from __future__ import annotations

import colorsys
import functools
import hashlib
import io
import json
import math
import re
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterable

import numpy as np
from PIL import Image


CUSTOM_EPOCH_MS = 1_704_067_200_000  # 2024-01-01T00:00:00Z
BUILD_BASE_MS = 1_784_764_800_000  # 2026-07-23T00:00:00Z

WORKER_IDS = {
    "record": 1,
    "raw": 2,
    "lut": 3,
    "family": 4,
    "raw_shard": 5,
    "normalized_shard": 6,
    "canonical_parquet": 7,
    "alias_parquet": 8,
    "raw_index_parquet": 9,
    "asset": 10,
    "report": 11,
}

DIRECTIVES = {
    "TITLE",
    "LUT_1D_SIZE",
    "LUT_3D_SIZE",
    "DOMAIN_MIN",
    "DOMAIN_MAX",
    "LUT_1D_INPUT_RANGE",
    "LUT_3D_INPUT_RANGE",
}

TECHNICAL_TERMS = {
    "rec709": ("rec709",),
    "rec.709": ("rec709",),
    "s-log3": ("s-log3",),
    "slog3": ("s-log3",),
    "s-log2": ("s-log2",),
    "slog2": ("s-log2",),
    "s-log": ("s-log",),
    "v-log": ("v-log",),
    "vlog": ("v-log",),
    "c-log": ("c-log",),
    "clog": ("c-log",),
    "log-c": ("log-c",),
    "logc": ("log-c",),
    "redlogfilm": ("redlogfilm",),
    "bmdfilm": ("bmdfilm",),
    "cineon": ("cineon",),
    "alexa": ("alexa",),
    "aces": ("aces",),
    "dci-p3": ("dci-p3",),
    "dcip3": ("dci-p3",),
    "s-gamut": ("s-gamut",),
    "sgamut": ("s-gamut",),
}

CAMERA_TERMS = {
    "sony": "sony",
    "canon": "canon",
    "panasonic": "panasonic",
    "blackmagic": "blackmagic",
    "bmd": "blackmagic",
    "arri": "arri",
    "alexa": "arri",
    "red camera": "red",
    "redlog": "red",
    "dji": "dji",
    "gopro": "gopro",
    "fuji": "fujifilm",
    "fujifilm": "fujifilm",
    "nikon": "nikon",
}

STYLE_TERMS = {
    "cinematic": "cinematic",
    "cinema": "cinematic",
    "movie": "cinematic",
    "film": "film",
    "vintage": "vintage",
    "retro": "retro",
    "wedding": "wedding",
    "portrait": "portrait",
    "travel": "travel",
    "landscape": "landscape",
    "drone": "drone",
    "hdr": "hdr",
    "teal": "teal",
    "orange": "orange",
    "warm": "warm",
    "cold": "cool",
    "cool": "cool",
    "noir": "noir",
    "monochrome": "monochrome",
    "black and white": "monochrome",
    "b&w": "monochrome",
    "cyberpunk": "cyberpunk",
    "kodak": "film-emulation",
    "fuji": "film-emulation",
    "food": "food",
    "nature": "nature",
    "city": "urban",
    "urban": "urban",
    "commercial": "commercial",
    "dramatic": "dramatic",
    "nostalgia": "nostalgic",
    "日系": "japanese-style",
    "婚礼": "wedding",
    "婚纱": "wedding",
    "人像": "portrait",
    "旅行": "travel",
    "风光": "landscape",
    "风景": "landscape",
    "电影": "cinematic",
    "胶片": "film",
    "复古": "vintage",
    "美食": "food",
    "冷色": "cool",
    "暖": "warm",
    "清新": "fresh",
    "儿童": "children",
}


