"""Image processors for FlexiCT Hugging Face model repos."""

from __future__ import annotations

from pathlib import Path
from typing import Any

import numpy as np
import torch
import torch.nn.functional as F
from transformers import BatchFeature
from transformers.image_processing_utils import ImageProcessingMixin


def _as_float_array(image: Any) -> tuple[np.ndarray, dict[str, Any]]:
    if isinstance(image, (str, Path)):
        return _load_medical_image_array(image)
    if isinstance(image, torch.Tensor):
        image = image.detach().cpu().numpy()
    array = np.asarray(image, dtype=np.float32)
    return array, {"source": "array"}


def _load_medical_image_array(path: str | Path) -> tuple[np.ndarray, dict[str, Any]]:
    try:
        import SimpleITK as sitk
    except ImportError as exc:  # pragma: no cover - runtime dependency branch.
        raise RuntimeError("SimpleITK is required to load CT paths with FlexiCTImageProcessor.") from exc

    image = sitk.ReadImage(str(Path(path).expanduser()))
    image = sitk.DICOMOrient(image, "LPS")
    array = sitk.GetArrayFromImage(image).astype(np.float32, copy=False)
    metadata = {
        "source": str(path),
        "spacing_xyz": [float(v) for v in image.GetSpacing()],
        "origin_xyz": [float(v) for v in image.GetOrigin()],
        "direction": [float(v) for v in image.GetDirection()],
        "loaded_shape_zyx": [int(v) for v in array.shape],
    }
    return array, metadata


def _resample_array_zyx(
    array: np.ndarray,
    input_spacing_xyz: tuple[float, float, float] | None,
    target_spacing_xyz: tuple[float, float, float],
) -> np.ndarray:
    if input_spacing_xyz is None:
        return array
    spacing_zyx = tuple(float(v) for v in input_spacing_xyz[::-1])
    target_zyx = tuple(float(v) for v in target_spacing_xyz[::-1])
    out_shape = [
        max(1, int(round(size * spacing / target)))
        for size, spacing, target in zip(array.shape, spacing_zyx, target_zyx)
    ]
    tensor = torch.from_numpy(array[None, None].astype(np.float32, copy=False))
    resized = F.interpolate(tensor, size=out_shape, mode="trilinear", align_corners=False)
    return resized[0, 0].cpu().numpy().astype(np.float32, copy=False)


def _clip_zscore(
    array: np.ndarray,
    clip_range: tuple[float, float],
    eps: float,
) -> tuple[np.ndarray, dict[str, float]]:
    clipped = np.clip(array.astype(np.float32, copy=False), clip_range[0], clip_range[1])
    mean = float(clipped.mean())
    std = float(clipped.std())
    if std < eps:
        std = 1.0
    normalized = (clipped - mean) / std
    return normalized.astype(np.float32, copy=False), {
        "clip_min": float(clip_range[0]),
        "clip_max": float(clip_range[1]),
        "mean": mean,
        "std": std,
    }


def _pad_to_shape(
    array: np.ndarray,
    target_shape: tuple[int, ...],
    fill_value: float,
) -> tuple[np.ndarray, list[int], list[int]]:
    pad_before: list[int] = []
    pad_after: list[int] = []
    pads = []
    for size, target in zip(array.shape, target_shape):
        total = max(0, int(target) - int(size))
        before = total // 2
        after = total - before
        pad_before.append(before)
        pad_after.append(after)
        pads.append((before, after))
    if any(before or after for before, after in pads):
        array = np.pad(array, pads, mode="constant", constant_values=float(fill_value))
    return array.astype(np.float32, copy=False), pad_before, pad_after


def _center_crop(array: np.ndarray, target_shape: tuple[int, ...]) -> tuple[np.ndarray, list[int]]:
    starts = [max(0, (int(size) - int(target)) // 2) for size, target in zip(array.shape, target_shape)]
    slices = tuple(slice(start, start + int(target)) for start, target in zip(starts, target_shape))
    return array[slices].astype(np.float32, copy=False), starts


def _resize_2d(array: np.ndarray, output_size: int) -> np.ndarray:
    tensor = torch.from_numpy(array[None, None].astype(np.float32, copy=False))
    resized = F.interpolate(tensor, size=(output_size, output_size), mode="bilinear", align_corners=False)
    return resized[0, 0].cpu().numpy().astype(np.float32, copy=False)


def _resize_3d(array: np.ndarray, output_size: tuple[int, int, int]) -> np.ndarray:
    tensor = torch.from_numpy(array[None, None].astype(np.float32, copy=False))
    resized = F.interpolate(tensor, size=output_size, mode="trilinear", align_corners=False)
    return resized[0, 0].cpu().numpy().astype(np.float32, copy=False)


def _listify_images(images: Any, spatial_dims: int) -> list[Any]:
    if isinstance(images, (str, Path)):
        return [images]
    if isinstance(images, torch.Tensor):
        ndim = images.dim()
    else:
        ndim = np.asarray(images).ndim
    if ndim == spatial_dims:
        return [images]
    if ndim == spatial_dims + 1:
        return [sample for sample in images]
    return list(images)


class FlexiCTImageProcessor(ImageProcessingMixin):
    """Preprocess CT arrays or image paths for FlexiCT model variants."""

