import os
import sys
import traceback
import logging

logger = logging.getLogger(__name__)

from time import time as ttime

import fairseq
import faiss
import numpy as np
import parselmouth
import pyworld
import scipy.signal as signal
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchcrepe

from infer.lib.infer_pack.models import (
    SynthesizerTrnMs256NSFsid,
    SynthesizerTrnMs256NSFsid_nono,
    SynthesizerTrnMs768NSFsid,
    SynthesizerTrnMs768NSFsid_nono,
)

now_dir = os.getcwd()
sys.path.append(now_dir)
from multiprocessing import Manager as M

from configs.config import Config

config = Config()

mm = M()
if config.dml == True:

    def forward_dml(ctx, x, scale):
        ctx.scale = scale
        res = x.clone().detach()
        return res

    fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml


# config.device=torch.device("cpu")########强制cpu测试
# config.is_half=False########强制cpu测试
class RVC:
    def __init__(
        self, key, pth_path, index_path, index_rate, n_cpu, inp_q, opt_q, device
    ) -> None:
        """
        初始化
        """
        try:
            global config
            self.inp_q = inp_q
            self.opt_q = opt_q
            # device="cpu"########强制cpu测试
            self.device = device
            self.f0_up_key = key
            self.time_step = 160 / 16000 * 1000
            self.f0_min = 50
            self.f0_max = 1100
            self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
            self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
            self.sr = 16000
            self.window = 160
            self.n_cpu = n_cpu
            if index_rate != 0:
                self.index = faiss.read_index(index_path)
                self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
                logger.info("Index search enabled")
            self.index_path = index_path
            self.index_rate = index_rate
            models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                ["assets/hubert/hubert_base.pt"],
                suffix="",
            )
            hubert_model = models[0]
            hubert_model = hubert_model.to(config.device)
            if config.is_half:
                hubert_model = hubert_model.half()
            else:
                hubert_model = hubert_model.float()
            hubert_model.eval()
            self.model = hubert_model
            cpt = torch.load(pth_path, map_location="cpu")
            self.tgt_sr = cpt["config"][-1]
            cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
            self.if_f0 = cpt.get("f0", 1)
            self.version = cpt.get("version", "v1")
            if self.version == "v1":
                if self.if_f0 == 1:
                    self.net_g = SynthesizerTrnMs256NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
            elif self.version == "v2":
                if self.if_f0 == 1:
                    self.net_g = SynthesizerTrnMs768NSFsid(
                        *cpt["config"], is_half=config.is_half
                    )
                else:
                    self.net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
            del self.net_g.enc_q
            logger.debug(self.net_g.load_state_dict(cpt["weight"], strict=False))
            self.net_g.eval().to(device)
            # print(2333333333,device,config.device,self.device)#net_g是device，hubert是config.device
            if config.is_half:
                self.net_g = self.net_g.half()
            else:
                self.net_g = self.net_g.float()
            self.is_half = config.is_half
        except:
            logger.warn(traceback.format_exc())

    def change_key(self, new_key):
        self.f0_up_key = new_key

    def change_index_rate(self, new_index_rate):
        if new_index_rate != 0 and self.index_rate == 0:
            self.index = faiss.read_index(self.index_path)
            self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
            logger.info("Index search enabled")
        self.index_rate = new_index_rate

    def get_f0_post(self, f0):
        f0_min = self.f0_min
        f0_max = self.f0_max
        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
        f0bak = f0.copy()
        f0_mel = 1127 * np.log(1 + f0 / 700)
        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
            f0_mel_max - f0_mel_min
        ) + 1
        f0_mel[f0_mel <= 1] = 1
        f0_mel[f0_mel > 255] = 255
        f0_coarse = np.rint(f0_mel).astype(np.int32)
        return f0_coarse, f0bak

    def get_f0(self, x, f0_up_key, n_cpu, method="harvest"):
        n_cpu = int(n_cpu)
        if method == "crepe":
            return self.get_f0_crepe(x, f0_up_key)
        if method == "rmvpe":
            return self.get_f0_rmvpe(x, f0_up_key)
        if method == "pm":
            p_len = x.shape[0] // 160
            f0 = (
                parselmouth.Sound(x, 16000)
                .to_pitch_ac(
                    time_step=0.01,
                    voicing_threshold=0.6,
                    pitch_floor=50,
                    pitch_ceiling=1100,
                )
                .selected_array["frequency"]
            )

            pad_size = (p_len - len(f0) + 1) // 2
            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
                # print(pad_size, p_len - len(f0) - pad_size)
                f0 = np.pad(
                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
                )

            f0 *= pow(2, f0_up_key / 12)
            return self.get_f0_post(f0)
        if n_cpu == 1:
            f0, t = pyworld.harvest(
                x.astype(np.double),
                fs=16000,
                f0_ceil=1100,
                f0_floor=50,
                frame_period=10,
            )
            f0 = signal.medfilt(f0, 3)
            f0 *= pow(2, f0_up_key / 12)
            return self.get_f0_post(f0)
        f0bak = np.zeros(x.shape[0] // 160, dtype=np.float64)
        length = len(x)
        part_length = int(length / n_cpu / 160) * 160
        ts = ttime()
        res_f0 = mm.dict()
        for idx in range(n_cpu):
            tail = part_length * (idx + 1) + 320
            if idx == 0:
                self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
            else:
                self.inp_q.put(
                    (idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
                )
        while 1:
            res_ts = self.opt_q.get()
            if res_ts == ts:
                break
        f0s = [i[1] for i in sorted(res_f0.items(), key=lambda x: x[0])]
        for idx, f0 in enumerate(f0s):
            if idx == 0:
                f0 = f0[:-3]
            elif idx != n_cpu - 1:
                f0 = f0[2:-3]
            else:
                f0 = f0[2:-1]
            f0bak[
                part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
            ] = f0
        f0bak = signal.medfilt(f0bak, 3)
        f0bak *= pow(2, f0_up_key / 12)
        return self.get_f0_post(f0bak)

