from typing import TYPE_CHECKING, Optional

import psutil
import torch

from vllm.logger import init_logger

from .interface import Platform, PlatformEnum, _Backend

logger = init_logger(__name__)

if TYPE_CHECKING:
    from vllm.config import VllmConfig
else:
    VllmConfig = None

logger = init_logger(__name__)


class CpuPlatform(Platform):
    _enum = PlatformEnum.CPU
    device_name: str = "cpu"
    device_type: str = "cpu"
    dispatch_key: str = "CPU"

    @classmethod
    def get_device_name(cls, device_id: int = 0) -> str:
        return "cpu"

    @classmethod
    def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
                             dtype: torch.dtype, kv_cache_dtype: Optional[str],
                             block_size: int, use_v1: bool) -> str:
        if selected_backend != _Backend.TORCH_SDPA:
            logger.info("Cannot use %s backend on CPU.", selected_backend)
        logger.info("Using Torch SDPA backend.")
        return "vllm.attention.backends.torch_sdpa.TorchSDPABackend"

    @classmethod
    def get_device_total_memory(cls, device_id: int = 0) -> int:
        return psutil.virtual_memory().total

    @classmethod
    def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
        return False

    @classmethod
    def inference_mode(cls):
        return torch.no_grad()

    @classmethod
    def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
        import vllm.envs as envs
        from vllm.utils import GiB_bytes
        model_config = vllm_config.model_config
        # Reminder: Please update docs/source/features/compatibility_matrix.md
        # If the feature combo become valid
        if not model_config.enforce_eager:
            logger.warning(
                "CUDA graph is not supported on CPU, fallback to the eager "
                "mode.")
            model_config.enforce_eager = True

        cache_config = vllm_config.cache_config

        if cache_config and cache_config.block_size is None:
            cache_config.block_size = 16

        kv_cache_space = envs.VLLM_CPU_KVCACHE_SPACE

        if kv_cache_space >= 0:
            if kv_cache_space == 0:
                cache_config.cpu_kvcache_space_bytes = 4 * GiB_bytes  # type: ignore
                logger.warning(
                    "Environment variable VLLM_CPU_KVCACHE_SPACE (GB) "
                    "for CPU backend is not set, using 4 by default.")
            else:
                cache_config.cpu_kvcache_space_bytes = kv_cache_space * GiB_bytes  # type: ignore # noqa
        else:
            raise RuntimeError(
                "Invalid environment variable VLLM_CPU_KVCACHE_SPACE"
                f" {kv_cache_space}, expect a positive integer value.")

        scheduler_config = vllm_config.scheduler_config
        if ((scheduler_config.chunked_prefill_enabled
             or cache_config.enable_prefix_caching)
                and model_config.dtype == torch.half):
            logger.warning("Chunked-prefill on the CPU backend only does not"
                           " support fp16 for now, cast to bf16.")
            model_config.dtype = torch.bfloat16

        parallel_config = vllm_config.parallel_config
        if (parallel_config.distributed_executor_backend is not None
                and parallel_config.distributed_executor_backend != "mp"):
            logger.warning(("%s is not supported on CPU, fallback to mp "
                            "distributed executor backend."),
                           parallel_config.distributed_executor_backend)
            parallel_config.distributed_executor_backend = "mp"
        if parallel_config.worker_cls == "auto":
            if vllm_config.speculative_config:
                parallel_config.worker_cls = \
                    "vllm.spec_decode.spec_decode_worker.create_spec_worker"
                parallel_config.sd_worker_cls = \
                    "vllm.worker.cpu_worker.CPUWorker"
            else:
                parallel_config.worker_cls = "vllm.worker.cpu_worker.CPUWorker"

    @classmethod
    def is_pin_memory_available(cls) -> bool:
        logger.warning("Pin memory is not supported on CPU.")
        return False