    model_input_names = ["pixel_values"]

    def __init__(
        self,
        model_variant: str = "3d",
        preset: str = "default",
        image_size: int | list[int] | tuple[int, ...] | None = None,
        clip_range: list[float] | tuple[float, float] = (-1000.0, 1000.0),
        target_spacing: list[float] | tuple[float, float, float] = (2.0, 2.0, 2.0),
        do_resample: bool = True,
        do_orient_lps: bool = True,
        eps: float = 1e-6,
        **kwargs: Any,
    ):
        super().__init__(**kwargs)
        model_variant = model_variant.lower()
        if model_variant not in {"2d", "3d", "vlm"}:
            raise ValueError("model_variant must be one of '2d', '3d', or 'vlm'")
        if preset not in {"default", "local_path", "retrieval_roi"}:
            raise ValueError("preset must be 'default', 'local_path', or 'retrieval_roi'")

        self.model_variant = model_variant
        self.preset = preset
        if image_size is None:
            image_size = 512 if model_variant == "2d" else [160, 160, 160]
        self.image_size = list(image_size) if isinstance(image_size, (list, tuple)) else int(image_size)
        self.clip_range = [float(clip_range[0]), float(clip_range[1])]
        self.target_spacing = [float(v) for v in target_spacing]
        self.do_resample = bool(do_resample)
        self.do_orient_lps = bool(do_orient_lps)
        self.eps = float(eps)

    def __call__(
        self,
        images: Any,
        return_tensors: str | None = "pt",
        return_metadata: bool = False,
        **kwargs: Any,
    ) -> BatchFeature:
        spatial_dims = 2 if self.model_variant == "2d" and np.asarray(images).ndim == 2 else 3
        samples = _listify_images(images, spatial_dims=spatial_dims)
        processed = []
        metadata = []
        for sample in samples:
            if self.model_variant == "2d":
                array, meta = self._process_2d(sample, **kwargs)
            else:
                array, meta = self._process_3d(sample, **kwargs)
            processed.append(array[None])
            metadata.append(meta)

        batch_array = np.stack(processed, axis=0).astype(np.float32, copy=False)
        data: dict[str, Any] = {"pixel_values": batch_array}
        if return_tensors == "pt":
            data["pixel_values"] = torch.from_numpy(batch_array)
        elif return_tensors not in {None, "np"}:
            raise ValueError("return_tensors must be 'pt', 'np', or None")
        if return_metadata:
            data["metadata"] = metadata
        return BatchFeature(data=data)

    def _process_2d(self, image: Any, slice_index: int | None = None, slice_axis: int = 0, **_: Any):
        array, metadata = _as_float_array(image)
        metadata["original_shape"] = [int(v) for v in array.shape]
        if array.ndim == 3:
            if slice_index is None:
                slice_index = array.shape[slice_axis] // 2
            array = np.take(array, int(slice_index), axis=int(slice_axis))
            metadata["slice_index"] = int(slice_index)
            metadata["slice_axis"] = int(slice_axis)
        if array.ndim != 2:
            raise ValueError(f"FlexiCT-2D expects a 2D slice or 3D volume, got shape {array.shape}")

        array, stats = _clip_zscore(array, tuple(self.clip_range), self.eps)
        side = max(array.shape)
        array, pad_before, pad_after = _pad_to_shape(array, (side, side), float(array.min()))
        output_size = int(self.image_size)
        array = _resize_2d(array, output_size)
        metadata.update(stats)
        metadata.update(
            {
                "pad_before_yx": pad_before,
                "pad_after_yx": pad_after,
                "processed_shape_yx": [output_size, output_size],
            }
        )
        return array, metadata

