import gradio as gr
import os, gc, torch
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 1024
title1 = "RWKV-4-Raven-7B-v9-Eng99%-Other1%-20230412-ctx8192"

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path1 = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title1}.pth")
model1 = RWKV(model=model_path1, strategy='cuda fp16i8 *8 -> cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model1, "20B_tokenizer.json")

import git

os.system('git clone https://github.com/Edresson/Coqui-TTS -b multilingual-torchaudio-SE TTS')
os.system('pip install -q -e TTS/')
os.system('pip install -q torchaudio==0.9.0')

os.system('pip install voicefixer --upgrade')
from voicefixer import VoiceFixer
voicefixer = VoiceFixer()


import sys
TTS_PATH = "TTS/"

# add libraries into environment
sys.path.append(TTS_PATH) # set this if TTS is not installed globally

import string
import time
import argparse
import json

import numpy as np
import IPython
from IPython.display import Audio

from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
try:
  from TTS.utils.audio import AudioProcessor
except:
  from TTS.utils.audio import AudioProcessor


from TTS.tts.models import setup_model
from TTS.config import load_config
from TTS.tts.models.vits import *  

OUT_PATH = 'out/'

# create output path
os.makedirs(OUT_PATH, exist_ok=True)

# model vars 
MODEL_PATH = '/home/user/app/best_model_latest.pth.tar'
CONFIG_PATH = '/home/user/app/config.json'
TTS_LANGUAGES = "/home/user/app/language_ids.json"
TTS_SPEAKERS = "/home/user/app/speakers.json"
USE_CUDA = torch.cuda.is_available()  

# load the config
C = load_config(CONFIG_PATH)


# load the audio processor
ap = AudioProcessor(**C.audio)

speaker_embedding = None

C.model_args['d_vector_file'] = TTS_SPEAKERS
C.model_args['use_speaker_encoder_as_loss'] = False

model = setup_model(C)
model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)
# print(model.language_manager.num_languages, model.embedded_language_dim)
# print(model.emb_l)
cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))
# remove speaker encoder
model_weights = cp['model'].copy()
for key in list(model_weights.keys()):
  if "speaker_encoder" in key:
    del model_weights[key]

model.load_state_dict(model_weights)


model.eval()

if USE_CUDA:
    model = model.cuda()

# synthesize voice
use_griffin_lim = False

os.system('pip install -q pydub ffmpeg-normalize')

CONFIG_SE_PATH = "config_se.json"
CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"

from TTS.tts.utils.speakers import SpeakerManager
from pydub import AudioSegment
import librosa

SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA)

def compute_spec(ref_file):
  y, sr = librosa.load(ref_file, sr=ap.sample_rate)
  spec = ap.spectrogram(y)
  spec = torch.FloatTensor(spec).unsqueeze(0)
  return spec
  

    
def greet(Text,Voicetoclone,VoiceMicrophone):
    text= "%s" % (Text)
    if Voicetoclone is not None:
      reference_files= "%s" % (Voicetoclone)
      print("path url")
      print(Voicetoclone)
      sample= str(Voicetoclone)
    else:
      reference_files= "%s" % (VoiceMicrophone)
      print("path url")
      print(VoiceMicrophone)
      sample= str(VoiceMicrophone)
    size= len(reference_files)*sys.getsizeof(reference_files)
    size2= size / 1000000
    if (size2 > 0.012) or len(text)>2000:
      message="File is greater than 30mb or Text inserted is longer than 2000 characters. Please re-try with smaller sizes."
      print(message)
      raise SystemExit("File is greater than 30mb. Please re-try or Text inserted is longer than 2000 characters. Please re-try with smaller sizes.")
    else:
      os.system('ffmpeg-normalize $sample -nt rms -t=-27 -o $sample -ar 16000 -f')
      reference_emb = SE_speaker_manager.compute_d_vector_from_clip(reference_files)
      model.length_scale = 1  # scaler for the duration predictor. The larger it is, the slower the speech.
      model.inference_noise_scale = 0.3 # defines the noise variance applied to the random z vector at inference.
      model.inference_noise_scale_dp = 0.3 # defines the noise variance applied to the duration predictor z vector at inference.
      text = text
      model.language_manager.language_id_mapping
      language_id = 0
    
