from typing import Union

import torch.nn.functional as F
import numpy as np
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
from torchaudio.transforms import Resample
import os
import librosa
import soundfile as sf
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
import math
from functools import partial

from einops import rearrange, repeat
from local_attention import LocalAttention
from torch import nn

os.environ["LRU_CACHE_CAPACITY"] = "3"

def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
    sampling_rate = None
    try:
        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
    except Exception as ex:
        print(f"'{full_path}' failed to load.\nException:")
        print(ex)
        if return_empty_on_exception:
            return [], sampling_rate or target_sr or 48000
        else:
            raise Exception(ex)
    
    if len(data.shape) > 1:
        data = data[:, 0]
        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
    
    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
    else: # if audio data is type fp32
        max_mag = max(np.amax(data), -np.amin(data))
        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
    
    data = torch.FloatTensor(data.astype(np.float32))/max_mag
    
    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
        return [], sampling_rate or target_sr or 48000
    if target_sr is not None and sampling_rate != target_sr:
        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
        sampling_rate = target_sr
    
    return data, sampling_rate

def dynamic_range_compression(x, C=1, clip_val=1e-5):
    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)

def dynamic_range_decompression(x, C=1):
    return np.exp(x) / C

def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
    return torch.log(torch.clamp(x, min=clip_val) * C)

def dynamic_range_decompression_torch(x, C=1):
    return torch.exp(x) / C

class STFT():
    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
        self.target_sr = sr
        
        self.n_mels     = n_mels
        self.n_fft      = n_fft
        self.win_size   = win_size
        self.hop_length = hop_length
        self.fmin     = fmin
        self.fmax     = fmax
        self.clip_val = clip_val
        self.mel_basis = {}
        self.hann_window = {}
    
    def get_mel(self, y, keyshift=0, speed=1, center=False, train=False):
        sampling_rate = self.target_sr
        n_mels     = self.n_mels
        n_fft      = self.n_fft
        win_size   = self.win_size
        hop_length = self.hop_length
        fmin       = self.fmin
        fmax       = self.fmax
        clip_val   = self.clip_val
        
        factor = 2 ** (keyshift / 12)       
        n_fft_new = int(np.round(n_fft * factor))
        win_size_new = int(np.round(win_size * factor))
        hop_length_new = int(np.round(hop_length * speed))
        if not train:
            mel_basis = self.mel_basis
            hann_window = self.hann_window
        else:
            mel_basis = {}
            hann_window = {}
        
        if torch.min(y) < -1.:
            print('min value is ', torch.min(y))
        if torch.max(y) > 1.:
            print('max value is ', torch.max(y))
        
        mel_basis_key = str(fmax)+'_'+str(y.device)
        if mel_basis_key not in mel_basis:
            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
            mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device)
        
        keyshift_key = str(keyshift)+'_'+str(y.device)
        if keyshift_key not in hann_window:
            hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device)
        
        pad_left = (win_size_new - hop_length_new) //2
        pad_right = max((win_size_new- hop_length_new + 1) //2, win_size_new - y.size(-1) - pad_left)
        if pad_right < y.size(-1):
            mode = 'reflect'
        else:
            mode = 'constant'
        y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode = mode)
        y = y.squeeze(1)
        
        spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=hann_window[keyshift_key],
                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)                          
        spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + (1e-9))
        if keyshift != 0:
            size = n_fft // 2 + 1
            resize = spec.size(1)
            if resize < size:
                spec = F.pad(spec, (0, 0, 0, size-resize))
            spec = spec[:, :size, :] * win_size / win_size_new   
        spec = torch.matmul(mel_basis[mel_basis_key], spec)
        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
        return spec
    
    def __call__(self, audiopath):
        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
        return spect

stft = STFT()

#import fast_transformers.causal_product.causal_product_cuda

def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device = None):
    b, h, *_ = data.shape
    # (batch size, head, length, model_dim)

    # normalize model dim
    data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.

    # what is ration?, projection_matrix.shape[0] --> 266
    
    ratio = (projection_matrix.shape[0] ** -0.5)

    projection = repeat(projection_matrix, 'j d -> b h j d', b = b, h = h)
    projection = projection.type_as(data)

    #data_dash = w^T x
    data_dash = torch.einsum('...id,...jd->...ij', (data_normalizer * data), projection)