@dataclass
class ParsedLUT:
    lut_3d: np.ndarray | None
    lut_1d: np.ndarray | None
    lut_3d_size: int | None
    lut_1d_size: int | None
    inferred_3d_size: bool
    domain_min: np.ndarray
    domain_max: np.ndarray
    domain_declared: bool
    one_d_input_range: tuple[float, float] | None
    title_present: bool
    encoding: str
    replacement_characters: bool
    unknown_line_count: int
    data_row_count: int
    expected_data_row_count: int
    comments_text: str


def snowflake_id(kind: str, ordinal: int) -> str:
    """Return a deterministic, classic 64-bit Snowflake ID as a decimal string."""
    if kind not in WORKER_IDS:
        raise KeyError(f"Unknown Snowflake kind: {kind}")
    if ordinal < 0:
        raise ValueError("Snowflake ordinal must be non-negative")
    timestamp_ms = BUILD_BASE_MS + ordinal // 4096
    sequence = ordinal % 4096
    value = (
        ((timestamp_ms - CUSTOM_EPOCH_MS) << 22)
        | (WORKER_IDS[kind] << 12)
        | sequence
    )
    if value >= 2**63:
        raise OverflowError("Snowflake ID exceeded signed 64-bit range")
    return str(value)


def decode_lut(raw: bytes) -> tuple[str, str, bool]:
    for encoding in ("utf-8-sig", "gb18030", "cp1252"):
        try:
            return raw.decode(encoding), encoding, False
        except UnicodeDecodeError:
            continue
    text = raw.decode("utf-8", "replace")
    return text, "utf-8-replace", "\ufffd" in text


def _parse_vector(value: str, expected: int = 3) -> np.ndarray | None:
    try:
        array = np.fromstring(value.replace(",", " "), sep=" ", dtype=np.float64)
    except ValueError:
        return None
    if array.size < expected:
        return None
    return array[:expected].astype(np.float32)


def _safe_fromstring(value: str, dtype: Any = np.float64) -> np.ndarray:
    try:
        return np.fromstring(value, sep=" ", dtype=dtype)
    except ValueError:
        return np.empty(0, dtype=dtype)


def _integer_cube_root(value: int) -> int | None:
    if value <= 0:
        return None
    guess = round(value ** (1.0 / 3.0))
    for candidate in range(max(1, guess - 2), guess + 3):
        if candidate**3 == value:
            return candidate
    return None


def parse_cube(raw: bytes) -> ParsedLUT:
    text, encoding, replacements = decode_lut(raw)
    directives: dict[str, str] = {}
    comments: list[str] = []
    unknown_lines = 0
    lines = text.splitlines()
    data_start: int | None = None

    for line_index, raw_line in enumerate(lines):
        stripped = raw_line.strip().lstrip("\ufeff")
        if not stripped:
            continue
        if stripped.startswith("#"):
            comments.append(stripped[1:].strip())
            continue

        content = stripped.split("#", 1)[0].strip()
        if not content:
            continue
        parts = content.split(None, 1)
        key = parts[0].upper()
        if key in DIRECTIVES:
            if key not in directives:
                directives[key] = parts[1].strip() if len(parts) > 1 else ""
            continue

        values = _safe_fromstring(content.replace(",", " "), dtype=np.float64)
        if values.size >= 3 and all(math.isfinite(float(item)) for item in values[:3]):
            data_start = line_index
            break
        else:
            unknown_lines += 1

    lut_3d_size = None
    lut_1d_size = None
    if directives.get("LUT_3D_SIZE", "").strip().isdigit():
        lut_3d_size = int(directives["LUT_3D_SIZE"].strip())
    if directives.get("LUT_1D_SIZE", "").strip().isdigit():
        lut_1d_size = int(directives["LUT_1D_SIZE"].strip())

    declared_expected = (lut_1d_size or 0) + ((lut_3d_size or 0) ** 3)
    values = np.empty((0, 3), dtype=np.float32)
    if data_start is not None:
        if declared_expected:
            candidate_lines = lines[data_start : data_start + declared_expected]
        else:
            candidate_lines = lines[data_start:]
        numeric_block = "\n".join(candidate_lines).replace(",", " ")
        flat = _safe_fromstring(numeric_block, dtype=np.float32)
        if flat.size % 3 == 0:
            values = flat.reshape(-1, 3)

