import os
import math
import torch

import numpy as np
import onnxruntime as ort
import torch.nn.functional as F

from torch import nn, einsum
from functools import partial
from torchaudio.transforms import Resample
from einops import rearrange, repeat, pack, unpack
from torch.nn.utils.parametrizations import weight_norm

from librosa.filters import mel as librosa_mel_fn

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

def exists(val):
    return val is not None

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

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

def decrypt_model(input_path):
    from io import BytesIO
    from Crypto.Cipher import AES
    from Crypto.Util.Padding import unpad

    with open(input_path, "rb") as f:
        data = f.read()

    with open(os.path.join("main", "configs", "decrypt.bin"), "rb") as f:
        key = f.read()

    return BytesIO(unpad(AES.new(key, AES.MODE_CBC, data[:16]).decrypt(data[16:]), AES.block_size)).read()

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)

def pad_to_multiple(tensor, multiple, dim=-1, value=0):
    seqlen = tensor.shape[dim]
    m = seqlen / multiple
    if m.is_integer(): return False, tensor
    return True, F.pad(tensor, (*((0,) * (-1 - dim) * 2), 0, (math.ceil(m) * multiple - seqlen)), value = value)

def look_around(x, backward = 1, forward = 0, pad_value = -1, dim = 2):
    t = x.shape[1]
    dims = (len(x.shape) - dim) * (0, 0)
    padded_x = F.pad(x, (*dims, backward, forward), value = pad_value)
    return torch.cat([padded_x[:, ind:(ind + t), ...] for ind in range(forward + backward + 1)], dim = dim)

def rotate_half(x):
    x1, x2 = rearrange(x, 'b ... (r d) -> b ... r d', r = 2).unbind(dim = -2)
    return torch.cat((-x2, x1), dim = -1)

def apply_rotary_pos_emb(q, k, freqs, scale = 1):
    q_len = q.shape[-2]
    q_freqs = freqs[..., -q_len:, :]
    inv_scale = scale ** -1
    if scale.ndim == 2: scale = scale[-q_len:, :]
    q = (q * q_freqs.cos() * scale) + (rotate_half(q) * q_freqs.sin() * scale)
    k = (k * freqs.cos() * inv_scale) + (rotate_half(k) * freqs.sin() * inv_scale)

    return q, k

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

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))
    if qr_uniform_q:
        d = torch.diag(r, 0)
        q *= d.sign()

    return q.t()

def linear_attention(q, k, v):
    return einsum("...ed,...nd->...ne", k, q) if v is None else einsum("...de,...nd,...n->...ne", einsum("...nd,...ne->...de", k, v), q, 1.0 / (einsum("...nd,...d->...n", q, k.sum(dim=-2).type_as(q)) + 1e-8))

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)
    block_list = []
    for _ in range(nb_full_blocks):
        block_list.append(orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device))

    remaining_rows = nb_rows - nb_full_blocks * nb_columns
    if remaining_rows > 0: block_list.append(orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device)[:remaining_rows])
    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"{scaling} != 0, 1")

    return torch.diag(multiplier) @ torch.cat(block_list)

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

def softmax_kernel(data, *, projection_matrix, is_query, normalize_data=True, eps=1e-4, device=None):
    b, h, *_ = data.shape
    
    data_normalizer = (data.shape[-1] ** -0.25) if normalize_data else 1.0
    ratio = projection_matrix.shape[0] ** -0.5
    data_dash = torch.einsum("...id,...jd->...ij", (data_normalizer * data), repeat(projection_matrix, "j d -> b h j d", b=b, h=h).type_as(data))
    diag_data = ((torch.sum(data**2, dim=-1) / 2.0) * (data_normalizer**2)).unsqueeze(dim=-1)

    return (ratio * (torch.exp(data_dash - diag_data - torch.max(data_dash, dim=-1, keepdim=True).values) + eps) if is_query else ratio * (torch.exp(data_dash - diag_data + eps))).type_as(data)

def torch_interp(x, xp, fp):
    sort_idx = torch.argsort(xp)
    xp = xp[sort_idx]
    fp = fp[sort_idx]

    right_idxs = torch.searchsorted(xp, x).clamp(max=len(xp) - 1)
    left_idxs = (right_idxs - 1).clamp(min=0)
    x_left = xp[left_idxs]
    y_left = fp[left_idxs]

    interp_vals = y_left + ((x - x_left) * (fp[right_idxs] - y_left) / (xp[right_idxs] - x_left))
    interp_vals[x < xp[0]] = fp[0]
    interp_vals[x > xp[-1]] = fp[-1]

    return interp_vals

def batch_interp_with_replacement_detach(uv, f0):
    result = f0.clone()
    for i in range(uv.shape[0]):
        interp_vals = torch_interp(torch.where(uv[i])[-1], torch.where(~uv[i])[-1], f0[i][~uv[i]]).detach()
        result[i][uv[i]] = interp_vals
        
    return result

def catch_none_args_must(x, func_name, warning_str):
    if x is None: raise ValueError(f'[Error] {warning_str}\n[Error] > {func_name}')
    else: return x

def catch_none_args_opti(x, default, func_name, warning_str=None, level='WARN'):
    return default if x is None else x

