from io import BytesIO
import os
from typing import List, Optional, Tuple, Union
import numpy as np
import torch

from infer.lib import jit

try:
    # Fix "Torch not compiled with CUDA enabled"
    import intel_extension_for_pytorch as ipex  # pylint: disable=import-error, unused-import

    if torch.xpu.is_available():
        from infer.modules.ipex import ipex_init

        ipex_init()
except Exception:  # pylint: disable=broad-exception-caught
    pass
import torch.nn as nn
import torch.nn.functional as F
from librosa.util import normalize, pad_center, tiny
from scipy.signal import get_window

import logging

logger = logging.getLogger(__name__)

from rvc.f0.mel import MelSpectrogram

from time import time as ttime


class BiGRU(nn.Module):
    def __init__(self, input_features, hidden_features, num_layers):
        super(BiGRU, self).__init__()
        self.gru = nn.GRU(
            input_features,
            hidden_features,
            num_layers=num_layers,
            batch_first=True,
            bidirectional=True,
        )

    def forward(self, x):
        return self.gru(x)[0]


class ConvBlockRes(nn.Module):
    def __init__(self, in_channels, out_channels, momentum=0.01):
        super(ConvBlockRes, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=(3, 3),
                stride=(1, 1),
                padding=(1, 1),
                bias=False,
            ),
            nn.BatchNorm2d(out_channels, momentum=momentum),
            nn.ReLU(),
            nn.Conv2d(
                in_channels=out_channels,
                out_channels=out_channels,
                kernel_size=(3, 3),
                stride=(1, 1),
                padding=(1, 1),
                bias=False,
            ),
            nn.BatchNorm2d(out_channels, momentum=momentum),
            nn.ReLU(),
        )
        # self.shortcut:Optional[nn.Module] = None
        if in_channels != out_channels:
            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))

    def forward(self, x: torch.Tensor):
        if not hasattr(self, "shortcut"):
            return self.conv(x) + x
        else:
            return self.conv(x) + self.shortcut(x)


class Encoder(nn.Module):
    def __init__(
        self,
        in_channels,
        in_size,
        n_encoders,
        kernel_size,
        n_blocks,
        out_channels=16,
        momentum=0.01,
    ):
        super(Encoder, self).__init__()
        self.n_encoders = n_encoders
        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
        self.layers = nn.ModuleList()
        self.latent_channels = []
        for i in range(self.n_encoders):
            self.layers.append(
                ResEncoderBlock(
                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
                )
            )
            self.latent_channels.append([out_channels, in_size])
            in_channels = out_channels
            out_channels *= 2
            in_size //= 2
        self.out_size = in_size
        self.out_channel = out_channels

    def forward(self, x: torch.Tensor):
        concat_tensors: List[torch.Tensor] = []
        x = self.bn(x)
        for i, layer in enumerate(self.layers):
            t, x = layer(x)
            concat_tensors.append(t)
        return x, concat_tensors


class ResEncoderBlock(nn.Module):
    def __init__(
        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
    ):
        super(ResEncoderBlock, self).__init__()
        self.n_blocks = n_blocks
        self.conv = nn.ModuleList()
        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
        for i in range(n_blocks - 1):
            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
        self.kernel_size = kernel_size
        if self.kernel_size is not None:
            self.pool = nn.AvgPool2d(kernel_size=kernel_size)

    def forward(self, x):
        for i, conv in enumerate(self.conv):
            x = conv(x)
        if self.kernel_size is not None:
            return x, self.pool(x)
        else:
            return x


class Intermediate(nn.Module):  #
    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
        super(Intermediate, self).__init__()
        self.n_inters = n_inters
        self.layers = nn.ModuleList()
        self.layers.append(
            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
        )
        for i in range(self.n_inters - 1):
            self.layers.append(
                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
            )