        target_rows = declared_expected or None
        if (
            (target_rows is not None and len(values) != target_rows)
            or (target_rows is None and len(values) == 0)
        ):
            fallback_rows: list[tuple[float, float, float]] = []
            for raw_line in lines[data_start:]:
                content = raw_line.split("#", 1)[0].strip()
                row = _safe_fromstring(
                    content.replace(",", " "), dtype=np.float64
                )
                if row.size >= 3 and all(
                    math.isfinite(float(item)) for item in row[:3]
                ):
                    fallback_rows.append(
                        (float(row[0]), float(row[1]), float(row[2]))
                    )
                    if target_rows is not None and len(fallback_rows) == target_rows:
                        break
                elif content and not content.startswith("#"):
                    unknown_lines += 1
            values = np.asarray(fallback_rows, dtype=np.float32)

    inferred_3d = False
    if lut_3d_size is None and lut_1d_size is None:
        inferred = _integer_cube_root(len(values))
        if inferred is not None:
            lut_3d_size = inferred
            inferred_3d = True

    expected = (lut_1d_size or 0) + ((lut_3d_size or 0) ** 3)
    if expected != len(values):
        raise ValueError(
            f"Data row mismatch: parsed={len(values)}, expected={expected}, "
            f"lut_1d_size={lut_1d_size}, lut_3d_size={lut_3d_size}"
        )

    offset = 0
    lut_1d = None
    lut_3d = None
    if lut_1d_size:
        lut_1d = values[:lut_1d_size].copy()
        offset = lut_1d_size
    if lut_3d_size:
        lut_3d = values[offset:].reshape(
            lut_3d_size, lut_3d_size, lut_3d_size, 3
        )

    domain_min = _parse_vector(directives.get("DOMAIN_MIN", ""))
    domain_max = _parse_vector(directives.get("DOMAIN_MAX", ""))
    domain_declared = domain_min is not None and domain_max is not None
    if domain_min is None:
        domain_min = np.zeros(3, dtype=np.float32)
    if domain_max is None:
        domain_max = np.ones(3, dtype=np.float32)

    one_d_input_range = None
    range_values = _parse_vector(
        directives.get("LUT_1D_INPUT_RANGE", ""), expected=2
    )
    if range_values is not None:
        one_d_input_range = (float(range_values[0]), float(range_values[1]))

    return ParsedLUT(
        lut_3d=lut_3d,
        lut_1d=lut_1d,
        lut_3d_size=lut_3d_size,
        lut_1d_size=lut_1d_size,
        inferred_3d_size=inferred_3d,
        domain_min=domain_min,
        domain_max=domain_max,
        domain_declared=domain_declared,
        one_d_input_range=one_d_input_range,
        title_present="TITLE" in directives,
        encoding=encoding,
        replacement_characters=replacements or "\ufffd" in text,
        unknown_line_count=unknown_lines,
        data_row_count=len(values),
        expected_data_row_count=expected,
        comments_text="\n".join(comments[:64]),
    )


def interpolate_axis(array: np.ndarray, axis: int, size: int) -> np.ndarray:
    if array.shape[axis] == size:
        return array.astype(np.float32, copy=False)
    positions = np.linspace(0, array.shape[axis] - 1, size, dtype=np.float32)
    low = np.floor(positions).astype(np.int32)
    high = np.minimum(low + 1, array.shape[axis] - 1)
    weight_shape = [1] * array.ndim
    weight_shape[axis] = size
    weight = (positions - low).reshape(weight_shape)
    left = np.take(array, low, axis=axis)
    right = np.take(array, high, axis=axis)
    return left * (1.0 - weight) + right * weight


def normalize_3d(lut: np.ndarray | None, size: int = 33) -> np.ndarray | None:
    if lut is None:
        return None
    result = lut.astype(np.float32, copy=False)
    for axis in range(3):
        result = interpolate_axis(result, axis, size)
    return result.astype(np.float16)


def normalize_1d(lut: np.ndarray | None, size: int = 4096) -> np.ndarray | None:
    if lut is None:
        return None
    result = interpolate_axis(lut.astype(np.float32, copy=False), 0, size)
    return result.astype(np.float16)


