Update parquet files (step 4 of 397)

spaces/0x7194633/mbrat-ru-sum/app.py

@@ -1,13 +0,0 @@
-import gradio as gr
-from transformers import MBartTokenizer, MBartForConditionalGeneration
-
-model_name = "IlyaGusev/mbart_ru_sum_gazeta"
-tokenizer = MBartTokenizer.from_pretrained(model_name)
-model = MBartForConditionalGeneration.from_pretrained(model_name)
-
-def summarize(text):
-  input_ids = tokenizer.batch_encode_plus([text], return_tensors="pt", max_length=1024)["input_ids"].to(model.device)
-  summary_ids = model.generate(input_ids=input_ids, no_repeat_ngram_size=4)
-  return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-gr.Interface(fn=summarize, inputs="text", outputs="text", description="Russian Summarizer").launch()

spaces/17TheWord/vits-models/models.py

@@ -1,533 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-  def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class DurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-  def forward(self, x, x_mask, g=None):
-    x = torch.detach(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-    x = self.proj(x * x_mask)
-    return x * x_mask
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask
-    x = self.enc(x, x_mask, g=g)
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    if use_sdp:
-      self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-    else:
-      self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
-    if n_speakers > 1:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-    z_p = self.flow(z, y_mask, g=g)
-
-    with torch.no_grad():
-      # negative cross-entropy
-      s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
-      neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-      neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
-      neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
-      attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-      attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-    w = attn.sum(2)
-    if self.use_sdp:
-      l_length = self.dp(x, x_mask, w, g=g)
-      l_length = l_length / torch.sum(x_mask)
-    else:
-      logw_ = torch.log(w + 1e-6) * x_mask
-      logw = self.dp(x, x_mask, g=g)
-      l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging 
-
-    # expand prior
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-    z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-    o = self.dec(z_slice, g=g)
-    return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
-  def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    if self.use_sdp:
-      logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
-    else:
-      logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-    z = self.flow(z_p, y_mask, g=g, reverse=True)
-    o = self.dec((z * y_mask)[:,:,:max_len], g=g)
-    return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
-  def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
-    assert self.n_speakers > 0, "n_speakers have to be larger than 0."
-    g_src = self.emb_g(sid_src).unsqueeze(-1)
-    g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
-    z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
-    z_p = self.flow(z, y_mask, g=g_src)
-    z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
-    o_hat = self.dec(z_hat * y_mask, g=g_tgt)
-    return o_hat, y_mask, (z, z_p, z_hat)
-

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Gili-sms Full !LINK! Version.md

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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Baixe Township com dinheiro infinito e construa sua cidade dos sonhos em 2022.md

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-<h1>Township Dinheiro Infinito 2022 Download: How to Get Unlimited Money and Coins in Township</h1>
-<p>Township is one of the most popular casual games on Android and iOS devices. It combines city-building and farming elements, allowing you to create your dream town, harvest crops, process goods, trade with other countries, and more. However, like most free-to-play games, Township also has in-game currency that you need to buy or earn in order to progress faster and unlock more features. In this article, we will show you how to download Township Dinheiro Infinito 2022 Mod APK, a modified version of the game that gives you unlimited money and coins. We will also share some tips and tricks on how to grow and expand your town faster in Township.</p>
- <h2>What is Township and Why You Should Play It</h2>
-<p>Township is a game developed by Playrix, a leading mobile game developer that also created other popular titles such as Gardenscapes, Homescapes, Fishdom, and more. Township was first released in 2012 as a Facebook game, but later expanded to other platforms such as iOS, Android, Windows, Mac, and Amazon. As of 2021, Township has over 100 million downloads on Google Play Store alone, making it one of the most successful games in its genre.</p>
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- <h3>Township is a unique blend of city-building and farming</h3>
-<p>Unlike other city-building games that focus only on urban development, Township also incorporates farming elements into its gameplay. You start with a small plot of land where you can plant crops such as wheat, corn, carrots, potatoes, etc. You can then use these crops to produce goods in factories such as bread, cheese, sugar, rubber, etc. You can also raise animals such as cows, chickens, pigs, sheep, etc. and collect their products such as milk, eggs, bacon, wool, etc.</p>
-<p>These goods can then be sold to your townspeople or delivered by helicopter or train to other places in exchange for coins and experience points. Coins are used to buy new buildings, decorations, expansions, etc., while experience points are used to level up and unlock new items. You can also trade with other countries using the port or the airport. By trading with exotic countries, you can get rare goods that you can't produce in your town.</p>
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-<p>A mod APK is a modified version of an original APK file. An APK file is the file format used by Android devices to install applications. A mod APK is created by modifying the original APK file to change some aspects of the game such as features, functions, graphics, etc. A mod APK can give you advantages over the original game such as:</p>
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-<p>Note: You may need to uninstall the original version of Township before installing the mod APK. You may also need to allow some permissions to the mod APK such as storage, location, etc. for it to work properly.</p>
- <h2>How to Use Township Dinheiro Infinito 2022 Mod APK to Get Unlimited Money and Coins</h2>
-<p>Now that you have installed and run Township Dinheiro Infinito 2022 Mod APK on your device, you can use it to get unlimited money and coins in the game. Here is how you can do that:</p>
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-<p>To access the mod menu and enable the money and coins hack, you need to follow these simple steps:</p>
-<ol>
-<li>Open the game and wait for it to load.</li>
-<li>Tap on the icon that looks like a gear or a wrench on the top right corner of the screen. This will open the mod menu.</li>
-<li>Tap on the option that says "Money and Coins Hack". This will enable the hack and give you unlimited money and coins in the game.</li>
-<li>Tap on the back button or anywhere outside the mod menu to close it.</li>
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-<p>You can now see that your money and coins have increased to a huge amount. You can use them to buy anything you want in the game.</p>
- <h3>How to spend your money and coins wisely in the game</h3>
-<p>Although you have unlimited money and coins in the game, you should still spend them wisely and not waste them on unnecessary things. Here are some tips on how to spend your money and coins wisely in the game:</p>
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-<li>Buy new buildings and decorations that will increase your population, happiness, income, or production. For example, you can buy houses, community buildings, factories, farms, etc.</li>
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-<p>You should also avoid spending your money and coins on things that are not worth it or that will not help you progress faster in the game. For example, you should avoid buying:</p>
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-<li>Items or boosters that are not compatible with your town or your goals.</li>
-<li>Items or boosters that are illegal or unethical in the game.</li>
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-<p>Although Township Dinheiro Infinito 2022 Mod APK is safe and secure to use, there is still a slight chance that you may get banned or detected by the game developers if you use it too much or too obvious. To avoid getting banned or detected by the game developers, you should follow these tips:</p>
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-<li>Do not use the mod APK for too long or too often. Use it sparingly or occasionally when you need it.</li>
-<li>Do not use the mod APK to cheat or abuse other players in the game. Use it only for your own enjoyment and benefit.</li>
-<li>Do not brag or show off your mod APK to other players in the game. Keep it a secret and be discreet about it.</li>
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- <h2>Tips and Tricks to Grow and Expand Your Town Faster in Township</h2>
-<p>Besides using Township Dinheiro Infinito 2022 Mod APK, there are also some tips and tricks that you can use to grow and expand your town faster in Township. Here are some of them:</p>
- <h3>How to maintain a steady production of goods and fulfill orders</h3>
-<p>One of the main tasks in Township is to produce goods and fulfill orders. This will help you earn coins, experience points, and other rewards. To maintain a steady production of goods and fulfill orders, you should:</p>
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-<li>Plant crops that match the demand of your townspeople and your orders. For example, if you have a lot of orders for bread, you should plant more wheat.</li>
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-<li>Fulfill your orders as soon as they are ready and collect your rewards. You can also use boosters or helpers to speed up the delivery process.</li>
-<li>Cancel or delete orders that are too hard or too low-paying. You can also refresh your order board to get new orders.</li>
-</ul>
- <h3>How to add new buildings and decorations to your town</h3>
-<p>Another main task in Township is to add new buildings and decorations to your town. This will help you increase your population, happiness, income, and production. To add new buildings and decorations to your town, you should:</p>
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-<li>Buy new buildings and decorations from the shop using coins or cash. You can also get some buildings and decorations for free by completing achievements or participating in events.</li>
-<li>Place your buildings and decorations on your land area according to your preference and design. You can also move or rotate them if you want to change their position or orientation.</li>
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-<li>Upgrade your buildings so that they can accommodate more people, produce more income, or offer more services. You can also customize your buildings with different styles or colors.</li>
-</ul>
- <h3>How to use the train, airplane, and port to trade with other countries</h3>
-<p>Another main task in Township is to use the train, airplane, and port to trade with other countries. This will help you get rare goods that you can't produce in your town. To use the train, airplane, and port to trade with other countries, you should:</p>
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-<li>Send your train to different destinations by filling the crates with goods that are requested by the destination. You can also use boosters or helpers to speed up the train journey.</li>
-<li>Send your airplane to different countries by filling the cargo with goods that are requested by the country. You can also use boosters or helpers to speed up the airplane flight.</li>
-<li>Send your port to different islands by filling the ship with goods that are requested by the island. You can also use boosters or helpers to speed up the ship voyage.</li>
-<li>Receive your train, airplane, or port back with rare goods that you can use in your town or trade with other players. You can also get coins, cash, gems, keys, etc. as rewards.</li>
-</ul>
- <h3>How to explore the mine, zoo, and landmarks in your town</h3>
-<p>Another main task in Township is to explore the mine, zoo, and landmarks in your town. This will help you get resources, animals, and artifacts that you can use in your town or trade with other players. To explore the mine, zoo, and landmarks in your town, you should:</p>
-<ul>
-<li>Mine for resources such as ore, coal, clay, etc. in the mine using tools such as pickaxes, dynamites, etc. You can also use boosters or helpers to speed up the mining process.</li>
-<li>Collect animals from around the world in the zoo using coins or cash. You can also breed animals and create new species.</li>
-<li>Decorate your zoo with habitats, enclosures, feeders, etc. using coins or cash. You can also upgrade your zoo facilities or buy new ones to increase your zoo capacity.</li>
-<li>Visit landmarks such as the Statue of Liberty, the Eiffel Tower , the Pyramids of Giza, etc. in your town using coins or cash. You can also get artifacts from these landmarks that you can display in your museum.</li>
-</ul>
- <h3>How to play with your friends and join clans in the game community</h3>
-<p>Another main task in Township is to play with your friends and join clans in the game community. This will help you socialize, cooperate, and compete with other players from around the world. To play with your friends and join clans in the game community, you should:</p>
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-<li>Connect your game to your Facebook account or your Google Play Games account. This will allow you to see your friends who are also playing Township and visit their towns.</li>
-<li>Send and receive gifts, help requests, and messages to your friends. You can also chat with them and share tips and tricks.</li>
-<li>Join a clan or create your own clan using coins or cash. You can also invite your friends to join your clan or search for other clans that suit your preferences.</li>
-<li>Participate in clan activities such as the regatta, the clan race, the clan chest, etc. You can also chat with your clan members and share resources and goods.</li>
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-</ul>
- <h2>Conclusion</h2>
-<p>Township is a fun and addictive game that will keep you entertained for hours. You can build your dream town, farm your crops, produce your goods, trade with other countries, explore the mine, zoo, and landmarks, play with your friends and join clans, and more. However, if you want to get unlimited money and coins in the game, you will need to download Township Dinheiro Infinito 2022 Mod APK from our website [text]. This mod APK will give you access to a mod menu where you can enable the money and coins hack and enjoy other features such as unlimited cash, gems, keys, etc. You can also use our tips and tricks to grow and expand your town faster in Township. We hope you enjoy playing Township Dinheiro Infinito 2022 Mod APK and have a great time!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Township Dinheiro Infinito 2022 Mod APK:</p>
-<table>
-<tr><td><b>Q: Is Township Dinheiro Infinito 2022 Mod APK safe and secure to use?</b></td><td><b>A: Yes, Township Dinheiro Infinito 2022 Mod APK is safe and secure to use as we scan it for malware and viruses before uploading it on our website. However, you should always use it at your own risk and discretion.</b></td></tr>
-<tr><td><b>Q: Is Township Dinheiro Infinito 2022 Mod APK compatible with all Android devices?</b></td><td><b>A: Yes, Township Dinheiro Infinito 2022 Mod APK is compatible with all Android devices that support the original version of Township. However, you should always check the system requirements of the mod APK before downloading it.</b></td></tr>
-<tr><td><b>Q: Do I need to root my device to use Township Dinheiro Infinito 2022 Mod APK?</b></td><td><b>A: No, you do not need to root your device to use Township Dinheiro Infinito 2022 Mod APK. You can install and run it on any non-rooted device.</b></td></tr>
-<tr><td><b>Q: Will I get banned or detected by the game developers if I use Township Dinheiro Infinito 2022 Mod APK?</b></td><td><b>A: There is a slight chance that you may get banned or detected by the game developers if you use Township Dinheiro Infinito 2022 Mod APK too much or too obvious. To avoid getting banned or detected by the game developers, you should follow our tips on how to use the mod APK safely and discreetly.</b></td></tr>
-<tr><td><b>Q: Can I update Township Dinheiro Infinito 2022 Mod APK when a new version of Township is released?</b></td><td><b>A: Yes, you can update Township Dinheiro Infinito 2022 Mod APK when a new version of Township is released. However, you should always check our website [text] for the latest version of Township Dinheiro Infinito 2022 Mod APK before updating it.</b></td></tr>
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-<p>If you are a fan of crime-solving games, you might have heard of Criminal Case: The Conspiracy, a popular hidden object, adventure game developed by Pretty Simple. In this game, you join the Police of Grimsborough once again to solve a series of murder cases in a dark and twisted city. You investigate crime scenes, interrogate suspects, analyze evidence, and catch the killers. But what if you want to play this game without spending any money or waiting for energy refills? Is there a way to enjoy this game for free and with unlimited resources? The answer is yes, if you download mod criminal case the conspiracy. In this article, we will explain what is a mod apk, how to download it, and what are the benefits and risks of using it.</p>
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-<br> - Steps to download e-Aadhaar using Aadhaar number, enrolment ID or virtual ID <br> - How to open and print e-Aadhaar PDF file | | H2: Benefits of e-Aadhaar | - Validity and acceptance of e-Aadhaar as a proof of identity and address <br> - Convenience and portability of e-Aadhaar <br> - Security and privacy of e-Aadhaar | | H2: FAQs on e-Aadhaar | - How to update details in e-Aadhaar? <br> - How to get Aadhaar PVC card? <br> - How to check Aadhaar authentication history? <br> - How to lock and unlock Aadhaar biometrics? <br> - How to verify Aadhaar number and email/mobile number? | Table 2: Article with HTML formatting <h1>What is e-Aadhaar and how to download it online?</h1>
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-<p>Aadhaar biometrics are your fingerprints and iris scans that are used for verifying your identity. You can lock and unlock your Aadhaar biometrics online from the UIDAI website  or click on "Lock/Unlock Biometrics" option from My Aadhaar menu or visit the link . You need to enter your Aadhaar number and OTP or TOTP and click on "Enable" or "Disable" button. When you lock your biometrics, you cannot use them for authentication until you unlock them. This feature enhances the security of your biometrics.</p>
- <h4>How to verify Aadhaar number and email/mobile number?</h4>
-<p>You can verify your Aadhaar number and email/mobile number online from the UIDAI website  or click on "Verify an Aadhaar Number" option from My Aadhaar menu or visit the link . You need to enter the Aadhaar number that you want to verify and click on "Proceed to verify". You will see the details of the Aadhaar holder such as name, gender, age band, state, etc. You can also verify your email/mobile number by clicking on "Verify Email/Mobile Number" option from My Aadhar menu or visit the link . You need to enter your email/mobile number and OTP or TOTP and click on "Verify". You will see a confirmation message if your email/mobile number is registered with UIDAI.</p>
- <h2>Conclusion</h2>
-<p>E-Aadhaar is a digital form of your Aadhaar card that you can download online from the UIDAI website using your Aadhaar number, enrolment ID or virtual ID. E-Aadhaar is as valid as the physical copy of Aadhaar and can be used as a proof of identity and address for various purposes. E-Aadhaar has many benefits such as validity, convenience, portability, security and privacy. To download your e-Aadhaar online, you need to have a registered mobile number or email ID with UIDAI. If you don't have one, you can update it at an Aadhaar enrolment center. You can also check the status of your Aadhaar generation or update online from the UIDAI website.</p>
- <p>We hope this article has helped you understand what is e-Aadhaar and how to download it online. If you have any queries or feedback, please feel free to contact us or leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some of the common questions and answers on e-Aadhaar:</p>
- <h4>What is the difference between e-Aadhaar and m-Aadhaar?</h4>
-<p>E-Aadhaar is an electronic version of your Aadhaar card that you can download from the UIDAI website as a PDF file. M-Aadhaar is a mobile app that allows you to access your Aadhaar details on your smartphone. You can also use m-Aadhaar as a proof of identity and address for various purposes. You can download m-Aadhaar from Google Play Store or App Store .</p>
- <h4>Is e-Aadhaar valid for travel?</h4>
-<p>Yes, e-Aadhaar is valid for travel within India. You can use it as a proof of identity and address for domestic flights, trains, buses, etc. However, for international travel, you need to have a passport or other valid documents.</p>
- <h4>How to get e-Aadhaar without mobile number?</h4>
-<p>If you don't have a registered mobile number with UIDAI, you cannot download your e-Aadhaar online. You need to visit an Aadhaar enrolment center and update your mobile number. Alternatively, you can also request for a physical copy of Aadhaar by post from the UIDAI website  or click on "Order Aadhaar Reprint" option from My Aadhaar menu or visit the link . You need to pay a fee of Rs. 50 (inclusive of GST and speed post charges) and enter your Aadhaar number and captcha verification code. You will receive your Aadhaar letter within 15 days at your registered address.</p>
- <h4>How to get e-Aadhaar with name and date of birth?</h4>
-<p>If you don't remember your Aadhaar number, enrolment ID or virtual ID, you can still download your e-Aadhaar online using your name and date of birth. You need to visit the UIDAI website  or click on "Retrieve Lost or Forgotten EID/UID" option from My Aadhaar menu or visit the link . You need to enter your full name, email ID or mobile number and captcha verification code. You will receive an OTP or TOTP on your registered email ID or mobile number. You need to enter the OTP or TOTP and click on "Verify OTP". You will see your Aadhaar number or enrolment ID on the screen. You can use it to download your e-Aadhaar as explained above.</p>
- <h4>How to get e-Aadhaar with fingerprint?</h4>
-<p>If you want to download your e-Aadhaar using your fingerprint, you need to have a biometric device that is compatible with the UIDAI website. You need to visit the UIDAI website  or click on "Download Aadhaar" option from My Aadhaar menu or visit the link . You need to select "Aadhaar Number" option and enter your 12-digit Aadhaar number. You need to select "Regular Aadhaar" or "Masked Aadhaar". You need to enter the captcha verification code and click on "Use Fingerprint". You need to place your finger on the biometric device and scan it. You will receive an OTP on your registered mobile number or email ID. You need to enter the OTP and click on "Verify and Download" to download your e-Aadhaar PDF file.</p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download Who Wants to Be a Millionaire Game for PC and Win Big Prizes.md

