"""LayeredDepth preprocessing (aligned with SeeGroup ``LayeredDepthSyn``).

This module is self-contained for Hugging Face dataset users. It mirrors the
logic in ``dataset/layereddepth_syn.py`` and ``dataset/hf_utils.py``.
"""

from __future__ import annotations

import io
import os
from typing import Iterable, Sequence

import numpy as np

DEFAULT_LAYER_IDS: tuple[int, ...] = (1, 3, 5, 7)


def decode_image(value) -> np.ndarray:
    if isinstance(value, np.ndarray):
        return value

    try:
        from PIL.Image import Image as PILImage
    except ImportError:
        PILImage = ()

    if isinstance(value, PILImage):
        return np.asarray(value.copy())

    if isinstance(value, dict):
        if value.get("bytes") is not None:
            from PIL import Image

            with Image.open(io.BytesIO(value["bytes"])) as image:
                return np.asarray(image.copy())
        if value.get("path") is not None:
            value = value["path"]

    if isinstance(value, (str, os.PathLike)):
        from PIL import Image

        with Image.open(value) as image:
            return np.asarray(image.copy())

    if hasattr(value, "__array__"):
        return np.asarray(value)

    raise TypeError(f"Unsupported image value type: {type(value)!r}")


def image_to_float_rgb(value) -> np.ndarray:
    image = decode_image(value)
    if image.ndim == 2:
        image = np.repeat(image[..., None], 3, axis=2)
    if image.ndim != 3 or image.shape[2] not in (3, 4):
        raise ValueError(f"Expected RGB image, got shape {image.shape}")
    if image.shape[2] == 4:
        image = image[..., :3]

    image = image.astype(np.float32, copy=False)
    if image.max(initial=0) > 1.0:
        scale = 65535.0 if image.max(initial=0) > 255.0 else 255.0
        image = image / scale
    return image


def depth_png_to_meters(value) -> np.ndarray:
    depth = decode_image(value)
    if depth.ndim == 3:
        depth = depth[..., 0]
    depth = depth.astype(np.float32, copy=False) / 1000.0
    depth[~np.isfinite(depth)] = 0
    depth[depth > 80] = 0
    depth[depth <= 0] = 0
    return depth


def get_row_value(row, names: Sequence[str]):
    for name in names:
        if name in row:
            return row[name]
    raise KeyError(f"None of the expected fields are present: {list(names)}")


def postprocess_layered_depth(depth_layers: Iterable[np.ndarray]) -> np.ndarray:
    """Collapse invalid target pixels into deeper valid layers (LayeredDepth convention)."""
    layers = [layer.copy() for layer in depth_layers]
    for current_layer in range(1, len(layers)):
        for target_layer in range(current_layer):
            valid_current = layers[current_layer] != 0
            valid_target = layers[target_layer] != 0
            collapse_region = valid_current & (~valid_target)
            layers[target_layer][collapse_region] = layers[current_layer][collapse_region]
            layers[current_layer][collapse_region] = 0
    return np.stack(layers, axis=-1)


def load_depth_layers_from_row(row, layer_ids: Sequence[int] = DEFAULT_LAYER_IDS) -> np.ndarray:
    layers = []
    for layer_id in layer_ids:
        layers.append(
            depth_png_to_meters(get_row_value(row, [f"depth_{layer_id}.png", f"depth{layer_id}.png"]))
        )
    return postprocess_layered_depth(layers)


def preprocess_sample(
    row,
    *,
    layer_ids: Sequence[int] = DEFAULT_LAYER_IDS,
    selected_layer_ids: Sequence[int] | None = None,
) -> dict:
    """Return a training-ready dict from a ``princeton-vl/LayeredDepth-Syn`` row."""
    image = image_to_float_rgb(get_row_value(row, ["image.png", "image", "rgb"]))
    depth = load_depth_layers_from_row(row, layer_ids=layer_ids)
    valid_mask = (depth > 0).astype(np.float32)

    sample = {
        "image": image,
        "depth": depth,
        "valid_mask": valid_mask,
        "sample_key": str(row.get("__key__", row.get("id", ""))),
    }

    if selected_layer_ids is not None:
        indices = [layer_ids.index(layer_id) for layer_id in selected_layer_ids]
        sample["depth_selected"] = depth[..., indices]
        sample["valid_mask_selected"] = valid_mask[..., indices]

    return sample


def sort_depth_with_mask(depth: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    valid_mask = depth > 0
    sort_key = np.where(valid_mask, depth, np.inf)
    order = np.argsort(sort_key, axis=-1)
    sorted_depth = np.take_along_axis(depth, order, axis=-1)
    sorted_mask = np.take_along_axis(valid_mask, order, axis=-1)
    return sorted_depth, sorted_mask


def compressed_layer_count_per_pixel(
    sorted_depth: np.ndarray,
    sorted_mask: np.ndarray,
    *,
    abs_gap_threshold: float = 1e-4,
    rel_gap_threshold: float = 0.0,
) -> np.ndarray:
    raw_count = sorted_mask.sum(axis=-1)
    raw_gap = sorted_depth[..., 1:] - sorted_depth[..., :-1]
    adjacent_valid = sorted_mask[..., 1:] & sorted_mask[..., :-1]
    threshold = np.maximum(abs_gap_threshold, rel_gap_threshold * np.abs(sorted_depth[..., :-1]))
    event_gap = adjacent_valid & (raw_gap > threshold)
    return (raw_count > 0).astype(np.int16) + event_gap.sum(axis=-1).astype(np.int16)