    
    # diag_data = D**2 
    diag_data = data ** 2
    diag_data = torch.sum(diag_data, dim=-1)
    diag_data = (diag_data / 2.0) * (data_normalizer ** 2)
    diag_data = diag_data.unsqueeze(dim=-1)
    
    #print ()
    if is_query:
        data_dash = ratio * (
            torch.exp(data_dash - diag_data -
                    torch.max(data_dash, dim=-1, keepdim=True).values) + eps)
    else:
        data_dash = ratio * (
            torch.exp(data_dash - diag_data + eps))#- torch.max(data_dash)) + eps)

    return data_dash.type_as(data)

def orthogonal_matrix_chunk(cols, qr_uniform_q = False, device = None):
    unstructured_block = torch.randn((cols, cols), device = device)
    q, r = torch.linalg.qr(unstructured_block.cpu(), mode='reduced')
    q, r = map(lambda t: t.to(device), (q, r))

    # proposed by @Parskatt
    # to make sure Q is uniform https://arxiv.org/pdf/math-ph/0609050.pdf
    if qr_uniform_q:
        d = torch.diag(r, 0)
        q *= d.sign()
    return q.t()
def exists(val):
    return val is not None

def empty(tensor):
    return tensor.numel() == 0

def default(val, d):
    return val if exists(val) else d

def cast_tuple(val):
    return (val,) if not isinstance(val, tuple) else val

class PCmer(nn.Module):
    """The encoder that is used in the Transformer model."""
    
    def __init__(self, 
                num_layers,
                num_heads,
                dim_model,
                dim_keys,
                dim_values,
                residual_dropout,
                attention_dropout):
        super().__init__()
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.dim_model = dim_model
        self.dim_values = dim_values
        self.dim_keys = dim_keys
        self.residual_dropout = residual_dropout
        self.attention_dropout = attention_dropout

        self._layers = nn.ModuleList([_EncoderLayer(self) for _ in range(num_layers)])
        
    #  METHODS  ########################################################################################################
    
    def forward(self, phone, mask=None):
        
        # apply all layers to the input
        for (i, layer) in enumerate(self._layers):
            phone = layer(phone, mask)
        # provide the final sequence
        return phone


# ==================================================================================================================== #
#  CLASS  _ E N C O D E R  L A Y E R                                                                                   #
# ==================================================================================================================== #


class _EncoderLayer(nn.Module):
    """One layer of the encoder.
    
    Attributes:
        attn: (:class:`mha.MultiHeadAttention`): The attention mechanism that is used to read the input sequence.
        feed_forward (:class:`ffl.FeedForwardLayer`): The feed-forward layer on top of the attention mechanism.
    """
    
    def __init__(self, parent: PCmer):
        """Creates a new instance of ``_EncoderLayer``.
        
        Args:
            parent (Encoder): The encoder that the layers is created for.
        """
        super().__init__()
        
        
        self.conformer = ConformerConvModule(parent.dim_model)
        self.norm = nn.LayerNorm(parent.dim_model)
        self.dropout = nn.Dropout(parent.residual_dropout)
        
        # selfatt -> fastatt: performer!
        self.attn = SelfAttention(dim = parent.dim_model,
                                  heads = parent.num_heads,
                                  causal = False)
        
    #  METHODS  ########################################################################################################

    def forward(self, phone, mask=None):
        
        # compute attention sub-layer
        phone = phone + (self.attn(self.norm(phone), mask=mask))
        
        phone = phone + (self.conformer(phone))
        
        return phone 

def calc_same_padding(kernel_size):
    pad = kernel_size // 2
    return (pad, pad - (kernel_size + 1) % 2)

# helper classes

class Swish(nn.Module):
    def forward(self, x):
        return x * x.sigmoid()

class Transpose(nn.Module):
    def __init__(self, dims):
        super().__init__()
        assert len(dims) == 2, 'dims must be a tuple of two dimensions'
        self.dims = dims

    def forward(self, x):
        return x.transpose(*self.dims)

class GLU(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x):
        out, gate = x.chunk(2, dim=self.dim)
        return out * gate.sigmoid()

class DepthWiseConv1d(nn.Module):
    def __init__(self, chan_in, chan_out, kernel_size, padding):
        super().__init__()
        self.padding = padding
        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups = chan_in)

    def forward(self, x):
        x = F.pad(x, self.padding)
        return self.conv(x)

