#!/usr/bin/env python3
# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""HPI engine."""

from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Type

from ....constants import MODEL_FILE_PREFIX
from ....utils.deps import is_dep_available
from ....utils.device import get_default_device, parse_device
from ..hpi import get_hpi_info
from ..runners.hpi import HPIConfig, HPIRunner
from ..runners.inference_runner import InferenceRunner
from ..utils.model_paths import LocalModelFormat
from ._base import RunnerBuilder, RunnerEngine


class HPIEngine(RunnerEngine):
    """Engine for HPI / UltraInfer inference."""

    entities = "hpi"

    @property
    def name(self) -> str:
        return "hpi"

    @property
    def engine_config_model(self) -> Type[HPIConfig]:
        return HPIConfig

    def get_supported_model_formats(
        self,
    ) -> Optional[Tuple[LocalModelFormat, ...]]:
        return ("paddle", "onnx", "om")

    def prepare_config_dict(
        self,
        raw: Dict[str, Any],
        *,
        model_name: Optional[str] = None,
        device: Optional[str] = None,
    ) -> Dict[str, Any]:
        raw.setdefault("model_name", model_name or "")
        self._apply_device(raw, device)
        if not device and "device_type" not in raw:
            raw["device_type"], _ = parse_device(get_default_device())
        return raw

    def get_config_dump_kwargs(self) -> Dict[str, Any]:
        return {"exclude_none": True, "by_alias": True}

    def ensure_environment(self) -> None:
        if not is_dep_available("ultra-infer"):
            raise RuntimeError(
                "Engine 'hpi' is unavailable because dependency "
                "'ultra-infer' is not installed."
            )

    def get_default_runner_builder(self) -> RunnerBuilder:
        def runner_builder(
            *,
            model_name: str,
            model_dir: Optional[Path],
            model_config: Optional[Dict[str, Any]],
            engine_config: Dict[str, Any],
            default_builder: Optional[RunnerBuilder] = None,
        ) -> InferenceRunner:
            del default_builder
            if model_dir is None:
                raise ValueError("`model_dir` is required for engine='hpi'.")
            hpi_cfg = dict(engine_config)
            hpi_cfg.setdefault("model_name", model_name)
            if "hpi_info" not in hpi_cfg:
                hpi_info = get_hpi_info(model_config)
                if hpi_info is not None:
                    hpi_cfg["hpi_info"] = hpi_info
            hpi_config = HPIConfig.model_validate(hpi_cfg)
            return HPIRunner(
                model_dir=model_dir,
                model_file_prefix=MODEL_FILE_PREFIX,
                config=hpi_config,
            )

        return runner_builder

    def validate_runner(self, runner: InferenceRunner) -> None:
        if not isinstance(runner, HPIRunner):
            raise TypeError(
                "Engine 'hpi' must build an HPIRunner, "
                f"but got {type(runner).__name__}."
            )