from typing import  Dict, Any
import librosa
import numpy as np
import torch
import pyewts
import noisereduce as nr
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from num2tib.core import convert
from num2tib.core import convert2text
import re
converter = pyewts.pyewts()
def replace_numbers_with_convert(sentence, wylie=True):
    pattern = r'\d+(\.\d+)?'
    def replace(match):
        return convert(match.group(), wylie)
    result = re.sub(pattern, replace, sentence)
    
    return result

def cleanup_text(inputs):
    for src, dst in replacements:
        inputs = inputs.replace(src, dst)
    return inputs

speaker_embeddings = {
    "Lhasa(female)": "female_2.npy",

}

replacements = [
    ('_', '_'),
    ('*', 'v'),
    ('`', ';'),
    ('~', ','),
    ('+', ','),
    ('\\', ';'),
    ('|', ';'),
    ('╚',''),
    ('╗','')
]





class EndpointHandler():
    def __init__(self, path=""):
        # load the model
        self.processor = SpeechT5Processor.from_pretrained("TenzinGayche/TTS_run3_ep20_174k_b")
        self.model = SpeechT5ForTextToSpeech.from_pretrained("TenzinGayche/TTS_run3_ep20_174k_b")
        self.model.to('cuda')
        self.vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")


    def __call__(self, data: Dict[str, Any]) -> bytes:
        """_summary_

        Args:
            data (Dict[str, Any]): _description_

        Returns:
            bytes: _description_
        """


        # process input
        text = data.get("inputs", None)

        if len(text.strip()) == 0:
            return (16000, np.zeros(0).astype(np.int16))
        text = converter.toWylie(text)
        text=cleanup_text(text)
        text=replace_numbers_with_convert(text)
        inputs = self.processor(text=text, return_tensors="pt")
        # limit input length
        input_ids = inputs["input_ids"]
        input_ids = input_ids[..., :self.model.config.max_text_positions]
        speaker_embedding = np.load(speaker_embeddings['Lhasa(female)'])
        speaker_embedding = torch.tensor(speaker_embedding)
        speech = self.model.generate_speech(input_ids.to('cuda'), speaker_embedding.to('cuda'), vocoder=vocoder.to('cuda'))
        speech = nr.reduce_noise(y=speech.to('cpu'), sr=16000)
        return speech.tobytes()