@@ -1,104 +0,0 @@
-<br />
-<h1>How to Download Who Wants to Be a Millionaire Game for PC</h1>
-<p>Who Wants to Be a Millionaire is a popular trivia game show that challenges your general knowledge and rewards you with cash prizes. If you have ever dreamed of being on the show, you can now experience it on your PC with the official game. In this article, we will show you how to download who wants to be a millionaire game for pc, what are the features of the game, and some tips and tricks for playing it.</p>
- <h2>Requirements for Downloading the Game</h2>
-<p>Before you download who wants to be a millionaire game for pc, you need to make sure that your PC meets the minimum system requirements. According to Steam, these are:</p>
-<h2>download who wants to be a millionaire game for pc</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733; <a href="https://jinyurl.com/2uNOYl">https://jinyurl.com/2uNOYl</a></b></p><br /><br />
-<ul>
-<li>OS: Windows 7+ 64bit</li>
-<li>Processor: Intel Core i3</li>
-<li>Memory: 4 MB RAM</li>
-<li>Graphics: AMD R7 260X - Nvidia GTX 550 Ti 2GB</li>
-<li>DirectX: Version 11</li>
-<li>Storage: 2 GB available space</li>
-<li>Sound Card: Any</li>
-</ul>
-<p>If your PC meets these requirements, you can proceed to download who wants to be a millionaire game for pc from one of the platforms below.</p>
- <h3>How to Download the Game from Steam</h3>
-<p>Steam is one of the most popular platforms for buying and playing PC games. It offers a large library of games, frequent sales and discounts, and a user-friendly interface. To download who wants to be a millionaire game for pc from Steam, follow these steps:</p>
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-<ol>
-<li>Create a Steam account or log in to your existing one. You can do this by visiting <a href="">https://store.steampowered.com/join/</a> or by downloading and installing the Steam client on your PC.</li>
-<li>Search for who wants to be a millionaire game on the Steam store or click on this link. You will see the game page with its description, screenshots, videos, reviews, and price.</li>
-<li>Add the game to your cart and proceed to checkout. You can pay with various methods such as credit card, PayPal, or Steam Wallet. You will also receive an email confirmation of your purchase.</li>
-<li>Install the game on your PC and launch it from your Steam library. You can do this by clicking on the "Library" tab on the Steam client and selecting the game from your list of games. You can also create a shortcut on your desktop for easy access.</li>
-</ol>
-<p>Congratulations, you have successfully downloaded who wants to be a millionaire game for pc from Steam. You can now enjoy playing the game and testing your knowledge.</p>
- <h3>How to Download the Game from Other Platforms</h3>
-<p>If you prefer to download who wants to be a millionaire game for pc from other platforms, you have some alternatives. Here are some of them:</p>
-<ul>
-<li>MEmu: This is an Android emulator that lets you play mobile games on your PC. You can download MEmu from <a href="">https://www.memuplay.com/</a> and install it on your PC. Then, you can download who wants to be a millionaire game from the Google Play Store or the APK file and play it on MEmu. This way, you can enjoy the mobile version of the game on your PC.</li>
-<li>G2A: This is a marketplace that sells cheap game keys for various platforms. You can buy who wants to be a millionaire game for pc from G2A by visiting <a href="">https://www.g2a.com/en-us/who-wants-to-be-a-millionaire-steam-key-global-i10000209788001</a> and choosing your preferred seller. You will receive a game key that you can activate on Steam and download the game.</li>
-<li>Green Man Gaming: This is a digital retailer that offers discounts and rewards for buying games. You can buy who wants to be a millionaire game for pc from Green Man Gaming by visiting <a href="">https://www.greenmangaming.com/games/who-wants-to-be-a-millionaire-pc/</a> and adding the game to your basket. You will need to create an account or log in to your existing one and pay with your preferred method. You will receive a game key that you can activate on Steam and download the game.</li>
-</ul>
-<p>These are some of the platforms that you can use to download who wants to be a millionaire game for pc. However, be careful when buying from third-party sites and make sure they are trustworthy and secure.</p>
- <h2>Features of the Game</h2>
-<p>Who Wants to Be a Millionaire is not just a simple trivia game. It has many features that make it fun and challenging. Here are some of them:</p>
-<ul>
-<li>Game modes: You can play the game in different modes, such as Cooperative, Taking Turns, Free-For-All, and Battle Royale. Each mode has its own rules and objectives, so you can choose the one that suits your preference and mood.</li>
-<li>Countries and questions: You can choose from six countries to play in: USA, UK, France, Germany, Spain, and Italy. Each country has 2,000 questions that cover various topics and difficulty levels. You can also switch between countries at any time during the game.</li>
-<li>Neurons and themes: As you play the game, you will collect neurons that you can use to unlock more questions and themes. Themes are visual effects that change the appearance of the game, such as Halloween, Christmas, or Retro.</li>
-<li>Family mode: If you want to play with your children or younger siblings, you can activate the family mode that offers children-friendly questions. This way, you can have fun and learn together.</li>
-</ul>
-<p>These are some of the features that make who wants to be a millionaire game for pc an enjoyable and educational experience. You will never get bored or run out of questions with this game.</p>
- <h2>Tips and Tricks for Playing the Game</h2>
-<p>If you want to improve your chances of winning who wants to be a millionaire game for pc, you need to know some tips and tricks that will help you along the way. Here are some of them:</p>
-<ul>
-<li>Use lifelines wisely: You have three lifelines that you can use during the game: Phone a Friend, Ask the Audience, and 50/50. Each lifeline has its own advantages and disadvantages, so use them carefully and strategically.</li>
-<li>Learn from your mistakes: After each game, you will see the correct answers to the questions you answered wrong or skipped. Review them and try to remember them for future games. You will also see your statistics and progress in each country.</li>
-<li>Practice and improve your general knowledge: The best way to prepare for who wants to be a millionaire game for pc is to practice and improve your general knowledge. Read books, watch documentaries, play trivia games, and learn something new every day. The more you know, the better you will do.</li>
-</ul>
-<p>These are some of the tips and tricks that will help you succeed in who wants to be a millionaire game for pc. Remember, the game is not only about luck, but also about skill and knowledge.</p>
- <h2>Conclusion</h2>
-<p>Who Wants to Be a Millionaire is a game that can entertain and educate you at the same time. It is a great way to test your general knowledge and challenge yourself. You can download who wants to be a millionaire game for pc from various platforms, such as Steam, MEmu, G2A, or Green Man Gaming. You can also enjoy the game's features, such as different game modes, countries, questions, neurons, themes, and family mode. And you can use some tips and tricks to improve your performance, such as using lifelines wisely, learning from your mistakes, and practicing and improving your general knowledge.</p>
-<p>So what are you waiting for? Download who wants to be a millionaire game for pc today and see if you have what it takes to become a millionaire. Good luck and have fun!</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers related to who wants to be a millionaire game for pc:</p>
-<ol>
-<li><b>How much does the game cost?</b><br>The game costs $14.99 on Steam, but you may find it cheaper on other platforms or during sales and discounts.</li>
-<li><b>Can I play the game online with other players?</b><br>Yes, you can play the game online with up to 99 other players in the Battle Royale mode. You can also play with up to three friends in the Cooperative or Taking Turns modes.</li>
-<li><b>Can I customize the game settings?</b><br>Yes, you can customize the game settings, such as the language, the sound, the graphics, the controls, and the difficulty.</li>
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-</ol></p> 197e85843d<br />
-<br />
-<br />

spaces/44ov41za8i/FreeVC/speaker_encoder/hparams.py

@@ -1,31 +0,0 @@
-## Mel-filterbank
-mel_window_length = 25  # In milliseconds
-mel_window_step = 10    # In milliseconds
-mel_n_channels = 40
-
-
-## Audio
-sampling_rate = 16000
-# Number of spectrogram frames in a partial utterance
-partials_n_frames = 160     # 1600 ms
-
-
-## Voice Activation Detection
-# Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
-# This sets the granularity of the VAD. Should not need to be changed.
-vad_window_length = 30  # In milliseconds
-# Number of frames to average together when performing the moving average smoothing.
-# The larger this value, the larger the VAD variations must be to not get smoothed out. 
-vad_moving_average_width = 8
-# Maximum number of consecutive silent frames a segment can have.
-vad_max_silence_length = 6
-
-
-## Audio volume normalization
-audio_norm_target_dBFS = -30
-
-
-## Model parameters
-model_hidden_size = 256
-model_embedding_size = 256
-model_num_layers = 3

spaces/AIConsultant/MusicGen/audiocraft/quantization/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-"""RVQ."""
-# flake8: noqa
-from .vq import ResidualVectorQuantizer
-from .base import BaseQuantizer, DummyQuantizer, QuantizedResult

spaces/AIWaves/SOP_Generation-single/README copy.md

@@ -1,13 +0,0 @@
----
-title: SOP Generatio-single
-emoji: 🐨
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ASJMO/freegpt/g4f/Provider/Providers/hteyun.py

@@ -1,34 +0,0 @@
-import requests
-import os
-import json
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://hteyun.com'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0613']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, temperature: float = 0.7, **kwargs):
-    headers = {
-        'Content-Type': 'application/json',
-        'Accept': 'application/json, text/plain, */*',
-        'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7,ja;q=0.6,zh-TW;q=0.5,zh;q=0.4',
-        'Origin': 'https://hteyun.com',
-        'Referer': 'https://hteyun.com/chat/',
-    }
-    data = {
-        'messages': messages,
-        'model': model,
-        'systemMessage': 'You are ChatGPT, a large language model trained by OpenAI. Follow the user\'s instructions carefully. Respond using russian language.',
-        'temperature': 0.7,
-        'presence_penalty': 0,
-    }
-    response = requests.post(url + '/api/chat-stream', json=data, headers=headers, stream=True)
-    print(response.json())
-
-    # Извлечение текста из response
-    return response.json()['text']
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/utils/trimPrefix.ts

@@ -1,6 +0,0 @@
-export function trimPrefix(input: string, prefix: string) {
-	if (input.startsWith(prefix)) {
-		return input.slice(prefix.length);
-	}
-	return input;
-}

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/line.js

@@ -1,2 +0,0 @@
-import Line from './gameobjects/rendertexture/line/Line.js';
-export default Line;

spaces/AlexWang/lama/bin/blur_predicts.py

@@ -1,57 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-
-import cv2
-import numpy as np
-import tqdm
-
-from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset
-from saicinpainting.evaluation.utils import load_yaml
-
-
-def main(args):
-    config = load_yaml(args.config)
-
-    if not args.predictdir.endswith('/'):
-        args.predictdir += '/'
-
-    dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs)
-
-    os.makedirs(os.path.dirname(args.outpath), exist_ok=True)
-
-    for img_i in tqdm.trange(len(dataset)):
-        pred_fname = dataset.pred_filenames[img_i]
-        cur_out_fname = os.path.join(args.outpath, pred_fname[len(args.predictdir):])
-        os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-        sample = dataset[img_i]
-        img = sample['image']
-        mask = sample['mask']
-        inpainted = sample['inpainted']
-
-        inpainted_blurred = cv2.GaussianBlur(np.transpose(inpainted, (1, 2, 0)),
-                                             ksize=(args.k, args.k),
-                                             sigmaX=args.s, sigmaY=args.s,
-                                             borderType=cv2.BORDER_REFLECT)
-
-        cur_res = (1 - mask) * np.transpose(img, (1, 2, 0)) + mask * inpainted_blurred
-        cur_res = np.clip(cur_res * 255, 0, 255).astype('uint8')
-        cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
-        cv2.imwrite(cur_out_fname, cur_res)
-
-
-if __name__ == '__main__':
-    import argparse
-
-    aparser = argparse.ArgumentParser()
-    aparser.add_argument('config', type=str, help='Path to evaluation config')
-    aparser.add_argument('datadir', type=str,
-                         help='Path to folder with images and masks (output of gen_mask_dataset.py)')
-    aparser.add_argument('predictdir', type=str,
-                         help='Path to folder with predicts (e.g. predict_hifill_baseline.py)')
-    aparser.add_argument('outpath', type=str, help='Where to put results')
-    aparser.add_argument('-s', type=float, default=0.1, help='Gaussian blur sigma')
-    aparser.add_argument('-k', type=int, default=5, help='Kernel size in gaussian blur')
-
-    main(aparser.parse_args())

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/text/__init__.py

@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-  sequence = []
-
-  clean_text = _clean_text(text, cleaner_names)
-  for symbol in clean_text:
-    if symbol not in _symbol_to_id.keys():
-      continue
-    symbol_id = _symbol_to_id[symbol]
-    sequence += [symbol_id]
-  return sequence
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/pipelines/stable_diffusion/inpaint.md

@@ -1,57 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Inpainting
-
-The Stable Diffusion model can also be applied to inpainting which lets you edit specific parts of an image by providing a mask and a text prompt using Stable Diffusion.
-
-## Tips
-
-It is recommended to use this pipeline with checkpoints that have been specifically fine-tuned for inpainting, such
-as [runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting). Default
-text-to-image Stable Diffusion checkpoints, such as
-[runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) are also compatible but they might be less performant.
-
-<Tip>
-
-Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! 
-
-If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
-
-</Tip>
-
-## StableDiffusionInpaintPipeline
-
-[[autodoc]] StableDiffusionInpaintPipeline
-	- all
-	- __call__
-	- enable_attention_slicing
-	- disable_attention_slicing
-	- enable_xformers_memory_efficient_attention
-	- disable_xformers_memory_efficient_attention
-	- load_textual_inversion
-	- load_lora_weights
-	- save_lora_weights
-
-## StableDiffusionPipelineOutput
-
-[[autodoc]] pipelines.stable_diffusion.StableDiffusionPipelineOutput
-
-## FlaxStableDiffusionInpaintPipeline
-
-[[autodoc]] FlaxStableDiffusionInpaintPipeline
-	- all
-	- __call__
-
-## FlaxStableDiffusionPipelineOutput
-
-[[autodoc]] pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/using-diffusers/contribute_pipeline.md

