"""Official FuXi-S2S ONNX adapter.

The public FuXi-S2S release contains an ONNX model and inference code. This
module exposes the official inference contract through ONNX Runtime.
"""

from pathlib import Path

import numpy as np


class FuXiS2SModel:
    def __init__(self, model_path, device="cpu", providers=None):
        self.model_path = Path(model_path)
        self.device = device
        self.providers = list(providers) if providers else self._default_providers(device)
        try:
            import onnxruntime as ort
        except ImportError as exc:
            raise RuntimeError("onnxruntime is required for official FuXi-S2S inference") from exc
        if not self.model_path.is_file():
            raise FileNotFoundError(f"FuXi-S2S ONNX model not found: {self.model_path}")
        external_data_path = self.model_path.parent / "fuxi_s2s"
        if not external_data_path.is_file():
            raise FileNotFoundError(
                f"FuXi-S2S ONNX external data not found: {external_data_path}. "
                "The official archive requires both fuxi_s2s.onnx and fuxi_s2s."
            )
        available_providers = ort.get_available_providers()
        missing_providers = [provider for provider in self.providers if provider not in available_providers]
        if missing_providers:
            raise RuntimeError(
                f"ONNX Runtime provider(s) unavailable: {missing_providers}. "
                f"Available providers: {available_providers}. "
                "Install the matching ONNX Runtime build and update model.providers."
            )
        session_options = ort.SessionOptions()
        if device == "dcu":
            # CPU constant folding can warn on kernels that are intended for the
            # accelerator. Let the configured DCU provider execute the graph.
            session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
        self.session = ort.InferenceSession(
            str(self.model_path),
            sess_options=session_options,
            providers=self.providers,
        )

    @staticmethod
    def _default_providers(device):
        if device == "cpu":
            return ["CPUExecutionProvider"]
        if device == "cuda":
            return ["CUDAExecutionProvider", "CPUExecutionProvider"]
        if device == "dcu":
            raise ValueError(
                "DCU inference requires an explicit ONNX Runtime provider in "
                "model.providers, for example ['DCUExecutionProvider', "
                "'CPUExecutionProvider']"
            )
        raise ValueError("device must be 'cpu', 'cuda', or 'dcu'")

    @property
    def input_names(self):
        return [item.name for item in self.session.get_inputs()]

    @property
    def output_names(self):
        return [item.name for item in self.session.get_outputs()]

    def step(self, inputs):
        payload = {}
        for name in self.input_names:
            if name not in inputs:
                raise KeyError(f"Missing FuXi-S2S ONNX input: {name}")
            payload[name] = np.asarray(inputs[name], dtype=np.float32)
        outputs = self.session.run(None, payload)
        return dict(zip(self.output_names, outputs))

    def __call__(self, inputs):
        return self.step(inputs)