def spawn_wav2mel(args, device = None):
    _type = args.mel.type
    if (str(_type).lower() == 'none') or (str(_type).lower() == 'default'): _type = 'default'
    elif str(_type).lower() == 'stft': _type = 'stft'
    wav2mel = Wav2MelModule(sr=catch_none_args_opti(args.mel.sr, default=16000, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.sr is None'), n_mels=catch_none_args_opti(args.mel.num_mels, default=128, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.num_mels is None'), n_fft=catch_none_args_opti(args.mel.n_fft, default=1024, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.n_fft is None'), win_size=catch_none_args_opti(args.mel.win_size, default=1024, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.win_size is None'), hop_length=catch_none_args_opti(args.mel.hop_size, default=160, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.hop_size is None'), fmin=catch_none_args_opti(args.mel.fmin, default=0, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.fmin is None'), fmax=catch_none_args_opti(args.mel.fmax, default=8000, func_name='torchfcpe.tools.spawn_wav2mel', warning_str='args.mel.fmax is None'), clip_val=1e-05, mel_type=_type)
    device = catch_none_args_opti(device, default='cpu', func_name='torchfcpe.tools.spawn_wav2mel', warning_str='.device is None')
    
    return wav2mel.to(torch.device(device))

def ensemble_f0(f0s, key_shift_list, tta_uv_penalty):
    device = f0s.device
    f0s = f0s / (torch.pow(2, torch.tensor(key_shift_list, device=device).to(device).unsqueeze(0).unsqueeze(0) / 12))
    notes = torch.log2(f0s / 440) * 12 + 69
    notes[notes < 0] = 0

    uv_penalty = tta_uv_penalty**2
    dp = torch.zeros_like(notes, device=device)
    backtrack = torch.zeros_like(notes, device=device).long()
    dp[:, 0, :] = (notes[:, 0, :] <= 0) * uv_penalty

    for t in range(1, notes.size(1)):
        penalty = torch.zeros([notes.size(0), notes.size(2), notes.size(2)], device=device)
        t_uv = notes[:, t, :] <= 0
        penalty += uv_penalty * t_uv.unsqueeze(1)

        t1_uv = notes[:, t - 1, :] <= 0
        l2 = torch.pow((notes[:, t - 1, :].unsqueeze(-1) - notes[:, t, :].unsqueeze(1)) * (~t1_uv).unsqueeze(-1) * (~t_uv).unsqueeze(1), 2) - 0.5
        l2 = l2 * (l2 > 0)

        penalty += l2
        penalty += t1_uv.unsqueeze(-1) * (~t_uv).unsqueeze(1) * uv_penalty * 2

        min_value, min_indices = torch.min(dp[:, t - 1, :].unsqueeze(-1) + penalty, dim=1)
        dp[:, t, :] = min_value
        backtrack[:, t, :] = min_indices

    t = f0s.size(1) - 1
    f0_result = torch.zeros_like(f0s[:, :, 0], device=device)
    min_indices = torch.argmin(dp[:, t, :], dim=-1)

    for i in range(0, t + 1):
        f0_result[:, t - i] = f0s[:, t - i, min_indices]
        min_indices = backtrack[:, t - i, min_indices]

    return f0_result.unsqueeze(-1)

class LocalAttention(nn.Module):
    def __init__(self, window_size, causal = False, look_backward = 1, look_forward = None, dropout = 0., shared_qk = False, rel_pos_emb_config = None, dim = None, autopad = False, exact_windowsize = False, scale = None, use_rotary_pos_emb = True, use_xpos = False, xpos_scale_base = None):
        super().__init__()
        look_forward = default(look_forward, 0 if causal else 1)
        assert not (causal and look_forward > 0)
        self.scale = scale
        self.window_size = window_size
        self.autopad = autopad
        self.exact_windowsize = exact_windowsize
        self.causal = causal
        self.look_backward = look_backward
        self.look_forward = look_forward
        self.dropout = nn.Dropout(dropout)
        self.shared_qk = shared_qk
        self.rel_pos = None
        self.use_xpos = use_xpos
        if use_rotary_pos_emb and (exists(rel_pos_emb_config) or exists(dim)): 
            if exists(rel_pos_emb_config): dim = rel_pos_emb_config[0]
            self.rel_pos = SinusoidalEmbeddings(dim, use_xpos = use_xpos, scale_base = default(xpos_scale_base, window_size // 2))

    def forward(self, q, k, v, mask = None, input_mask = None, attn_bias = None, window_size = None):
        mask = default(mask, input_mask)
        assert not (exists(window_size) and not self.use_xpos)

        _, autopad, pad_value, window_size, causal, look_backward, look_forward, shared_qk = q.shape, self.autopad, -1, default(window_size, self.window_size), self.causal, self.look_backward, self.look_forward, self.shared_qk
        (q, packed_shape), (k, _), (v, _) = map(lambda t: pack([t], '* n d'), (q, k, v))

        if autopad:
            orig_seq_len = q.shape[1]
            (_, q), (_, k), (_, v) = map(lambda t: pad_to_multiple(t, self.window_size, dim = -2), (q, k, v))

        b, n, dim_head, device, dtype = *q.shape, q.device, q.dtype
        scale = default(self.scale, dim_head ** -0.5)

        assert (n % window_size) == 0
        windows = n // window_size

        if shared_qk: k = F.normalize(k, dim = -1).type(k.dtype)