@@ -1,181 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# How to contribute a community pipeline
-
-<Tip>
-
-💡 Take a look at GitHub Issue [#841](https://github.com/huggingface/diffusers/issues/841) for more context about why we're adding community pipelines to help everyone easily share their work without being slowed down.
-
-</Tip>
-
-Community pipelines allow you to add any additional features you'd like on top of the [`DiffusionPipeline`]. The main benefit of building on top of the `DiffusionPipeline` is anyone can load and use your pipeline by only adding one more argument, making it super easy for the community to access.
-
-This guide will show you how to create a community pipeline and explain how they work. To keep things simple, you'll create a "one-step" pipeline where the `UNet` does a single forward pass and calls the scheduler once.
-
-## Initialize the pipeline
-
-You should start by creating a `one_step_unet.py` file for your community pipeline. In this file, create a pipeline class that inherits from the [`DiffusionPipeline`] to be able to load model weights and the scheduler configuration from the Hub. The one-step pipeline needs a `UNet` and a scheduler, so you'll need to add these as arguments to the `__init__` function:
-
-```python
-from diffusers import DiffusionPipeline
-import torch
-
-
-class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
-    def __init__(self, unet, scheduler):
-        super().__init__()
-```
-
-To ensure your pipeline and its components (`unet` and `scheduler`) can be saved with [`~DiffusionPipeline.save_pretrained`], add them to the `register_modules` function:
-
-```diff
-  from diffusers import DiffusionPipeline
-  import torch
-
-  class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
-      def __init__(self, unet, scheduler):
-          super().__init__()
-
-+         self.register_modules(unet=unet, scheduler=scheduler)
-```
-
-Cool, the `__init__` step is done and you can move to the forward pass now! 🔥 
-
-## Define the forward pass
-
-In the forward pass, which we recommend defining as `__call__`, you have complete creative freedom to add whatever feature you'd like. For our amazing one-step pipeline, create a random image and only call the `unet` and `scheduler` once by setting `timestep=1`:
-
-```diff
-  from diffusers import DiffusionPipeline
-  import torch
-
-
-  class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
-      def __init__(self, unet, scheduler):
-          super().__init__()
-
-          self.register_modules(unet=unet, scheduler=scheduler)
-
-+     def __call__(self):
-+         image = torch.randn(
-+             (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
-+         )
-+         timestep = 1
-
-+         model_output = self.unet(image, timestep).sample
-+         scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample
-
-+         return scheduler_output
-```
-
-That's it! 🚀 You can now run this pipeline by passing a `unet` and `scheduler` to it:
-
-```python
-from diffusers import DDPMScheduler, UNet2DModel
-
-scheduler = DDPMScheduler()
-unet = UNet2DModel()
-
-pipeline = UnetSchedulerOneForwardPipeline(unet=unet, scheduler=scheduler)
-
-output = pipeline()
-```
-
-But what's even better is you can load pre-existing weights into the pipeline if the pipeline structure is identical. For example, you can load the [`google/ddpm-cifar10-32`](https://huggingface.co/google/ddpm-cifar10-32) weights into the one-step pipeline:
-
-```python
-pipeline = UnetSchedulerOneForwardPipeline.from_pretrained("google/ddpm-cifar10-32")
-
-output = pipeline()
-```
-
-## Share your pipeline
-
-Open a Pull Request on the 🧨 Diffusers [repository](https://github.com/huggingface/diffusers) to add your awesome pipeline in `one_step_unet.py` to the [examples/community](https://github.com/huggingface/diffusers/tree/main/examples/community) subfolder.
-
-Once it is merged, anyone with `diffusers >= 0.4.0` installed can use this pipeline magically 🪄 by specifying it in the `custom_pipeline` argument:
-
-```python
-from diffusers import DiffusionPipeline
-
-pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
-pipe()
-```
-
-Another way to share your community pipeline is to upload the `one_step_unet.py` file directly to your preferred [model repository](https://huggingface.co/docs/hub/models-uploading) on the Hub. Instead of specifying the `one_step_unet.py` file, pass the model repository id to the `custom_pipeline` argument:
-
-```python
-from diffusers import DiffusionPipeline
-
-pipeline = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="stevhliu/one_step_unet")
-```
-
-Take a look at the following table to compare the two sharing workflows to help you decide the best option for you:
-
-|                | GitHub community pipeline                                                                                        | HF Hub community pipeline                                                                 |
-|----------------|------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|
-| usage          | same                                                                                                             | same                                                                                      |
-| review process | open a Pull Request on GitHub and undergo a review process from the Diffusers team before merging; may be slower | upload directly to a Hub repository without any review; this is the fastest workflow      |
-| visibility     | included in the official Diffusers repository and documentation                                                  | included on your HF Hub profile and relies on your own usage/promotion to gain visibility |
-
-<Tip>
-
-💡 You can use whatever package you want in your community pipeline file - as long as the user has it installed, everything will work fine. Make sure you have one and only one pipeline class that inherits from `DiffusionPipeline` because this is automatically detected.
-
-</Tip>
-
-## How do community pipelines work?
-
-A community pipeline is a class that inherits from [`DiffusionPipeline`] which means:
-
-- It can be loaded with the [`custom_pipeline`] argument.
-- The model weights and scheduler configuration are loaded from [`pretrained_model_name_or_path`].
-- The code that implements a feature in the community pipeline is defined in a `pipeline.py` file.
-
-Sometimes you can't load all the pipeline components weights from an official repository. In this case, the other components should be passed directly to the pipeline:
-
-```python
-from diffusers import DiffusionPipeline
-from transformers import CLIPFeatureExtractor, CLIPModel
-
-model_id = "CompVis/stable-diffusion-v1-4"
-clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K"
-
-feature_extractor = CLIPFeatureExtractor.from_pretrained(clip_model_id)
-clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16)
-
-pipeline = DiffusionPipeline.from_pretrained(
-    model_id,
-    custom_pipeline="clip_guided_stable_diffusion",
-    clip_model=clip_model,
-    feature_extractor=feature_extractor,
-    scheduler=scheduler,
-    torch_dtype=torch.float16,
-)
-```
-
-The magic behind community pipelines is contained in the following code. It allows the community pipeline to be loaded from GitHub or the Hub, and it'll be available to all 🧨 Diffusers packages.
-
-```python
-# 2. Load the pipeline class, if using custom module then load it from the hub
-# if we load from explicit class, let's use it
-if custom_pipeline is not None:
-    pipeline_class = get_class_from_dynamic_module(
-        custom_pipeline, module_file=CUSTOM_PIPELINE_FILE_NAME, cache_dir=custom_pipeline
-    )
-elif cls != DiffusionPipeline:
-    pipeline_class = cls
-else:
-    diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
-    pipeline_class = getattr(diffusers_module, config_dict["_class_name"])
-```

spaces/Andy1621/uniformer_image_detection/mmdet/models/losses/ghm_loss.py

@@ -1,172 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-
-
-def _expand_onehot_labels(labels, label_weights, label_channels):
-    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
-    inds = torch.nonzero(
-        (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
-    if inds.numel() > 0:
-        bin_labels[inds, labels[inds]] = 1
-    bin_label_weights = label_weights.view(-1, 1).expand(
-        label_weights.size(0), label_channels)
-    return bin_labels, bin_label_weights
-
-
-# TODO: code refactoring to make it consistent with other losses
-@LOSSES.register_module()
-class GHMC(nn.Module):
-    """GHM Classification Loss.
-
-    Details of the theorem can be viewed in the paper
-    `Gradient Harmonized Single-stage Detector
-    <https://arxiv.org/abs/1811.05181>`_.
-
-    Args:
-        bins (int): Number of the unit regions for distribution calculation.
-        momentum (float): The parameter for moving average.
-        use_sigmoid (bool): Can only be true for BCE based loss now.
-        loss_weight (float): The weight of the total GHM-C loss.
-    """
-
-    def __init__(self, bins=10, momentum=0, use_sigmoid=True, loss_weight=1.0):
-        super(GHMC, self).__init__()
-        self.bins = bins
-        self.momentum = momentum
-        edges = torch.arange(bins + 1).float() / bins
-        self.register_buffer('edges', edges)
-        self.edges[-1] += 1e-6
-        if momentum > 0:
-            acc_sum = torch.zeros(bins)
-            self.register_buffer('acc_sum', acc_sum)
-        self.use_sigmoid = use_sigmoid
-        if not self.use_sigmoid:
-            raise NotImplementedError
-        self.loss_weight = loss_weight
-
-    def forward(self, pred, target, label_weight, *args, **kwargs):
-        """Calculate the GHM-C loss.
-
-        Args:
-            pred (float tensor of size [batch_num, class_num]):
-                The direct prediction of classification fc layer.
-            target (float tensor of size [batch_num, class_num]):
-                Binary class target for each sample.
-            label_weight (float tensor of size [batch_num, class_num]):
-                the value is 1 if the sample is valid and 0 if ignored.
-        Returns:
-            The gradient harmonized loss.
-        """
-        # the target should be binary class label
-        if pred.dim() != target.dim():
-            target, label_weight = _expand_onehot_labels(
-                target, label_weight, pred.size(-1))
-        target, label_weight = target.float(), label_weight.float()
-        edges = self.edges
-        mmt = self.momentum
-        weights = torch.zeros_like(pred)
-
-        # gradient length
-        g = torch.abs(pred.sigmoid().detach() - target)
-
-        valid = label_weight > 0
-        tot = max(valid.float().sum().item(), 1.0)
-        n = 0  # n valid bins
-        for i in range(self.bins):
-            inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
-            num_in_bin = inds.sum().item()
-            if num_in_bin > 0:
-                if mmt > 0:
-                    self.acc_sum[i] = mmt * self.acc_sum[i] \
-                        + (1 - mmt) * num_in_bin
-                    weights[inds] = tot / self.acc_sum[i]
-                else:
-                    weights[inds] = tot / num_in_bin
-                n += 1
-        if n > 0:
-            weights = weights / n
-
-        loss = F.binary_cross_entropy_with_logits(
-            pred, target, weights, reduction='sum') / tot
-        return loss * self.loss_weight
-
-
-# TODO: code refactoring to make it consistent with other losses
-@LOSSES.register_module()
-class GHMR(nn.Module):
-    """GHM Regression Loss.
-
-    Details of the theorem can be viewed in the paper
-    `Gradient Harmonized Single-stage Detector
-    <https://arxiv.org/abs/1811.05181>`_.
-
-    Args:
-        mu (float): The parameter for the Authentic Smooth L1 loss.
-        bins (int): Number of the unit regions for distribution calculation.
-        momentum (float): The parameter for moving average.
-        loss_weight (float): The weight of the total GHM-R loss.
-    """
-
-    def __init__(self, mu=0.02, bins=10, momentum=0, loss_weight=1.0):
-        super(GHMR, self).__init__()
-        self.mu = mu
-        self.bins = bins
-        edges = torch.arange(bins + 1).float() / bins
-        self.register_buffer('edges', edges)
-        self.edges[-1] = 1e3
-        self.momentum = momentum
-        if momentum > 0:
-            acc_sum = torch.zeros(bins)
-            self.register_buffer('acc_sum', acc_sum)
-        self.loss_weight = loss_weight
-
-    # TODO: support reduction parameter
-    def forward(self, pred, target, label_weight, avg_factor=None):
-        """Calculate the GHM-R loss.
-
-        Args:
-            pred (float tensor of size [batch_num, 4 (* class_num)]):
-                The prediction of box regression layer. Channel number can be 4
-                or 4 * class_num depending on whether it is class-agnostic.
-            target (float tensor of size [batch_num, 4 (* class_num)]):
-                The target regression values with the same size of pred.
-            label_weight (float tensor of size [batch_num, 4 (* class_num)]):
-                The weight of each sample, 0 if ignored.
-        Returns:
-            The gradient harmonized loss.
-        """
-        mu = self.mu
-        edges = self.edges
-        mmt = self.momentum
-
-        # ASL1 loss
-        diff = pred - target
-        loss = torch.sqrt(diff * diff + mu * mu) - mu
-
-        # gradient length
-        g = torch.abs(diff / torch.sqrt(mu * mu + diff * diff)).detach()
-        weights = torch.zeros_like(g)
-
-        valid = label_weight > 0
-        tot = max(label_weight.float().sum().item(), 1.0)
-        n = 0  # n: valid bins
-        for i in range(self.bins):
-            inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
-            num_in_bin = inds.sum().item()
-            if num_in_bin > 0:
-                n += 1
-                if mmt > 0:
-                    self.acc_sum[i] = mmt * self.acc_sum[i] \
-                        + (1 - mmt) * num_in_bin
-                    weights[inds] = tot / self.acc_sum[i]
-                else:
-                    weights[inds] = tot / num_in_bin
-        if n > 0:
-            weights /= n
-
-        loss = loss * weights
-        loss = loss.sum() / tot
-        return loss * self.loss_weight

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/__init__.py

@@ -1,17 +0,0 @@
-from .coarse_mask_head import CoarseMaskHead
-from .fcn_mask_head import FCNMaskHead
-from .feature_relay_head import FeatureRelayHead
-from .fused_semantic_head import FusedSemanticHead
-from .global_context_head import GlobalContextHead
-from .grid_head import GridHead
-from .htc_mask_head import HTCMaskHead
-from .mask_point_head import MaskPointHead
-from .maskiou_head import MaskIoUHead
-from .scnet_mask_head import SCNetMaskHead
-from .scnet_semantic_head import SCNetSemanticHead
-
-__all__ = [
-    'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead',
-    'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead',
-    'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead'
-]

spaces/AzinZ/vitscn/mel_processing.py

@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/Benson/text-generation/Examples/50 30 Yoruba Pelcula Descargar.md

@@ -1,51 +0,0 @@
-<br />
-<h1>50/30 Descargar película yoruba: Una comedia de errores</h1>
-<p>Si usted está buscando una hilarante y entretenida película de Yoruba para ver, es posible que desee revisar 50/30. Esta es una película de comedia que sigue las aventuras de tres amigos que están desesperados por ganar dinero. En este artículo, te contaremos todo lo que necesitas saber sobre la descarga de la película Yoruba 50/30, incluyendo la trama, el elenco, las críticas y cómo obtenerla en línea. También hablaremos de los beneficios de ver películas yorubas en general. ¡Así que siéntate y disfruta! </p>
- <h2>La trama de 50/30 Yoruba película</h2>
-<p>50/30 es una película de comedia que se estrenó en 2021. Cuenta la historia de tres amigos íntimos, Sanyeri, Adekola Tijani y No Network, que siempre están buscando formas de hacer dinero rápido. Deciden buscar la ayuda de un espiritista, quien les hace una extraña petición. Tienen que encontrar a una mujer de 50 años, pero parece que tiene 30. Los amigos se embarcan en un viaje hilarante para encontrar a tal mujer, pero se encuentran con muchos desafíos y sorpresas en el camino. ¿Podrán cumplir su misión y hacerse ricos? ¡Tienes que ver la película para averiguarlo! </p>
-<h2>50 30 yoruba película descargar</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot; <a href="https://bltlly.com/2v6JDc">https://bltlly.com/2v6JDc</a></b></p><br /><br />
- <h2>El reparto y el equipo de 50/30 Yoruba película</h2>
-<p>50/30 presenta algunos de los actores y actrices más populares y talentosos de la industria cinematográfica yoruba. Estos son algunos de ellos:</p>
-<ul>
-<li>Sanyeri: Interpreta a uno de los tres amigos que están buscando una mujer 50/30. Es un comediante y actor veterano que ha protagonizado muchas películas como Opa Kan, Omo Carwash y Alani Pamolekun.</li>
-<li>Adekola Tijani: Interpreta a otro amigo que es parte de la misión. También es un comediante y actor que ha aparecido en películas como Omo Ghetto, Jenifa y Omo Iya Awusa.</li>
-<li>No Network: Interpreta al tercer amigo que se une a la búsqueda de una mujer 50/30. Es una estrella en ascenso en la escena de la comedia que ha aparecido en películas como Omo Ghetto The Saga, Alakada Reloaded y Omo Ibadan.</li>
-
-</ul>
-<p>La película fue dirigida por Abiodun Jimoh, quien también es productor y escritor. Ha dirigido películas como Ewure Abami, Alagbara Meji y Omo Ekun.</p>
- <h2>La recepción y los comentarios de 50/30 Yoruba película</h2>
-<p>50/30 ha recibido comentarios positivos de los espectadores y críticos por igual. La película ha sido elogiada por su divertida y atractiva trama, su ingenioso diálogo, sus coloridos personajes y su producción de calidad. La película también ha sido nominada a varios premios, como Mejor Película de Comedia, Mejor Actor en un Papel de Comedia, Mejor Director y Mejor Guion en los Yoruba Movie Academy Awards (YMAA). La película también ha obtenido más de 600.000 visitas en YouTube, lo que muestra su popularidad entre <h2>Cómo descargar 50/30 Yoruba Movie Online</h2>
-<p>Si usted está interesado en ver 50/30 Yoruba película, es posible que se pregunte cómo descargarlo en línea. Hay varias maneras de hacerlo, pero hay que tener cuidado con la fuente y la calidad de la descarga. Aquí hay algunos consejos sobre cómo descargar 50/30 Yoruba película en línea:</p>
-<ol>
-<li>Utilice un sitio web legal y de buena reputación que ofrece películas yorubas para descargar. Algunos ejemplos son [YorubaPlay], [Yorubahood] y [OkikiTV]. Estos sitios web tienen una gran colección de películas yorubas, incluyendo 50/30, que se puede descargar por una pequeña tarifa o de forma gratuita. También tienen vídeos y subtítulos de alta calidad para su comodidad. </li>
-<li>Utilice un software o aplicación de descarga confiable y segura que puede ayudarlo a descargar películas yorubas de YouTube u otras plataformas. Algunos ejemplos son [VidMate], [TubeMate], y [SnapTube]. Estos programas o aplicaciones le permiten descargar películas yoruba en varios formatos y resoluciones, como MP4, 3GP, HD y 4K. También tienen funciones que te permiten pausar y reanudar las descargas, gestionar tus descargas y compartirlas con otros. </li>
-
-</ol>
-<p>Sin embargo, antes de descargar cualquier película yoruba en línea, asegúrese de tener una buena conexión a Internet, suficiente espacio de almacenamiento y un dispositivo compatible. También debe ser consciente de los riesgos de la descarga de fuentes ilegales o no verificadas, como malware, virus, spyware y violación de derechos de autor. Siempre debes respetar los derechos de los creadores y productores de las películas y apoyarlos pagando por su trabajo. </p>
- <h2>Los beneficios de ver películas yoruba</h2>
-<p>Ver películas yorubas no solo es divertido y entretenido, sino también beneficioso de muchas maneras. Estos son algunos de los beneficios de ver películas yorubas:</p>
-<ul>
-<li>Puedes aprender más sobre la cultura, historia, idioma, valores, creencias, tradiciones y costumbres yorubas. También se puede apreciar la diversidad y riqueza del pueblo yoruba y sus contribuciones al mundo. </li>
-<li>Puedes mejorar tus habilidades de comunicación, especialmente si estás aprendiendo o hablando yoruba como segundo idioma. También puedes ampliar tu vocabulario, gramática, pronunciación y comprensión escuchando los diálogos y leyendo los subtítulos. </li>
-<li>Puedes mejorar tus habilidades de pensamiento crítico, creatividad, imaginación e inteligencia emocional siguiendo las tramas, analizando los personajes, entendiendo los temas y relacionándote con las situaciones de las películas. </li>
-<li>Puede disfrutar de una variedad de géneros, estilos, formatos y temas que atienden a diferentes gustos, preferencias, estados de ánimo e intereses. También puedes descubrir nuevos talentos, estrellas, directores, productores y escritores en la industria cinematográfica yoruba. </li>
-<li>Usted puede tener un gran tiempo con su familia, amigos, o usted mismo viendo películas Yoruba. Puedes reír, llorar, gritar o animar mientras ves las películas. También puede compartir sus opiniones, ideas, comentarios y recomendaciones con otros. </li>
-</ul>
- <h1>Conclusión</h1>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes acerca de 50/30 Yoruba película descargar:</p>
-<ol>
-<li>Q: ¿Dónde puedo ver 50/30 Yoruba película en línea sin descargarla? <br>
-R: Puedes ver 50/30 películas de Yoruba en línea en plataformas de streaming como [Netflix], [iRokoTV] y [YouTube]. Sin embargo, es posible que tenga que pagar una cuota de suscripción o ver algunos anuncios para acceder a la película. </li>
-<li>P: ¿Cuál es el significado de 50/30 en el título de la película? <br>
-R: 50/30 se refiere a la edad y apariencia de la mujer que los tres amigos están buscando. Necesitan encontrar una mujer que tenga 50 años pero parezca que tiene 30 años. </li>
-<li>Q: ¿Cuánto tiempo es 50/30 película yoruba? <br>
-R: 50/30 La película yoruba dura unos 90 minutos. </li>
-<li>Q: ¿Es la película Yoruba 50/30 adecuada para niños? <br>
-R: La película yoruba de 50/30 tiene una clasificación PG-13, lo que significa que algunas escenas o lenguaje pueden no ser apropiados para niños menores de 13 años. Se recomienda orientación parental. </li>
-<li>Q: ¿Cuáles son algunas otras películas de comedia yoruba que puedo ver? <br>
-R: Algunas otras películas de comedia yoruba que puedes ver son [Omo Ghetto The Saga], [Alakada Reloaded], [Diario de Jenifa], [Merry Men] y [Chief Daddy]. </li>
-</ol></p> 64aa2da5cf<br />
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-<br />