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = layer(x)
        return x


class ResDecoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
        super(ResDecoderBlock, self).__init__()
        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
        self.n_blocks = n_blocks
        self.conv1 = nn.Sequential(
            nn.ConvTranspose2d(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=(3, 3),
                stride=stride,
                padding=(1, 1),
                output_padding=out_padding,
                bias=False,
            ),
            nn.BatchNorm2d(out_channels, momentum=momentum),
            nn.ReLU(),
        )
        self.conv2 = nn.ModuleList()
        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
        for i in range(n_blocks - 1):
            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))

    def forward(self, x, concat_tensor):
        x = self.conv1(x)
        x = torch.cat((x, concat_tensor), dim=1)
        for i, conv2 in enumerate(self.conv2):
            x = conv2(x)
        return x


class Decoder(nn.Module):
    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
        super(Decoder, self).__init__()
        self.layers = nn.ModuleList()
        self.n_decoders = n_decoders
        for i in range(self.n_decoders):
            out_channels = in_channels // 2
            self.layers.append(
                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
            )
            in_channels = out_channels

    def forward(self, x: torch.Tensor, concat_tensors: List[torch.Tensor]):
        for i, layer in enumerate(self.layers):
            x = layer(x, concat_tensors[-1 - i])
        return x


class DeepUnet(nn.Module):
    def __init__(
        self,
        kernel_size,
        n_blocks,
        en_de_layers=5,
        inter_layers=4,
        in_channels=1,
        en_out_channels=16,
    ):
        super(DeepUnet, self).__init__()
        self.encoder = Encoder(
            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
        )
        self.intermediate = Intermediate(
            self.encoder.out_channel // 2,
            self.encoder.out_channel,
            inter_layers,
            n_blocks,
        )
        self.decoder = Decoder(
            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, concat_tensors = self.encoder(x)
        x = self.intermediate(x)
        x = self.decoder(x, concat_tensors)
        return x


class E2E(nn.Module):
    def __init__(
        self,
        n_blocks,
        n_gru,
        kernel_size,
        en_de_layers=5,
        inter_layers=4,
        in_channels=1,
        en_out_channels=16,
    ):
        super(E2E, self).__init__()
        self.unet = DeepUnet(
            kernel_size,
            n_blocks,
            en_de_layers,
            inter_layers,
            in_channels,
            en_out_channels,
        )
        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
        if n_gru:
            self.fc = nn.Sequential(
                BiGRU(3 * 128, 256, n_gru),
                nn.Linear(512, 360),
                nn.Dropout(0.25),
                nn.Sigmoid(),
            )
        else:
            self.fc = nn.Sequential(
                nn.Linear(3 * nn.N_MELS, nn.N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
            )

    def forward(self, mel):
        # print(mel.shape)
        mel = mel.transpose(-1, -2).unsqueeze(1)
        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
        x = self.fc(x)
        # print(x.shape)
        return x


class RMVPE:
    def __init__(self, model_path: str, is_half, device=None, use_jit=False):
        self.resample_kernel = {}
        self.resample_kernel = {}
        self.is_half = is_half
        if device is None:
            device = "cuda:0" if torch.cuda.is_available() else "cpu"
        self.device = device
        self.mel_extractor = MelSpectrogram(
            is_half=is_half,
            n_mel_channels=128,
            sampling_rate=16000,
            win_length=1024,
            hop_length=160,
            mel_fmin=30,
            mel_fmax=8000,
            device=device,
        ).to(device)
        if "privateuseone" in str(device):
            import onnxruntime as ort

            ort_session = ort.InferenceSession(
                "%s/rmvpe.onnx" % os.environ["rmvpe_root"],
                providers=["DmlExecutionProvider"],
            )
            self.model = ort_session
        else:
            if str(self.device) == "cuda":
                self.device = torch.device("cuda:0")

            def get_jit_model():
                jit_model_path = model_path.rstrip(".pth")
                jit_model_path += ".half.jit" if is_half else ".jit"
                ckpt = None
                if os.path.exists(jit_model_path):
                    ckpt = jit.load(jit_model_path)
                    model_device = ckpt["device"]
                    if model_device != str(self.device):
                        del ckpt
                        ckpt = None

                if ckpt is None:
                    ckpt = jit.rmvpe_jit_export(
                        model_path=model_path,
                        mode="script",
                        inputs_path=None,
                        save_path=jit_model_path,
                        device=device,
                        is_half=is_half,
                    )

                model = torch.jit.load(BytesIO(ckpt["model"]), map_location=device)
                return model