        seq = torch.arange(n, device = device)
        b_t = rearrange(seq, '(w n) -> 1 w n', w = windows, n = window_size)
        bq, bk, bv = map(lambda t: rearrange(t, 'b (w n) d -> b w n d', w = windows), (q, k, v))

        bq = bq * scale
        look_around_kwargs = dict(backward =  look_backward, forward =  look_forward, pad_value = pad_value)

        bk = look_around(bk, **look_around_kwargs)
        bv = look_around(bv, **look_around_kwargs)

        if exists(self.rel_pos):
            pos_emb, xpos_scale = self.rel_pos(bk)
            bq, bk = apply_rotary_pos_emb(bq, bk, pos_emb, scale = xpos_scale)

        bq_t = b_t
        bq_k = look_around(b_t, **look_around_kwargs)
        bq_t = rearrange(bq_t, '... i -> ... i 1')
        bq_k = rearrange(bq_k, '... j -> ... 1 j')

        pad_mask = bq_k == pad_value
        sim = einsum('b h i e, b h j e -> b h i j', bq, bk)

        if exists(attn_bias):
            heads = attn_bias.shape[0]
            assert (b % heads) == 0

            attn_bias = repeat(attn_bias, 'h i j -> (b h) 1 i j', b = b // heads)
            sim = sim + attn_bias

        mask_value = -torch.finfo(sim.dtype).max
        if shared_qk:
            self_mask = bq_t == bq_k
            sim = sim.masked_fill(self_mask, -5e4)
            del self_mask

        if causal:
            causal_mask = bq_t < bq_k
            if self.exact_windowsize: causal_mask = causal_mask | (bq_t > (bq_k + (self.window_size * self.look_backward)))
            sim = sim.masked_fill(causal_mask, mask_value)
            del causal_mask

        sim = sim.masked_fill(((bq_k - (self.window_size * self.look_forward)) > bq_t) | (bq_t > (bq_k + (self.window_size * self.look_backward))) | pad_mask, mask_value) if not causal and self.exact_windowsize else sim.masked_fill(pad_mask, mask_value)

        if exists(mask):
            batch = mask.shape[0]
            assert (b % batch) == 0

            h = b // mask.shape[0]
            if autopad: _, mask = pad_to_multiple(mask, window_size, dim = -1, value = False)

            mask = repeat(rearrange(look_around(rearrange(mask, '... (w n) -> (...) w n', w = windows, n = window_size), **{**look_around_kwargs, 'pad_value': False}), '... j -> ... 1 j'), 'b ... -> (b h) ...', h = h)
            sim = sim.masked_fill(~mask, mask_value)

            del mask

        out = rearrange(einsum('b h i j, b h j e -> b h i e', self.dropout(sim.softmax(dim = -1)), bv), 'b w n d -> b (w n) d')
        if autopad: out = out[:, :orig_seq_len, :]

        out, *_ = unpack(out, packed_shape, '* n d')
        return out
    
class SinusoidalEmbeddings(nn.Module):
    def __init__(self, dim, scale_base = None, use_xpos = False, theta = 10000):
        super().__init__()
        inv_freq = 1. / (theta ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer('inv_freq', inv_freq)
        self.use_xpos = use_xpos
        self.scale_base = scale_base
        assert not (use_xpos and not exists(scale_base))
        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
        self.register_buffer('scale', scale, persistent = False)

    def forward(self, x):
        seq_len, device = x.shape[-2], x.device
        t = torch.arange(seq_len, device = x.device).type_as(self.inv_freq)

        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
        freqs =  torch.cat((freqs, freqs), dim = -1)

        if not self.use_xpos: return freqs, torch.ones(1, device = device)

        power = (t - (seq_len // 2)) / self.scale_base
        scale = self.scale ** rearrange(power, 'n -> n 1')

        return freqs, torch.cat((scale, scale), dim = -1)

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):
        n_fft = self.n_fft
        win_size = self.win_size
        hop_length = self.hop_length
        fmax = self.fmax
        factor = 2 ** (keyshift / 12)
        win_size_new = int(np.round(win_size * factor))
        hop_length_new = int(np.round(hop_length * speed))
        mel_basis = self.mel_basis if not train else {}
        hann_window = self.hann_window if not train else {}
        mel_basis_key = str(fmax) + "_" + str(y.device)

        if mel_basis_key not in mel_basis: mel_basis[mel_basis_key] = torch.from_numpy(librosa_mel_fn(sr=self.target_sr, n_fft=n_fft, n_mels=self.n_mels, fmin=self.fmin, fmax=fmax)).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)
        spec = torch.stft(F.pad(y.unsqueeze(1), (pad_left, pad_right), mode="reflect" if pad_right < y.size(-1) else "constant").squeeze(1), int(np.round(n_fft * factor)), 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)
            spec = (F.pad(spec, (0, 0, 0, size - resize)) if resize < size else spec[:, :size, :]) * win_size / win_size_new

        return dynamic_range_compression_torch(torch.matmul(mel_basis[mel_basis_key], spec), clip_val=self.clip_val)

class PCmer(nn.Module):
    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)])

    def forward(self, phone, mask=None):
        for layer in self._layers:
            phone = layer(phone, mask)