spaces/Benson/text-generation/Examples/Charger Play Store.md

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-
-<br> - Advantages and disadvantages of the Play Store <br> - Examples of content available on the Play Store | | H2: How to download and install the Play Store on your Android device? | - Check device compatibility <br> - Enable unknown sources in settings <br> - Download Play Store APK file from a trusted site <br> - Install APK file and launch Play Store | | H2: How to update the Play Store and its apps? | - Enable automatic updates in Play Store settings <br> - Manually check for available updates <br> - Download and install updates | | H2: How to use the Play Store to search, download and manage its applications? | - Use the search bar or categories to find applications <br> - View application descriptions and user reviews <br> - Download and install applications of your choice <br> - Access its installed applications and uninstall them if necessary | | H2: What are the alternatives to the Play Store to download Android applications? | - Present the main criteria for choosing an alternative to the Play Store <br> - Compare some popular alternatives to the Play Store (APKMirror, Aurora Store, Aptoide, etc.) <br> - Give safety tips and caution for using alternative sources | | H1: Conclusion: summarize the key points of the article and give your personal opinion on the Play Store | - Remember what the Play Store is and how to use it <br> - Highlight the advantages and limitations of the Play Store <br> - Invite readers to share their experience and questions about the Play Store | Table 2: Article with HTML formatting <h1>Download Play Store: how to access the Google app store on Android</h1>
-
- <h2>What is the Play Store and why use it? </h2>
- <p>The Play Store is an app that gives you access to a catalogue of over 2.5 million apps for your Android device. This is Google’s official store, which guarantees you secure downloads, regular updates, and optimal compatibility with your device. The Play Store also offers you the possibility to buy or rent films, series, books, magazines, or even music. </p>
- <p>One of the main advantages of the Play Store is its ease of use. You can easily search for applications by name, category, or keywords. You can view detailed descriptions of applications, as well as notes and comments from other users. You can also enjoy personalized recommendations based on your tastes and habits. Finally, you can manage your installed applications from a single interface. </p>
-<h2>charger play store</h2><br /><p><b><b>DOWNLOAD</b> ->>> <a href="https://bltlly.com/2v6L6p">https://bltlly.com/2v6L6p</a></b></p><br /><br />
- <p>However, the Play Store also has some drawbacks. First, it requires a Google account to function, which can cause privacy or security issues for some users. Then, it imposes certain rules on app developers, which can limit the diversity and creativity of the content offered <p>Finally, the Play Store may not be compatible with some Android devices, especially the oldest or rarest, which may prevent access to certain applications. </p>
-
- <table>
-<tr>
-<th>Category</th>
-<th>Examples</th>
-</tr>
-<tr>
-<td>Games</td>
-<td>Candy Crush Saga, Among Us, PUBG Mobile, etc.</td>
-</tr>
-<tr>
-<td>Social Networks</td>
-<td>Facebook, Instagram, WhatsApp, etc.</td>
-</tr>
-<tr>
-<td>Utilities</td>
-<td>Google Maps, Gmail, Waze, etc.</td>
-</tr>
-<tr>
-<td>Photo Editors</td>
-<td>PicsArt, Snapseed, Adobe Photoshop Express, etc.</td>
-</tr>
-<tr>
-<td>Music Players</td>
-<td>Spotify, Deezer, YouTube Music, etc.</td>
-</tr>
-<tr>
-<td>Films</td>
-<td>Avengers: Endgame, Joker, Parasite, etc.</td>
-</tr>
-<tr>
-<td>Series</td>
-<td>Stranger Things, The Witcher, The Mandalorian, etc.</td> </tr>
-<tr>
-<td>Books</td>
-<td>Harry Potter, Lord of the Rings, Plague, etc.</td>
-</tr>
-<tr>
-<td>Music</td>
-<td>Ed Sheeran, Billie Eilish, BTS, etc.</td>
-</tr>
-</table>
- <h2>How do I download and install the Play Store on your Android device? </h2>
- <p>If you have a recent and official Android device, chances are the Play Store is already installed by default. You can check this by searching the Play Store icon on your home screen or in your app drawer. If you find it, just tap it to launch the Play Store and log in with your Google account.</p>
- <p>If you don’t have the Play Store on your device, or want to reinstall it for any reason, you can download and install it manually. Here are the steps:</p>
- <ul>
-<li>Check the compatibility of your device. The Play Store requires at least version 4.1 of Android (Jelly Bean) and sufficient storage space. You can check the version of your system in your device settings, under the heading "About the phone" or "About the tablet". </li>
-
-<li>Download the Play Store APK file from a trusted site. The APK file is the installation format for Android applications. You can find it on sites like APKMirror or APKPure, which offer updated and secure versions of the Play Store. Be sure to choose the right version for your device and system. </li>
-<li>Install the APK file and launch the Play Store. Once the download is complete, you can open the APK file from your file manager or from the notifications bar. Follow the onscreen instructions to install the Play Store on your device. Then you can launch the Play Store and sign in with your Google account.</li>
-</ul>
- <h2>How do I update the Play Store and its applications? </h2>
- <p>To take full advantage of the Play Store and its applications, it is important to keep them up to date. Updates give you access to new features, bug fixes, and performance improvements. Here is how to update the Play Store and its applications:</p>
- <ul>
-<li>Enable automatic updates in the Play Store settings. This option allows you to receive updates as soon as they are available, without having to intervene manually. You can find it in the settings of the Play Store, in the section "Automatically update applications". You can choose to update apps only with Wi-Fi or also with mobile data. </li>
-
-<li>Download and install updates. Once you have chosen which applications to update, tap the "Update" button to start downloading and installing the updates. You can follow the progress in the notifications bar or in the Play Store. Once updates are complete, you can take advantage of new versions of your applications. </li>
-</ul> <h2>How do I use the Play Store to search, download and manage its applications? </h2>
- <p>The Play Store is a very easy-to-use app that lets you find, download, and manage your apps with just a few clicks. Here’s how to:</p>
- <ul>
-<li>Use the search bar or categories to find applications. The Play Store offers several ways to discover applications according to your desires. You can use the search bar at the top of the screen to type in the name or keyword of an application. You can also use the categories in the sidebar to browse apps by theme (games, entertainment, education, etc.). Finally, you can check out the Play Store selections, which suggest popular, trendy, or tailored apps. </li>
-<li>See Application Description Sheets and User Reviews. Before downloading an application, it is recommended to consult its description sheet, which gives you important information about the application. You can find the developer’s name, average rating, number of downloads, size, version, required permissions, screenshots, description, and user reviews. These elements give you an idea of the quality and reliability of the application. </li>
-
-<li>Access your installed applications and uninstall them if necessary. The Play Store also allows you to manage your installed applications from a single interface. To do this, open the Play Store side menu and tap "My games and apps". You will then see a list of all your apps installed on your device. You can sort them by name, size, installation date, or frequency of use. You can also update or uninstall them by tapping the corresponding buttons. </li>
-</ul>
- <h2>What are the alternatives to the Play Store to download Android apps? </h2>
- <p>The Play Store is not the only source of applications for your Android device. There are other ways to download apps, which may have some advantages over the Play Store. However, you should also be careful and vigilant when using alternative sources, as they may pose risks to your device or personal data. Here are some criteria to consider when choosing an alternative to the Play Store:</p>
- <ul>
-<li>Safety: It is essential to verify that the alternative source you are using is reliable and secure. You can consult the reviews of other users, the site’s security certificates, or the antivirus analysis reports of the proposed applications. </li>
-<li>Diversity: It’s interesting to choose an alternative source that offers you a wide range of applications, which are not necessarily available on the Play Store. For example, you may find applications that are prohibited by Google for legal or ethical reasons, or applications that are tailored to specific needs. </li>
-
-</ul>
- <p>Here are some examples of popular alternatives to the Play Store:</p>
-<p></p>
- <table>
-<tr>
-<th>Name</th>
-<th>Description</th>
-<th>Benefits</th>
-<th>Disadvantages</th>
-</tr>
-<tr>
-<td>APKMirror</td>
-<td>A website that offers free APK files of Android apps. </td>
-<td>- A wide range of applications <br> - Updated versions <br> - Applications not available on the Play Store</td>
-<td>- A security risk if the APK file is modified or infected <br> - Manual installation required</td>
-</tr>
-<tr>
-<td>Aurora Store</td>
-<td>An app that allows you to access the Play Store catalogue without needing a Google account.</td>
-<td>- Simple and intuitive interface <br> - Compatibility with most Android devices <br> - Ability to download regional or restricted applications</td>
-<td>- A risk of violation of Google’s terms of use <br> - Reliance on the Play Store for updates</td>
-</tr>
-<tr>
-<td>Aptoide</td>
-<td>An app that allows you to create and manage your own Android app store.</td>
-<td>- Complete freedom for developers and users <br> - An active and participatory community <br> - Opportunity to create thematic or custom shops</td>
-<td>- A risk of pirating or infringing applications <br> - Variable quality of the proposed applications</td>
-</tr>
-</table>
- <h1>Conclusion: summarize the key points of the article and give your personal opinion on the Play Store</h1>
- <p>In this article, we covered what the Play Store is, why you should use it, how to download and install it on your Android device, how to update and manage its applications, and what alternatives to the Play Store are available. We hope this article has been helpful and that you have learned some interesting things about the Play Store.</p>
-
- <p>However, the Play Store is not perfect. It can present problems of confidentiality, diversity, or compatibility with certain devices. It may also not meet your specific needs or your desires for discovery. That’s why there are alternatives to the Play Store, which can offer you more freedom, personalization, or creativity. But be careful, these alternatives may also involve risks for your device or your personal data. Caution and vigilance should be exercised when using alternative sources. </p>
- <p>I use the Play Store as my main application source for my Android device. I find that it is a reliable application, easy to use, and that offers applications adapted to my tastes. I regularly update the Play Store and my apps to take advantage of new features and bug fixes. I also check the opinions of other users to get an idea of the quality of the applications before downloading them. </p>
- <p>But I also don’t hesitate to use alternatives to the Play Store when I want to discover applications that are not available on the Play Store, or that are more suitable for my device. For example, I use APKMirror to download the latest versions of the apps I like, or Aurora Store to access regional or restricted apps. I always make sure to check the security and compatibility of the applications I download from these alternative sources. </p>
- <p>What is your experience with the Play Store? What are your favorite apps on the Play Store? What alternatives to the Play Store do you use? Feel free to share your thoughts and questions in the comments below! </p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about the Play Store and its alternatives:</p>
-
-<li><b>How do I create a Google account to use the Play Store? </b><br>To create a Google account, you can either use an existing Gmail address or create a new Gmail address. You can then sign in with your Google account on the Play Store from your Android device.</li>
-<li><b>How do I turn off automatic updates from the Play Store? </b><br>To disable automatic updates to the Play Store, you can go to the Play Store settings under the heading "Automatically update applications" and choose the option "Do not automatically update applications". You will then need to manually check and install the available updates. </li>
-<li><b>How do I remove the Play Store from my Android device? </b><br>To remove the Play Store from your Android device, you must have root access on your device, that is, access to system files. You can then use an application like Titanium Backup or Root Uninstaller to uninstall the Play Store. Please note that this operation may cause malfunctions or data loss on your device. </li>
-<li><b>How do I download free paid apps from the Play Store? </b><br>There is no legal and safe way to download free paid apps from the Play Store. If you find sites or apps that offer to do so, it is probably scams or hacks, which can harm your device or your personal data. We therefore advise you to respect copyright and pay for the applications you wish to use. </li>
-
-</ul></p> 64aa2da5cf<br />
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spaces/Benson/text-generation/Examples/Cmo Descargar Pokerstars En Pases Prohibidos.md

