"""
RegexFeatureExtractor - token feature gating.
Extends build_code_features (4-dim) to a 7-dim per-token feature vector plus
a running SyntaxState (bracket stack, quote open/closed) used as a cheap
pre-model sanity gate.

Dimensions per token:
  [0] is_code_like       (indent / brackets / operators / newlines)
  [1] indent_depth       (normalized leading whitespace)
  [2] bracket_balance    (1.0 open, 0.5 neutral, 0.0 close)
  [3] has_newline
  [4] keyword_hit        (NEW)
  [5] quote_state        (running open/closed string literal)   (NEW)
  [6] numeric_literal    (NEW)
"""

import re
import torch
from dataclasses import dataclass, field
from typing import List, Optional, Tuple

CODE_CHARS = set("{}()[];=<>!&|+-*/%'\"`#@.,:")
KEYWORD_RE = re.compile(
    r"\b(def|class|import|return|if|else|elif|for|while|try|except|finally|"
    r"lambda|pass|with|as|yield|from|async|await)\b"
)
NUMERIC_RE = re.compile(r"\b\d+(\.\d+)?\b")

OPEN_BRACKETS = {"{": 1, "[": 1, "(": 1}
CLOSE_BRACKETS = {"}": 1, "]": 1, ")": 1}


@dataclass
class SyntaxState:
    bracket_stack: List[str] = field(default_factory=list)
    quote_open: Optional[str] = None  # '"' or "'" while a string literal is open
    depth: int = 0

    def is_balanced(self) -> bool:
        return not self.bracket_stack and self.quote_open is None

    def to_dict(self) -> dict:
        return {
            "balanced": self.is_balanced(),
            "bracket_depth": len(self.bracket_stack),
            "quote_open": self.quote_open,
        }


class RegexFeatureExtractor:
    def __init__(self, num_features: int = 7):
        self.num_features = num_features

    def extract(self, tokenizer, input_ids: torch.Tensor) -> Tuple[torch.Tensor, List[SyntaxState]]:
        """Per-token features (B, T, F) + one SyntaxState per row."""
        feats: List[List[List[float]]] = []
        states: List[SyntaxState] = []
        for row in input_ids.tolist():
            tokens = tokenizer.convert_ids_to_tokens(row)
            state = SyntaxState()
            row_feats = []
            for tok in tokens:
                is_code = any(c in CODE_CHARS for c in tok)
                indent = 0.0
                stripped = tok.lstrip()
                if stripped and tok != stripped:
                    indent = min((len(tok) - len(stripped)) / 8.0, 1.0)
                    is_code = True
                bal = 0.5
                for c in tok:
                    if c in OPEN_BRACKETS:
                        bal = 1.0
                        state.bracket_stack.append(c)
                    elif c in CLOSE_BRACKETS:
                        bal = 0.0
                        if state.bracket_stack:
                            state.bracket_stack.pop()
                # running quote state
                for c in tok:
                    if c in ('"', "'"):
                        if state.quote_open is None:
                            state.quote_open = c
                        elif state.quote_open == c:
                            state.quote_open = None
                quote = 1.0 if state.quote_open is not None else 0.0
                newline = 1.0 if "\n" in tok else 0.0
                kw = 1.0 if KEYWORD_RE.search(tok) else 0.0
                num = 1.0 if NUMERIC_RE.search(tok) else 0.0
                row_feats.append([1.0 if is_code else 0.0, indent, bal, newline, kw, quote, num])
                state.depth = len(state.bracket_stack)
            row_feats = row_feats[: input_ids.shape[1]]
            feats.append(row_feats)
            states.append(state)
        max_len = max(len(r) for r in feats)
        padded = [
            r + [[0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0]] * (max_len - len(r))
            for r in feats
        ]
        t = torch.tensor(padded, dtype=torch.float32)
        if t.shape[-1] > self.num_features:
            t = t[..., : self.num_features]
        return t, states

    def extract_text(self, text: str) -> SyntaxState:
        """Run a string-only pass for the pre-model gate (no tokenizer)."""
        state = SyntaxState()
        for c in text:
            if c in OPEN_BRACKETS:
                state.bracket_stack.append(c)
            elif c in CLOSE_BRACKETS and state.bracket_stack:
                state.bracket_stack.pop()
            elif c in ('"', "'"):
                if state.quote_open is None:
                    state.quote_open = c
                elif state.quote_open == c:
                    state.quote_open = None
        state.depth = len(state.bracket_stack)
        return state

    def gate(self, syntax_state: SyntaxState) -> str:
        """Returns 'pass' | 'warn' | 'block'."""
        if syntax_state.is_balanced():
            return "pass"
        return "block" if syntax_state.depth > 4 else "warn"


# drop-in replacement for architecture.build_code_features with 7-dim output
def build_code_features_v2(tokenizer, input_ids: torch.Tensor) -> torch.Tensor:
    return RegexFeatureExtractor(num_features=7).extract(tokenizer, input_ids)[0]