class ConformerConvModule(nn.Module):
    def __init__(
        self,
        dim,
        causal = False,
        expansion_factor = 2,
        kernel_size = 31,
        dropout = 0.):
        super().__init__()

        inner_dim = dim * expansion_factor
        padding = calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0)

        self.net = nn.Sequential(
            nn.LayerNorm(dim),
            Transpose((1, 2)),
            nn.Conv1d(dim, inner_dim * 2, 1),
            GLU(dim=1),
            DepthWiseConv1d(inner_dim, inner_dim, kernel_size = kernel_size, padding = padding),
            #nn.BatchNorm1d(inner_dim) if not causal else nn.Identity(),
            Swish(),
            nn.Conv1d(inner_dim, dim, 1),
            Transpose((1, 2)),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        return self.net(x)

def linear_attention(q, k, v):
    if v is None:
        #print (k.size(), q.size())
        out = torch.einsum('...ed,...nd->...ne', k, q)
        return out

    else:
        k_cumsum = k.sum(dim = -2) 
        #k_cumsum = k.sum(dim = -2)
        D_inv = 1. / (torch.einsum('...nd,...d->...n', q, k_cumsum.type_as(q)) + 1e-8)

        context = torch.einsum('...nd,...ne->...de', k, v)
        #print ("TRUEEE: ", context.size(), q.size(), D_inv.size())
        out = torch.einsum('...de,...nd,...n->...ne', context, q, D_inv)
        return out

def gaussian_orthogonal_random_matrix(nb_rows, nb_columns, scaling = 0, qr_uniform_q = False, device = None):
    nb_full_blocks = int(nb_rows / nb_columns)
    #print (nb_full_blocks)
    block_list = []

    for _ in range(nb_full_blocks):
        q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q = qr_uniform_q, device = device)
        block_list.append(q)
    # block_list[n] is a orthogonal matrix ... (model_dim * model_dim)
    #print (block_list[0].size(), torch.einsum('...nd,...nd->...n', block_list[0], torch.roll(block_list[0],1,1)))
    #print (nb_rows, nb_full_blocks, nb_columns)
    remaining_rows = nb_rows - nb_full_blocks * nb_columns
    #print (remaining_rows)
    if remaining_rows > 0:
        q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q = qr_uniform_q, device = device)
        #print (q[:remaining_rows].size())
        block_list.append(q[:remaining_rows])

    final_matrix = torch.cat(block_list)
    
    if scaling == 0:
        multiplier = torch.randn((nb_rows, nb_columns), device = device).norm(dim = 1)
    elif scaling == 1:
        multiplier = math.sqrt((float(nb_columns))) * torch.ones((nb_rows,), device = device)
    else:
        raise ValueError(f'Invalid scaling {scaling}')

    return torch.diag(multiplier) @ final_matrix

class FastAttention(nn.Module):
    def __init__(self, dim_heads, nb_features = None, ortho_scaling = 0, causal = False, generalized_attention = False, kernel_fn = nn.ReLU(), qr_uniform_q = False, no_projection = False):
        super().__init__()
        nb_features = default(nb_features, int(dim_heads * math.log(dim_heads)))

        self.dim_heads = dim_heads
        self.nb_features = nb_features
        self.ortho_scaling = ortho_scaling

        self.create_projection = partial(gaussian_orthogonal_random_matrix, nb_rows = self.nb_features, nb_columns = dim_heads, scaling = ortho_scaling, qr_uniform_q = qr_uniform_q)
        projection_matrix = self.create_projection()
        self.register_buffer('projection_matrix', projection_matrix)

        self.generalized_attention = generalized_attention
        self.kernel_fn = kernel_fn

        # if this is turned on, no projection will be used
        # queries and keys will be softmax-ed as in the original efficient attention paper
        self.no_projection = no_projection

        self.causal = causal

    @torch.no_grad()
    def redraw_projection_matrix(self):
        projections = self.create_projection()
        self.projection_matrix.copy_(projections)
        del projections

    def forward(self, q, k, v):
        device = q.device

        if self.no_projection:
            q = q.softmax(dim = -1)
            k = torch.exp(k) if self.causal else k.softmax(dim = -2)
        else:
            create_kernel = partial(softmax_kernel, projection_matrix = self.projection_matrix, device = device)
            
            q = create_kernel(q, is_query = True)
            k = create_kernel(k, is_query = False)