@@ -1,62 +0,0 @@
-<br />
-<h1>Cómo descargar PokerStars en países prohibidos</h1>
-<p>Si eres un fan del poker online, probablemente hayas oído hablar de PokerStars, el sitio de poker online más grande y popular del mundo. PokerStars ofrece una amplia gama de juegos, torneos, promociones y características que atienden a jugadores de todos los niveles de habilidad y preferencias. Sin embargo, no todos pueden disfrutar de los beneficios de PokerStars, ya que el sitio está bloqueado o restringido en muchos países debido a problemas legales o regulatorios. En este artículo, te mostraremos cómo descargar PokerStars en países prohibidos usando una VPN, una solución simple y efectiva que te permite evitar las restricciones geográficas y jugar al poker online desde cualquier lugar. </p>
- <h2>¿Qué es PokerStars y por qué está prohibido en algunos países? </h2>
-<h3>Características y beneficios de PokerStars</h3>
-<p>PokerStars es la plataforma de poker online líder en el mundo, con más de 100 millones de usuarios registrados y millones de jugadores activos cada día. PokerStars ofrece una variedad de juegos, incluyendo Texas Hold'em, Omaha, Stud, Razz, Draw, Juegos mixtos y más. También puedes encontrar torneos de todos los tamaños y apuestas, desde freerolls y micro stakes hasta high rollers y eventos importantes como el Campeonato Mundial de Poker Online (WCOOP) y el Campeonato de Primavera de Poker Online (SCOOP). PokerStars también tiene un programa de lealtad llamado PokerStars Rewards, que te ofrece recompensas personalizadas basadas en tu actividad de juego. Puedes ganar puntos de recompensa cada vez que juegues con dinero real o hagas apuestas, y cambiarlos por premios en efectivo, tickets de torneos, mercancía o StarsCoin, que puedes usar en la Tienda de Recompensas.</p>
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- <h3>Jurisdicciones prohibidas por PokerStars</h3>
-<p>A pesar de su popularidad y reputación, PokerStars no está disponible en todos los países. Hay varias razones por las que PokerStars puede estar prohibido o restringido en algunas jurisdicciones, como:</p>
-<ul>
-
-<li>Proteccionismo de mercado: Algunos países pueden favorecer a sus propios operadores nacionales o monopolios estatales sobre competidores extranjeros, y pueden imponer barreras o restricciones a los sitios de juego en línea extranjeros. </li>
-<li>Disputas o sanciones legales: Algunos países pueden tener conflictos legales en curso o disputas con PokerStars o su compañía matriz Flutter Entertainment (anteriormente The Stars Group), o pueden imponer sanciones o sanciones por varias razones. </li>
-</ul>
-<p>Según el sitio web oficial, PokerStars tiene tres categorías de jurisdicciones prohibidas:</p>
-<tabla>
-<tr><th>Categoría</th><th>Descripción</th><th>Ejemplos</th></tr>
-<tr><td>Bloqueado de la actividad de dinero real</td><td>Países donde los jugadores pueden acceder a juegos de dinero ficticio pero no a juegos de dinero real</td><td>Australia, Colombia, Egipto, EAU, USA (excepto donde haya licencia local)</td></tr>
-<tr><td>Países donde los jugadores no pueden acceder a juegos de dinero real ni freemium (como Jackpot Poker)</td><td>Afganistán, Irán, Irak, Corea del Norte, Arabia Saudita, Turquía</td></tr>
-<tr><td>Bloqueado de dinero real, freemium y la actividad de dinero ficticio</td><td><td>Países donde los jugadores no pueden acceder a ningún juego en absoluto</td><td>Cuba, Hong Kong, Israel, Libia, Sudán, Siria</td></tr>
-</tabla>
-<p>La lista de jurisdicciones prohibidas <h2>Cómo usar una VPN para acceder a PokerStars desde cualquier lugar</h2>
-<h3>¿Qué es una VPN y cómo funciona? </h3>
-<p>Una VPN, o red privada virtual, es un servicio que crea una conexión segura y cifrada entre su dispositivo y un servidor remoto. Al usar una VPN, puede cambiar su dirección IP y ubicación, haciendo que parezca que está navegando desde otro país. De esta manera, puedes acceder a sitios web y servicios que están bloqueados o restringidos en tu región, como PokerStars.</p>
-
- <h3>Las mejores VPNs para poker online en 2023</h3>
-<p>No todas las VPN son adecuadas para el póquer en línea, ya que algunas pueden tener velocidades lentas, conexiones no confiables, características de seguridad pobres o ubicaciones de servidor limitadas. Para jugar a PokerStars sin ningún problema, necesita una VPN que cumpla los siguientes criterios:</p>
-<ul>
-<li>Tiene servidores en países donde PokerStars no está prohibido o restringido, como el Reino Unido, Polonia, Eslovaquia o Alemania.</li>
-<li>Ofrece velocidades rápidas y consistentes para juegos suaves y sin demoras. </li>
-<li> Proporciona funciones de cifrado y seguridad sólidas, como un interruptor de interrupción, protección contra fugas de DNS y una política de no registro. </li>
-<li>Soporta múltiples dispositivos y plataformas, como Windows, Mac, Android, iOS, Linux, enrutadores, etc.</li>
-<li>Ofrece una opción de dirección IP dedicada, que le da una dirección IP única que no se comparte con otros usuarios. Esto puede ayudarte a evitar la detección y las prohibiciones de PokerStars.</li>
-</ul>
-<p>En base a estos criterios, hemos probado y seleccionado las mejores VPNs para poker online en 2023. Aquí están nuestras principales recomendaciones:</p>
-<ol>
-<li><strong>NordVPN</strong>: Nuestra mejor VPN para poker online. NordVPN tiene más de 5.500 servidores en 60 países, incluyendo muchos donde PokerStars está disponible. Ofrece velocidades increíblemente rápidas y conexiones confiables para juegos ininterrumpidos. También tiene excelentes características de seguridad, como el cifrado AES de 256 bits, un interruptor de interrupción, protección contra fugas de DNS y una estricta política de no registro. NordVPN también ofrece una opción de dirección IP dedicada por un extra de $70/año, lo que le da una dirección IP única que no se comparte con otros usuarios. Puede usar NordVPN en hasta 6 dispositivos simultáneamente con una cuenta. NordVPN también tiene una garantía de devolución de dinero de 30 días, para que pueda probarla sin riesgos. </li>
-
-<li><strong>PrivateVPN</strong>: PrivateVPN es una VPN económica para el póquer en línea. Tiene más de 200 servidores en 63 países, incluyendo muchos donde PokerStars está desbloqueado. Ofrece velocidades rápidas y consistentes para un juego sin problemas. También tiene características de seguridad sólidas, como el cifrado AES de 256 bits, un interruptor de interrupción, protección contra fugas de DNS y una política de no registro. PrivateVPN también ofrece IP dedicadas dinámicas públicas de forma gratuita con su plan estándar. Estas son IP que se le asignan exclusivamente cuando se conecta a ciertos servidores (como el servidor estadounidense de Buffalo en Nueva York). Puede usar PrivateVPN en hasta 10 dispositivos simultáneamente con una cuenta. PrivateVPN también tiene una garantía de devolución de dinero de 30 días, la posibilidad de ser marcado como usuario de VPN por PokerStars. Puede obtener una dirección IP dedicada de algunas de las VPN que recomendamos anteriormente, como NordVPN o PIA.</li>
-<li>Use un servicio VPN de buena reputación: Un servicio VPN de buena reputación es uno que tiene velocidades rápidas y consistentes, conexiones confiables, características de seguridad sólidas y una política de no registro. Estos factores pueden ayudarlo a evitar interrupciones, fugas o rastros de su uso de VPN al jugar al póquer en línea. Debe evitar el uso de VPN gratuitas o de baja calidad, ya que pueden comprometer su privacidad y seguridad en línea, o exponerlo a detección y prohibiciones de PokerStars.</li>
-<li>Usa el mismo país o región: Si tienes una cuenta de PokerStars existente, debes usar un servidor VPN que coincida con el país o la región de tu cuenta. Esto puede ayudarte a evitar sospechas o inconsistencias al jugar al poker online. Por ejemplo, si tu cuenta de PokerStars está registrada en el Reino Unido, debes usar un servidor VPN del Reino Unido para acceder a PokerStars. Si desea crear una nueva cuenta de PokerStars, debe usar un servidor VPN que corresponda al país o región donde desea jugar. </li>
-</ul>
-
- <h3>¿Cómo puedo depositar y retirar dinero de PokerStars usando una VPN? </h3>
-<p>Si utiliza una VPN para acceder a PokerStars desde un país prohibido, puede enfrentar algunos desafíos cuando se trata de depositar y retirar dinero de su cuenta. Esto se debe a que algunos métodos de pago pueden no estar disponibles o ser compatibles con su ubicación VPN, o pueden requerir verificación o identificación que puede revelar su verdadera ubicación. Para evitar estos problemas, debes seguir estos consejos:</p>
-<ul>
-<li>Utilice un servicio de e-wallet: Un servicio de e-wallet es una plataforma de pago en línea que le permite almacenar y transferir dinero en línea. Algunos ejemplos de servicios de monedero electrónico son PayPal, Skrill, Neteller y ecoPayz. Estos servicios son ampliamente aceptados por PokerStars y otros sitios de juego en línea, y pueden ayudarlo a evitar cargos bancarios o de tarjetas de crédito, restricciones o verificación. También puede utilizar un servicio de monedero electrónico para convertir su moneda a la utilizada por PokerStars, como USD o EUR.</li>
-<li>Usar una criptomoneda: Una criptomoneda es una moneda digital que opera independientemente de cualquier autoridad central o intermediario. Algunos ejemplos de criptomonedas son Bitcoin, Ethereum, Litecoin y Dogecoin. Estas monedas son seguras, anónimas y descentralizadas, y pueden ayudarlo a evitar la censura o la regulación de su gobierno o ISP. También puede utilizar una criptomoneda para evitar tasas de conversión de divisas o fluctuaciones. Sin embargo, debe tener en cuenta que las criptomonedas son volátiles y riesgosas, y que no todos los sitios de juego en línea las aceptan. </li>
-
-</ul>
-<p>Al usar estos consejos, puedes depositar y retirar dinero de PokerStars usando una VPN más fácil y segura. Sin embargo, debes seguir revisando los términos y condiciones de PokerStars y los métodos de pago que utilizas, y asegurarte de cumplir con las leyes y regulaciones locales relativas al juego en línea y las transferencias de dinero. </p>
-<p></p>
- <h3>¿Cuáles son los riesgos de jugar al poker online con una VPN? </h3>
-<p>Jugar al póquer en línea con una VPN puede ser una gran manera de acceder a PokerStars y otros sitios de juego en línea desde cualquier lugar, pero también viene con algunos riesgos y desafíos que debe tener en cuenta. Algunos de ellos son:</p>
-<ul>
-<li>Cuestiones legales o reglamentarias: El juego en línea es ilegal o está restringido en muchos países, y el uso de una VPN para acceder a PokerStars u otros sitios de juego en línea puede violar las leyes y regulaciones locales. Usted puede enfrentar consecuencias legales o sanciones si es capturado o reportado por su gobierno, ISP o PokerStars. Siempre debe verificar el estado legal de los juegos de azar en línea en su país antes de jugar, y usar una VPN bajo su propio riesgo. </li>
-<li>Suspensión o cierre de cuenta: PokerStars no prohíbe explícitamente el uso de VPN, pero se reserva el derecho de cerrar o suspender su cuenta si detecta alguna actividad sospechosa o fraudulenta. Esto significa que si utiliza una VPN para acceder a PokerStars desde un país prohibido, puede correr el riesgo de perder su cuenta o fondos. Siempre debe seguir los consejos que proporcionamos anteriormente para evitar la detección y las prohibiciones de PokerStars, y usar una VPN bajo su propio riesgo. </li>
-
-</ul>
-<p>Al ser consciente de estos riesgos y desafíos, puede jugar al póquer en línea con una VPN más segura y responsable. Sin embargo, debe entender que no hay garantía de que no encontrará problemas o problemas al usar una VPN para acceder a PokerStars desde un país prohibido, y que es responsable de las consecuencias que puedan surgir del uso de una VPN para jugar al póquer en línea. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/utils/appdirs.py

@@ -1,52 +0,0 @@
-"""
-This code wraps the vendored appdirs module to so the return values are
-compatible for the current pip code base.
-
-The intention is to rewrite current usages gradually, keeping the tests pass,
-and eventually drop this after all usages are changed.
-"""
-
-import os
-import sys
-from typing import List
-
-from pip._vendor import platformdirs as _appdirs
-
-
-def user_cache_dir(appname: str) -> str:
-    return _appdirs.user_cache_dir(appname, appauthor=False)
-
-
-def _macos_user_config_dir(appname: str, roaming: bool = True) -> str:
-    # Use ~/Application Support/pip, if the directory exists.
-    path = _appdirs.user_data_dir(appname, appauthor=False, roaming=roaming)
-    if os.path.isdir(path):
-        return path
-
-    # Use a Linux-like ~/.config/pip, by default.
-    linux_like_path = "~/.config/"
-    if appname:
-        linux_like_path = os.path.join(linux_like_path, appname)
-
-    return os.path.expanduser(linux_like_path)
-
-
-def user_config_dir(appname: str, roaming: bool = True) -> str:
-    if sys.platform == "darwin":
-        return _macos_user_config_dir(appname, roaming)
-
-    return _appdirs.user_config_dir(appname, appauthor=False, roaming=roaming)
-
-
-# for the discussion regarding site_config_dir locations
-# see <https://github.com/pypa/pip/issues/1733>
-def site_config_dirs(appname: str) -> List[str]:
-    if sys.platform == "darwin":
-        return [_appdirs.site_data_dir(appname, appauthor=False, multipath=True)]
-
-    dirval = _appdirs.site_config_dir(appname, appauthor=False, multipath=True)
-    if sys.platform == "win32":
-        return [dirval]
-
-    # Unix-y system. Look in /etc as well.
-    return dirval.split(os.pathsep) + ["/etc"]