        return phone

class _EncoderLayer(nn.Module):
    def __init__(self, parent):
        super().__init__()
        self.conformer = ConformerConvModule_LEGACY(parent.dim_model)
        self.norm = nn.LayerNorm(parent.dim_model)
        self.dropout = nn.Dropout(parent.residual_dropout)
        self.attn = SelfAttention(dim=parent.dim_model, heads=parent.num_heads, causal=False)

    def forward(self, phone, mask=None):
        phone = phone + (self.attn(self.norm(phone), mask=mask))
        return phone + (self.conformer(phone))

class ConformerNaiveEncoder(nn.Module):
    def __init__(self, num_layers, num_heads, dim_model, use_norm = False, conv_only = False, conv_dropout = 0, atten_dropout = 0):
        super().__init__()
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.dim_model = dim_model
        self.use_norm = use_norm
        self.residual_dropout = 0.1  
        self.attention_dropout = 0.1  
        self.encoder_layers = nn.ModuleList([CFNEncoderLayer(dim_model, num_heads, use_norm, conv_only, conv_dropout, atten_dropout) for _ in range(num_layers)])

    def forward(self, x, mask=None):
        for (_, layer) in enumerate(self.encoder_layers):
            x = layer(x, mask)

        return x 

class CFNaiveMelPE(nn.Module):
    def __init__(self, input_channels, out_dims, hidden_dims = 512, n_layers = 6, n_heads = 8, f0_max = 1975.5, f0_min = 32.70, use_fa_norm = False, conv_only = False, conv_dropout = 0, atten_dropout = 0, use_harmonic_emb = False):
        super().__init__()
        self.input_channels = input_channels
        self.out_dims = out_dims
        self.hidden_dims = hidden_dims
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.f0_max = f0_max
        self.f0_min = f0_min
        self.use_fa_norm = use_fa_norm
        self.residual_dropout = 0.1  
        self.attention_dropout = 0.1  
        self.harmonic_emb = nn.Embedding(9, hidden_dims) if use_harmonic_emb else None
        self.input_stack = nn.Sequential(nn.Conv1d(input_channels, hidden_dims, 3, 1, 1), nn.GroupNorm(4, hidden_dims), nn.LeakyReLU(), nn.Conv1d(hidden_dims, hidden_dims, 3, 1, 1))
        self.net = ConformerNaiveEncoder(num_layers=n_layers, num_heads=n_heads, dim_model=hidden_dims, use_norm=use_fa_norm, conv_only=conv_only, conv_dropout=conv_dropout, atten_dropout=atten_dropout)
        self.norm = nn.LayerNorm(hidden_dims)
        self.output_proj = weight_norm(nn.Linear(hidden_dims, out_dims))
        self.cent_table_b = torch.linspace(self.f0_to_cent(torch.Tensor([f0_min]))[0], self.f0_to_cent(torch.Tensor([f0_max]))[0], out_dims).detach()
        self.register_buffer("cent_table", self.cent_table_b)
        self.gaussian_blurred_cent_mask_b = (1200 * torch.log2(torch.Tensor([self.f0_max / 10.])))[0].detach()
        self.register_buffer("gaussian_blurred_cent_mask", self.gaussian_blurred_cent_mask_b)

    def forward(self, x, _h_emb=None):
        x = self.input_stack(x.transpose(-1, -2)).transpose(-1, -2)
        if self.harmonic_emb is not None: x = x + self.harmonic_emb(torch.LongTensor([0]).to(x.device)) if _h_emb is None else x + self.harmonic_emb(torch.LongTensor([int(_h_emb)]).to(x.device))
        return torch.sigmoid(self.output_proj(self.norm(self.net(x))))

    @torch.no_grad()
    def latent2cents_decoder(self, y, threshold = 0.05, 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)  

        if mask:
            confident = torch.max(y, dim=-1, keepdim=True)[0]
            confident_mask = torch.ones_like(confident)
            confident_mask[confident <= threshold] = float("-INF")
            rtn = rtn * confident_mask

        return rtn  

    @torch.no_grad()
    def latent2cents_local_decoder(self, y, threshold = 0.05, 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.out_dims] = self.out_dims - 1

        y_l = torch.gather(y, -1, local_argmax_index)
        rtn = torch.sum(torch.gather(ci, -1, local_argmax_index) * y_l, dim=-1, keepdim=True) / torch.sum(y_l, dim=-1, keepdim=True) 

        if mask:
            confident_mask = torch.ones_like(confident)
            confident_mask[confident <= threshold] = float("-INF")
            rtn = rtn * confident_mask

        return rtn  

    @torch.no_grad()
    def infer(self, mel, decoder = "local_argmax", threshold = 0.05):
        latent = self.forward(mel)
        if decoder == "argmax": cents = self.latent2cents_local_decoder
        elif decoder == "local_argmax": cents = self.latent2cents_local_decoder

        return self.cent_to_f0(cents(latent, threshold=threshold))  

    @torch.no_grad()
    def cent_to_f0(self, cent: torch.Tensor) -> torch.Tensor:
        return 10 * 2 ** (cent / 1200)

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

class CFNEncoderLayer(nn.Module):
    def __init__(self, dim_model, num_heads = 8, use_norm = False, conv_only = False, conv_dropout = 0, atten_dropout = 0):
        super().__init__()
        self.conformer = nn.Sequential(ConformerConvModule(dim_model), nn.Dropout(conv_dropout)) if conv_dropout > 0 else ConformerConvModule(dim_model)
        self.norm = nn.LayerNorm(dim_model)
        self.dropout = nn.Dropout(0.1)  
        self.attn = SelfAttention(dim=dim_model, heads=num_heads, causal=False, use_norm=use_norm, dropout=atten_dropout) if not conv_only else None

    def forward(self, x, mask=None):
        if self.attn is not None: x = x + (self.attn(self.norm(x), mask=mask))
        return x + (self.conformer(x)) 