def semantic_sha256(
    lut_3d: np.ndarray | None,
    lut_1d: np.ndarray | None,
    domain_min: np.ndarray,
    domain_max: np.ndarray,
    one_d_input_range: tuple[float, float] | None,
) -> str:
    digest = hashlib.sha256()
    if lut_3d is None:
        digest.update(b"3d:none\0")
    else:
        digest.update(b"3d:33:f16\0")
        digest.update(np.ascontiguousarray(lut_3d).tobytes())
    if lut_1d is None:
        digest.update(b"1d:none\0")
    else:
        digest.update(b"1d:4096:f16\0")
        digest.update(np.ascontiguousarray(lut_1d).tobytes())
    digest.update(np.asarray(domain_min, dtype="<f4").tobytes())
    digest.update(np.asarray(domain_max, dtype="<f4").tobytes())
    if one_d_input_range is None:
        digest.update(b"1d-range:none\0")
    else:
        digest.update(np.asarray(one_d_input_range, dtype="<f4").tobytes())
    return digest.hexdigest()


def identity_grid(
    size: int, domain_min: np.ndarray, domain_max: np.ndarray
) -> np.ndarray:
    unit = np.linspace(0.0, 1.0, size, dtype=np.float32)
    blue, green, red = np.meshgrid(unit, unit, unit, indexing="ij")
    rgb = np.stack([red, green, blue], axis=-1)
    return domain_min + rgb * (domain_max - domain_min)


def calculate_metrics(
    lut_3d: np.ndarray | None,
    lut_1d: np.ndarray | None,
    domain_min: np.ndarray,
    domain_max: np.ndarray,
) -> dict[str, float | None]:
    arrays = [item.astype(np.float32) for item in (lut_3d, lut_1d) if item is not None]
    merged = np.concatenate([item.reshape(-1, 3) for item in arrays], axis=0)
    metrics: dict[str, float | None] = {
        "value_min": float(np.min(merged)),
        "value_max": float(np.max(merged)),
        "out_of_unit_fraction": float(
            np.mean((merged < 0.0) | (merged > 1.0))
        ),
        "clipped_fraction": float(
            np.mean((merged <= 0.0) | (merged >= 1.0))
        ),
        "identity_rmse": None,
        "mean_abs_change": None,
        "neutral_rmse": None,
        "luma_shift": None,
        "saturation_mean": None,
        "monotonicity_violation_fraction": None,
    }
    if lut_3d is not None:
        table = lut_3d.astype(np.float32)
        identity = identity_grid(table.shape[0], domain_min, domain_max)
        delta = table - identity
        diagonal = table[
            np.arange(table.shape[0]),
            np.arange(table.shape[0]),
            np.arange(table.shape[0]),
        ]
        neutral = identity[
            np.arange(identity.shape[0]),
            np.arange(identity.shape[0]),
            np.arange(identity.shape[0]),
        ]
        luma_weights = np.asarray([0.2126, 0.7152, 0.0722], dtype=np.float32)
        monotonic = np.concatenate(
            [
                np.diff(table[..., 0], axis=2).reshape(-1),
                np.diff(table[..., 1], axis=1).reshape(-1),
                np.diff(table[..., 2], axis=0).reshape(-1),
            ]
        )
        metrics.update(
            {
                "identity_rmse": float(np.sqrt(np.mean(delta**2))),
                "mean_abs_change": float(np.mean(np.abs(delta))),
                "neutral_rmse": float(
                    np.sqrt(np.mean((diagonal - neutral) ** 2))
                ),
                "luma_shift": float(
                    np.mean(table @ luma_weights) - np.mean(identity @ luma_weights)
                ),
                "saturation_mean": float(
                    np.mean(np.max(table, axis=-1) - np.min(table, axis=-1))
                ),
                "monotonicity_violation_fraction": float(
                    np.mean(monotonic < -1e-5)
                ),
            }
        )
    return metrics


def _stable_unique(values: Iterable[str]) -> list[str]:
    return sorted(set(values))