      print(" > text: {}".format(text))
      wav, alignment, _, _ = synthesis(
                        model,
                        text,
                        C,
                        "cuda" in str(next(model.parameters()).device),
                        ap,
                        speaker_id=None,
                        d_vector=reference_emb,
                        style_wav=None,
                        language_id=language_id,
                        enable_eos_bos_chars=C.enable_eos_bos_chars,
                        use_griffin_lim=True,
                        do_trim_silence=False,
                    ).values()
      print("Generated Audio")
      IPython.display.display(Audio(wav, rate=ap.sample_rate))
      #file_name = text.replace(" ", "_")
      #file_name = file_name.translate(str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
      file_name="Audio.wav"
      out_path = os.path.join(OUT_PATH, file_name)
      print(" > Saving output to {}".format(out_path))
      ap.save_wav(wav, out_path)

      voicefixer.restore(input=out_path, # input wav file path
                      output="audio1.wav", # output wav file path
                      cuda=True, # whether to use gpu acceleration'
                      mode = 0) # You can try out mode 0, 1, or 2 to find out the best result
    
      return "audio1.wav"

def generate_prompt(instruction, input=None):
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Input:
{input}
# Response:
"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Response:
"""

def evaluate(
    instruction,
    input=None,
#    token_count=200,
#    temperature=1.0,
#    top_p=0.7,
#    presencePenalty = 0.1,
#    countPenalty = 0.1,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(1.0)), top_p = float(0.5),
                     alpha_frequency = 0.4,
                     alpha_presence = 0.4,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here

    instruction = instruction.strip()
    input = input.strip()
    ctx = generate_prompt(instruction, input)
    
    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')
    
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(200)):
        out, state = model1.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1
    gc.collect()
    torch.cuda.empty_cache()
    yield out_str.strip()


block = gr.Blocks()

with block:
    with gr.Group():
        gr.Markdown(
            """ # <center>🥳💬💕 - TalktoAI，随时随地，谈天说地！</center>
            ## <center>🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！TalktoAI - Enable the future！</center>
            ### <center>注意❗：请不要输入或生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及娱乐使用。用户输入或生成的内容与程序开发者无关，请自觉合法合规使用，违反者一切后果自负。</center>
            
            ### <center>Model by [Raven](https://huggingface.co/spaces/BlinkDL/Raven-RWKV-7B). Thanks to [PENG Bo](https://github.com/BlinkDL). Please follow me on [Bilibili](https://space.bilibili.com/501495851?spm_id_from=333.1007.0.0).</center>
            
      """
        )
        
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
              
                inp1 = gr.components.Textbox(lines=2, label="说些什么吧(中英皆可，英文对话效果更好)", value="Tell me a joke.")
                inp2 = gr.components.Textbox(lines=2, label="对话的背景信息(选填，请合理合规使用此程序)", placeholder="none")

                btn = gr.Button("开始对话吧")
        
        text = gr.Textbox(lines=5, label="Raven的回答")
              
        btn.click(evaluate, [inp1, inp2], [text])

        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                inp3 = text
                inp4 = gr.Audio(source="upload", label = "请上传您喜欢的声音(wav/mp3文件, max. 30mb)", type="filepath")
                inp5 = gr.Audio(source="microphone", type="filepath", label = '请用麦克风上传您喜欢的声音，与文件上传二选一即可')

                btn1 = gr.Button("用喜欢的声音听一听吧")

        out1 = gr.Audio(label="合成的专属声音")

        btn1.click(greet, [inp3, inp4, inp5], [out1])
        
        gr.HTML('''
        <div class="footer">
                    <p>🎶🖼️🎡 - It’s the intersection of technology and liberal arts that makes our hearts sing. - Steve Jobs
                    </p>
        </div>
        ''')


block.launch(show_error=True)