        attn_fn = linear_attention if not self.causal else self.causal_linear_fn
        if v is None:
            out = attn_fn(q, k, None)
            return out
        else:
            out = attn_fn(q, k, v)
            return out
class SelfAttention(nn.Module):
    def __init__(self, dim, causal = False, heads = 8, dim_head = 64, local_heads = 0, local_window_size = 256, nb_features = None, feature_redraw_interval = 1000, generalized_attention = False, kernel_fn = nn.ReLU(), qr_uniform_q = False, dropout = 0., no_projection = False):
        super().__init__()
        assert dim % heads == 0, 'dimension must be divisible by number of heads'
        dim_head = default(dim_head, dim // heads)
        inner_dim = dim_head * heads
        self.fast_attention = FastAttention(dim_head, nb_features, causal = causal, generalized_attention = generalized_attention, kernel_fn = kernel_fn, qr_uniform_q = qr_uniform_q, no_projection = no_projection)

        self.heads = heads
        self.global_heads = heads - local_heads
        self.local_attn = LocalAttention(window_size = local_window_size, causal = causal, autopad = True, dropout = dropout, look_forward = int(not causal), rel_pos_emb_config = (dim_head, local_heads)) if local_heads > 0 else None

        #print (heads, nb_features, dim_head)
        #name_embedding = torch.zeros(110, heads, dim_head, dim_head)
        #self.name_embedding = nn.Parameter(name_embedding, requires_grad=True)
        

        self.to_q = nn.Linear(dim, inner_dim)
        self.to_k = nn.Linear(dim, inner_dim)
        self.to_v = nn.Linear(dim, inner_dim)
        self.to_out = nn.Linear(inner_dim, dim)
        self.dropout = nn.Dropout(dropout)

    @torch.no_grad()
    def redraw_projection_matrix(self):
        self.fast_attention.redraw_projection_matrix()
        #torch.nn.init.zeros_(self.name_embedding)
        #print (torch.sum(self.name_embedding))
    def forward(self, x, context = None, mask = None, context_mask = None, name=None, inference=False, **kwargs):
        _, _, _, h, gh = *x.shape, self.heads, self.global_heads
        
        cross_attend = exists(context)

        context = default(context, x)
        context_mask = default(context_mask, mask) if not cross_attend else context_mask
        #print (torch.sum(self.name_embedding))
        q, k, v = self.to_q(x), self.to_k(context), self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
        (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v))

        attn_outs = []
        #print (name)
        #print (self.name_embedding[name].size())
        if not empty(q):
            if exists(context_mask):
                global_mask = context_mask[:, None, :, None]
                v.masked_fill_(~global_mask, 0.)
            if cross_attend:
                pass
                #print (torch.sum(self.name_embedding))
                #out = self.fast_attention(q,self.name_embedding[name],None)
                #print (torch.sum(self.name_embedding[...,-1:]))
            else:
                out = self.fast_attention(q, k, v)
            attn_outs.append(out)

        if not empty(lq):
            assert not cross_attend, 'local attention is not compatible with cross attention'
            out = self.local_attn(lq, lk, lv, input_mask = mask)
            attn_outs.append(out)

        out = torch.cat(attn_outs, dim = 1)
        out = rearrange(out, 'b h n d -> b n (h d)')
        out =  self.to_out(out)
        return self.dropout(out)

def l2_regularization(model, l2_alpha):
    l2_loss = []
    for module in model.modules():
        if type(module) is nn.Conv2d:
            l2_loss.append((module.weight ** 2).sum() / 2.0)
    return l2_alpha * sum(l2_loss)


class FCPE(nn.Module):
    def __init__(
            self,
            input_channel=128,
            out_dims=360,
            n_layers=12,
            n_chans=512,
            use_siren=False,
            use_full=False,
            loss_mse_scale=10,
            loss_l2_regularization=False,
            loss_l2_regularization_scale=1,
            loss_grad1_mse=False,
            loss_grad1_mse_scale=1,
            f0_max=1975.5,
            f0_min=32.70,
            confidence=False,
            threshold=0.05,
            use_input_conv=True
    ):
        super().__init__()
        if use_siren is True:
            raise ValueError("Siren is not supported yet.")
        if use_full is True:
            raise ValueError("Full model is not supported yet.")

