from __future__ import annotations

from dataclasses import dataclass, field
from enum import Enum, auto

import torch
from tokenizer import (
    BOS, EOS, HLD_ON, MASK, META_BPM, META_END, PAD, RST, SEP, SLD_MID, SLD_ON,
    BRK_TO_ID, DUR, HLD_TO_ID, ID_TO_DIV, SIM_BEG, SIM_END, SLD_BEG,
    SLD_END_TOKEN, SLD_TO_ID, TAP_TO_ID, TCH_TO_ID,
    SIM_COUNT_2, DUR_NUM_TO_ID, DUR_DEN_TO_ID,
    CONFIG_BASE,
)


class GrammarMode(Enum):
    NORMAL = auto()
    SIM_COUNT = auto()
    SIM_BODY = auto()
    NEED_DUR = auto()
    DUR_NUM = auto()
    DUR_DEN = auto()
    SLIDE_BODY = auto()


NOTE_TOKENS = (
    set(TAP_TO_ID.values())
    | set(BRK_TO_ID.values())
    | set(HLD_TO_ID.values())
    | set(SLD_TO_ID.values())
    | set(TCH_TO_ID.values())
)
HOLD_TOKENS = set(HLD_TO_ID.values())
SLIDE_POINT_TOKENS = set(SLD_TO_ID.values())
META_TOKENS = set(range(META_BPM, META_END + 1))
ENGINE_ONLY_TOKENS = {PAD, BOS, EOS, SEP, MASK, HLD_ON, SLD_ON, SLD_MID} | META_TOKENS
# Duration parameters are raw integer tokens, not normal vocabulary tokens.
# Keep them in the same compact domain produced by real charts; allowing the
# whole uint8 range lets generation sample special/control-looking ids and
# absurdly long holds/slides.
VALID_DUR_NUMS = set(DUR_NUM_TO_ID.values())
VALID_DUR_DENS = set(DUR_DEN_TO_ID.values())


def config_tokens() -> set[int]:
    # Config vocab can be extended after importing this module when a checkpoint
    # loads its learned config_vocab. Keep this dynamic instead of freezing at import.
    import tokenizer as chart_tokenizer
    return set(chart_tokenizer.ID_TO_CONFIG.keys())


@dataclass
class ChartGrammarState:
    """Hard token grammar for chart generation.

    This keeps the sequence parseable. It intentionally does not enforce chart
    quality or muri rules; those belong in a later event/window validator.
    """

    min_division: int = 1
    max_sim: int = 2
    mode: GrammarMode = GrammarMode.NORMAL
    sim_target: int = 0
    sim_seen: int = 0
    sim_has_hold: bool = False
    slide_points: int = 0
    last_event_token: int | None = None
    can_take_duration: bool = False
    active_slide: bool = False
    allowed_div_tokens: set[int] = field(default_factory=set)

    def __post_init__(self) -> None:
        self.allowed_div_tokens = {
            div_id for div_id, div_value in ID_TO_DIV.items()
            if div_value >= self.min_division
        }

    def allowed_tokens(self) -> set[int]:
        if self.mode is GrammarMode.SIM_COUNT:
            return {SIM_COUNT_2} if self.max_sim >= 2 else set()

        if self.mode is GrammarMode.SIM_BODY:
            if self.sim_seen >= self.sim_target:
                return {SIM_END}
            return NOTE_TOKENS - SLIDE_POINT_TOKENS

        if self.mode is GrammarMode.NEED_DUR:
            return {DUR}

        if self.mode is GrammarMode.DUR_NUM:
            return VALID_DUR_NUMS

        if self.mode is GrammarMode.DUR_DEN:
            return VALID_DUR_DENS

        if self.mode is GrammarMode.SLIDE_BODY:
            allowed = set(SLIDE_POINT_TOKENS)
            if self.slide_points >= 2:
                allowed.add(SLD_END_TOKEN)
            return allowed

        allowed = {RST, SIM_BEG, SLD_BEG} | NOTE_TOKENS | config_tokens() | self.allowed_div_tokens
        return allowed - ENGINE_ONLY_TOKENS

    def apply_logits_mask(self, logits: torch.Tensor) -> torch.Tensor:
        allowed = self.allowed_tokens()
        masked = torch.full_like(logits, float("-inf"))
        idx = torch.tensor(sorted(allowed), dtype=torch.long, device=logits.device)
        masked[idx] = logits[idx]
        return masked

    def step(self, token: int) -> None:
        if self.mode is GrammarMode.SIM_COUNT:
            self.sim_target = 2 if token == SIM_COUNT_2 else min(max(int(token), 1), self.max_sim)
            self.sim_seen = 0
            self.sim_has_hold = False
            self.mode = GrammarMode.SIM_BODY
            return

        if self.mode is GrammarMode.SIM_BODY:
            if token == SIM_END:
                self.last_event_token = SIM_END
                if self.sim_has_hold:
                    self.mode = GrammarMode.NEED_DUR
                    self.can_take_duration = True
                else:
                    self.mode = GrammarMode.NORMAL
                    self.can_take_duration = False
            else:
                if token in HOLD_TOKENS:
                    self.sim_has_hold = True
                self.sim_seen += 1
            return

        if self.mode is GrammarMode.NEED_DUR:
            if token == DUR:
                self.mode = GrammarMode.DUR_NUM
            return

        if self.mode is GrammarMode.DUR_NUM:
            self.mode = GrammarMode.DUR_DEN
            return

        if self.mode is GrammarMode.DUR_DEN:
            self.mode = GrammarMode.NORMAL
            self.last_event_token = None
            self.can_take_duration = False
            return

        if self.mode is GrammarMode.SLIDE_BODY:
            if token == SLD_END_TOKEN:
                self.mode = GrammarMode.NEED_DUR
                self.active_slide = False
                self.last_event_token = SLD_END_TOKEN
                self.can_take_duration = True
            elif token in SLIDE_POINT_TOKENS:
                self.slide_points += 1
            return

        if token == SIM_BEG:
            self.mode = GrammarMode.SIM_COUNT
            self.last_event_token = SIM_BEG
            self.can_take_duration = False
        elif token == SLD_BEG:
            self.mode = GrammarMode.SLIDE_BODY
            self.slide_points = 0
            self.active_slide = True
            self.last_event_token = SLD_BEG
            self.can_take_duration = False
        elif token == DUR:
            self.mode = GrammarMode.DUR_NUM
        elif token in ID_TO_DIV:
            return
        elif token == RST or token in NOTE_TOKENS or token >= CONFIG_BASE:
            self.last_event_token = token
            if token in HOLD_TOKENS:
                self.mode = GrammarMode.NEED_DUR
                self.can_take_duration = True
            else:
                self.can_take_duration = False