from __future__ import annotations
import logging, typing as t, openllm
if t.TYPE_CHECKING: import transformers

logger = logging.getLogger(__name__)
class OPT(openllm.LLM["transformers.OPTForCausalLM", "transformers.GPT2Tokenizer"]):
  __openllm_internal__ = True

  @property
  def import_kwargs(self) -> tuple[dict[str, t.Any], dict[str, t.Any]]:
    import torch
    return {"torch_dtype": torch.float16 if torch.cuda.is_available() else torch.float32}, {}

  def generate(self, prompt: str, **attrs: t.Any) -> list[str]:
    import torch
    with torch.inference_mode():
      return self.tokenizer.batch_decode(self.model.generate(**self.tokenizer(prompt, return_tensors="pt").to(self.device), do_sample=True, generation_config=self.config.model_construct_env(**attrs).to_generation_config()), skip_special_tokens=True)