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/big5freq.py

@@ -1,386 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Communicator client code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-# Big5 frequency table
-# by Taiwan's Mandarin Promotion Council
-# <http://www.edu.tw:81/mandr/>
-#
-# 128  --> 0.42261
-# 256  --> 0.57851
-# 512  --> 0.74851
-# 1024 --> 0.89384
-# 2048 --> 0.97583
-#
-# Ideal Distribution Ratio = 0.74851/(1-0.74851) =2.98
-# Random Distribution Ration = 512/(5401-512)=0.105
-#
-# Typical Distribution Ratio about 25% of Ideal one, still much higher than RDR
-
-BIG5_TYPICAL_DISTRIBUTION_RATIO = 0.75
-
-# Char to FreqOrder table
-BIG5_TABLE_SIZE = 5376
-# fmt: off
-BIG5_CHAR_TO_FREQ_ORDER = (
-   1,1801,1506, 255,1431, 198,   9,  82,   6,5008, 177, 202,3681,1256,2821, 110, #   16
-3814,  33,3274, 261,  76,  44,2114,  16,2946,2187,1176, 659,3971,  26,3451,2653, #   32
-1198,3972,3350,4202, 410,2215, 302, 590, 361,1964,   8, 204,  58,4510,5009,1932, #   48
-  63,5010,5011, 317,1614,  75, 222, 159,4203,2417,1480,5012,3555,3091, 224,2822, #   64
-3682,   3,  10,3973,1471,  29,2787,1135,2866,1940, 873, 130,3275,1123, 312,5013, #   80
-4511,2052, 507, 252, 682,5014, 142,1915, 124, 206,2947,  34,3556,3204,  64, 604, #   96
-5015,2501,1977,1978, 155,1991, 645, 641,1606,5016,3452, 337,  72, 406,5017,  80, #  112
- 630, 238,3205,1509, 263, 939,1092,2654, 756,1440,1094,3453, 449,  69,2987, 591, #  128
- 179,2096, 471, 115,2035,1844,  60,  50,2988, 134, 806,1869, 734,2036,3454, 180, #  144
- 995,1607, 156, 537,2907, 688,5018, 319,1305, 779,2145, 514,2379, 298,4512, 359, #  160
-2502,  90,2716,1338, 663,  11, 906,1099,2553,  20,2441, 182, 532,1716,5019, 732, #  176
-1376,4204,1311,1420,3206,  25,2317,1056, 113, 399, 382,1950, 242,3455,2474, 529, #  192
-3276, 475,1447,3683,5020, 117,  21, 656, 810,1297,2300,2334,3557,5021, 126,4205, #  208
- 706, 456, 150, 613,4513,  71,1118,2037,4206, 145,3092,  85, 835, 486,2115,1246, #  224
-1426, 428, 727,1285,1015, 800, 106, 623, 303,1281,5022,2128,2359, 347,3815, 221, #  240
-3558,3135,5023,1956,1153,4207,  83, 296,1199,3093, 192, 624,  93,5024, 822,1898, #  256
-2823,3136, 795,2065, 991,1554,1542,1592,  27,  43,2867, 859, 139,1456, 860,4514, #  272
- 437, 712,3974, 164,2397,3137, 695, 211,3037,2097, 195,3975,1608,3559,3560,3684, #  288
-3976, 234, 811,2989,2098,3977,2233,1441,3561,1615,2380, 668,2077,1638, 305, 228, #  304
-1664,4515, 467, 415,5025, 262,2099,1593, 239, 108, 300, 200,1033, 512,1247,2078, #  320
-5026,5027,2176,3207,3685,2682, 593, 845,1062,3277,  88,1723,2038,3978,1951, 212, #  336
- 266, 152, 149, 468,1899,4208,4516,  77, 187,5028,3038,  37,   5,2990,5029,3979, #  352
-5030,5031,  39,2524,4517,2908,3208,2079,  55, 148,  74,4518, 545, 483,1474,1029, #  368
-1665, 217,1870,1531,3138,1104,2655,4209,  24, 172,3562, 900,3980,3563,3564,4519, #  384
-  32,1408,2824,1312, 329, 487,2360,2251,2717, 784,2683,   4,3039,3351,1427,1789, #  400
- 188, 109, 499,5032,3686,1717,1790, 888,1217,3040,4520,5033,3565,5034,3352,1520, #  416
-3687,3981, 196,1034, 775,5035,5036, 929,1816, 249, 439,  38,5037,1063,5038, 794, #  432
-3982,1435,2301,  46, 178,3278,2066,5039,2381,5040, 214,1709,4521, 804,  35, 707, #  448
- 324,3688,1601,2554, 140, 459,4210,5041,5042,1365, 839, 272, 978,2262,2580,3456, #  464
-2129,1363,3689,1423, 697, 100,3094,  48,  70,1231, 495,3139,2196,5043,1294,5044, #  480
-2080, 462, 586,1042,3279, 853, 256, 988, 185,2382,3457,1698, 434,1084,5045,3458, #  496
- 314,2625,2788,4522,2335,2336, 569,2285, 637,1817,2525, 757,1162,1879,1616,3459, #  512
- 287,1577,2116, 768,4523,1671,2868,3566,2526,1321,3816, 909,2418,5046,4211, 933, #  528
-3817,4212,2053,2361,1222,4524, 765,2419,1322, 786,4525,5047,1920,1462,1677,2909, #  544
-1699,5048,4526,1424,2442,3140,3690,2600,3353,1775,1941,3460,3983,4213, 309,1369, #  560
-1130,2825, 364,2234,1653,1299,3984,3567,3985,3986,2656, 525,1085,3041, 902,2001, #  576
-1475, 964,4527, 421,1845,1415,1057,2286, 940,1364,3141, 376,4528,4529,1381,   7, #  592
-2527, 983,2383, 336,1710,2684,1846, 321,3461, 559,1131,3042,2752,1809,1132,1313, #  608
- 265,1481,1858,5049, 352,1203,2826,3280, 167,1089, 420,2827, 776, 792,1724,3568, #  624
-4214,2443,3281,5050,4215,5051, 446, 229, 333,2753, 901,3818,1200,1557,4530,2657, #  640
-1921, 395,2754,2685,3819,4216,1836, 125, 916,3209,2626,4531,5052,5053,3820,5054, #  656
-5055,5056,4532,3142,3691,1133,2555,1757,3462,1510,2318,1409,3569,5057,2146, 438, #  672
-2601,2910,2384,3354,1068, 958,3043, 461, 311,2869,2686,4217,1916,3210,4218,1979, #  688
- 383, 750,2755,2627,4219, 274, 539, 385,1278,1442,5058,1154,1965, 384, 561, 210, #  704
-  98,1295,2556,3570,5059,1711,2420,1482,3463,3987,2911,1257, 129,5060,3821, 642, #  720
- 523,2789,2790,2658,5061, 141,2235,1333,  68, 176, 441, 876, 907,4220, 603,2602, #  736
- 710, 171,3464, 404, 549,  18,3143,2398,1410,3692,1666,5062,3571,4533,2912,4534, #  752
-5063,2991, 368,5064, 146, 366,  99, 871,3693,1543, 748, 807,1586,1185,  22,2263, #  768
- 379,3822,3211,5065,3212, 505,1942,2628,1992,1382,2319,5066, 380,2362, 218, 702, #  784
-1818,1248,3465,3044,3572,3355,3282,5067,2992,3694, 930,3283,3823,5068,  59,5069, #  800
- 585, 601,4221, 497,3466,1112,1314,4535,1802,5070,1223,1472,2177,5071, 749,1837, #  816
- 690,1900,3824,1773,3988,1476, 429,1043,1791,2236,2117, 917,4222, 447,1086,1629, #  832
-5072, 556,5073,5074,2021,1654, 844,1090, 105, 550, 966,1758,2828,1008,1783, 686, #  848
-1095,5075,2287, 793,1602,5076,3573,2603,4536,4223,2948,2302,4537,3825, 980,2503, #  864
- 544, 353, 527,4538, 908,2687,2913,5077, 381,2629,1943,1348,5078,1341,1252, 560, #  880
-3095,5079,3467,2870,5080,2054, 973, 886,2081, 143,4539,5081,5082, 157,3989, 496, #  896
-4224,  57, 840, 540,2039,4540,4541,3468,2118,1445, 970,2264,1748,1966,2082,4225, #  912
-3144,1234,1776,3284,2829,3695, 773,1206,2130,1066,2040,1326,3990,1738,1725,4226, #  928
- 279,3145,  51,1544,2604, 423,1578,2131,2067, 173,4542,1880,5083,5084,1583, 264, #  944
- 610,3696,4543,2444, 280, 154,5085,5086,5087,1739, 338,1282,3096, 693,2871,1411, #  960
-1074,3826,2445,5088,4544,5089,5090,1240, 952,2399,5091,2914,1538,2688, 685,1483, #  976
-4227,2475,1436, 953,4228,2055,4545, 671,2400,  79,4229,2446,3285, 608, 567,2689, #  992
-3469,4230,4231,1691, 393,1261,1792,2401,5092,4546,5093,5094,5095,5096,1383,1672, # 1008
-3827,3213,1464, 522,1119, 661,1150, 216, 675,4547,3991,1432,3574, 609,4548,2690, # 1024
-2402,5097,5098,5099,4232,3045,   0,5100,2476, 315, 231,2447, 301,3356,4549,2385, # 1040
-5101, 233,4233,3697,1819,4550,4551,5102,  96,1777,1315,2083,5103, 257,5104,1810, # 1056
-3698,2718,1139,1820,4234,2022,1124,2164,2791,1778,2659,5105,3097, 363,1655,3214, # 1072
-5106,2993,5107,5108,5109,3992,1567,3993, 718, 103,3215, 849,1443, 341,3357,2949, # 1088
-1484,5110,1712, 127,  67, 339,4235,2403, 679,1412, 821,5111,5112, 834, 738, 351, # 1104
-2994,2147, 846, 235,1497,1881, 418,1993,3828,2719, 186,1100,2148,2756,3575,1545, # 1120
-1355,2950,2872,1377, 583,3994,4236,2581,2995,5113,1298,3699,1078,2557,3700,2363, # 1136
-  78,3829,3830, 267,1289,2100,2002,1594,4237, 348, 369,1274,2197,2178,1838,4552, # 1152
-1821,2830,3701,2757,2288,2003,4553,2951,2758, 144,3358, 882,4554,3995,2759,3470, # 1168
-4555,2915,5114,4238,1726, 320,5115,3996,3046, 788,2996,5116,2831,1774,1327,2873, # 1184
-3997,2832,5117,1306,4556,2004,1700,3831,3576,2364,2660, 787,2023, 506, 824,3702, # 1200
- 534, 323,4557,1044,3359,2024,1901, 946,3471,5118,1779,1500,1678,5119,1882,4558, # 1216
- 165, 243,4559,3703,2528, 123, 683,4239, 764,4560,  36,3998,1793, 589,2916, 816, # 1232
- 626,1667,3047,2237,1639,1555,1622,3832,3999,5120,4000,2874,1370,1228,1933, 891, # 1248
-2084,2917, 304,4240,5121, 292,2997,2720,3577, 691,2101,4241,1115,4561, 118, 662, # 1264
-5122, 611,1156, 854,2386,1316,2875,   2, 386, 515,2918,5123,5124,3286, 868,2238, # 1280
-1486, 855,2661, 785,2216,3048,5125,1040,3216,3578,5126,3146, 448,5127,1525,5128, # 1296
-2165,4562,5129,3833,5130,4242,2833,3579,3147, 503, 818,4001,3148,1568, 814, 676, # 1312
-1444, 306,1749,5131,3834,1416,1030, 197,1428, 805,2834,1501,4563,5132,5133,5134, # 1328
-1994,5135,4564,5136,5137,2198,  13,2792,3704,2998,3149,1229,1917,5138,3835,2132, # 1344
-5139,4243,4565,2404,3580,5140,2217,1511,1727,1120,5141,5142, 646,3836,2448, 307, # 1360
-5143,5144,1595,3217,5145,5146,5147,3705,1113,1356,4002,1465,2529,2530,5148, 519, # 1376
-5149, 128,2133,  92,2289,1980,5150,4003,1512, 342,3150,2199,5151,2793,2218,1981, # 1392
-3360,4244, 290,1656,1317, 789, 827,2365,5152,3837,4566, 562, 581,4004,5153, 401, # 1408
-4567,2252,  94,4568,5154,1399,2794,5155,1463,2025,4569,3218,1944,5156, 828,1105, # 1424
-4245,1262,1394,5157,4246, 605,4570,5158,1784,2876,5159,2835, 819,2102, 578,2200, # 1440
-2952,5160,1502, 436,3287,4247,3288,2836,4005,2919,3472,3473,5161,2721,2320,5162, # 1456
-5163,2337,2068,  23,4571, 193, 826,3838,2103, 699,1630,4248,3098, 390,1794,1064, # 1472
-3581,5164,1579,3099,3100,1400,5165,4249,1839,1640,2877,5166,4572,4573, 137,4250, # 1488
- 598,3101,1967, 780, 104, 974,2953,5167, 278, 899, 253, 402, 572, 504, 493,1339, # 1504
-5168,4006,1275,4574,2582,2558,5169,3706,3049,3102,2253, 565,1334,2722, 863,  41, # 1520
-5170,5171,4575,5172,1657,2338,  19, 463,2760,4251, 606,5173,2999,3289,1087,2085, # 1536
-1323,2662,3000,5174,1631,1623,1750,4252,2691,5175,2878, 791,2723,2663,2339, 232, # 1552
-2421,5176,3001,1498,5177,2664,2630, 755,1366,3707,3290,3151,2026,1609, 119,1918, # 1568
-3474, 862,1026,4253,5178,4007,3839,4576,4008,4577,2265,1952,2477,5179,1125, 817, # 1584
-4254,4255,4009,1513,1766,2041,1487,4256,3050,3291,2837,3840,3152,5180,5181,1507, # 1600
-5182,2692, 733,  40,1632,1106,2879, 345,4257, 841,2531, 230,4578,3002,1847,3292, # 1616
-3475,5183,1263, 986,3476,5184, 735, 879, 254,1137, 857, 622,1300,1180,1388,1562, # 1632
-4010,4011,2954, 967,2761,2665,1349, 592,2134,1692,3361,3003,1995,4258,1679,4012, # 1648
-1902,2188,5185, 739,3708,2724,1296,1290,5186,4259,2201,2202,1922,1563,2605,2559, # 1664
-1871,2762,3004,5187, 435,5188, 343,1108, 596,  17,1751,4579,2239,3477,3709,5189, # 1680
-4580, 294,3582,2955,1693, 477, 979, 281,2042,3583, 643,2043,3710,2631,2795,2266, # 1696
-1031,2340,2135,2303,3584,4581, 367,1249,2560,5190,3585,5191,4582,1283,3362,2005, # 1712
- 240,1762,3363,4583,4584, 836,1069,3153, 474,5192,2149,2532, 268,3586,5193,3219, # 1728
-1521,1284,5194,1658,1546,4260,5195,3587,3588,5196,4261,3364,2693,1685,4262, 961, # 1744
-1673,2632, 190,2006,2203,3841,4585,4586,5197, 570,2504,3711,1490,5198,4587,2633, # 1760
-3293,1957,4588, 584,1514, 396,1045,1945,5199,4589,1968,2449,5200,5201,4590,4013, # 1776
- 619,5202,3154,3294, 215,2007,2796,2561,3220,4591,3221,4592, 763,4263,3842,4593, # 1792
-5203,5204,1958,1767,2956,3365,3712,1174, 452,1477,4594,3366,3155,5205,2838,1253, # 1808
-2387,2189,1091,2290,4264, 492,5206, 638,1169,1825,2136,1752,4014, 648, 926,1021, # 1824
-1324,4595, 520,4596, 997, 847,1007, 892,4597,3843,2267,1872,3713,2405,1785,4598, # 1840
-1953,2957,3103,3222,1728,4265,2044,3714,4599,2008,1701,3156,1551,  30,2268,4266, # 1856
-5207,2027,4600,3589,5208, 501,5209,4267, 594,3478,2166,1822,3590,3479,3591,3223, # 1872
- 829,2839,4268,5210,1680,3157,1225,4269,5211,3295,4601,4270,3158,2341,5212,4602, # 1888
-4271,5213,4015,4016,5214,1848,2388,2606,3367,5215,4603, 374,4017, 652,4272,4273, # 1904
- 375,1140, 798,5216,5217,5218,2366,4604,2269, 546,1659, 138,3051,2450,4605,5219, # 1920
-2254, 612,1849, 910, 796,3844,1740,1371, 825,3845,3846,5220,2920,2562,5221, 692, # 1936
- 444,3052,2634, 801,4606,4274,5222,1491, 244,1053,3053,4275,4276, 340,5223,4018, # 1952
-1041,3005, 293,1168,  87,1357,5224,1539, 959,5225,2240, 721, 694,4277,3847, 219, # 1968
-1478, 644,1417,3368,2666,1413,1401,1335,1389,4019,5226,5227,3006,2367,3159,1826, # 1984
- 730,1515, 184,2840,  66,4607,5228,1660,2958, 246,3369, 378,1457, 226,3480, 975, # 2000
-4020,2959,1264,3592, 674, 696,5229, 163,5230,1141,2422,2167, 713,3593,3370,4608, # 2016
-4021,5231,5232,1186,  15,5233,1079,1070,5234,1522,3224,3594, 276,1050,2725, 758, # 2032
-1126, 653,2960,3296,5235,2342, 889,3595,4022,3104,3007, 903,1250,4609,4023,3481, # 2048
-3596,1342,1681,1718, 766,3297, 286,  89,2961,3715,5236,1713,5237,2607,3371,3008, # 2064
-5238,2962,2219,3225,2880,5239,4610,2505,2533, 181, 387,1075,4024, 731,2190,3372, # 2080
-5240,3298, 310, 313,3482,2304, 770,4278,  54,3054, 189,4611,3105,3848,4025,5241, # 2096
-1230,1617,1850, 355,3597,4279,4612,3373, 111,4280,3716,1350,3160,3483,3055,4281, # 2112
-2150,3299,3598,5242,2797,4026,4027,3009, 722,2009,5243,1071, 247,1207,2343,2478, # 2128
-1378,4613,2010, 864,1437,1214,4614, 373,3849,1142,2220, 667,4615, 442,2763,2563, # 2144
-3850,4028,1969,4282,3300,1840, 837, 170,1107, 934,1336,1883,5244,5245,2119,4283, # 2160
-2841, 743,1569,5246,4616,4284, 582,2389,1418,3484,5247,1803,5248, 357,1395,1729, # 2176
-3717,3301,2423,1564,2241,5249,3106,3851,1633,4617,1114,2086,4285,1532,5250, 482, # 2192
-2451,4618,5251,5252,1492, 833,1466,5253,2726,3599,1641,2842,5254,1526,1272,3718, # 2208
-4286,1686,1795, 416,2564,1903,1954,1804,5255,3852,2798,3853,1159,2321,5256,2881, # 2224
-4619,1610,1584,3056,2424,2764, 443,3302,1163,3161,5257,5258,4029,5259,4287,2506, # 2240
-3057,4620,4030,3162,2104,1647,3600,2011,1873,4288,5260,4289, 431,3485,5261, 250, # 2256
-  97,  81,4290,5262,1648,1851,1558, 160, 848,5263, 866, 740,1694,5264,2204,2843, # 2272
-3226,4291,4621,3719,1687, 950,2479, 426, 469,3227,3720,3721,4031,5265,5266,1188, # 2288
- 424,1996, 861,3601,4292,3854,2205,2694, 168,1235,3602,4293,5267,2087,1674,4622, # 2304
-3374,3303, 220,2565,1009,5268,3855, 670,3010, 332,1208, 717,5269,5270,3603,2452, # 2320
-4032,3375,5271, 513,5272,1209,2882,3376,3163,4623,1080,5273,5274,5275,5276,2534, # 2336
-3722,3604, 815,1587,4033,4034,5277,3605,3486,3856,1254,4624,1328,3058,1390,4035, # 2352
-1741,4036,3857,4037,5278, 236,3858,2453,3304,5279,5280,3723,3859,1273,3860,4625, # 2368
-5281, 308,5282,4626, 245,4627,1852,2480,1307,2583, 430, 715,2137,2454,5283, 270, # 2384
- 199,2883,4038,5284,3606,2727,1753, 761,1754, 725,1661,1841,4628,3487,3724,5285, # 2400
-5286, 587,  14,3305, 227,2608, 326, 480,2270, 943,2765,3607, 291, 650,1884,5287, # 2416
-1702,1226, 102,1547,  62,3488, 904,4629,3489,1164,4294,5288,5289,1224,1548,2766, # 2432
- 391, 498,1493,5290,1386,1419,5291,2056,1177,4630, 813, 880,1081,2368, 566,1145, # 2448
-4631,2291,1001,1035,2566,2609,2242, 394,1286,5292,5293,2069,5294,  86,1494,1730, # 2464
-4039, 491,1588, 745, 897,2963, 843,3377,4040,2767,2884,3306,1768, 998,2221,2070, # 2480
- 397,1827,1195,1970,3725,3011,3378, 284,5295,3861,2507,2138,2120,1904,5296,4041, # 2496
-2151,4042,4295,1036,3490,1905, 114,2567,4296, 209,1527,5297,5298,2964,2844,2635, # 2512
-2390,2728,3164, 812,2568,5299,3307,5300,1559, 737,1885,3726,1210, 885,  28,2695, # 2528
-3608,3862,5301,4297,1004,1780,4632,5302, 346,1982,2222,2696,4633,3863,1742, 797, # 2544
-1642,4043,1934,1072,1384,2152, 896,4044,3308,3727,3228,2885,3609,5303,2569,1959, # 2560
-4634,2455,1786,5304,5305,5306,4045,4298,1005,1308,3728,4299,2729,4635,4636,1528, # 2576
-2610, 161,1178,4300,1983, 987,4637,1101,4301, 631,4046,1157,3229,2425,1343,1241, # 2592
-1016,2243,2570, 372, 877,2344,2508,1160, 555,1935, 911,4047,5307, 466,1170, 169, # 2608
-1051,2921,2697,3729,2481,3012,1182,2012,2571,1251,2636,5308, 992,2345,3491,1540, # 2624
