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import torch

from tokenspeed.runtime.configs.device_config import DeviceConfig
from tokenspeed.runtime.configs.load_config import LoadConfig
from tokenspeed.runtime.configs.model_config import ModelConfig
from tokenspeed.runtime.model_loader import get_model
from tokenspeed.runtime.utils import (
    get_available_gpu_memory,
    get_colorful_logger,
    set_cuda_arch,
)
from tokenspeed.runtime.utils.server_args import ServerArgs
from tokenspeed.runtime.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter

logger = get_colorful_logger(__name__)


class WeightLoader:
    """Handles model weight loading from disk.

    This class is stateless and does not modify external state.
    It returns LoadedModel with all necessary information.
    """

    @staticmethod
    def load_model(
        model_config: ModelConfig,
        server_args: ServerArgs,
        device: str,
        gpu_id: int,
        memory_saver_adapter: TorchMemorySaverAdapter,
    ):
        """Load model from disk.

        Args:
            model_config: Model configuration
            server_args: Server arguments
            device: Device type ("cuda", "cpu")
            gpu_id: GPU ID
            memory_saver_adapter: Memory saver adapter

        Returns:
            LoadedModel with model and dtype
        """
        logger.info(
            "Load weight begin. avail mem=%.2f GB",
            get_available_gpu_memory(device, gpu_id),
        )

        # Reduce thread conflicts during weight loading
        if device != "cpu":
            torch.set_num_threads(1)

        set_cuda_arch()

        # Create load config
        load_config = LoadConfig(
            load_format=server_args.load_format,
            download_dir=server_args.download_dir,
            ext_yaml=server_args.ext_yaml,
            weight_loader_prefetch_checkpoints=server_args.weight_loader_prefetch_checkpoints,
            weight_loader_prefetch_num_threads=server_args.weight_loader_prefetch_num_threads,
        )

        # Load model with memory saver context. Tag as "weights" with CPU backup
        # so release_memory_occupation offloads (and restores) them byte-exact.
        with memory_saver_adapter.region(tag="weights", enable_cpu_backup=True):
            model = get_model(
                model_config=model_config,
                load_config=load_config,
                device_config=DeviceConfig(device),
            )

        # Load KV cache scaling factors if using FP8
        if server_args.kv_cache_dtype == "fp8_e4m3":
            if server_args.quantization_param_path is not None:
                if callable(getattr(model, "load_kv_cache_scales", None)):
                    model.load_kv_cache_scales(server_args.quantization_param_path)
                    logger.info(
                        "Loaded KV cache scaling factors from %s",
                        server_args.quantization_param_path,
                    )
                else:
                    raise RuntimeError(
                        "Using FP8 KV cache and scaling factors provided but "
                        f"model {model.__class__} does not support loading scaling factors."
                    )
            else:
                logger.warning(
                    "Using FP8 KV cache but no scaling factors provided. "
                    "Defaulting to scaling factors of 1.0. "
                    "This may lead to less accurate results!"
                )

        dtype = model_config.dtype

        logger.info(
            "Load weight end. type=%s, dtype=%s, avail mem=%.2f GB",
            type(model).__name__,
            dtype,
            get_available_gpu_memory(device, gpu_id),
        )

        return model