import torch
from transformers import AutoProcessor, VibeVoiceAsrForConditionalGeneration


class EndpointHandler:
    def __init__(self, path: str = ""):
        self.processor = AutoProcessor.from_pretrained(path)
        self.model = VibeVoiceAsrForConditionalGeneration.from_pretrained(
            path,
            torch_dtype=torch.float16,
            device_map="auto",
        )

    def __call__(self, data):
        inputs_data = data.pop("inputs", data)
        prompt = data.pop("prompt", None)
        inputs = self.processor.apply_transcription_request(
            audio=inputs_data,
            prompt=prompt,
            return_tensors="pt",
        ).to(self.model.device, self.model.dtype)

        with torch.no_grad():
            output_ids = self.model.generate(**inputs)

        generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
        transcription = self.processor.decode(
            generated_ids,
            return_format="transcription_only",
        )[0]
        return {"text": transcription}