-2730,1201,2071,2406,1997,2482,5309,4638, 528,1923,2191,1503,1874,1570,2369,3379, # 2640
-3309,5310, 557,1073,5311,1828,3492,2088,2271,3165,3059,3107, 767,3108,2799,4639, # 2656
-1006,4302,4640,2346,1267,2179,3730,3230, 778,4048,3231,2731,1597,2667,5312,4641, # 2672
-5313,3493,5314,5315,5316,3310,2698,1433,3311, 131,  95,1504,4049, 723,4303,3166, # 2688
-1842,3610,2768,2192,4050,2028,2105,3731,5317,3013,4051,1218,5318,3380,3232,4052, # 2704
-4304,2584, 248,1634,3864, 912,5319,2845,3732,3060,3865, 654,  53,5320,3014,5321, # 2720
-1688,4642, 777,3494,1032,4053,1425,5322, 191, 820,2121,2846, 971,4643, 931,3233, # 2736
- 135, 664, 783,3866,1998, 772,2922,1936,4054,3867,4644,2923,3234, 282,2732, 640, # 2752
-1372,3495,1127, 922, 325,3381,5323,5324, 711,2045,5325,5326,4055,2223,2800,1937, # 2768
-4056,3382,2224,2255,3868,2305,5327,4645,3869,1258,3312,4057,3235,2139,2965,4058, # 2784
-4059,5328,2225, 258,3236,4646, 101,1227,5329,3313,1755,5330,1391,3314,5331,2924, # 2800
-2057, 893,5332,5333,5334,1402,4305,2347,5335,5336,3237,3611,5337,5338, 878,1325, # 2816
-1781,2801,4647, 259,1385,2585, 744,1183,2272,4648,5339,4060,2509,5340, 684,1024, # 2832
-4306,5341, 472,3612,3496,1165,3315,4061,4062, 322,2153, 881, 455,1695,1152,1340, # 2848
- 660, 554,2154,4649,1058,4650,4307, 830,1065,3383,4063,4651,1924,5342,1703,1919, # 2864
-5343, 932,2273, 122,5344,4652, 947, 677,5345,3870,2637, 297,1906,1925,2274,4653, # 2880
-2322,3316,5346,5347,4308,5348,4309,  84,4310, 112, 989,5349, 547,1059,4064, 701, # 2896
-3613,1019,5350,4311,5351,3497, 942, 639, 457,2306,2456, 993,2966, 407, 851, 494, # 2912
-4654,3384, 927,5352,1237,5353,2426,3385, 573,4312, 680, 921,2925,1279,1875, 285, # 2928
- 790,1448,1984, 719,2168,5354,5355,4655,4065,4066,1649,5356,1541, 563,5357,1077, # 2944
-5358,3386,3061,3498, 511,3015,4067,4068,3733,4069,1268,2572,3387,3238,4656,4657, # 2960
-5359, 535,1048,1276,1189,2926,2029,3167,1438,1373,2847,2967,1134,2013,5360,4313, # 2976
-1238,2586,3109,1259,5361, 700,5362,2968,3168,3734,4314,5363,4315,1146,1876,1907, # 2992
-4658,2611,4070, 781,2427, 132,1589, 203, 147, 273,2802,2407, 898,1787,2155,4071, # 3008
-4072,5364,3871,2803,5365,5366,4659,4660,5367,3239,5368,1635,3872, 965,5369,1805, # 3024
-2699,1516,3614,1121,1082,1329,3317,4073,1449,3873,  65,1128,2848,2927,2769,1590, # 3040
-3874,5370,5371,  12,2668,  45, 976,2587,3169,4661, 517,2535,1013,1037,3240,5372, # 3056
-3875,2849,5373,3876,5374,3499,5375,2612, 614,1999,2323,3877,3110,2733,2638,5376, # 3072
-2588,4316, 599,1269,5377,1811,3735,5378,2700,3111, 759,1060, 489,1806,3388,3318, # 3088
-1358,5379,5380,2391,1387,1215,2639,2256, 490,5381,5382,4317,1759,2392,2348,5383, # 3104
-4662,3878,1908,4074,2640,1807,3241,4663,3500,3319,2770,2349, 874,5384,5385,3501, # 3120
-3736,1859,  91,2928,3737,3062,3879,4664,5386,3170,4075,2669,5387,3502,1202,1403, # 3136
-3880,2969,2536,1517,2510,4665,3503,2511,5388,4666,5389,2701,1886,1495,1731,4076, # 3152
-2370,4667,5390,2030,5391,5392,4077,2702,1216, 237,2589,4318,2324,4078,3881,4668, # 3168
-4669,2703,3615,3504, 445,4670,5393,5394,5395,5396,2771,  61,4079,3738,1823,4080, # 3184
-5397, 687,2046, 935, 925, 405,2670, 703,1096,1860,2734,4671,4081,1877,1367,2704, # 3200
-3389, 918,2106,1782,2483, 334,3320,1611,1093,4672, 564,3171,3505,3739,3390, 945, # 3216
-2641,2058,4673,5398,1926, 872,4319,5399,3506,2705,3112, 349,4320,3740,4082,4674, # 3232
-3882,4321,3741,2156,4083,4675,4676,4322,4677,2408,2047, 782,4084, 400, 251,4323, # 3248
-1624,5400,5401, 277,3742, 299,1265, 476,1191,3883,2122,4324,4325,1109, 205,5402, # 3264
-2590,1000,2157,3616,1861,5403,5404,5405,4678,5406,4679,2573, 107,2484,2158,4085, # 3280
-3507,3172,5407,1533, 541,1301, 158, 753,4326,2886,3617,5408,1696, 370,1088,4327, # 3296
-4680,3618, 579, 327, 440, 162,2244, 269,1938,1374,3508, 968,3063,  56,1396,3113, # 3312
-2107,3321,3391,5409,1927,2159,4681,3016,5410,3619,5411,5412,3743,4682,2485,5413, # 3328
-2804,5414,1650,4683,5415,2613,5416,5417,4086,2671,3392,1149,3393,4087,3884,4088, # 3344
-5418,1076,  49,5419, 951,3242,3322,3323, 450,2850, 920,5420,1812,2805,2371,4328, # 3360
-1909,1138,2372,3885,3509,5421,3243,4684,1910,1147,1518,2428,4685,3886,5422,4686, # 3376
-2393,2614, 260,1796,3244,5423,5424,3887,3324, 708,5425,3620,1704,5426,3621,1351, # 3392
-1618,3394,3017,1887, 944,4329,3395,4330,3064,3396,4331,5427,3744, 422, 413,1714, # 3408
-3325, 500,2059,2350,4332,2486,5428,1344,1911, 954,5429,1668,5430,5431,4089,2409, # 3424
-4333,3622,3888,4334,5432,2307,1318,2512,3114, 133,3115,2887,4687, 629,  31,2851, # 3440
-2706,3889,4688, 850, 949,4689,4090,2970,1732,2089,4335,1496,1853,5433,4091, 620, # 3456
-3245, 981,1242,3745,3397,1619,3746,1643,3326,2140,2457,1971,1719,3510,2169,5434, # 3472
-3246,5435,5436,3398,1829,5437,1277,4690,1565,2048,5438,1636,3623,3116,5439, 869, # 3488
-2852, 655,3890,3891,3117,4092,3018,3892,1310,3624,4691,5440,5441,5442,1733, 558, # 3504
-4692,3747, 335,1549,3065,1756,4336,3748,1946,3511,1830,1291,1192, 470,2735,2108, # 3520
-2806, 913,1054,4093,5443,1027,5444,3066,4094,4693, 982,2672,3399,3173,3512,3247, # 3536
-3248,1947,2807,5445, 571,4694,5446,1831,5447,3625,2591,1523,2429,5448,2090, 984, # 3552
-4695,3749,1960,5449,3750, 852, 923,2808,3513,3751, 969,1519, 999,2049,2325,1705, # 3568
-5450,3118, 615,1662, 151, 597,4095,2410,2326,1049, 275,4696,3752,4337, 568,3753, # 3584
-3626,2487,4338,3754,5451,2430,2275, 409,3249,5452,1566,2888,3514,1002, 769,2853, # 3600
- 194,2091,3174,3755,2226,3327,4339, 628,1505,5453,5454,1763,2180,3019,4096, 521, # 3616
-1161,2592,1788,2206,2411,4697,4097,1625,4340,4341, 412,  42,3119, 464,5455,2642, # 3632
-4698,3400,1760,1571,2889,3515,2537,1219,2207,3893,2643,2141,2373,4699,4700,3328, # 3648
-1651,3401,3627,5456,5457,3628,2488,3516,5458,3756,5459,5460,2276,2092, 460,5461, # 3664
-4701,5462,3020, 962, 588,3629, 289,3250,2644,1116,  52,5463,3067,1797,5464,5465, # 3680
-5466,1467,5467,1598,1143,3757,4342,1985,1734,1067,4702,1280,3402, 465,4703,1572, # 3696
- 510,5468,1928,2245,1813,1644,3630,5469,4704,3758,5470,5471,2673,1573,1534,5472, # 3712
-5473, 536,1808,1761,3517,3894,3175,2645,5474,5475,5476,4705,3518,2929,1912,2809, # 3728
-5477,3329,1122, 377,3251,5478, 360,5479,5480,4343,1529, 551,5481,2060,3759,1769, # 3744
-2431,5482,2930,4344,3330,3120,2327,2109,2031,4706,1404, 136,1468,1479, 672,1171, # 3760
-3252,2308, 271,3176,5483,2772,5484,2050, 678,2736, 865,1948,4707,5485,2014,4098, # 3776
-2971,5486,2737,2227,1397,3068,3760,4708,4709,1735,2931,3403,3631,5487,3895, 509, # 3792
-2854,2458,2890,3896,5488,5489,3177,3178,4710,4345,2538,4711,2309,1166,1010, 552, # 3808
- 681,1888,5490,5491,2972,2973,4099,1287,1596,1862,3179, 358, 453, 736, 175, 478, # 3824
-1117, 905,1167,1097,5492,1854,1530,5493,1706,5494,2181,3519,2292,3761,3520,3632, # 3840
-4346,2093,4347,5495,3404,1193,2489,4348,1458,2193,2208,1863,1889,1421,3331,2932, # 3856
-3069,2182,3521, 595,2123,5496,4100,5497,5498,4349,1707,2646, 223,3762,1359, 751, # 3872
-3121, 183,3522,5499,2810,3021, 419,2374, 633, 704,3897,2394, 241,5500,5501,5502, # 3888
- 838,3022,3763,2277,2773,2459,3898,1939,2051,4101,1309,3122,2246,1181,5503,1136, # 3904
-2209,3899,2375,1446,4350,2310,4712,5504,5505,4351,1055,2615, 484,3764,5506,4102, # 3920
- 625,4352,2278,3405,1499,4353,4103,5507,4104,4354,3253,2279,2280,3523,5508,5509, # 3936
-2774, 808,2616,3765,3406,4105,4355,3123,2539, 526,3407,3900,4356, 955,5510,1620, # 3952
-4357,2647,2432,5511,1429,3766,1669,1832, 994, 928,5512,3633,1260,5513,5514,5515, # 3968
-1949,2293, 741,2933,1626,4358,2738,2460, 867,1184, 362,3408,1392,5516,5517,4106, # 3984
-4359,1770,1736,3254,2934,4713,4714,1929,2707,1459,1158,5518,3070,3409,2891,1292, # 4000
-1930,2513,2855,3767,1986,1187,2072,2015,2617,4360,5519,2574,2514,2170,3768,2490, # 4016
-3332,5520,3769,4715,5521,5522, 666,1003,3023,1022,3634,4361,5523,4716,1814,2257, # 4032
- 574,3901,1603, 295,1535, 705,3902,4362, 283, 858, 417,5524,5525,3255,4717,4718, # 4048
-3071,1220,1890,1046,2281,2461,4107,1393,1599, 689,2575, 388,4363,5526,2491, 802, # 4064
-5527,2811,3903,2061,1405,2258,5528,4719,3904,2110,1052,1345,3256,1585,5529, 809, # 4080
-5530,5531,5532, 575,2739,3524, 956,1552,1469,1144,2328,5533,2329,1560,2462,3635, # 4096
-3257,4108, 616,2210,4364,3180,2183,2294,5534,1833,5535,3525,4720,5536,1319,3770, # 4112
-3771,1211,3636,1023,3258,1293,2812,5537,5538,5539,3905, 607,2311,3906, 762,2892, # 4128
-1439,4365,1360,4721,1485,3072,5540,4722,1038,4366,1450,2062,2648,4367,1379,4723, # 4144
-2593,5541,5542,4368,1352,1414,2330,2935,1172,5543,5544,3907,3908,4724,1798,1451, # 4160
-5545,5546,5547,5548,2936,4109,4110,2492,2351, 411,4111,4112,3637,3333,3124,4725, # 4176
-1561,2674,1452,4113,1375,5549,5550,  47,2974, 316,5551,1406,1591,2937,3181,5552, # 4192
-1025,2142,3125,3182, 354,2740, 884,2228,4369,2412, 508,3772, 726,3638, 996,2433, # 4208
-3639, 729,5553, 392,2194,1453,4114,4726,3773,5554,5555,2463,3640,2618,1675,2813, # 4224
- 919,2352,2975,2353,1270,4727,4115,  73,5556,5557, 647,5558,3259,2856,2259,1550, # 4240
-1346,3024,5559,1332, 883,3526,5560,5561,5562,5563,3334,2775,5564,1212, 831,1347, # 4256
-4370,4728,2331,3909,1864,3073, 720,3910,4729,4730,3911,5565,4371,5566,5567,4731, # 4272
-5568,5569,1799,4732,3774,2619,4733,3641,1645,2376,4734,5570,2938, 669,2211,2675, # 4288
-2434,5571,2893,5572,5573,1028,3260,5574,4372,2413,5575,2260,1353,5576,5577,4735, # 4304
-3183, 518,5578,4116,5579,4373,1961,5580,2143,4374,5581,5582,3025,2354,2355,3912, # 4320
- 516,1834,1454,4117,2708,4375,4736,2229,2620,1972,1129,3642,5583,2776,5584,2976, # 4336
-1422, 577,1470,3026,1524,3410,5585,5586, 432,4376,3074,3527,5587,2594,1455,2515, # 4352
-2230,1973,1175,5588,1020,2741,4118,3528,4737,5589,2742,5590,1743,1361,3075,3529, # 4368
-2649,4119,4377,4738,2295, 895, 924,4378,2171, 331,2247,3076, 166,1627,3077,1098, # 4384
-5591,1232,2894,2231,3411,4739, 657, 403,1196,2377, 542,3775,3412,1600,4379,3530, # 4400
-5592,4740,2777,3261, 576, 530,1362,4741,4742,2540,2676,3776,4120,5593, 842,3913, # 4416
-5594,2814,2032,1014,4121, 213,2709,3413, 665, 621,4380,5595,3777,2939,2435,5596, # 4432
-2436,3335,3643,3414,4743,4381,2541,4382,4744,3644,1682,4383,3531,1380,5597, 724, # 4448
-2282, 600,1670,5598,1337,1233,4745,3126,2248,5599,1621,4746,5600, 651,4384,5601, # 4464
-1612,4385,2621,5602,2857,5603,2743,2312,3078,5604, 716,2464,3079, 174,1255,2710, # 4480
-4122,3645, 548,1320,1398, 728,4123,1574,5605,1891,1197,3080,4124,5606,3081,3082, # 4496
-3778,3646,3779, 747,5607, 635,4386,4747,5608,5609,5610,4387,5611,5612,4748,5613, # 4512
-3415,4749,2437, 451,5614,3780,2542,2073,4388,2744,4389,4125,5615,1764,4750,5616, # 4528
-4390, 350,4751,2283,2395,2493,5617,4391,4126,2249,1434,4127, 488,4752, 458,4392, # 4544
-4128,3781, 771,1330,2396,3914,2576,3184,2160,2414,1553,2677,3185,4393,5618,2494, # 4560
-2895,2622,1720,2711,4394,3416,4753,5619,2543,4395,5620,3262,4396,2778,5621,2016, # 4576
-2745,5622,1155,1017,3782,3915,5623,3336,2313, 201,1865,4397,1430,5624,4129,5625, # 4592
-5626,5627,5628,5629,4398,1604,5630, 414,1866, 371,2595,4754,4755,3532,2017,3127, # 4608
-4756,1708, 960,4399, 887, 389,2172,1536,1663,1721,5631,2232,4130,2356,2940,1580, # 4624
-5632,5633,1744,4757,2544,4758,4759,5634,4760,5635,2074,5636,4761,3647,3417,2896, # 4640
-4400,5637,4401,2650,3418,2815, 673,2712,2465, 709,3533,4131,3648,4402,5638,1148, # 4656
- 502, 634,5639,5640,1204,4762,3649,1575,4763,2623,3783,5641,3784,3128, 948,3263, # 4672
- 121,1745,3916,1110,5642,4403,3083,2516,3027,4132,3785,1151,1771,3917,1488,4133, # 4688
-1987,5643,2438,3534,5644,5645,2094,5646,4404,3918,1213,1407,2816, 531,2746,2545, # 4704
-3264,1011,1537,4764,2779,4405,3129,1061,5647,3786,3787,1867,2897,5648,2018, 120, # 4720
-4406,4407,2063,3650,3265,2314,3919,2678,3419,1955,4765,4134,5649,3535,1047,2713, # 4736
-1266,5650,1368,4766,2858, 649,3420,3920,2546,2747,1102,2859,2679,5651,5652,2000, # 4752
-5653,1111,3651,2977,5654,2495,3921,3652,2817,1855,3421,3788,5655,5656,3422,2415, # 4768
-2898,3337,3266,3653,5657,2577,5658,3654,2818,4135,1460, 856,5659,3655,5660,2899, # 4784
-2978,5661,2900,3922,5662,4408, 632,2517, 875,3923,1697,3924,2296,5663,5664,4767, # 4800
-3028,1239, 580,4768,4409,5665, 914, 936,2075,1190,4136,1039,2124,5666,5667,5668, # 4816
-5669,3423,1473,5670,1354,4410,3925,4769,2173,3084,4137, 915,3338,4411,4412,3339, # 4832
-1605,1835,5671,2748, 398,3656,4413,3926,4138, 328,1913,2860,4139,3927,1331,4414, # 4848
-3029, 937,4415,5672,3657,4140,4141,3424,2161,4770,3425, 524, 742, 538,3085,1012, # 4864
-5673,5674,3928,2466,5675, 658,1103, 225,3929,5676,5677,4771,5678,4772,5679,3267, # 4880
-1243,5680,4142, 963,2250,4773,5681,2714,3658,3186,5682,5683,2596,2332,5684,4774, # 4896
-5685,5686,5687,3536, 957,3426,2547,2033,1931,2941,2467, 870,2019,3659,1746,2780, # 4912
-2781,2439,2468,5688,3930,5689,3789,3130,3790,3537,3427,3791,5690,1179,3086,5691, # 4928
-3187,2378,4416,3792,2548,3188,3131,2749,4143,5692,3428,1556,2549,2297, 977,2901, # 4944
-2034,4144,1205,3429,5693,1765,3430,3189,2125,1271, 714,1689,4775,3538,5694,2333, # 4960
-3931, 533,4417,3660,2184, 617,5695,2469,3340,3539,2315,5696,5697,3190,5698,5699, # 4976
-3932,1988, 618, 427,2651,3540,3431,5700,5701,1244,1690,5702,2819,4418,4776,5703, # 4992
-3541,4777,5704,2284,1576, 473,3661,4419,3432, 972,5705,3662,5706,3087,5707,5708, # 5008
-4778,4779,5709,3793,4145,4146,5710, 153,4780, 356,5711,1892,2902,4420,2144, 408, # 5024
- 803,2357,5712,3933,5713,4421,1646,2578,2518,4781,4782,3934,5714,3935,4422,5715, # 5040
-2416,3433, 752,5716,5717,1962,3341,2979,5718, 746,3030,2470,4783,4423,3794, 698, # 5056
-4784,1893,4424,3663,2550,4785,3664,3936,5719,3191,3434,5720,1824,1302,4147,2715, # 5072
-3937,1974,4425,5721,4426,3192, 823,1303,1288,1236,2861,3542,4148,3435, 774,3938, # 5088
-5722,1581,4786,1304,2862,3939,4787,5723,2440,2162,1083,3268,4427,4149,4428, 344, # 5104
-1173, 288,2316, 454,1683,5724,5725,1461,4788,4150,2597,5726,5727,4789, 985, 894, # 5120
-5728,3436,3193,5729,1914,2942,3795,1989,5730,2111,1975,5731,4151,5732,2579,1194, # 5136
- 425,5733,4790,3194,1245,3796,4429,5734,5735,2863,5736, 636,4791,1856,3940, 760, # 5152
-1800,5737,4430,2212,1508,4792,4152,1894,1684,2298,5738,5739,4793,4431,4432,2213, # 5168
- 479,5740,5741, 832,5742,4153,2496,5743,2980,2497,3797, 990,3132, 627,1815,2652, # 5184
-4433,1582,4434,2126,2112,3543,4794,5744, 799,4435,3195,5745,4795,2113,1737,3031, # 5200
-1018, 543, 754,4436,3342,1676,4796,4797,4154,4798,1489,5746,3544,5747,2624,2903, # 5216
-4155,5748,5749,2981,5750,5751,5752,5753,3196,4799,4800,2185,1722,5754,3269,3270, # 5232
-1843,3665,1715, 481, 365,1976,1857,5755,5756,1963,2498,4801,5757,2127,3666,3271, # 5248
- 433,1895,2064,2076,5758, 602,2750,5759,5760,5761,5762,5763,3032,1628,3437,5764, # 5264
-3197,4802,4156,2904,4803,2519,5765,2551,2782,5766,5767,5768,3343,4804,2905,5769, # 5280
-4805,5770,2864,4806,4807,1221,2982,4157,2520,5771,5772,5773,1868,1990,5774,5775, # 5296
-5776,1896,5777,5778,4808,1897,4158, 318,5779,2095,4159,4437,5780,5781, 485,5782, # 5312
- 938,3941, 553,2680, 116,5783,3942,3667,5784,3545,2681,2783,3438,3344,2820,5785, # 5328
-3668,2943,4160,1747,2944,2983,5786,5787, 207,5788,4809,5789,4810,2521,5790,3033, # 5344
- 890,3669,3943,5791,1878,3798,3439,5792,2186,2358,3440,1652,5793,5794,5795, 941, # 5360
-2299, 208,3546,4161,2020, 330,4438,3944,2906,2499,3799,4439,4811,5796,5797,5798, # 5376
-)
-# fmt: on