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 == 2"
        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_LEGACY(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):
        return self.conv(F.pad(x, self.padding))

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

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

class ConformerConvModule_LEGACY(nn.Module):
    def __init__(self, dim, causal=False, expansion_factor=2, kernel_size=31, dropout=0.0):
        super().__init__()
        inner_dim = dim * expansion_factor
        self.net = nn.Sequential(nn.LayerNorm(dim), Transpose((1, 2)), nn.Conv1d(dim, inner_dim * 2, 1), GLU(dim=1), DepthWiseConv1d_LEGACY(inner_dim, inner_dim, kernel_size=kernel_size, padding=(calc_same_padding(kernel_size) if not causal else (kernel_size - 1, 0))), Swish(), nn.Conv1d(inner_dim, dim, 1), Transpose((1, 2)), nn.Dropout(dropout))

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

class ConformerConvModule(nn.Module):
    def __init__(self, dim, expansion_factor=2, kernel_size=31, dropout=0):
        super().__init__()
        inner_dim = dim * expansion_factor
        self.net = nn.Sequential(nn.LayerNorm(dim), Transpose((1, 2)), nn.Conv1d(dim, inner_dim * 2, 1), nn.GLU(dim=1), DepthWiseConv1d(inner_dim, inner_dim, kernel_size=kernel_size, padding=calc_same_padding(kernel_size)[0], groups=inner_dim), nn.SiLU(), nn.Conv1d(inner_dim, dim, 1), Transpose((1, 2)), nn.Dropout(dropout))

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

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

        attn_fn = linear_attention if not self.causal else self.causal_linear_fn
        return attn_fn(q, k, None) if v is None else attn_fn(q, k, v)

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=0.0, no_projection=False):
        super().__init__()
        assert dim % heads == 0
        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)
        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()

    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

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (self.to_q(x), self.to_k(context), self.to_v(context)))
        (q, lq), (k, lk), (v, lv) = map(lambda t: (t[:, :gh], t[:, gh:]), (q, k, v))

        attn_outs = []

        if not empty(q):
            if exists(context_mask): v.masked_fill_(~context_mask[:, None, :, None], 0.0)
            if cross_attend: pass  
            else: out = self.fast_attention(q, k, v)

            attn_outs.append(out)

        if not empty(lq):
            assert (not cross_attend), "not cross_attend"

            out = self.local_attn(lq, lk, lv, input_mask=mask)
            attn_outs.append(out)

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

class HannWindow(torch.nn.Module):
    def __init__(self, win_size):
        super().__init__()
        self.register_buffer('window', torch.hann_window(win_size), persistent=False)

    def forward(self):
        return self.window

class FCPE_LEGACY(nn.Module):
    def __init__(self, input_channel=128, out_dims=360, n_layers=12, n_chans=512, 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__()
        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)
        self.stack = nn.Sequential(nn.Conv1d(input_channel, n_chans, 3, 1, 1), nn.GroupNorm(4, n_chans), nn.LeakyReLU(), nn.Conv1d(n_chans, n_chans, 3, 1, 1))
        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)
        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", output_interp_target_length=None):
        if cdecoder == "argmax": self.cdecoder = self.cents_decoder
        elif cdecoder == "local_argmax": self.cdecoder = self.cents_local_decoder

        x = torch.sigmoid(self.dense_out(self.norm(self.decoder((self.stack(mel.transpose(1, 2)).transpose(1, 2) if self.use_input_conv else mel)))))

        if not infer:
            loss_all = self.loss_mse_scale * F.binary_cross_entropy(x, self.gaussian_blurred_cent(self.f0_to_cent(gt_f0)))
            if self.loss_l2_regularization: loss_all = loss_all + l2_regularization(model=self, l2_alpha=self.loss_l2_regularization_scale)
            x = loss_all
        else:
            x = self.cent_to_f0(self.cdecoder(x))
            x = (1 + x / 700).log() if not return_hz_f0 else x

        if output_interp_target_length is not None: 
            x = F.interpolate(torch.where(x == 0, float("nan"), x).transpose(1, 2), size=int(output_interp_target_length), mode="linear").transpose(1, 2)
            x = torch.where(x.isnan(), float(0.0), x)

        return x

    def cents_decoder(self, y, mask=True):
        B, N, _ = y.size()
        rtn = torch.sum(self.cent_table[None, None, :].expand(B, N, -1) * y, dim=-1, keepdim=True) / torch.sum(y, dim=-1, keepdim=True)

