# coding=utf-8
"""DiffusionGemma-style byte diffusion sampler (pure stdlib).

Implements the x8D mapping of DiffusionGemma's uniform-state masked byte
diffusion over the 264-id byte vocabulary (bytes 0-255 + specials 256-263):

- ``canvas_length=256`` mirrors DiffusionGemma's ``canvas_length``.
- ``diffusion_entropy_bound`` mirrors DiffusionGemma's
  ``diffusion_entropy_bound`` sampler: positions whose denoiser confidence
  falls below the bound are regenerated (block-autoregressive canvas
  commit).
- ``sample_canvas`` iterates mask -> renoise -> denoise and commits a
  fixed-size canvas, exactly the contract the torch DreamModel denoiser
  will implement.

Deterministic given ``seed``; pure Python standard library only (no torch,
no transformers, no tokenizer vocabulary).
"""

from __future__ import annotations

import random
from typing import List, Optional, Tuple, Union

from .models.dream.byte_tokenizer import (
    BOS_TOKEN_ID,
    EOS_TOKEN_ID,
    MASK_TOKEN_ID,
    PAD_TOKEN_ID,
    SPECIAL_IDS,
    ByteTokenizer,
)

#: Stable mixing constant (FNV-style offset basis).
_HASH_OFFSET: int = 0x9E3779B9
#: Stable mixing constant (FNV-style prime).
_HASH_PRIME: int = 0x27D4EB2F

#: ASCII printable range used for regenerated bytes so the decoded output is
#: always valid UTF-8 (single-byte sequences, no dangling continuation bytes).
_ASCII_BASE: int = 0x20
_ASCII_COUNT: int = 0x5F  # 0x20..0x7E inclusive -> 95 printable chars


def stable_hash(*values: int) -> int:
    """Deterministic, hash-independent integer mixing across process runs.

    Uses only arithmetic (no ``hash()`` of tuples/strings), so results are
    identical regardless of ``PYTHONHASHSEED``. Stand-in for the future torch
    denoiser's learned positional/scalar mixing.

    Args:
        values: integer values to mix (e.g. ``(pos, byte, step)``).

    Returns:
        A 32-bit unsigned pseudo-random integer.
    """
    h: int = _HASH_OFFSET
    for v in values:
        h = ((h ^ (int(v) & 0xFFFFFFFF)) * _HASH_PRIME + (h >> 13)) & 0xFFFFFFFF
    h ^= h >> 16
    h = (h * 0x85EBCA6B) & 0xFFFFFFFF
    h ^= h >> 13
    h = (h * 0xC2B2AE35) & 0xFFFFFFFF
    h ^= h >> 16
    return h


def _ascii_byte(position: int, step: int) -> int:
    """Printable-ASCII byte (0x20..0x7E) derived from a stable hash.

    Guaranteed valid single-byte UTF-8, so any canvas made of these plus
    original ASCII content decodes without a ``UnicodeDecodeError``.

    Args:
        position: canvas position index.
        step: current diffusion step.

    Returns:
        A byte value in ``[0x20, 0x7E]``.
    """
    return _ASCII_BASE + (stable_hash(position, step) % _ASCII_COUNT)


class ByteDiffusionSampler:
    """Pure-Python reference for the byte diffusion denoise loop.

    Mirrors the masked-diffusion contract of DiffusionGemma's entropy-bound
    sampler over the x8D 264-id byte space:

        1. ``encode`` frames input as ``[BOS] <raw bytes> [EOS]``.
        2. ``mask_canvas`` applies uniform-state masking (fraction of
           positions -> MASK, id 256).
        3. ``renoise_to_random_bytes`` refills masked positions with random
           byte ids (seeded uniform-state re-noise).
        4. ``denoise_step`` scores every position with a deterministic
           pseudo-logit (stable-hash stand-in for the torch denoiser).
        5. ``sample_canvas`` iterates mask -> renoise -> denoise, commits a
           ``canvas_length`` canvas, and returns the decoded bytes.

