"""Utilities for selecting and loading models."""
import contextlib
from typing import Tuple, Type

import torch
from torch import nn

from vllm.config import ModelConfig
from vllm.model_executor.models import ModelRegistry


@contextlib.contextmanager
def set_default_torch_dtype(dtype: torch.dtype):
    """Sets the default torch dtype to the given dtype."""
    old_dtype = torch.get_default_dtype()
    torch.set_default_dtype(dtype)
    yield
    torch.set_default_dtype(old_dtype)


def get_model_architecture(
        model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
    architectures = getattr(model_config.hf_config, "architectures", None) or []
    logger.warning("[DEBUG-ARCH] get_model_architecture: "
                   "type(hf_config)=%s, architectures=%s, "
                   "id(hf_config)=%s, has_text_config=%s",
                   type(model_config.hf_config).__name__,
                   getattr(model_config.hf_config, "architectures", "MISSING"),
                   id(model_config.hf_config),
                   hasattr(model_config.hf_config, "text_config"))
    # Special handling for quantized Mixtral.
    # FIXME(woosuk): This is a temporary hack.
    mixtral_supported = [
        "fp8", "compressed-tensors", "gptq_marlin", "awq_marlin"
    ]

    if (model_config.quantization is not None
            and model_config.quantization not in mixtral_supported
            and "MixtralForCausalLM" in architectures):
        architectures = ["QuantMixtralForCausalLM"]

    return ModelRegistry.resolve_model_cls(
        architectures,
        model_path=model_config.model,
        revision=model_config.revision,
        trust_remote_code=model_config.trust_remote_code,
        hf_config=model_config.hf_config,
    )


def get_architecture_class_name(model_config: ModelConfig) -> str:
    return get_model_architecture(model_config)[1]