import whisper
import gradio as gr
import time
import os
from typing import BinaryIO, Union, Tuple, List
import numpy as np
import torch

from modules.whisper_base import WhisperBase
from modules.whisper_parameter import *


class WhisperInference(WhisperBase):
    def __init__(self):
        super().__init__(
            model_dir=os.path.join("models", "Whisper")
        )

    def transcribe(self,
                   audio: Union[str, np.ndarray, torch.Tensor],
                   progress: gr.Progress,
                   *whisper_params,
                   ) -> Tuple[List[dict], float]:
        """
        transcribe method for faster-whisper.

        Parameters
        ----------
        audio: Union[str, BinaryIO, np.ndarray]
            Audio path or file binary or Audio numpy array
        progress: gr.Progress
            Indicator to show progress directly in gradio.
        *whisper_params: tuple
            Gradio components related to Whisper. see whisper_data_class.py for details.

        Returns
        ----------
        segments_result: List[dict]
            list of dicts that includes start, end timestamps and transcribed text
        elapsed_time: float
            elapsed time for transcription
        """
        start_time = time.time()
        params = WhisperValues(*whisper_params)

        if params.model_size != self.current_model_size or self.model is None or self.current_compute_type != params.compute_type:
            self.update_model(params.model_size, params.compute_type, progress)

        if params.lang == "Automatic Detection":
            params.lang = None

        def progress_callback(progress_value):
            progress(progress_value, desc="Transcribing..")

        segments_result = self.model.transcribe(audio=audio,
                                                language=params.lang,
                                                verbose=False,
                                                beam_size=params.beam_size,
                                                logprob_threshold=params.log_prob_threshold,
                                                no_speech_threshold=params.no_speech_threshold,
                                                task="translate" if params.is_translate and self.current_model_size in self.translatable_models else "transcribe",
                                                fp16=True if params.compute_type == "float16" else False,
                                                best_of=params.best_of,
                                                patience=params.patience,
                                                temperature=params.temperature,
                                                compression_ratio_threshold=params.compression_ratio_threshold,
                                                progress_callback=progress_callback,)["segments"]
        elapsed_time = time.time() - start_time

        return segments_result, elapsed_time

    def update_model(self,
                     model_size: str,
                     compute_type: str,
                     progress: gr.Progress,
                     ):
        """
        Update current model setting

        Parameters
        ----------
        model_size: str
            Size of whisper model
        compute_type: str
            Compute type for transcription.
            see more info : https://opennmt.net/CTranslate2/quantization.html
        progress: gr.Progress
            Indicator to show progress directly in gradio.
        """
        progress(0, desc="Initializing Model..")
        self.current_compute_type = compute_type
        self.current_model_size = model_size
        self.model = whisper.load_model(
            name=model_size,
            device=self.device,
            download_root=self.model_dir
        )