def classify_lut(
    original_name: str,
    comments_text: str,
    has_3d: bool,
    has_1d: bool,
) -> dict[str, Any]:
    searchable = f"{original_name}\n{comments_text}".casefold()
    technical_tags: list[str] = []
    camera_tags: list[str] = []
    style_tags: list[str] = []

    for needle, tags in TECHNICAL_TERMS.items():
        if needle in searchable:
            technical_tags.extend(tags)
    for needle, tag in CAMERA_TERMS.items():
        if needle in searchable:
            camera_tags.append(tag)
    for needle, tag in STYLE_TERMS.items():
        if needle in searchable:
            style_tags.append(tag)

    technical_signals = (
        technical_tags
        or "source input" in searchable
        or "input shaper" in searchable
        or "output:" in searchable
        or "to-rec709" in searchable
        or "to rec709" in searchable
    )
    if has_3d and has_1d:
        function_type = "hybrid_transform"
    elif technical_signals:
        function_type = "technical_or_hybrid"
    elif style_tags:
        function_type = "creative_look"
    else:
        function_type = "unknown"

    if has_3d and has_1d:
        lut_type = "1d+3d"
    elif has_3d:
        lut_type = "3d"
    elif has_1d:
        lut_type = "1d"
    else:
        lut_type = "invalid"

    return {
        "lut_type": lut_type,
        "function_type": function_type,
        "technical_tags": _stable_unique(technical_tags),
        "camera_tags": _stable_unique(camera_tags),
        "style_tags": _stable_unique(style_tags),
    }


def family_key(original_name: str) -> str:
    stem = original_name
    while stem.casefold().endswith(".cube"):
        stem = stem[:-5]
    stem = stem.casefold()
    stem = re.sub(r"\s*\((?:copy|\d+)\)\s*$", "", stem)
    stem = re.sub(r"\s*\[\d+\]\s*", " ", stem)
    stem = re.sub(r"(?:[-_ #]|\s)+\d+\s*$", "", stem)
    stem = re.sub(r"\s*[-_ ]?copy\s*$", "", stem)
    stem = re.sub(r"\s+", " ", stem).strip(" -_")
    if not stem:
        return "__unnamed__"
    return stem


@functools.lru_cache(maxsize=1)
def build_reference_image(width: int = 256, height: int = 144) -> np.ndarray:
    image = np.zeros((height, width, 3), dtype=np.float32)
    hue_height = int(height * 0.62)
    neutral_height = int(height * 0.18)
    channel_height = height - hue_height - neutral_height

    for y in range(hue_height):
        saturation = y / max(1, hue_height - 1)
        value = 0.95 - 0.35 * (y / max(1, hue_height - 1))
        for x in range(width):
            hue = x / max(1, width - 1)
            image[y, x] = colorsys.hsv_to_rgb(hue, saturation, value)

    ramp = np.linspace(0.0, 1.0, width, dtype=np.float32)
    neutral_start = hue_height
    image[neutral_start : neutral_start + neutral_height] = ramp[None, :, None]

    channel_start = neutral_start + neutral_height
    third = max(1, channel_height // 3)
    image[channel_start : channel_start + third, :, 0] = ramp
    image[channel_start + third : channel_start + 2 * third, :, 1] = ramp
    image[channel_start + 2 * third :, :, 2] = ramp
    return image


def apply_1d(image: np.ndarray, lut: np.ndarray) -> np.ndarray:
    positions = np.clip(image, 0.0, 1.0) * (lut.shape[0] - 1)
    low = np.floor(positions).astype(np.int32)
    high = np.minimum(low + 1, lut.shape[0] - 1)
    weight = positions - low
    result = np.empty_like(image, dtype=np.float32)
    for channel in range(3):
        result[..., channel] = (
            lut[low[..., channel], channel] * (1.0 - weight[..., channel])
            + lut[high[..., channel], channel] * weight[..., channel]
        )
    return result