        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

        return (rtn, confident) if self.confidence else rtn

    def cents_local_decoder(self, y, mask=True):
        B, N, _ = y.size()

        confident, max_index = torch.max(y, dim=-1, keepdim=True)
        local_argmax_index = torch.clamp(torch.arange(0, 9).to(max_index.device) + (max_index - 4), 0, self.n_out - 1)
        y_l = torch.gather(y, -1, local_argmax_index)
        rtn = torch.sum(torch.gather(self.cent_table[None, None, :].expand(B, N, -1), -1, local_argmax_index) * y_l, dim=-1, keepdim=True) / torch.sum(y_l, dim=-1, keepdim=True)

        if mask:
            confident_mask = torch.ones_like(confident)
            confident_mask[confident <= self.threshold] = float("-INF")
            rtn = rtn * confident_mask

        return (rtn, confident) if self.confidence else rtn

    def cent_to_f0(self, cent):
        return 10.0 * 2 ** (cent / 1200.0)

    def f0_to_cent(self, f0):
        return 1200.0 * torch.log2(f0 / 10.0)

    def gaussian_blurred_cent(self, cents):
        B, N, _ = cents.size()
        return torch.exp(-torch.square(self.cent_table[None, None, :].expand(B, N, -1) - cents) / 1250) * (cents > 0.1) & (cents < (1200.0 * np.log2(self.f0_max / 10.0))).float()

class InferCFNaiveMelPE(torch.nn.Module):
    def __init__(self, args, state_dict):
        super().__init__()
        self.wav2mel = spawn_wav2mel(args, device="cpu")
        self.model = CFNaiveMelPE(input_channels=catch_none_args_must(args.mel.num_mels, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.mel.num_mels is None"), out_dims=catch_none_args_must(args.model.out_dims, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.out_dims is None"), hidden_dims=catch_none_args_must(args.model.hidden_dims, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.hidden_dims is None"), n_layers=catch_none_args_must(args.model.n_layers, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.n_layers is None"), n_heads=catch_none_args_must(args.model.n_heads, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.n_heads is None"), f0_max=catch_none_args_must(args.model.f0_max, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.f0_max is None"), f0_min=catch_none_args_must(args.model.f0_min, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.f0_min is None"), use_fa_norm=catch_none_args_must(args.model.use_fa_norm, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.use_fa_norm is None"), conv_only=catch_none_args_opti(args.model.conv_only, default=False, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.conv_only is None"), conv_dropout=catch_none_args_opti(args.model.conv_dropout, default=0.0, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.conv_dropout is None"), atten_dropout=catch_none_args_opti(args.model.atten_dropout, default=0.0, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.atten_dropout is None"), use_harmonic_emb=catch_none_args_opti(args.model.use_harmonic_emb, default=False, func_name="torchfcpe.tools.spawn_cf_naive_mel_pe", warning_str="args.model.use_harmonic_emb is None"))
        self.model.load_state_dict(state_dict)
        self.model.eval()
        self.args_dict = dict(args)
        self.register_buffer("tensor_device_marker", torch.tensor(1.0).float(), persistent=False)

    def forward(self, wav, sr, decoder_mode = "local_argmax", threshold = 0.006, key_shifts = [0]):
        with torch.no_grad():
            mels = rearrange(torch.stack([self.wav2mel(wav.to(self.tensor_device_marker.device), sr, keyshift=keyshift) for keyshift in key_shifts], -1), "B T C K -> (B K) T C")
            f0s = rearrange(self.model.infer(mels, decoder=decoder_mode, threshold=threshold), "(B K) T 1 -> B T (K 1)", K=len(key_shifts))

        return f0s 

    def infer(self, wav, sr, decoder_mode = "local_argmax", threshold = 0.006, f0_min = None, f0_max = None, interp_uv = False, output_interp_target_length = None, return_uv = False, test_time_augmentation = False, tta_uv_penalty = 12.0, tta_key_shifts = [0, -12, 12], tta_use_origin_uv=False):
        if test_time_augmentation:
            assert len(tta_key_shifts) > 0
            flag = 0
            if tta_use_origin_uv:
                if 0 not in tta_key_shifts:
                    flag = 1
                    tta_key_shifts.append(0)

            tta_key_shifts.sort(key=lambda x: (x if x >= 0 else -x / 2))
            f0s = self.__call__(wav, sr, decoder_mode, threshold, tta_key_shifts)
            f0 = ensemble_f0(f0s[:, :, flag:], tta_key_shifts[flag:], tta_uv_penalty)
            f0_for_uv = f0s[:, :, [0]] if tta_use_origin_uv else f0
        else:
            f0 = self.__call__(wav, sr, decoder_mode, threshold)
            f0_for_uv = f0

        if f0_min is None: f0_min = self.args_dict["model"]["f0_min"]
        uv = (f0_for_uv < f0_min).type(f0_for_uv.dtype)
        f0 = f0 * (1 - uv)

        if interp_uv: f0 = batch_interp_with_replacement_detach(uv.squeeze(-1).bool(), f0.squeeze(-1)).unsqueeze(-1)
        if f0_max is not None: f0[f0 > f0_max] = f0_max
        if output_interp_target_length is not None: 
            f0 = F.interpolate(torch.where(f0 == 0, float("nan"), f0).transpose(1, 2), size=int(output_interp_target_length), mode="linear").transpose(1, 2)
            f0 = torch.where(f0.isnan(), float(0.0), f0)

        if return_uv: return f0, F.interpolate(uv.transpose(1, 2), size=int(output_interp_target_length), mode="nearest").transpose(1, 2)
        else: return f0

class FCPEInfer_LEGACY:
    def __init__(self, model_path, device=None, dtype=torch.float32, providers=None, onnx=False, f0_min=50, f0_max=1100):
        if device is None: device = "cuda" if torch.cuda.is_available() else "cpu"
        self.device = device
        self.dtype = dtype
        self.onnx = onnx
        self.f0_min = f0_min
        self.f0_max = f0_max

        if self.onnx:
            sess_options = ort.SessionOptions()
            sess_options.log_severity_level = 3
            self.model = ort.InferenceSession(decrypt_model(model_path), sess_options=sess_options, providers=providers)
        else:
            ckpt = torch.load(model_path, map_location=torch.device(self.device))
            self.args = DotDict(ckpt["config"])
            model = FCPE_LEGACY(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, 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.f0_max, f0_min=self.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