        self.loss_mse_scale = loss_mse_scale if (loss_mse_scale is not None) else 10
        self.loss_l2_regularization = loss_l2_regularization if (loss_l2_regularization is not None) else False
        self.loss_l2_regularization_scale = loss_l2_regularization_scale if (loss_l2_regularization_scale
                                                                             is not None) else 1
        self.loss_grad1_mse = loss_grad1_mse if (loss_grad1_mse is not None) else False
        self.loss_grad1_mse_scale = loss_grad1_mse_scale if (loss_grad1_mse_scale is not None) else 1
        self.f0_max = f0_max if (f0_max is not None) else 1975.5
        self.f0_min = f0_min if (f0_min is not None) else 32.70
        self.confidence = confidence if (confidence is not None) else False
        self.threshold = threshold if (threshold is not None) else 0.05
        self.use_input_conv = use_input_conv if (use_input_conv is not None) else True

        self.cent_table_b = torch.Tensor(
            np.linspace(self.f0_to_cent(torch.Tensor([f0_min]))[0], self.f0_to_cent(torch.Tensor([f0_max]))[0],
                        out_dims))
        self.register_buffer("cent_table", self.cent_table_b)

        # conv in stack
        _leaky = nn.LeakyReLU()
        self.stack = nn.Sequential(
            nn.Conv1d(input_channel, n_chans, 3, 1, 1),
            nn.GroupNorm(4, n_chans),
            _leaky,
            nn.Conv1d(n_chans, n_chans, 3, 1, 1))

        # transformer
        self.decoder = PCmer(
            num_layers=n_layers,
            num_heads=8,
            dim_model=n_chans,
            dim_keys=n_chans,
            dim_values=n_chans,
            residual_dropout=0.1,
            attention_dropout=0.1)
        self.norm = nn.LayerNorm(n_chans)

        # out
        self.n_out = out_dims
        self.dense_out = weight_norm(
            nn.Linear(n_chans, self.n_out))

    def forward(self, mel, infer=True, gt_f0=None, return_hz_f0=False, cdecoder = "local_argmax"):
        """
        input:
            B x n_frames x n_unit
        return:
            dict of B x n_frames x feat
        """
        if cdecoder == "argmax":
            self.cdecoder = self.cents_decoder
        elif cdecoder == "local_argmax":
            self.cdecoder = self.cents_local_decoder
        if self.use_input_conv:
            x = self.stack(mel.transpose(1, 2)).transpose(1, 2)
        else:
            x = mel
        x = self.decoder(x)
        x = self.norm(x)
        x = self.dense_out(x)  # [B,N,D]
        x = torch.sigmoid(x)
        if not infer:
            gt_cent_f0 = self.f0_to_cent(gt_f0)  # mel f0  #[B,N,1]
            gt_cent_f0 = self.gaussian_blurred_cent(gt_cent_f0)  # #[B,N,out_dim]
            loss_all = self.loss_mse_scale * F.binary_cross_entropy(x, gt_cent_f0)  # bce loss
            # l2 regularization
            if self.loss_l2_regularization:
                loss_all = loss_all + l2_regularization(model=self, l2_alpha=self.loss_l2_regularization_scale)
            x = loss_all
        if infer:
            x = self.cdecoder(x)
            x = self.cent_to_f0(x)
            if not return_hz_f0:
                x = (1 + x / 700).log()
        return x

    def cents_decoder(self, y, mask=True):
        B, N, _ = y.size()
        ci = self.cent_table[None, None, :].expand(B, N, -1)
        rtn = torch.sum(ci * y, dim=-1, keepdim=True) / torch.sum(y, dim=-1, keepdim=True)  # cents: [B,N,1]
        if mask:
            confident = torch.max(y, dim=-1, keepdim=True)[0]
            confident_mask = torch.ones_like(confident)
            confident_mask[confident <= self.threshold] = float("-INF")
            rtn = rtn * confident_mask
        if self.confidence:
            return rtn, confident
        else:
            return rtn
        
    def cents_local_decoder(self, y, mask=True):
        B, N, _ = y.size()
        ci = self.cent_table[None, None, :].expand(B, N, -1)
        confident, max_index = torch.max(y, dim=-1, keepdim=True)
        local_argmax_index = torch.arange(0,9).to(max_index.device) + (max_index - 4)
        local_argmax_index[local_argmax_index<0] = 0
        local_argmax_index[local_argmax_index>=self.n_out] = self.n_out - 1
        ci_l = torch.gather(ci,-1,local_argmax_index)
        y_l = torch.gather(y,-1,local_argmax_index)
        rtn = torch.sum(ci_l * y_l, dim=-1, keepdim=True) / torch.sum(y_l, dim=-1, keepdim=True)  # cents: [B,N,1]
        if mask:
            confident_mask = torch.ones_like(confident)
            confident_mask[confident <= self.threshold] = float("-INF")
            rtn = rtn * confident_mask
        if self.confidence:
            return rtn, confident
        else:
            return rtn

    def cent_to_f0(self, cent):
        return 10. * 2 ** (cent / 1200.)