def apply_3d(
    image: np.ndarray,
    lut: np.ndarray,
    domain_min: np.ndarray,
    domain_max: np.ndarray,
) -> np.ndarray:
    scale = np.where(domain_max != domain_min, domain_max - domain_min, 1.0)
    unit = np.clip((image - domain_min) / scale, 0.0, 1.0)
    coordinates = unit * (lut.shape[0] - 1)
    low = np.floor(coordinates).astype(np.int32)
    high = np.minimum(low + 1, lut.shape[0] - 1)
    weight = coordinates - low

    r0, g0, b0 = low[..., 0], low[..., 1], low[..., 2]
    r1, g1, b1 = high[..., 0], high[..., 1], high[..., 2]
    wr, wg, wb = weight[..., 0:1], weight[..., 1:2], weight[..., 2:3]

    c000 = lut[b0, g0, r0]
    c100 = lut[b0, g0, r1]
    c010 = lut[b0, g1, r0]
    c110 = lut[b0, g1, r1]
    c001 = lut[b1, g0, r0]
    c101 = lut[b1, g0, r1]
    c011 = lut[b1, g1, r0]
    c111 = lut[b1, g1, r1]

    c00 = c000 * (1.0 - wr) + c100 * wr
    c10 = c010 * (1.0 - wr) + c110 * wr
    c01 = c001 * (1.0 - wr) + c101 * wr
    c11 = c011 * (1.0 - wr) + c111 * wr
    c0 = c00 * (1.0 - wg) + c10 * wg
    c1 = c01 * (1.0 - wg) + c11 * wg
    return c0 * (1.0 - wb) + c1 * wb


def render_preview(
    lut_3d: np.ndarray | None,
    lut_1d: np.ndarray | None,
    domain_min: np.ndarray,
    domain_max: np.ndarray,
    quality: int = 84,
) -> bytes:
    source = build_reference_image()
    transformed = source
    if lut_1d is not None:
        transformed = apply_1d(transformed, lut_1d.astype(np.float32))
    if lut_3d is not None:
        transformed = apply_3d(
            transformed,
            lut_3d.astype(np.float32),
            domain_min.astype(np.float32),
            domain_max.astype(np.float32),
        )
    source_u8 = np.round(np.clip(source, 0.0, 1.0) * 255).astype(np.uint8)
    transformed_u8 = np.round(np.clip(transformed, 0.0, 1.0) * 255).astype(
        np.uint8
    )
    separator = np.full((source.shape[0], 4, 3), 235, dtype=np.uint8)
    joined = np.concatenate([source_u8, separator, transformed_u8], axis=1)
    buffer = io.BytesIO()
    Image.fromarray(joined, mode="RGB").save(
        buffer, format="WEBP", quality=quality, method=4
    )
    return buffer.getvalue()


def normalized_npz_bytes(
    lut_id: str,
    lut_3d: np.ndarray | None,
    lut_1d: np.ndarray | None,
    domain_min: np.ndarray,
    domain_max: np.ndarray,
    one_d_input_range: tuple[float, float] | None,
) -> bytes:
    payload: dict[str, np.ndarray] = {
        "lut_id": np.asarray(lut_id),
        "domain_min": np.asarray(domain_min, dtype=np.float32),
        "domain_max": np.asarray(domain_max, dtype=np.float32),
    }
    if lut_3d is not None:
        payload["lut_3d"] = np.asarray(lut_3d, dtype=np.float16)
    if lut_1d is not None:
        payload["lut_1d"] = np.asarray(lut_1d, dtype=np.float16)
    if one_d_input_range is not None:
        payload["lut_1d_input_range"] = np.asarray(
            one_d_input_range, dtype=np.float32
        )
    buffer = io.BytesIO()
    np.savez_compressed(buffer, **payload)
    return buffer.getvalue()


def safe_json_dumps(value: Any) -> str:
    return json.dumps(
        value,
        ensure_ascii=False,
        sort_keys=True,
        separators=(",", ":"),
        allow_nan=False,
    )


def atomic_write(path: Path, data: bytes) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    temporary = path.with_suffix(path.suffix + ".partial")
    temporary.write_bytes(data)
    temporary.replace(path)