import librosa
import numpy as np
import argparse

from parse import parse
from scipy.signal import savgol_filter
import torch
from model import EmoTalk
import random
import os, subprocess
import shlex
from munch import Munch


@torch.no_grad()
def test(model, speech_array, sampling_rate):
    args = Munch(
        bs_dim=52,
        feature_dim=832,
        period=30,
        device="cuda",
        model_path="./pretrain_model/EmoTalk.pth",
        max_seq_len=5000,
        num_workers=0,
        batch_size=1,
        post_processing=True,
        blender_path="./blender/blender")

    eye1 = np.array([0.36537236, 0.950235724, 0.95593375, 0.916715622, 0.367256105, 0.119113259, 0.025357503])
    eye2 = np.array([0.234776169, 0.909951985, 0.944758058, 0.777862132, 0.191071674, 0.235437036, 0.089163929])
    eye3 = np.array([0.870040774, 0.949833691, 0.949418545, 0.695911646, 0.191071674, 0.072576277, 0.007108896])
    eye4 = np.array([0.000307991, 0.556701422, 0.952656746, 0.942345619, 0.425857186, 0.148335218, 0.017659493])
    # speech_array, sampling_rate = librosa.load(os.path.join(wav_path), sr=16000)
    audio = torch.FloatTensor(speech_array).unsqueeze(0).to(args.device)
    level = torch.tensor([1]).to(args.device)
    person = torch.tensor([0]).to(args.device)
    prediction = model.predict(audio, level, person)
    prediction = prediction.squeeze().detach().cpu().numpy()
    if args.post_processing:
        output = np.zeros((prediction.shape[0], prediction.shape[1]))
        for i in range(prediction.shape[1]):
            output[:, i] = savgol_filter(prediction[:, i], 5, 2)
        output[:, 8] = 0
        output[:, 9] = 0
        i = random.randint(0, 60)
        while i < output.shape[0] - 7:
            eye_num = random.randint(1, 4)
            if eye_num == 1:
                output[i:i + 7, 8] = eye1
                output[i:i + 7, 9] = eye1
            elif eye_num == 2:
                output[i:i + 7, 8] = eye2
                output[i:i + 7, 9] = eye2
            elif eye_num == 3:
                output[i:i + 7, 8] = eye3
                output[i:i + 7, 9] = eye3
            else:
                output[i:i + 7, 8] = eye4
                output[i:i + 7, 9] = eye4
            time1 = random.randint(60, 180)
            i = i + time1
        return output
    else:
        return prediction


def render_video(wav_name, model_name):
    args = Munch(
        bs_dim=52,
        feature_dim=832,
        period=30,
        device="cuda",
        model_path="./pretrain_model/EmoTalk.pth",
        max_seq_len=5000,
        num_workers=0,
        batch_size=1,
        post_processing=True,
        blender_path="./blender/blender")

    # wav_name = args.wav_path.split('/')[-1].split('.')[0]
    image_path = os.path.join("./audio", wav_name)
    os.makedirs(image_path, exist_ok=True)
    blender_path = args.blender_path

    python_path = f"./{model_name}.py"
    blend_path = f"./{model_name}.blend"
    print(python_path, blend_path)
    # python_path = "./render.py"
    # blend_path = "./render.blend"
    cmd = '{} -t 64 -b {} -P {} -- "{}" "{}" '.format(blender_path,
                                                      blend_path,
                                                      python_path,
                                                      "./audio/",
                                                      wav_name)
    cmd = shlex.split(cmd)
    p = subprocess.Popen(cmd,
                         shell=False,
                         stdout=subprocess.PIPE,
                         stderr=subprocess.STDOUT)
    while p.poll() is None:
        line = p.stdout.readline().decode('utf-8')
        line = line.strip()
        if line and line.startswith('Saved: '):
            fname = parse("Saved: '{}'", line).fixed[0]
            yield fname
        else:
            print(line)

    if p.returncode == 0:
        print('Subprogram success')
    else:
        print('Subprogram failed')


def construct_video(wav_name):
    image_path = os.path.join("./audio", wav_name)
    os.makedirs(image_path, exist_ok=True)
    image_temp = image_path + "/%d.png"
    output_path = os.path.join("./audio", wav_name + ".mp4")
    cmd = 'ffmpeg -r 30 -i "{}" -i "{}" -pix_fmt yuv420p -s 512x768 "{}" -y'.format(image_temp,
                                                                                    f"./audio/{wav_name}.wav",
                                                                                    output_path)
    subprocess.call(cmd, shell=True)
    cmd = 'rm -rf "{}"'.format(image_path)
    subprocess.call(cmd, shell=True)


class PieInfer(object):
    def __init__(self):
        args = Munch(
            bs_dim=52,
            feature_dim=832,
            period=30,
            device="cuda",
            model_path="./pretrain_model/EmoTalk.pth",
            max_seq_len=5000,
            num_workers=0,
            batch_size=1,
            post_processing=True,
            blender_path="./blender/blender")
        #"""
        model = EmoTalk(args)
        model.load_state_dict(torch.load(args.model_path, map_location=torch.device(args.device)), strict=False)
        model = model.to(args.device)
        model.eval()
        #"""
        # model = None
        self.model = model

    def __call__(self,
                 speech_array,
                 sampling_rate):
        return test(self.model, speech_array, sampling_rate)