    def f0_to_cent(self, f0):
        return 1200. * torch.log2(f0 / 10.)

    def gaussian_blurred_cent(self, cents):  # cents: [B,N,1]
        mask = (cents > 0.1) & (cents < (1200. * np.log2(self.f0_max / 10.)))
        B, N, _ = cents.size()
        ci = self.cent_table[None, None, :].expand(B, N, -1)
        return torch.exp(-torch.square(ci - cents) / 1250) * mask.float()


class FCPEInfer:
    def __init__(self, model_path, device=None, dtype=torch.float32):
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.device = device
        ckpt = torch.load(model_path, map_location=torch.device(self.device))
        self.args = DotDict(ckpt["config"])
        self.dtype = dtype
        model = FCPE(
            input_channel=self.args.model.input_channel,
            out_dims=self.args.model.out_dims,
            n_layers=self.args.model.n_layers,
            n_chans=self.args.model.n_chans,
            use_siren=self.args.model.use_siren,
            use_full=self.args.model.use_full,
            loss_mse_scale=self.args.loss.loss_mse_scale,
            loss_l2_regularization=self.args.loss.loss_l2_regularization,
            loss_l2_regularization_scale=self.args.loss.loss_l2_regularization_scale,
            loss_grad1_mse=self.args.loss.loss_grad1_mse,
            loss_grad1_mse_scale=self.args.loss.loss_grad1_mse_scale,
            f0_max=self.args.model.f0_max,
            f0_min=self.args.model.f0_min,
            confidence=self.args.model.confidence,
        )
        model.to(self.device).to(self.dtype)
        model.load_state_dict(ckpt['model'])
        model.eval()
        self.model = model
        self.wav2mel = Wav2Mel(self.args, dtype=self.dtype, device=self.device)

    @torch.no_grad()
    def __call__(self, audio, sr, threshold=0.05):
        self.model.threshold = threshold
        audio = audio[None,:]
        mel = self.wav2mel(audio=audio, sample_rate=sr).to(self.dtype)
        f0 = self.model(mel=mel, infer=True, return_hz_f0=True)
        return f0


class Wav2Mel:

    def __init__(self, args, device=None, dtype=torch.float32):
        # self.args = args
        self.sampling_rate = args.mel.sampling_rate
        self.hop_size = args.mel.hop_size
        if device is None:
            device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.device = device
        self.dtype = dtype
        self.stft = STFT(
            args.mel.sampling_rate,
            args.mel.num_mels,
            args.mel.n_fft,
            args.mel.win_size,
            args.mel.hop_size,
            args.mel.fmin,
            args.mel.fmax
        )
        self.resample_kernel = {}

    def extract_nvstft(self, audio, keyshift=0, train=False):
        mel = self.stft.get_mel(audio, keyshift=keyshift, train=train).transpose(1, 2)  # B, n_frames, bins
        return mel

    def extract_mel(self, audio, sample_rate, keyshift=0, train=False):
        audio = audio.to(self.dtype).to(self.device)
        # resample
        if sample_rate == self.sampling_rate:
            audio_res = audio
        else:
            key_str = str(sample_rate)
            if key_str not in self.resample_kernel:
                self.resample_kernel[key_str] = Resample(sample_rate, self.sampling_rate, lowpass_filter_width=128)
            self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.dtype).to(self.device)
            audio_res = self.resample_kernel[key_str](audio)

        # extract
        mel = self.extract_nvstft(audio_res, keyshift=keyshift, train=train)  # B, n_frames, bins
        n_frames = int(audio.shape[1] // self.hop_size) + 1
        if n_frames > int(mel.shape[1]):
            mel = torch.cat((mel, mel[:, -1:, :]), 1)
        if n_frames < int(mel.shape[1]):
            mel = mel[:, :n_frames, :]
        return mel

    def __call__(self, audio, sample_rate, keyshift=0, train=False):
        return self.extract_mel(audio, sample_rate, keyshift=keyshift, train=train)


class DotDict(dict):
    def __getattr__(*args):
        val = dict.get(*args)
        return DotDict(val) if type(val) is dict else val