    Deterministic given ``seed``.
    """

    def __init__(
        self,
        vocab_size: int = 264,
        canvas_length: int = 256,
        diffusion_entropy_bound: float = 0.1,
        seed: int = 0,
    ) -> None:
        """Initialize the sampler.

        Args:
            vocab_size: total vocabulary size (default 264 = 256 bytes + 8
                specials). Must be at least 264 to cover specials 256-263.
            canvas_length: fixed canvas length (default 256, DiffusionGemma
                parity).
            diffusion_entropy_bound: entropy-bound threshold; positions whose
                denoiser confidence is below this are regenerated (default
                0.1, DiffusionGemma parity).
            seed: RNG seed for all masking/re-noise randomness.
        """
        if vocab_size < 264:
            raise ValueError(f"vocab_size must be >= 264 to cover specials, got {vocab_size}")
        if canvas_length < 1:
            raise ValueError(f"canvas_length must be >= 1, got {canvas_length}")
        if not 0.0 <= diffusion_entropy_bound <= 1.0:
            raise ValueError(
                f"diffusion_entropy_bound must be in [0, 1], got {diffusion_entropy_bound}"
            )
        self.vocab_size: int = vocab_size
        self.canvas_length: int = canvas_length
        self.diffusion_entropy_bound: float = diffusion_entropy_bound
        self.seed: int = seed
        self._rng = random.Random(seed)
        self._step: int = 0
        self._decoder = ByteTokenizer()

    def encode(self, text_or_bytes: Union[str, bytes, bytearray, List[int]]) -> List[int]:
        """Encode text/bytes into byte ids wrapped with BOS(258)/EOS(259).

        Content ids are always the raw 0-255 byte states; there is no
        vocabulary lookup or encoding step.

        Args:
            text_or_bytes: UTF-8 text, raw bytes, or an iterable of byte
                ints.

        Returns:
            ``[BOS, *byte_ids, EOS]``.
        """
        if isinstance(text_or_bytes, str):
            raw = text_or_bytes.encode("utf-8")
        elif isinstance(text_or_bytes, bytearray):
            raw = bytes(text_or_bytes)
        elif isinstance(text_or_bytes, bytes):
            raw = text_or_bytes
        else:
            raw = bytes(int(b) & 0xFF for b in text_or_bytes)
        return [BOS_TOKEN_ID, *raw, EOS_TOKEN_ID]

    def decode(
        self,
        ids: Union[List[int], bytes],
        skip_special_tokens: bool = True,
        as_bytes: bool = True,
    ) -> Union[bytes, str]:
        """Decode byte ids back into bytes (or UTF-8 text).

        Args:
            ids: iterable of byte ids (0-263).
            skip_special_tokens: drop ids >= 256 when True (default).
            as_bytes: return ``bytes`` when True (default), else UTF-8 str.

        Returns:
            The reconstructed byte string, or its UTF-8 text.
        """
        return self._decoder.decode(ids, skip_special_tokens=skip_special_tokens, as_bytes=as_bytes)

    def mask_canvas(
        self, ids: List[int], mask_ratio: float = 0.7
    ) -> Tuple[List[int], List[int]]:
        """Uniform-state masking: mask a fraction of positions.

        A ``round(len(ids) * mask_ratio)`` subset of positions is chosen
        uniformly at random (seeded) and replaced with MASK(256).

        Args:
            ids: byte-id sequence to mask.
            mask_ratio: fraction of positions to mask, in ``[0, 1]``.

        Returns:
            ``(masked, truth)``: the masked ids and the untouched original.
        """
        masked = list(ids)
        truth = list(ids)
        n = len(masked)
        n_mask = int(round(n * mask_ratio))
        n_mask = max(0, min(n_mask, n))
        if n_mask:
            positions = self._rng.sample(range(n), n_mask)
            for p in positions:
                masked[p] = MASK_TOKEN_ID
        return masked, truth

    def renoise_to_random_bytes(self, masked_ids: List[int]) -> List[int]:
        """Uniform-state re-noise: refill every MASK position with a random byte.

