r"remember\s+(this|the\s+following)\s+(for\s+(future|all|every)|always)",
            r"from\s+now\s+on\s+(you\s+)?(must|will|should|always)",
            r"update\s+your\s+(instructions?|rules?|behaviour|system\s+prompt)",
        ]

        cls._INJECTION_PATTERNS = [re.compile(p, re.IGNORECASE) for p in raw]
        return cls._INJECTION_PATTERNS

    def _rule_based_check(self, text: str) -> bool:
        """Block on any known injection pattern; permissive otherwise."""
        for pattern in self._build_patterns():
            if pattern.search(text):
                return False
        return True