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# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
# ruff: noqa: E741, F841, RUF005
"""Upsampling in python"""

import math

import numpy as np

from tvm.topi.utils import nchw_pack_layout


def get_inx(x, image_width, target_width, coordinate_transformation_mode):
    """Infer input x from output x with various coordinate transformation methods"""
    scale = image_width / target_width
    if coordinate_transformation_mode == "half_pixel":
        in_x = (x + 0.5) * scale - 0.5
    elif coordinate_transformation_mode == "align_corners":
        in_x = (image_width - 1) / (target_width - 1) * x if target_width > 1 else 0
    elif coordinate_transformation_mode == "asymmetric":
        in_x = scale * x
    else:
        raise ValueError(
            f"Unsupported coordinate_transformation_mode: {coordinate_transformation_mode}"
        )
    return in_x


def get_index(x, image_width, target_width, coordinate_transformation_mode, rounding_method=""):
    """get and round the nearest index for nearest_neighbor"""
    in_x = get_inx(x, image_width, target_width, coordinate_transformation_mode)

    effective_rounding_method = rounding_method
    if not effective_rounding_method:
        if coordinate_transformation_mode == "align_corners":
            effective_rounding_method = "round"
        else:
            effective_rounding_method = "floor"

    if effective_rounding_method == "floor":
        out = math.floor(in_x)
    elif effective_rounding_method == "round":
        out = round(in_x)
    elif effective_rounding_method == "round_prefer_floor":
        out = math.ceil(in_x - 0.5)
    elif effective_rounding_method == "round_prefer_ceil":
        out = math.floor(in_x + 0.5)
    elif effective_rounding_method == "ceil":
        out = math.ceil(in_x)
    else:
        raise ValueError(f"Unknown rounding method: {rounding_method!r}")

    out = max(min(out, image_width - 1), 0)
    return int(out)


def resize3d_nearest(arr, scale, coordinate_transformation_mode, rounding_method=""):
    """Populate the array by scale factor"""
    d, h, w = arr.shape
    out_d, out_h, out_w = [round(i * s) for i, s in zip(arr.shape, scale)]
    out = np.empty((out_d, out_h, out_w))
    for z in range(out_d):
        for y in range(out_h):
            for x in range(out_w):
                in_z = get_index(z, d, out_d, coordinate_transformation_mode, rounding_method)
                in_y = get_index(y, h, out_h, coordinate_transformation_mode, rounding_method)
                in_x = get_index(x, w, out_w, coordinate_transformation_mode, rounding_method)
                out[z, y, x] = arr[in_z, in_y, in_x]
    return out


def resize3d_linear(data_in, scale, coordinate_transformation_mode):
    """Trilinear 3d scaling using python"""
    dtype = data_in.dtype
    d, h, w = data_in.shape
    new_d, new_h, new_w = [round(i * s) for i, s in zip(data_in.shape, scale)]
    data_out = np.ones((new_d, new_h, new_w))

    indexes = np.mgrid[0:2, 0:2, 0:2]

    def _get_patch(zint, yint, xint):
        # Get the surrounding values
        indices = indexes.copy()
        indices[0] = np.maximum(np.minimum(indexes[0] + zint, d - 1), 0)
        indices[1] = np.maximum(np.minimum(indexes[1] + yint, h - 1), 0)
        indices[2] = np.maximum(np.minimum(indexes[2] + xint, w - 1), 0)
        p = data_in[indices[0], indices[1], indices[2]]
        return p

    for m in range(new_d):
        for j in range(new_h):
            for k in range(new_w):
                in_z = get_inx(m, d, new_d, coordinate_transformation_mode)
                in_y = get_inx(j, h, new_h, coordinate_transformation_mode)
                in_x = get_inx(k, w, new_w, coordinate_transformation_mode)
                zint = math.floor(in_z)
                zfract = in_z - math.floor(in_z)

                yint = math.floor(in_y)
                yfract = in_y - math.floor(in_y)

                xint = math.floor(in_x)
                xfract = in_x - math.floor(in_x)

                wz = np.array([1.0 - zfract, zfract], dtype=dtype)
                wy = np.array([1.0 - yfract, yfract], dtype=dtype)
                wx = np.array([1.0 - xfract, xfract], dtype=dtype)

                p = _get_patch(zint, yint, xint)
                l = np.sum(p * wx, axis=-1)
                col = np.sum(l * wy, axis=-1)
                data_out[m, j, k] = np.sum(col * wz)

    return data_out


def resize3d_cubic(data_in, scale, coordinate_transformation_mode):
    """Tricubic 3d scaling using python"""
    dtype = data_in.dtype
    d, h, w = data_in.shape
    new_d, new_h, new_w = [round(i * s) for i, s in zip(data_in.shape, scale)]
    data_out = np.ones((new_d, new_h, new_w))

    def _cubic_spline_weights(t, alpha=-0.5):
        """create cubic spline weights in 1D"""
        t2 = t * t
        t3 = t * t * t
        w1 = alpha * (t3 - 2 * t2 + t)
        w2 = (alpha + 2) * t3 - (3 + alpha) * t2 + 1
        w3 = -(alpha + 2) * t3 + (3 + 2 * alpha) * t2 - alpha * t
        w4 = -alpha * t3 + alpha * t2
        return np.array([w1, w2, w3, w4])

