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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from audiotools import AudioSignal
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from audiotools import ml
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from audiotools import STFTParams
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from einops import rearrange
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from torch.nn.utils import weight_norm
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def WNConv1d(*args, **kwargs):
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act = kwargs.pop("act", True)
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conv = weight_norm(nn.Conv1d(*args, **kwargs))
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if not act:
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return conv
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return nn.Sequential(conv, nn.LeakyReLU(0.1))
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def WNConv2d(*args, **kwargs):
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act = kwargs.pop("act", True)
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conv = weight_norm(nn.Conv2d(*args, **kwargs))
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if not act:
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return conv
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return nn.Sequential(conv, nn.LeakyReLU(0.1))
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class MPD(nn.Module):
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def __init__(self, period):
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super().__init__()
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self.period = period
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self.convs = nn.ModuleList(
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[
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WNConv2d(1, 32, (5, 1), (3, 1), padding=(2, 0)),
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WNConv2d(32, 128, (5, 1), (3, 1), padding=(2, 0)),
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WNConv2d(128, 512, (5, 1), (3, 1), padding=(2, 0)),
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WNConv2d(512, 1024, (5, 1), (3, 1), padding=(2, 0)),
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WNConv2d(1024, 1024, (5, 1), 1, padding=(2, 0)),
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]
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)
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self.conv_post = WNConv2d(
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1024, 1, kernel_size=(3, 1), padding=(1, 0), act=False
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)
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def pad_to_period(self, x):
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t = x.shape[-1]
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x = F.pad(x, (0, self.period - t % self.period), mode="reflect")
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return x
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def forward(self, x):
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fmap = []
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x = self.pad_to_period(x)
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x = rearrange(x, "b c (l p) -> b c l p", p=self.period)
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for layer in self.convs:
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x = layer(x)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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class MSD(nn.Module):
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def __init__(self, rate: int = 1, sample_rate: int = 44100):
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super().__init__()
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self.convs = nn.ModuleList(
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[
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WNConv1d(1, 16, 15, 1, padding=7),
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WNConv1d(16, 64, 41, 4, groups=4, padding=20),
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WNConv1d(64, 256, 41, 4, groups=16, padding=20),
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WNConv1d(256, 1024, 41, 4, groups=64, padding=20),
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WNConv1d(1024, 1024, 41, 4, groups=256, padding=20),
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WNConv1d(1024, 1024, 5, 1, padding=2),
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]
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)
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self.conv_post = WNConv1d(1024, 1, 3, 1, padding=1, act=False)
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self.sample_rate = sample_rate
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self.rate = rate
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def forward(self, x):
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x = AudioSignal(x, self.sample_rate)
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x.resample(self.sample_rate // self.rate)
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x = x.audio_data
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fmap = []
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for l in self.convs:
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x = l(x)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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BANDS = [(0.0, 0.1), (0.1, 0.25), (0.25, 0.5), (0.5, 0.75), (0.75, 1.0)]
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class MRD(nn.Module):
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def __init__(
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self,
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window_length: int,
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hop_factor: float = 0.25,
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sample_rate: int = 44100,
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bands: list = BANDS,
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):
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"""Complex multi-band spectrogram discriminator.
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Parameters
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----------
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window_length : int
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Window length of STFT.
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hop_factor : float, optional
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Hop factor of the STFT, defaults to ``0.25 * window_length``.
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sample_rate : int, optional
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Sampling rate of audio in Hz, by default 44100
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bands : list, optional
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Bands to run discriminator over.
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"""
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super().__init__()
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self.window_length = window_length
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self.hop_factor = hop_factor
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self.sample_rate = sample_rate
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self.stft_params = STFTParams(
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window_length=window_length,
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hop_length=int(window_length * hop_factor),
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match_stride=True,
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)
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n_fft = window_length // 2 + 1
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bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
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self.bands = bands
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ch = 32
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convs = lambda: nn.ModuleList(
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[
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WNConv2d(2, ch, (3, 9), (1, 1), padding=(1, 4)),
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WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
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WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
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WNConv2d(ch, ch, (3, 9), (1, 2), padding=(1, 4)),
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WNConv2d(ch, ch, (3, 3), (1, 1), padding=(1, 1)),
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]
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)
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self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
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self.conv_post = WNConv2d(ch, 1, (3, 3), (1, 1), padding=(1, 1), act=False)
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def spectrogram(self, x):
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x = AudioSignal(x, self.sample_rate, stft_params=self.stft_params)
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x = torch.view_as_real(x.stft())
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x = rearrange(x, "b 1 f t c -> (b 1) c t f")
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x_bands = [x[..., b[0] : b[1]] for b in self.bands]
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return x_bands
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def forward(self, x):
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x_bands = self.spectrogram(x)
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fmap = []
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x = []
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for band, stack in zip(x_bands, self.band_convs):
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for layer in stack:
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band = layer(band)
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fmap.append(band)
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x.append(band)
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x = torch.cat(x, dim=-1)
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x = self.conv_post(x)
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fmap.append(x)
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return fmap
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class Discriminator(nn.Module):
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def __init__(
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self,
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rates: list = [],
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periods: list = [2, 3, 5, 7, 11],
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fft_sizes: list = [2048, 1024, 512],
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sample_rate: int = 44100,
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bands: list = BANDS,
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):
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"""Discriminator that combines multiple discriminators.
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Parameters
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----------
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rates : list, optional
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sampling rates (in Hz) to run MSD at, by default []
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If empty, MSD is not used.
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periods : list, optional
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periods (of samples) to run MPD at, by default [2, 3, 5, 7, 11]
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fft_sizes : list, optional
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Window sizes of the FFT to run MRD at, by default [2048, 1024, 512]
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sample_rate : int, optional
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Sampling rate of audio in Hz, by default 44100
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bands : list, optional
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Bands to run MRD at, by default `BANDS`
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"""
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super().__init__()
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discs = []
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discs += [MPD(p) for p in periods]
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discs += [MSD(r, sample_rate=sample_rate) for r in rates]
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discs += [MRD(f, sample_rate=sample_rate, bands=bands) for f in fft_sizes]
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self.discriminators = nn.ModuleList(discs)
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def preprocess(self, y):
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y = y - y.mean(dim=-1, keepdims=True)
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y = 0.8 * y / (y.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
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return y
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def forward(self, x):
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x = self.preprocess(x)
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fmaps = [d(x) for d in self.discriminators]
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return fmaps
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if __name__ == "__main__":
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disc = Discriminator()
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x = torch.zeros(1, 1, 44100)
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results = disc(x)
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for i, result in enumerate(results):
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print(f"disc{i}")
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for i, r in enumerate(result):
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print(r.shape, r.mean(), r.min(), r.max())
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print()
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