    def _process_3d(
        self,
        image: Any,
        input_spacing: tuple[float, float, float] | None = None,
        roi_center: tuple[int, int, int] | None = None,
        roi_size: int | tuple[int, int, int] | None = None,
        bbox: tuple[int, int, int, int, int, int] | None = None,
        mask: Any | None = None,
        **_: Any,
    ):
        array, metadata = _as_float_array(image)
        if array.ndim != 3:
            raise ValueError(f"FlexiCT-3D expects a 3D volume, got shape {array.shape}")
        if input_spacing is None and "spacing_xyz" in metadata:
            input_spacing = tuple(metadata["spacing_xyz"])
        if self.do_resample and input_spacing is not None:
            array = _resample_array_zyx(array, input_spacing, tuple(self.target_spacing))
            metadata["resampled_shape_zyx"] = [int(v) for v in array.shape]

        metadata["original_shape_zyx"] = [int(v) for v in array.shape]
        array, stats = _clip_zscore(array, tuple(self.clip_range), self.eps)
        metadata.update(stats)

        target_shape = tuple(int(v) for v in self.image_size)
        if self.preset == "default":
            return self._default_3d(array, target_shape, metadata)
        if self.preset == "local_path":
            return self._local_path_3d(array, target_shape, metadata)
        return self._retrieval_roi_3d(array, target_shape, metadata, roi_center, roi_size, bbox, mask)

    def _default_3d(self, array: np.ndarray, target_shape: tuple[int, int, int], metadata: dict[str, Any]):
        array, pad_before, pad_after = _pad_to_shape(array, target_shape, float(array.min()))
        array, crop_start = _center_crop(array, target_shape)
        metadata.update(
            {
                "pad_before_zyx": pad_before,
                "pad_after_zyx": pad_after,
                "crop_start_zyx": crop_start,
                "processed_shape_zyx": [int(v) for v in array.shape],
            }
        )
        return array, metadata

    def _local_path_3d(self, array: np.ndarray, target_shape: tuple[int, int, int], metadata: dict[str, Any]):
        side = max(int(v) for v in array.shape)
        array, pad_before, pad_after = _pad_to_shape(array, (side, side, side), float(array.min()))
        metadata.update(
            {
                "cubic_pad_before_zyx": pad_before,
                "cubic_pad_after_zyx": pad_after,
                "cubic_padded_shape_zyx": [int(v) for v in array.shape],
            }
        )
        array = _resize_3d(array, target_shape)
        metadata.update({"processed_shape_zyx": [int(v) for v in array.shape], "resize_mode": "trilinear"})
        return array, metadata

    def _retrieval_roi_3d(
        self,
        array: np.ndarray,
        target_shape: tuple[int, int, int],
        metadata: dict[str, Any],
        roi_center: tuple[int, int, int] | None,
        roi_size: int | tuple[int, int, int] | None,
        bbox: tuple[int, int, int, int, int, int] | None,
        mask: Any | None,
    ):
        if roi_size is None:
            roi_size = target_shape
        roi_shape = tuple([int(roi_size)] * 3) if isinstance(roi_size, int) else tuple(int(v) for v in roi_size)

        if bbox is not None:
            z0, y0, x0, z1, y1, x1 = [int(v) for v in bbox]
            roi_center = ((z0 + z1) // 2, (y0 + y1) // 2, (x0 + x1) // 2)
        elif mask is not None:
            mask_array = np.asarray(mask)
            coords = np.argwhere(mask_array > 0)
            if coords.size == 0:
                raise ValueError("mask does not contain any foreground voxels")
            roi_center = tuple(int(v) for v in coords.mean(axis=0).round())
        elif roi_center is None:
            roi_center = tuple(int(v // 2) for v in array.shape)

        starts = [int(center) - size // 2 for center, size in zip(roi_center, roi_shape)]
        ends = [start + size for start, size in zip(starts, roi_shape)]
        src_starts = [max(0, start) for start in starts]
        src_ends = [min(dim, end) for dim, end in zip(array.shape, ends)]
        crop = array[tuple(slice(start, end) for start, end in zip(src_starts, src_ends))]

        pad_before = [src - start for src, start in zip(src_starts, starts)]
        pad_after = [end - src for end, src in zip(ends, src_ends)]
        crop = np.pad(
            crop,
            tuple(zip(pad_before, pad_after)),
            mode="constant",
            constant_values=float(array.min()),
        ).astype(np.float32, copy=False)
        resized = _resize_3d(crop, target_shape)
        metadata.update(
            {
                "roi_center_zyx": [int(v) for v in roi_center],
                "roi_crop_start_zyx": src_starts,
                "roi_crop_end_zyx": src_ends,
                "roi_pad_before_zyx": pad_before,
                "roi_pad_after_zyx": pad_after,
                "roi_padded_shape_zyx": [int(v) for v in crop.shape],
                "processed_shape_zyx": [int(v) for v in resized.shape],
                "resize_mode": "trilinear",
            }
        )
        return resized, metadata