        Args:
            masked_ids: byte-id sequence possibly containing MASK(256).

        Returns:
            The sequence with every MASK position replaced by a seeded
            uniform random byte id in 0-255.
        """
        out = list(masked_ids)
        for i, tok in enumerate(out):
            if tok == MASK_TOKEN_ID:
                out[i] = self._rng.randrange(256)
        return out

    def denoise_step(self, canvas: List[int]) -> List[float]:
        """Deterministic pseudo-logits per canvas position.

        Stand-in for the future torch denoiser's output logits. Masked
        positions score in ``[0, 0.5]`` (low confidence), committed content
        positions score in ``(0.5, 1]``, both via ``stable_hash`` of
        ``(position, token, step)``. Position ``i`` of the returned list is
        the pseudo-logit for canvas position ``i``.

        Args:
            canvas: byte-id canvas to score.

        Returns:
            One float pseudo-logit per position, each in ``[0, 1]``.
        """
        logits: List[float] = []
        for i, tok in enumerate(canvas):
            h = stable_hash(i, tok if tok != MASK_TOKEN_ID else -1, self._step) & 0xFF
            if tok == MASK_TOKEN_ID:
                logits.append((h / 255.0) * 0.5)
            else:
                logits.append(0.5 + (h / 255.0) * 0.5)
        return logits

    def sample_canvas(
        self,
        prompt_bytes: Union[str, bytes, bytearray, List[int]],
        steps: int = 8,
        mask_ratio: float = 0.7,
        entropy_bound: Optional[float] = None,
    ) -> bytes:
        """Canvas-based block sampling over the byte diffusion loop.

        Iterates ``mask -> renoise -> denoise`` for ``steps`` rounds over a
        fixed ``canvas_length`` canvas. Each round:

        1. mask ``mask_ratio`` of the canvas (uniform state).
        2. re-noise masked positions to random byte ids.
        3. score positions with ``denoise_step``.
        4. commit: confident positions (score >= entropy_bound) recover the
           pre-mask byte when it is ASCII-safe; low-confidence positions are
           regenerated from a stable hash into printable ASCII.

        A final pass forces every remaining non-special byte into printable
        ASCII so the decoded output is always valid UTF-8.

        Args:
            prompt_bytes: prompt text/bytes to seed the canvas.
            steps: number of diffusion rounds.
            mask_ratio: fraction of positions masked per round.
            entropy_bound: threshold override; defaults to
                ``self.diffusion_entropy_bound``.

        Returns:
            Decoded content bytes (BOS/EOS/PAD/MASK and other specials
            stripped).
        """
        bound = self.diffusion_entropy_bound if entropy_bound is None else entropy_bound
        if not 0.0 <= bound <= 1.0:
            raise ValueError(f"entropy_bound must be in [0, 1], got {bound}")

        # Reset the RNG so every call with this seed reproduces the exact
        # same canvas sequence, regardless of prior calls on this instance.
        self._rng = random.Random(self.seed)
        self._step = 0

        ids = self.encode(prompt_bytes)
        if len(ids) > self.canvas_length:
            keep = self.canvas_length - 2
            ids = [BOS_TOKEN_ID, *ids[1 : 1 + keep], EOS_TOKEN_ID]
        canvas = ids + [PAD_TOKEN_ID] * (self.canvas_length - len(ids))

        for step in range(steps):
            self._step = step
            masked, truth = self.mask_canvas(canvas, mask_ratio=mask_ratio)
            canvas = self.renoise_to_random_bytes(masked)
            confidences = self.denoise_step(canvas)
            for i in range(self.canvas_length):
                if confidences[i] < bound:
                    canvas[i] = _ascii_byte(i, step)
                else:
                    pre = truth[i]
                    if pre < 0x80:
                        canvas[i] = pre

        for i in range(self.canvas_length):
            b = canvas[i]
            if b < 0x80 or b in SPECIAL_IDS:
                continue
            canvas[i] = _ascii_byte(i, self._step)

        return bytes(self.decode(canvas, skip_special_tokens=True))