    indexes = np.mgrid[-1:3, -1:3, -1:3]

    def _get_patch(zint, yint, xint):
        # Get the surrounding values
        indices = indexes.copy()
        indices[0] = np.maximum(np.minimum(indexes[0] + zint, d - 1), 0)
        indices[1] = np.maximum(np.minimum(indexes[1] + yint, h - 1), 0)
        indices[2] = np.maximum(np.minimum(indexes[2] + xint, w - 1), 0)
        p = data_in[indices[0], indices[1], indices[2]]
        return p

    for m in range(new_d):
        for j in range(new_h):
            for k in range(new_w):
                in_z = get_inx(m, d, new_d, coordinate_transformation_mode)
                in_y = get_inx(j, h, new_h, coordinate_transformation_mode)
                in_x = get_inx(k, w, new_w, coordinate_transformation_mode)
                zint = math.floor(in_z)
                zfract = in_z - math.floor(in_z)

                yint = math.floor(in_y)
                yfract = in_y - math.floor(in_y)

                xint = math.floor(in_x)
                xfract = in_x - math.floor(in_x)

                wz = _cubic_spline_weights(zfract)
                wy = _cubic_spline_weights(yfract)
                wx = _cubic_spline_weights(xfract)

                p = _get_patch(zint, yint, xint)

                l = np.sum(p * wx, axis=-1)
                col = np.sum(l * wy, axis=-1)
                data_out[m, j, k] = np.sum(col * wz)

    return data_out


def resize3d_ncdhw(
    data,
    scale,
    method="nearest_neighbor",
    coordinate_transformation_mode="align_corners",
    rounding_method="",
):
    """reference kernel for 3D image resizing"""
    ishape = data.shape

    oshape = (
        ishape[0],
        ishape[1],
        round(ishape[2] * scale[0]),
        round(ishape[3] * scale[1]),
        round(ishape[4] * scale[2]),
    )

    output_np = np.zeros(oshape, dtype=data.dtype)

    for b in range(oshape[0]):
        for c in range(oshape[1]):
            if method == "nearest_neighbor":
                output_np[b, c, :, :, :] = resize3d_nearest(
                    data[b, c, :, :, :], scale, coordinate_transformation_mode, rounding_method
                )
            elif method == "linear":
                output_np[b, c, :, :, :] = resize3d_linear(
                    data[b, c, :, :, :], scale, coordinate_transformation_mode
                )
            elif method == "cubic":
                output_np[b, c, :, :, :] = resize3d_cubic(
                    data[b, c, :, :, :], scale, coordinate_transformation_mode
                )
            else:
                raise ValueError("Unknown resize method", method)

    return output_np


def resize1d_python(
    data,
    scale,
    layout="NCW",
    method="nearest_neighbor",
    coordinate_transformation_mode="align_corners",
    rounding_method="",
):
    """Python version of 3D scaling using nearest neighbour"""

    if layout == "NWC":
        data = data.transpose([0, 2, 1])

    data = np.expand_dims(data, axis=[2, 3])
    output_np = resize3d_ncdhw(
        data, (1, 1) + scale, method, coordinate_transformation_mode, rounding_method
    )
    output_np = np.squeeze(output_np, axis=2)
    output_np = np.squeeze(output_np, axis=2)

    if layout == "NWC":
        output_np = output_np.transpose([0, 2, 1])

    return output_np


def resize2d_python(
    data,
    scale,
    layout="NCHW",
    method="nearest_neighbor",
    coordinate_transformation_mode="align_corners",
    rounding_method="",
):
    """Python version of scaling using nearest neighbour"""

    if layout == "NHWC":
        data = data.transpose([0, 3, 1, 2])
    elif nchw_pack_layout(layout):
        ishape = data.shape
        transposed = data.transpose([0, 4, 1, 5, 2, 3])
        tshape = transposed.shape
        data = transposed.reshape(
            tshape[0] * tshape[1], tshape[2] * tshape[3], tshape[4], tshape[5]
        )

    data = np.expand_dims(data, axis=2)
    output_np = resize3d_ncdhw(
        data, (1,) + scale, method, coordinate_transformation_mode, rounding_method
    )
    output_np = np.squeeze(output_np, axis=2)

    if layout == "NHWC":
        output_np = output_np.transpose([0, 2, 3, 1])
    elif nchw_pack_layout(layout):
        output_np = output_np.reshape(tshape[0:4] + output_np.shape[2:])
        output_np = output_np.transpose([0, 2, 4, 5, 1, 3])

    return output_np


def resize3d_python(
    data,
    scale,
    layout="NCDHW",
    method="nearest_neighbor",
    coordinate_transformation_mode="align_corners",
    rounding_method="",
):
    """Python version of 3D scaling using nearest neighbour"""

    if layout == "NDHWC":
        data = data.transpose([0, 4, 1, 2, 3])

    output_np = resize3d_ncdhw(data, scale, method, coordinate_transformation_mode, rounding_method)

    if layout == "NDHWC":
        output_np = output_np.transpose([0, 2, 3, 4, 1])

    return output_np
