# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import torch
from vllm.logger import init_logger
from vllm_ascend.platform import NPUPlatform

from vllm_omni.diffusion.attention.backends.registry import DiffusionAttentionBackendEnum
from vllm_omni.platforms.interface import OmniPlatform, OmniPlatformEnum

logger = init_logger(__name__)


class NPUOmniPlatform(OmniPlatform, NPUPlatform):
    """NPU/Ascend implementation of OmniPlatform.

    Inherits all NPU-specific implementations from vllm-ascend's NPUPlatform,
    and adds Omni-specific interfaces from OmniPlatform.
    """

    _omni_enum = OmniPlatformEnum.NPU
    dist_backend: str = "hccl"

    @classmethod
    def get_omni_ar_worker_cls(cls) -> str:
        return "vllm_omni.platforms.npu.worker.npu_ar_worker.NPUARWorker"

    @classmethod
    def get_omni_generation_worker_cls(cls) -> str:
        return "vllm_omni.platforms.npu.worker.npu_generation_worker.NPUGenerationWorker"

    @classmethod
    def get_default_stage_config_path(cls) -> str:
        return "vllm_omni/platforms/npu/stage_configs"

    @classmethod
    def get_diffusion_attn_backend_cls(
        cls,
        selected_backend: str | None,
        head_size: int,
    ) -> str:
        from importlib.util import find_spec

        if selected_backend is not None:
            backend_upper = selected_backend.upper()
            backend = DiffusionAttentionBackendEnum[backend_upper]
            logger.info("Using diffusion attention backend '%s'", backend_upper)
            return backend.get_path()

        # Try FLASH_ATTN if mindiesd is available, otherwise fall back to SDPA
        if find_spec("mindiesd"):
            logger.info("Defaulting to diffusion attention backend FLASH_ATTN")
            return DiffusionAttentionBackendEnum.FLASH_ATTN.get_path()

        logger.info("Falling back to diffusion attention backend SDPA")
        return DiffusionAttentionBackendEnum.TORCH_SDPA.get_path()

    @classmethod
    def supports_torch_inductor(cls) -> bool:
        return False

    @classmethod
    def get_torch_device(cls, local_rank: int | None = None) -> torch.device:
        if local_rank is None:
            return torch.device("npu")
        return torch.device("npu", local_rank)

    @classmethod
    def get_device_count(cls) -> int:
        return torch.npu.device_count()

    @classmethod
    def get_device_version(cls) -> str | None:
        return None

    @classmethod
    def synchronize(cls) -> None:
        torch.npu.synchronize()

    @classmethod
    def get_free_memory(cls, device: torch.device | None = None) -> int:
        free, _ = torch.npu.mem_get_info(device)
        return free

    @classmethod
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        device_props = torch.npu.get_device_properties(device_id)
        return device_props.total_memory