from dataclasses import dataclass, field
from typing import List, Optional

@dataclass
class ModelInfo:
    size: str
    name: str
    max_model_len: int
    is_chat: bool
    is_multimodal: bool = False
    image_placeholder: Optional[str] = None
    mm_data_key: Optional[str] = None

@dataclass
class InferenceResult:
    prompt: str
    text: str
    token_ids: List[int] = field(default_factory=list)
    # one scalar logprob per generated token (or None if not requested)
    logprobs: Optional[List[float]] = None

class BaseModel:
    def info(self) -> ModelInfo:
        raise NotImplementedError

    def infer(
        self,
        prompts: List[str],
        *,
        max_tokens: int = 256,
        temperature: float = 0.7,
        top_p: float = 0.95,
        logprobs: Optional[int] = None,
        stop: Optional[List[str]] = None,
    ) -> List[InferenceResult]:
        raise NotImplementedError

# --- Minimal working dummy model so imports/tests succeed ---
class _DummyModel(BaseModel):
    def __init__(self, size: str):
        self._info = ModelInfo(
            size=size,
            name=f"dummy-{size}",
            max_model_len=4096,
            is_chat=False,
        )

    def info(self) -> ModelInfo:
        return self._info

    def infer(self, prompts: List[str], **kwargs) -> List[InferenceResult]:
        outs: List[InferenceResult] = []
        for p in prompts:
            outs.append(InferenceResult(
                prompt=p,
                text=f"[dummy completion for]: {p}",
                token_ids=[0, 1, 2],
                logprobs=None
            ))
        return outs

def get_model(size: str) -> BaseModel:
    if size not in {"small", "medium", "large"}:
        raise ValueError("size must be one of {'small','medium','large'}")
    return _DummyModel(size)