"""Layer 1 — Regex-based PII detection.

Sub-millisecond detection for structured PII: IBAN, email, phone, SSN, credit cards.
All patterns are pre-compiled at import time.
"""

from __future__ import annotations

import re
from dataclasses import dataclass, field


@dataclass
class DetectedEntity:
    entity_type: str
    original_value: str
    start: int
    end: int
    confidence: float = 1.0
    detection_layer: str = "regex"


# ---------------------------------------------------------------------------
# Luhn algorithm (credit card validation)
# ---------------------------------------------------------------------------


def _luhn_valid(number: str) -> bool:
    """Return True if *number* (digits only) passes the Luhn check."""
    digits = [int(d) for d in number]
    odd_sum = sum(digits[-1::-2])
    even_sum = sum(sum(divmod(d * 2, 10)) for d in digits[-2::-2])
    return (odd_sum + even_sum) % 10 == 0


# ---------------------------------------------------------------------------
# IBAN MOD-97 checksum validation (ISO 13616)
# ---------------------------------------------------------------------------

_IBAN_LETTER_MAP = {c: str(ord(c) - ord("A") + 10) for c in "ABCDEFGHIJKLMNOPQRSTUVWXYZ"}


def _iban_valid(raw: str) -> bool:
    """Return True if *raw* (spaces allowed) is a valid IBAN checksum."""
    iban = raw.replace(" ", "").upper()
    if len(iban) < 5:
        return False
    rearranged = iban[4:] + iban[:4]
    numeric = "".join(_IBAN_LETTER_MAP.get(c, c) for c in rearranged)
    try:
        return int(numeric) % 97 == 1
    except ValueError:
        return False


# ---------------------------------------------------------------------------
# Pre-compiled patterns
# ---------------------------------------------------------------------------

# IBAN: 2-letter country code, 2 check digits, up to 30 alphanumeric chars (grouped by 4)
_RE_IBAN = re.compile(
    r"\b([A-Z]{2}\d{2}(?:\s?[0-9A-Z]{4}){2,7}\s?[0-9A-Z]{1,4})\b"
)

# Email (RFC 5321 simplified)
_RE_EMAIL = re.compile(
    r"\b[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,}\b"
)

# French phone (mobile 06/07 + landlines 01-05, 08, 09), optional +33/0033 prefix
_RE_PHONE_FR = re.compile(
    r"(?:(?:\+|00)33\s?|0)[1-9](?:[\s.\-]?\d{2}){4}"
)

# International phone: + followed by 1-3 digit country code + 6-12 digits
_RE_PHONE_INTL = re.compile(
    r"(?<!\d)\+[1-9]\d{0,2}[\s.\-]?\d{4,14}(?!\d)"
)

# French SSN (NIR): 13 digits + 2-digit key, with optional spaces/hyphens between groups
# Format: [12] YY MM DEP COM NNN CC  — e.g. "1 78 07 75 115 423 45" or "1780775115 42345"
_RE_FR_SSN = re.compile(
    r"\b([12][\s\-]?\d{2}[\s\-]?(?:0[1-9]|1[0-2]|20)[\s\-]?\d{2}[\s\-]?\d{3}[\s\-]?\d{3}[\s\-]?\d{2})\b"
)

# Credit card: 16 digits in groups of 4 (Visa, Mastercard, etc.)
# Also handles Amex: 4+6+5 (not validated by Luhn here — Luhn handles it)
_RE_CREDIT_CARD = re.compile(
    r"\b(\d{4}[\s\-]?\d{4}[\s\-]?\d{4}[\s\-]?\d{4})\b"
)

# ---------------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------------


class RegexLayer:
    """Detect structured PII using pre-compiled regular expressions."""

    def detect(self, text: str) -> list[DetectedEntity]:
        """Return all PII entities found in *text*, in document order."""
        entities: list[DetectedEntity] = []

        entities.extend(self._find_ibans(text))
        entities.extend(self._find_emails(text))
        entities.extend(self._find_phones(text))
        entities.extend(self._find_ssns(text))
        entities.extend(self._find_credit_cards(text))

        return sorted(entities, key=lambda e: e.start)

    # -----------------------------------------------------------------------
    # Private detection helpers
    # -----------------------------------------------------------------------

    def _find_ibans(self, text: str) -> list[DetectedEntity]:
        results = []
        for m in _RE_IBAN.finditer(text):
            value = m.group(1)
            # Accept IBANs with invalid MOD-97 checksum at lower confidence (useful for
            # test/demo data where checksums may not be real).
            confidence = 1.0 if _iban_valid(value) else 0.75
            results.append(
                DetectedEntity(
                    entity_type="IBAN",
                    original_value=value,
                    start=m.start(1),
                    end=m.end(1),
                    confidence=confidence,
                )
            )
        return results

    def _find_emails(self, text: str) -> list[DetectedEntity]:
        return [
            DetectedEntity(
                entity_type="EMAIL",
                original_value=m.group(),
                start=m.start(),
                end=m.end(),
            )
            for m in _RE_EMAIL.finditer(text)
        ]

    def _find_phones(self, text: str) -> list[DetectedEntity]:
        results = []
        seen: set[tuple[int, int]] = set()

        for m in _RE_PHONE_FR.finditer(text):
            span = (m.start(), m.end())
            if span not in seen:
                seen.add(span)
                results.append(
                    DetectedEntity(
                        entity_type="PHONE_FR",
                        original_value=m.group(),
                        start=m.start(),
                        end=m.end(),
                    )
                )

        for m in _RE_PHONE_INTL.finditer(text):
            span = (m.start(), m.end())
            if span not in seen:
                seen.add(span)
                results.append(
                    DetectedEntity(
                        entity_type="PHONE_INTL",
                        original_value=m.group(),
                        start=m.start(),
                        end=m.end(),
                    )
                )

        return results

    def _find_ssns(self, text: str) -> list[DetectedEntity]:
        return [
            DetectedEntity(
                entity_type="FR_SSN",
                original_value=m.group(1),
                start=m.start(1),
                end=m.end(1),
            )
            for m in _RE_FR_SSN.finditer(text)
        ]

    def _find_credit_cards(self, text: str) -> list[DetectedEntity]:
        results = []
        for m in _RE_CREDIT_CARD.finditer(text):
            digits_only = re.sub(r"[\s\-]", "", m.group(1))
            # Accept cards that fail Luhn at lower confidence (test/demo card numbers
            # often don't have a valid Luhn checksum).
            confidence = 1.0 if _luhn_valid(digits_only) else 0.75
            results.append(
                DetectedEntity(
                    entity_type="CREDIT_CARD",
                    original_value=m.group(1),
                    start=m.start(1),
                    end=m.end(1),
                    confidence=confidence,
                )
            )
        return results