            def get_default_model():
                model = E2E(4, 1, (2, 2))
                ckpt = torch.load(model_path, map_location="cpu")
                model.load_state_dict(ckpt)
                model.eval()
                if is_half:
                    model = model.half()
                else:
                    model = model.float()
                return model

            if use_jit:
                if is_half and "cpu" in str(self.device):
                    logger.warning(
                        "Use default rmvpe model. \
                                 Jit is not supported on the CPU for half floating point"
                    )
                    self.model = get_default_model()
                else:
                    self.model = get_jit_model()
            else:
                self.model = get_default_model()

            self.model = self.model.to(device)
        cents_mapping = 20 * np.arange(360) + 1997.3794084376191
        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368

    def mel2hidden(self, mel):
        with torch.no_grad():
            n_frames = mel.shape[-1]
            n_pad = 32 * ((n_frames - 1) // 32 + 1) - n_frames
            if n_pad > 0:
                mel = F.pad(mel, (0, n_pad), mode="constant")
            if "privateuseone" in str(self.device):
                onnx_input_name = self.model.get_inputs()[0].name
                onnx_outputs_names = self.model.get_outputs()[0].name
                hidden = self.model.run(
                    [onnx_outputs_names],
                    input_feed={onnx_input_name: mel.cpu().numpy()},
                )[0]
            else:
                mel = mel.half() if self.is_half else mel.float()
                hidden = self.model(mel)
            return hidden[:, :n_frames]

    def decode(self, hidden, thred=0.03):
        cents_pred = self.to_local_average_cents(hidden, thred=thred)
        f0 = 10 * (2 ** (cents_pred / 1200))
        f0[f0 == 10] = 0
        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
        return f0

    def infer_from_audio(self, audio, thred=0.03):
        # torch.cuda.synchronize()
        # t0 = ttime()
        if not torch.is_tensor(audio):
            audio = torch.from_numpy(audio)
        mel = self.mel_extractor(
            audio.float().to(self.device).unsqueeze(0), center=True
        )
        # print(123123123,mel.device.type)
        # torch.cuda.synchronize()
        # t1 = ttime()
        hidden = self.mel2hidden(mel)
        # torch.cuda.synchronize()
        # t2 = ttime()
        # print(234234,hidden.device.type)
        if "privateuseone" not in str(self.device):
            hidden = hidden.squeeze(0).cpu().numpy()
        else:
            hidden = hidden[0]
        if self.is_half == True:
            hidden = hidden.astype("float32")

        f0 = self.decode(hidden, thred=thred)
        # torch.cuda.synchronize()
        # t3 = ttime()
        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
        return f0

    def to_local_average_cents(self, salience, thred=0.05):
        # t0 = ttime()
        center = np.argmax(salience, axis=1)  # 帧长#index
        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368
        # t1 = ttime()
        center += 4
        todo_salience = []
        todo_cents_mapping = []
        starts = center - 4
        ends = center + 5
        for idx in range(salience.shape[0]):
            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
        # t2 = ttime()
        todo_salience = np.array(todo_salience)  # 帧长，9
        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9
        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
        weight_sum = np.sum(todo_salience, 1)  # 帧长
        devided = product_sum / weight_sum  # 帧长
        # t3 = ttime()
        maxx = np.max(salience, axis=1)  # 帧长
        devided[maxx <= thred] = 0
        # t4 = ttime()
        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
        return devided


if __name__ == "__main__":
    import librosa
    import soundfile as sf

    audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
    if len(audio.shape) > 1:
        audio = librosa.to_mono(audio.transpose(1, 0))
    audio_bak = audio.copy()
    if sampling_rate != 16000:
        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
    model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
    thred = 0.03  # 0.01
    device = "cuda" if torch.cuda.is_available() else "cpu"
    rmvpe = RMVPE(model_path, is_half=False, device=device)
    t0 = ttime()
    f0 = rmvpe.infer_from_audio(audio, thred=thred)
    # f0 = rmvpe.infer_from_audio(audio, thred=thred)
    # f0 = rmvpe.infer_from_audio(audio, thred=thred)
    # f0 = rmvpe.infer_from_audio(audio, thred=thred)
    # f0 = rmvpe.infer_from_audio(audio, thred=thred)
    t1 = ttime()
    logger.info("%s %.2f", f0.shape, t1 - t0)