    @torch.no_grad()
    def __call__(self, audio, sr, threshold=0.05, p_len=None):
        if not self.onnx: self.model.threshold = threshold
        self.wav2mel = Wav2Mel(device=self.device, dtype=self.dtype)

        return (torch.as_tensor(self.model.run([self.model.get_outputs()[0].name], {self.model.get_inputs()[0].name: self.wav2mel(audio=audio[None, :], sample_rate=sr).to(self.dtype).detach().cpu().numpy(), self.model.get_inputs()[1].name: np.array(threshold, dtype=np.float32)})[0], dtype=self.dtype, device=self.device) if self.onnx else self.model(mel=self.wav2mel(audio=audio[None, :], sample_rate=sr).to(self.dtype), infer=True, return_hz_f0=True, output_interp_target_length=p_len))

class FCPEInfer:
    def __init__(self, model_path, device=None, dtype=torch.float32, providers=None, onnx=False, f0_min=50, f0_max=1100):
        if device is None: device = "cuda" if torch.cuda.is_available() else "cpu"
        self.device = device
        self.dtype = dtype
        self.onnx = onnx
        self.f0_min = f0_min
        self.f0_max = f0_max

        if self.onnx:
            sess_options = ort.SessionOptions()
            sess_options.log_severity_level = 3
            self.model = ort.InferenceSession(decrypt_model(model_path), sess_options=sess_options, providers=providers)
        else:
            ckpt = torch.load(model_path, map_location=torch.device(device))
            ckpt["config_dict"]["model"]["conv_dropout"] = ckpt["config_dict"]["model"]["atten_dropout"] = 0.0
            self.args = DotDict(ckpt["config_dict"])
            model = InferCFNaiveMelPE(self.args, ckpt["model"])
            model = model.to(device).to(self.dtype)
            model.eval()
            self.model = model

    @torch.no_grad()
    def __call__(self, audio, sr, threshold=0.05, p_len=None):
        if self.onnx: self.wav2mel = Wav2Mel(device=self.device, dtype=self.dtype)
        return (torch.as_tensor(self.model.run([self.model.get_outputs()[0].name], {self.model.get_inputs()[0].name: self.wav2mel(audio=audio[None, :], sample_rate=sr).to(self.dtype).detach().cpu().numpy(), self.model.get_inputs()[1].name: np.array(threshold, dtype=np.float32)})[0], dtype=self.dtype, device=self.device) if self.onnx else self.model.infer(audio[None, :], sr, threshold=threshold, f0_min=self.f0_min, f0_max=self.f0_max, output_interp_target_length=p_len))

class MelModule(torch.nn.Module):
    def __init__(self, sr, n_mels, n_fft, win_size, hop_length, fmin = None, fmax = None, clip_val = 1e-5, out_stft = False):
        super().__init__()
        if fmin is None: fmin = 0
        if fmax is None: fmax = sr / 2
        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.register_buffer('mel_basis', torch.tensor(librosa_mel_fn(sr=sr, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)).float(), persistent=False)
        self.hann_window = torch.nn.ModuleDict()
        self.out_stft = out_stft

    @torch.no_grad()
    def __call__(self, y, key_shift = 0, speed = 1, center = False, no_cache_window = False):
        n_fft = self.n_fft
        win_size = self.win_size
        hop_length = self.hop_length
        clip_val = self.clip_val
        factor = 2 ** (key_shift / 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))

        y = y.squeeze(-1)
        if torch.min(y) < -1: print('[error with torchfcpe.mel_extractor.MelModule] min ', torch.min(y))
        if torch.max(y) > 1: print('[error with torchfcpe.mel_extractor.MelModule] max ', torch.max(y))

        key_shift_key = str(key_shift)
        if not no_cache_window:
            if key_shift_key in self.hann_window: hann_window = self.hann_window[key_shift_key]
            else:
                hann_window = HannWindow(win_size_new).to(self.mel_basis.device)
                self.hann_window[key_shift_key] = hann_window

            hann_window_tensor = hann_window()
        else: hann_window_tensor = torch.hann_window(win_size_new).to(self.mel_basis.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)
        mode = 'reflect' if pad_right < y.size(-1) else 'constant'
        spec = torch.stft(F.pad(y.unsqueeze(1), (pad_left, pad_right), mode=mode).squeeze(1), n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=hann_window_tensor, 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 key_shift != 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 = spec[:, :512, :] if self.out_stft else torch.matmul(self.mel_basis, spec)
        return dynamic_range_compression_torch(spec, clip_val=clip_val).transpose(-1, -2)