    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

class F0Predictor(object):
    def compute_f0(self,wav,p_len):
        '''
        input: wav:[signal_length]
               p_len:int
        output: f0:[signal_length//hop_length]
        '''
        pass

    def compute_f0_uv(self,wav,p_len):
        '''
        input: wav:[signal_length]
               p_len:int
        output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
        '''
        pass

class FCPE(F0Predictor):
    def __init__(self, model_path, hop_length=512, f0_min=50, f0_max=1100, dtype=torch.float32, device=None, sampling_rate=44100,
                 threshold=0.05):
        self.fcpe = FCPEInfer(model_path, device=device, dtype=dtype)
        self.hop_length = hop_length
        self.f0_min = f0_min
        self.f0_max = f0_max
        if device is None:
            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        else:
            self.device = device
        self.threshold = threshold
        self.sampling_rate = sampling_rate
        self.dtype = dtype
        self.name = "fcpe"

    def repeat_expand(
            self, content: Union[torch.Tensor, np.ndarray], target_len: int, mode: str = "nearest"
    ):
        ndim = content.ndim

        if content.ndim == 1:
            content = content[None, None]
        elif content.ndim == 2:
            content = content[None]

        assert content.ndim == 3

        is_np = isinstance(content, np.ndarray)
        if is_np:
            content = torch.from_numpy(content)

        results = torch.nn.functional.interpolate(content, size=target_len, mode=mode)

        if is_np:
            results = results.numpy()

        if ndim == 1:
            return results[0, 0]
        elif ndim == 2:
            return results[0]

    def post_process(self, x, sampling_rate, f0, pad_to):
        if isinstance(f0, np.ndarray):
            f0 = torch.from_numpy(f0).float().to(x.device)

        if pad_to is None:
            return f0

        f0 = self.repeat_expand(f0, pad_to)

        vuv_vector = torch.zeros_like(f0)
        vuv_vector[f0 > 0.0] = 1.0
        vuv_vector[f0 <= 0.0] = 0.0

        # 去掉0频率, 并线性插值
        nzindex = torch.nonzero(f0).squeeze()
        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
        time_org = self.hop_length / sampling_rate * nzindex.cpu().numpy()
        time_frame = np.arange(pad_to) * self.hop_length / sampling_rate

        vuv_vector = F.interpolate(vuv_vector[None, None, :], size=pad_to)[0][0]

        if f0.shape[0] <= 0:
            return torch.zeros(pad_to, dtype=torch.float, device=x.device).cpu().numpy(), vuv_vector.cpu().numpy()
        if f0.shape[0] == 1:
            return (torch.ones(pad_to, dtype=torch.float, device=x.device) * f0[
                0]).cpu().numpy(), vuv_vector.cpu().numpy()

        # 大概可以用 torch 重写?
        f0 = np.interp(time_frame, time_org, f0, left=f0[0], right=f0[-1])
        # vuv_vector = np.ceil(scipy.ndimage.zoom(vuv_vector,pad_to/len(vuv_vector),order = 0))

        return f0, vuv_vector.cpu().numpy()

    def compute_f0(self, wav, p_len=None):
        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
        if p_len is None:
            print("fcpe p_len is None")
            p_len = x.shape[0] // self.hop_length
        #else:
#            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
        f0 = self.fcpe(x, sr=self.sampling_rate, threshold=self.threshold)[0,:,0]
        if torch.all(f0 == 0):
            rtn = f0.cpu().numpy() if p_len is None else np.zeros(p_len)
            return rtn, rtn
        return self.post_process(x, self.sampling_rate, f0, p_len)[0]

    def compute_f0_uv(self, wav, p_len=None):
        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
        if p_len is None:
            p_len = x.shape[0] // self.hop_length
        #else:
#            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
        f0 = self.fcpe(x, sr=self.sampling_rate, threshold=self.threshold)[0,:,0]
        if torch.all(f0 == 0):
            rtn = f0.cpu().numpy() if p_len is None else np.zeros(p_len)
            return rtn, rtn
        return self.post_process(x, self.sampling_rate, f0, p_len)