"""ACE 40-channel input, 44-channel output and autoregressive rollout."""

from __future__ import annotations

from dataclasses import asdict, dataclass
from typing import Callable

import torch
from torch import nn

from ACE.model.variables import (
    FORCING_CHANNELS,
    INPUT_CHANNELS,
    OUTPUT_CHANNELS,
    PROGNOSTIC_CHANNELS,
    split_output,
    validate_channels,
)
from ACE.model.sfno import SFNOAdapter, SFNOConfig


@dataclass
class ACEModelConfig:
    nlat: int = 180
    nlon: int = 360
    input_channels: int = len(INPUT_CHANNELS)
    output_channels: int = len(OUTPUT_CHANNELS)
    prognostic_channels: int = len(PROGNOSTIC_CHANNELS)
    forcing_channels: int = len(FORCING_CHANNELS)
    embed_dim: int = 256
    num_layers: int = 8
    filter_type: str = "linear"
    operator_type: str = "dhconv"
    scale_factor: int = 1
    spectral_layers: int = 3
    grid: str = "legendre-gauss"
    grid_internal: str = "legendre-gauss"
    mlp_ratio: float = 2.0
    fallback: bool = False

    def to_dict(self) -> dict:
        return asdict(self)


class ACEModel(nn.Module):
    def __init__(self, config: ACEModelConfig | None = None) -> None:
        super().__init__()
        self.config = config or ACEModelConfig()
        if self.config.input_channels != len(INPUT_CHANNELS) or self.config.output_channels != len(OUTPUT_CHANNELS):
            raise ValueError("ACE channel contract must remain 40 input and 44 output channels")
        sfno_config = SFNOConfig(
            nlat=self.config.nlat,
            nlon=self.config.nlon,
            in_channels=self.config.input_channels,
            out_channels=self.config.output_channels,
            embed_dim=self.config.embed_dim,
            num_layers=self.config.num_layers,
            filter_type=self.config.filter_type,
            operator_type=self.config.operator_type,
            scale_factor=self.config.scale_factor,
            spectral_layers=self.config.spectral_layers,
            grid=self.config.grid,
            grid_internal=self.config.grid_internal,
            mlp_ratio=self.config.mlp_ratio,
            fallback=self.config.fallback,
        )
        self.sfno = SFNOAdapter(sfno_config)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        validate_channels(x, self.config.input_channels, "ACE input")
        return self.sfno(x)

    def step(self, prognostic: torch.Tensor, forcing: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        validate_channels(prognostic, self.config.prognostic_channels, "prognostic state")
        validate_channels(forcing, self.config.forcing_channels, "forcing")
        predicted = self.forward(torch.cat([prognostic, forcing], dim=1))
        return split_output(predicted)

    @torch.no_grad()
    def rollout(
        self,
        initial_prognostic: torch.Tensor,
        forcings: torch.Tensor | Callable[[int, torch.Tensor], torch.Tensor],
        steps: int | None = None,
    ) -> torch.Tensor:
        """Return predictions with shape ``[B,T,44,H,W]``.

        ``forcings`` is either `[B,T,6,H,W]` or a callable receiving
        `(step, current_prognostic)` and returning `[B,6,H,W]`.
        """
        validate_channels(initial_prognostic, self.config.prognostic_channels, "initial prognostic")
        if callable(forcings):
            if steps is None or steps < 1:
                raise ValueError("steps is required for callable forcing")
            forcing_steps = steps
        else:
            if forcings.ndim != 5 or forcings.shape[2] != self.config.forcing_channels:
                raise ValueError("tensor forcings must have shape [B,T,6,H,W]")
            forcing_steps = forcings.shape[1] if steps is None else min(steps, forcings.shape[1])
        state = initial_prognostic
        outputs = []
        for step in range(forcing_steps):
            forcing = forcings[:, step] if not callable(forcings) else forcings(step, state)
            predicted_state, diagnostics = self.step(state, forcing)
            outputs.append(torch.cat([predicted_state, diagnostics], dim=1))
            state = predicted_state
        return torch.stack(outputs, dim=1)