import logging
import torch
import os

from pyannote.audio import Pipeline
from transformers import pipeline, AutoModelForCausalLM
from diarization_utils import diarize
from huggingface_hub import HfApi
from transformers.pipelines.audio_utils import ffmpeg_read
from pydantic import Json, BaseModel, ValidationError


logger = logging.getLogger(__name__)


class InferenceConfig(BaseModel):
    task: Literal["transcribe", "translate"] = "transcribe"
    batch_size: int = 24
    assisted: bool = False
    chunk_length_s: int = 30
    sampling_rate: int = 16000
    language: Optional[str] = None
    num_speakers: Optional[int] = None
    min_speakers: Optional[int] = None
    max_speakers: Optional[int] = None


class EndpointHandler():
    def __init__(self):

        device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
        logger.info(f"Using device: {device.type}")
        torch_dtype = torch.float32 if device.type == "cpu" else torch.float16

        self.assistant_model = AutoModelForCausalLM.from_pretrained(
            os.getenv("ASSISTANT_MODEL"),
            torch_dtype=torch_dtype,
            low_cpu_mem_usage=True,
            use_safetensors=True
        ) if os.getenv("ASSISTANT_MODEL") else None

        if self.assistant_model:
            self.assistant_model.to(device)

        self.asr_pipeline = pipeline(
            "automatic-speech-recognition",
            model=os.getenv("ASR_MODEL"),
            torch_dtype=torch_dtype,
            device=device
        )

        if os.getenv("DIARIZATION_MODEL"):
            # diarization pipeline doesn't raise if there is no token
            HfApi().whoami(model_settings.hf_token)
            self.diarization_pipeline = Pipeline.from_pretrained(
                checkpoint_path=os.getenv("DIARIZATION_MODEL"),
                use_auth_token=os.getenv("HF_TOKEN"),
            )
            self.diarization_pipeline.to(device)
        else:
            self.diarization_pipeline = None
    
    async def __call__(self, file, parameters):
        try:
            parameters = InferenceConfig(**parameters)
        except ValidationError as e:
            logger.error(f"Error validating parameters: {e}")
            raise ValidationError(f"Error validating parameters: {e}")
            
        logger.info(f"inference parameters: {parameters}")

        generate_kwargs = {
            "task": parameters.task, 
            "language": parameters.language,
            "assistant_model": self.assistant_model if parameters.assisted else None
        }

        try:
            asr_outputs = self.asr_pipeline(
                file,
                chunk_length_s=parameters.chunk_length_s,
                batch_size=parameters.batch_size,
                generate_kwargs=generate_kwargs,
                return_timestamps=True,
            )
        except RuntimeError as e:
            logger.error(f"ASR inference error: {str(e)}")
            raise HTTPException(status_code=400, detail=f"ASR inference error: {str(e)}")
        except Exception as e:
            logger.error(f"Unknown error diring ASR inference: {str(e)}")
            raise HTTPException(status_code=500, detail=f"Unknown error diring ASR inference: {str(e)}")

        if self.diarization_pipeline:
            try:
                transcript = diarize(self.diarization_pipeline, file, parameters, asr_outputs)
            except RuntimeError as e:
                logger.error(f"Diarization inference error: {str(e)}")
                raise HTTPException(status_code=400, detail=f"Diarization inference error: {str(e)}")
            except Exception as e:
                logger.error(f"Unknown error during diarization: {str(e)}")
                raise HTTPException(status_code=500, detail=f"Unknown error during diarization: {str(e)}")
        else:
            transcript = []

        return {
            "speakers": transcript,
            "chunks": asr_outputs["chunks"],
            "text": asr_outputs["text"],
        }