class Wav2MelModule(torch.nn.Module):
    def __init__(self, sr, n_mels, n_fft, win_size, hop_length, fmin = None, fmax = None, clip_val = 1e-5, mel_type="default"):
        super().__init__()
        if fmin is None: fmin = 0
        if fmax is None: fmax = sr / 2
        self.sampling_rate = sr
        self.n_mels = n_mels
        self.n_fft = n_fft
        self.win_size = win_size
        self.hop_size = hop_length
        self.fmin = fmin
        self.fmax = fmax
        self.clip_val = clip_val
        self.register_buffer('tensor_device_marker', torch.tensor(1.0).float(), persistent=False)
        self.resample_kernel = torch.nn.ModuleDict()
        if mel_type == "default": self.mel_extractor = MelModule(sr, n_mels, n_fft, win_size, hop_length, fmin, fmax, clip_val, out_stft=False)
        elif mel_type == "stft": self.mel_extractor = MelModule(sr, n_mels, n_fft, win_size, hop_length, fmin, fmax, clip_val, out_stft=True)
        self.mel_type = mel_type

    @torch.no_grad()
    def __call__(self, audio, sample_rate, keyshift = 0, no_cache_window = False):
        if sample_rate == self.sampling_rate: audio_res = audio
        else:
            key_str = str(sample_rate)
            if key_str not in self.resample_kernel:
                if len(self.resample_kernel) > 8: self.resample_kernel.clear()
                self.resample_kernel[key_str] = Resample(sample_rate, self.sampling_rate, lowpass_filter_width=128).to(self.tensor_device_marker.device)

            audio_res = self.resample_kernel[key_str](audio.squeeze(-1)).unsqueeze(-1)

        mel = self.mel_extractor(audio_res, keyshift, no_cache_window=no_cache_window)
        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 

class Wav2Mel:
    def __init__(self, device=None, dtype=torch.float32):
        self.sample_rate = 16000
        self.hop_size = 160
        if device is None: device = "cuda" if torch.cuda.is_available() else "cpu"
        self.device = device
        self.dtype = dtype
        self.stft = STFT(16000, 128, 1024, 1024, 160, 0, 8000)
        self.resample_kernel = {}

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

    def extract_mel(self, audio, sample_rate, keyshift=0, train=False):
        audio = audio.to(self.dtype).to(self.device)
        if sample_rate == self.sample_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.sample_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)

        mel = self.extract_nvstft(audio_res, keyshift=keyshift, train=train) 
        n_frames = int(audio.shape[1] // self.hop_size) + 1
        mel = (torch.cat((mel, mel[:, -1:, :]), 1) if n_frames > int(mel.shape[1]) else mel)
        return mel[:, :n_frames, :] if n_frames < int(mel.shape[1]) else 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 FCPE:
    def __init__(self, model_path, hop_length=512, f0_min=50, f0_max=1100, dtype=torch.float32, device=None, sample_rate=16000, threshold=0.05, providers=None, onnx=False, legacy=False):
        self.model = FCPEInfer_LEGACY if legacy else FCPEInfer
        self.fcpe = self.model(model_path, device=device, dtype=dtype, providers=providers, onnx=onnx, f0_min=f0_min, f0_max=f0_max)
        self.hop_length = hop_length
        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
        self.threshold = threshold
        self.sample_rate = sample_rate
        self.dtype = dtype
        self.legacy = legacy

    def repeat_expand(self, content, target_len, mode = "nearest"):
        ndim = content.ndim
        content = (content[None, None] if ndim == 1 else content[None] if ndim == 2 else content)

        assert content.ndim == 3
        is_np = isinstance(content, np.ndarray)

        results = F.interpolate(torch.from_numpy(content) if is_np else content, size=target_len, mode=mode)
        results = results.numpy() if is_np else results
        return results[0, 0] if ndim == 1 else results[0] if ndim == 2 else results

    def post_process(self, x, sample_rate, f0, pad_to):
        f0 = (torch.from_numpy(f0).float().to(x.device) if isinstance(f0, np.ndarray) else f0)
        f0 = self.repeat_expand(f0, pad_to) if pad_to is not None else f0

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

        nzindex = torch.nonzero(f0).squeeze()
        f0 = torch.index_select(f0, dim=0, index=nzindex).cpu().numpy()
        vuv_vector = F.interpolate(vuv_vector[None, None, :], size=pad_to)[0][0]

        if f0.shape[0] <= 0: return np.zeros(pad_to), vuv_vector.cpu().numpy()
        if f0.shape[0] == 1: return np.ones(pad_to) * f0[0], vuv_vector.cpu().numpy()
        
        return np.interp(np.arange(pad_to) * self.hop_length / sample_rate, self.hop_length / sample_rate * nzindex.cpu().numpy(), f0, left=f0[0], right=f0[-1]), vuv_vector.cpu().numpy()

    def compute_f0(self, wav, p_len=None):
        x = torch.FloatTensor(wav).to(self.dtype).to(self.device)
        p_len = x.shape[0] // self.hop_length if p_len is None else p_len

        f0 = self.fcpe(x, sr=self.sample_rate, threshold=self.threshold, p_len=p_len)
        f0 = f0[:] if f0.dim() == 1 else f0[0, :, 0]

        if torch.all(f0 == 0): return f0.cpu().numpy() if p_len is None else np.zeros(p_len), (f0.cpu().numpy() if p_len is None else np.zeros(p_len))
        return self.post_process(x, self.sample_rate, f0, p_len)[0]