    def get_f0_crepe(self, x, f0_up_key):
        if "privateuseone" in str(self.device):  ###不支持dml，cpu又太慢用不成，拿pm顶替
            return self.get_f0(x, f0_up_key, 1, "pm")
        audio = torch.tensor(np.copy(x))[None].float()
        # print("using crepe,device:%s"%self.device)
        f0, pd = torchcrepe.predict(
            audio,
            self.sr,
            160,
            self.f0_min,
            self.f0_max,
            "full",
            batch_size=512,
            # device=self.device if self.device.type!="privateuseone" else "cpu",###crepe不用半精度全部是全精度所以不愁###cpu延迟高到没法用
            device=self.device,
            return_periodicity=True,
        )
        pd = torchcrepe.filter.median(pd, 3)
        f0 = torchcrepe.filter.mean(f0, 3)
        f0[pd < 0.1] = 0
        f0 = f0[0].cpu().numpy()
        f0 *= pow(2, f0_up_key / 12)
        return self.get_f0_post(f0)

    def get_f0_rmvpe(self, x, f0_up_key):
        if hasattr(self, "model_rmvpe") == False:
            from infer.lib.rmvpe import RMVPE

            logger.info("Loading rmvpe model")
            self.model_rmvpe = RMVPE(
                # "rmvpe.pt", is_half=self.is_half if self.device.type!="privateuseone" else False, device=self.device if self.device.type!="privateuseone"else "cpu"####dml时强制对rmvpe用cpu跑
                #  "rmvpe.pt", is_half=False, device=self.device####dml配置
                # "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置
                "assets/rmvpe/rmvpe.pt",
                is_half=self.is_half,
                device=self.device,  ####正常逻辑
            )
            # self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
        f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
        f0 *= pow(2, f0_up_key / 12)
        return self.get_f0_post(f0)

    def infer(
        self,
        feats: torch.Tensor,
        indata: np.ndarray,
        rate1,
        rate2,
        cache_pitch,
        cache_pitchf,
        f0method,
    ) -> np.ndarray:
        feats = feats.view(1, -1)
        if config.is_half:
            feats = feats.half()
        else:
            feats = feats.float()
        feats = feats.to(self.device)
        t1 = ttime()
        with torch.no_grad():
            padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
            inputs = {
                "source": feats,
                "padding_mask": padding_mask,
                "output_layer": 9 if self.version == "v1" else 12,
            }
            logits = self.model.extract_features(**inputs)
            feats = (
                self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
            )
        t2 = ttime()
        try:
            if hasattr(self, "index") and self.index_rate != 0:
                leng_replace_head = int(rate1 * feats[0].shape[0])
                npy = feats[0][-leng_replace_head:].cpu().numpy().astype("float32")
                score, ix = self.index.search(npy, k=8)
                weight = np.square(1 / score)
                weight /= weight.sum(axis=1, keepdims=True)
                npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
                if config.is_half:
                    npy = npy.astype("float16")
                feats[0][-leng_replace_head:] = (
                    torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
                    + (1 - self.index_rate) * feats[0][-leng_replace_head:]
                )
            else:
                logger.warn("Index search FAILED or disabled")
        except:
            traceback.print_exc()
            logger.warn("Index search FAILED")
        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
        t3 = ttime()
        if self.if_f0 == 1:
            pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method)
            cache_pitch[:] = np.append(cache_pitch[pitch[:-1].shape[0] :], pitch[:-1])
            cache_pitchf[:] = np.append(
                cache_pitchf[pitchf[:-1].shape[0] :], pitchf[:-1]
            )
            p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
        else:
            cache_pitch, cache_pitchf = None, None
            p_len = min(feats.shape[1], 13000)
        t4 = ttime()
        feats = feats[:, :p_len, :]
        if self.if_f0 == 1:
            cache_pitch = cache_pitch[:p_len]
            cache_pitchf = cache_pitchf[:p_len]
            cache_pitch = torch.LongTensor(cache_pitch).unsqueeze(0).to(self.device)
            cache_pitchf = torch.FloatTensor(cache_pitchf).unsqueeze(0).to(self.device)
        p_len = torch.LongTensor([p_len]).to(self.device)
        ii = 0  # sid
        sid = torch.LongTensor([ii]).to(self.device)
        with torch.no_grad():
            if self.if_f0 == 1:
                # print(12222222222,feats.device,p_len.device,cache_pitch.device,cache_pitchf.device,sid.device,rate2)
                infered_audio = (
                    self.net_g.infer(
                        feats, p_len, cache_pitch, cache_pitchf, sid, rate2
                    )[0][0, 0]
                    .data.cpu()
                    .float()
                )
            else:
                infered_audio = (
                    self.net_g.infer(feats, p_len, sid, rate2)[0][0, 0]
                    .data.cpu()
                    .float()
                )
        t5 = ttime()
        logger.info(
            "Spent time: fea = %s, index = %s, f0 = %s, model = %s",
            t2 - t1,
            t3 - t2,
            t4 - t3,
            t5 - t4,
        )
        return infered_audio