# coding=utf-8
#
# SPDX-FileCopyrightText: Copyright (c) 2022 The torch-harmonics Authors. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# This module is adapted from the official Spherical Fourier Neural Operator
# reference implementation of Boris Bonev et al. (ICML 2023), published in the
# NVIDIA/torch-harmonics repository (BSD-3-Clause). Only a thin configurable
# wrapper is added so that a single YAML config can drive the model.
#
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import torch
import torch.nn as nn

from torch_harmonics.examples.models.sfno import SphericalFourierNeuralOperator


class SFNO(nn.Module):
    """
    Configurable wrapper around the official SFNO (Spherical Fourier Neural
    Operator, Bonev et al. 2023, ICML).

    The underlying network is provided by ``torch-harmonics``
    (``torch_harmonics.examples.models.sfno.SphericalFourierNeuralOperator``),
    which replaces the plain FFT of a vanilla FNO by a Spherical Harmonic
    Transform (SHT) so that the learned convolution respects the geometry of
    the sphere.

    Model inputs / outputs are deterministic global fields of shape
    ``(Batch, C, H, W)``: a single 6-hour state ``u_t`` is mapped to the next
    state ``u_{t+1}`` (trained with weighted L2 losses and 1-2 step rollout).
    """

    def __init__(
        self,
        img_size=(32, 64),
        scale_factor=2,
        in_chans=4,
        out_chans=4,
        embed_dim=16,
        num_layers=2,
        activation_function="gelu",
        use_mlp=True,
        mlp_ratio=2.0,
        drop_rate=0.0,
        drop_path_rate=0.0,
        normalization_layer="instance_norm",
        hard_thresholding_fraction=1.0,
        residual_prediction=False,
        pos_embed="none",
        bias=False,
    ):
        super().__init__()
        self.img_size = tuple(img_size)
        self.in_chans = int(in_chans)
        self.out_chans = int(out_chans)
        self.model = SphericalFourierNeuralOperator(
            img_size=self.img_size,
            scale_factor=int(scale_factor),
            in_chans=self.in_chans,
            out_chans=self.out_chans,
            embed_dim=int(embed_dim),
            num_layers=int(num_layers),
            activation_function=activation_function,
            use_mlp=use_mlp,
            mlp_ratio=mlp_ratio,
            drop_rate=drop_rate,
            drop_path_rate=drop_path_rate,
            normalization_layer=normalization_layer,
            hard_thresholding_fraction=hard_thresholding_fraction,
            residual_prediction=residual_prediction,
            pos_embed=pos_embed,
            bias=bias,
        )

    def forward(self, x):
        return self.model(x)