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/platformdirs/version.py

@@ -1,4 +0,0 @@
-# file generated by setuptools_scm
-# don't change, don't track in version control
-__version__ = version = '3.2.0'
-__version_tuple__ = version_tuple = (3, 2, 0)

spaces/Boadiwaa/Recipes/openai/openai_object.py

@@ -1,294 +0,0 @@
-import json
-from copy import deepcopy
-from typing import Optional
-
-import openai
-from openai import api_requestor, util
-from openai.openai_response import OpenAIResponse
-from openai.util import ApiType
-
-
-class OpenAIObject(dict):
-    api_base_override = None
-
-    def __init__(
-        self,
-        id=None,
-        api_key=None,
-        api_version=None,
-        api_type=None,
-        organization=None,
-        response_ms: Optional[int] = None,
-        api_base=None,
-        engine=None,
-        **params,
-    ):
-        super(OpenAIObject, self).__init__()
-
-        if response_ms is not None and not isinstance(response_ms, int):
-            raise TypeError(f"response_ms is a {type(response_ms).__name__}.")
-        self._response_ms = response_ms
-
-        self._retrieve_params = params
-
-        object.__setattr__(self, "api_key", api_key)
-        object.__setattr__(self, "api_version", api_version)
-        object.__setattr__(self, "api_type", api_type)
-        object.__setattr__(self, "organization", organization)
-        object.__setattr__(self, "api_base_override", api_base)
-        object.__setattr__(self, "engine", engine)
-
-        if id:
-            self["id"] = id
-
-    @property
-    def response_ms(self) -> Optional[int]:
-        return self._response_ms
-
-    def __setattr__(self, k, v):
-        if k[0] == "_" or k in self.__dict__:
-            return super(OpenAIObject, self).__setattr__(k, v)
-
-        self[k] = v
-        return None
-
-    def __getattr__(self, k):
-        if k[0] == "_":
-            raise AttributeError(k)
-        try:
-            return self[k]
-        except KeyError as err:
-            raise AttributeError(*err.args)
-
-    def __delattr__(self, k):
-        if k[0] == "_" or k in self.__dict__:
-            return super(OpenAIObject, self).__delattr__(k)
-        else:
-            del self[k]
-
-    def __setitem__(self, k, v):
-        if v == "":
-            raise ValueError(
-                "You cannot set %s to an empty string. "
-                "We interpret empty strings as None in requests."
-                "You may set %s.%s = None to delete the property" % (k, str(self), k)
-            )
-        super(OpenAIObject, self).__setitem__(k, v)
-
-    def __delitem__(self, k):
-        raise NotImplementedError("del is not supported")
-
-    # Custom unpickling method that uses `update` to update the dictionary
-    # without calling __setitem__, which would fail if any value is an empty
-    # string
-    def __setstate__(self, state):
-        self.update(state)
-
-    # Custom pickling method to ensure the instance is pickled as a custom
-    # class and not as a dict, otherwise __setstate__ would not be called when
-    # unpickling.
-    def __reduce__(self):
-        reduce_value = (
-            type(self),  # callable
-            (  # args
-                self.get("id", None),
-                self.api_key,
-                self.api_version,
-                self.api_type,
-                self.organization,
-            ),
-            dict(self),  # state
-        )
-        return reduce_value
-
-    @classmethod
-    def construct_from(
-        cls,
-        values,
-        api_key: Optional[str] = None,
-        api_version=None,
-        organization=None,
-        engine=None,
-        response_ms: Optional[int] = None,
-    ):
-        instance = cls(
-            values.get("id"),
-            api_key=api_key,
-            api_version=api_version,
-            organization=organization,
-            engine=engine,
-            response_ms=response_ms,
-        )
-        instance.refresh_from(
-            values,
-            api_key=api_key,
-            api_version=api_version,
-            organization=organization,
-            response_ms=response_ms,
-        )
-        return instance
-
-    def refresh_from(
-        self,
-        values,
-        api_key=None,
-        api_version=None,
-        api_type=None,
-        organization=None,
-        response_ms: Optional[int] = None,
-    ):
-        self.api_key = api_key or getattr(values, "api_key", None)
-        self.api_version = api_version or getattr(values, "api_version", None)
-        self.api_type = api_type or getattr(values, "api_type", None)
-        self.organization = organization or getattr(values, "organization", None)
-        self._response_ms = response_ms or getattr(values, "_response_ms", None)
-
-        # Wipe old state before setting new.
-        self.clear()
-        for k, v in values.items():
-            super(OpenAIObject, self).__setitem__(
-                k, util.convert_to_openai_object(v, api_key, api_version, organization)
-            )
-
-        self._previous = values
-
-    @classmethod
-    def api_base(cls):
-        return None
-
-    def request(
-        self,
-        method,
-        url,
-        params=None,
-        headers=None,
-        stream=False,
-        plain_old_data=False,
-        request_id: Optional[str] = None,
-    ):
-        if params is None:
-            params = self._retrieve_params
-        requestor = api_requestor.APIRequestor(
-            key=self.api_key,
-            api_base=self.api_base_override or self.api_base(),
-            api_type=self.api_type,
-            api_version=self.api_version,
-            organization=self.organization,
-        )
-        response, stream, api_key = requestor.request(
-            method,
-            url,
-            params=params,
-            stream=stream,
-            headers=headers,
-            request_id=request_id,
-        )
-
-        if stream:
-            assert not isinstance(response, OpenAIResponse)  # must be an iterator
-            return (
-                util.convert_to_openai_object(
-                    line,
-                    api_key,
-                    self.api_version,
-                    self.organization,
-                    plain_old_data=plain_old_data,
-                )
-                for line in response
-            )
-        else:
-            return util.convert_to_openai_object(
-                response,
-                api_key,
-                self.api_version,
-                self.organization,
-                plain_old_data=plain_old_data,
-            )
-
-    def __repr__(self):
-        ident_parts = [type(self).__name__]
-
-        obj = self.get("object")
-        if isinstance(obj, str):
-            ident_parts.append(obj)
-
-        if isinstance(self.get("id"), str):
-            ident_parts.append("id=%s" % (self.get("id"),))
-
-        unicode_repr = "<%s at %s> JSON: %s" % (
-            " ".join(ident_parts),
-            hex(id(self)),
-            str(self),
-        )
-
-        return unicode_repr
-
-    def __str__(self):
-        obj = self.to_dict_recursive()
-        return json.dumps(obj, sort_keys=True, indent=2)
-
-    def to_dict(self):
-        return dict(self)
-
-    def to_dict_recursive(self):
-        d = dict(self)
-        for k, v in d.items():
-            if isinstance(v, OpenAIObject):
-                d[k] = v.to_dict_recursive()
-            elif isinstance(v, list):
-                d[k] = [
-                    e.to_dict_recursive() if isinstance(e, OpenAIObject) else e
-                    for e in v
-                ]
-        return d
-
-    @property
-    def openai_id(self):
-        return self.id
-
-    @property
-    def typed_api_type(self):
-        return (
-            ApiType.from_str(self.api_type)
-            if self.api_type
-            else ApiType.from_str(openai.api_type)
-        )
-
-    # This class overrides __setitem__ to throw exceptions on inputs that it
-    # doesn't like. This can cause problems when we try to copy an object
-    # wholesale because some data that's returned from the API may not be valid
-    # if it was set to be set manually. Here we override the class' copy
-    # arguments so that we can bypass these possible exceptions on __setitem__.
-    def __copy__(self):
-        copied = OpenAIObject(
-            self.get("id"),
-            self.api_key,
-            api_version=self.api_version,
-            api_type=self.api_type,
-            organization=self.organization,
-        )
-
-        copied._retrieve_params = self._retrieve_params
-
-        for k, v in self.items():
-            # Call parent's __setitem__ to avoid checks that we've added in the
-            # overridden version that can throw exceptions.
-            super(OpenAIObject, copied).__setitem__(k, v)
-
-        return copied
-
-    # This class overrides __setitem__ to throw exceptions on inputs that it
-    # doesn't like. This can cause problems when we try to copy an object
-    # wholesale because some data that's returned from the API may not be valid
-    # if it was set to be set manually. Here we override the class' copy
-    # arguments so that we can bypass these possible exceptions on __setitem__.
-    def __deepcopy__(self, memo):
-        copied = self.__copy__()
-        memo[id(self)] = copied
-
-        for k, v in self.items():
-            # Call parent's __setitem__ to avoid checks that we've added in the
-            # overridden version that can throw exceptions.
-            super(OpenAIObject, copied).__setitem__(k, deepcopy(v, memo))
-
-        return copied

spaces/Bradjan310/ehartford-Wizard-Vicuna-30B-Uncensored/README.md

@@ -1,12 +0,0 @@
----
-title: Ehartford Wizard Vicuna 30B Uncensored
-emoji: 🏃
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/CVPR/Dual-Key_Backdoor_Attacks/bottom-up-attention-vqa/base_model.py

@@ -1,60 +0,0 @@
-import torch
-import torch.nn as nn
-from attention import Attention, NewAttention
-from language_model import WordEmbedding, QuestionEmbedding
-from classifier import SimpleClassifier
-from fc import FCNet
-
-
-class BaseModel(nn.Module):
-    def __init__(self, w_emb, q_emb, v_att, q_net, v_net, classifier):
-        super(BaseModel, self).__init__()
-        self.w_emb = w_emb
-        self.q_emb = q_emb
-        self.v_att = v_att
-        self.q_net = q_net
-        self.v_net = v_net
-        self.classifier = classifier
-
-    def forward(self, v, b, q, labels):
-        """Forward
-
-        v: [batch, num_objs, obj_dim]
-        b: [batch, num_objs, b_dim]
-        q: [batch_size, seq_length]
-
-        return: logits, not probs
-        """
-        w_emb = self.w_emb(q)
-        q_emb = self.q_emb(w_emb) # [batch, q_dim]
-
-        att = self.v_att(v, q_emb)
-        v_emb = (att * v).sum(1) # [batch, v_dim]
-
-        q_repr = self.q_net(q_emb)
-        v_repr = self.v_net(v_emb)
-        joint_repr = q_repr * v_repr
-        logits = self.classifier(joint_repr)
-        return logits
-
-
-def build_baseline0(dataset, num_hid):
-    w_emb = WordEmbedding(dataset.dictionary.ntoken, 300, 0.0)
-    q_emb = QuestionEmbedding(300, num_hid, 1, False, 0.0)
-    v_att = Attention(dataset.v_dim, q_emb.num_hid, num_hid)
-    q_net = FCNet([num_hid, num_hid])
-    v_net = FCNet([dataset.v_dim, num_hid])
-    classifier = SimpleClassifier(
-        num_hid, 2 * num_hid, dataset.num_ans_candidates, 0.5)
-    return BaseModel(w_emb, q_emb, v_att, q_net, v_net, classifier)
-
-
-def build_baseline0_newatt(dataset, num_hid):
-    w_emb = WordEmbedding(dataset.dictionary.ntoken, 300, 0.0)
-    q_emb = QuestionEmbedding(300, num_hid, 1, False, 0.0)
-    v_att = NewAttention(dataset.v_dim, q_emb.num_hid, num_hid)
-    q_net = FCNet([q_emb.num_hid, num_hid])
-    v_net = FCNet([dataset.v_dim, num_hid])
-    classifier = SimpleClassifier(
-        num_hid, num_hid * 2, dataset.num_ans_candidates, 0.5)
-    return BaseModel(w_emb, q_emb, v_att, q_net, v_net, classifier)

spaces/CVPR/lama-example/models/ade20k/segm_lib/utils/data/distributed.py

@@ -1,58 +0,0 @@
-import math
-import torch
-from .sampler import Sampler
-from torch.distributed import get_world_size, get_rank
-
-
-class DistributedSampler(Sampler):
-    """Sampler that restricts data loading to a subset of the dataset.
-
-    It is especially useful in conjunction with
-    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
-    process can pass a DistributedSampler instance as a DataLoader sampler,
-    and load a subset of the original dataset that is exclusive to it.
-
-    .. note::
-        Dataset is assumed to be of constant size.
-
-    Arguments:
-        dataset: Dataset used for sampling.
-        num_replicas (optional): Number of processes participating in
-            distributed training.
-        rank (optional): Rank of the current process within num_replicas.
-    """
-
-    def __init__(self, dataset, num_replicas=None, rank=None):
-        if num_replicas is None:
-            num_replicas = get_world_size()
-        if rank is None:
-            rank = get_rank()
-        self.dataset = dataset
-        self.num_replicas = num_replicas
-        self.rank = rank
-        self.epoch = 0
-        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
-        self.total_size = self.num_samples * self.num_replicas
-
-    def __iter__(self):
-        # deterministically shuffle based on epoch
-        g = torch.Generator()
-        g.manual_seed(self.epoch)
-        indices = list(torch.randperm(len(self.dataset), generator=g))
-
-        # add extra samples to make it evenly divisible
-        indices += indices[:(self.total_size - len(indices))]
-        assert len(indices) == self.total_size
-
-        # subsample
-        offset = self.num_samples * self.rank
-        indices = indices[offset:offset + self.num_samples]
-        assert len(indices) == self.num_samples
-
-        return iter(indices)
-
-    def __len__(self):
-        return self.num_samples
-
-    def set_epoch(self, epoch):
-        self.epoch = epoch

spaces/CVPR/regionclip-demo/detectron2/export/caffe2_inference.py

@@ -1,161 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-import numpy as np
-from itertools import count
-import torch
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core
-
-from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
-from .shared import ScopedWS, get_pb_arg_vali, get_pb_arg_vals, infer_device_type
-
-logger = logging.getLogger(__name__)
-
-
-# ===== ref: mobile-vision predictor's 'Caffe2Wrapper' class ======
-class ProtobufModel(torch.nn.Module):
-    """
-    Wrapper of a caffe2's protobuf model.
-    It works just like nn.Module, but running caffe2 under the hood.
-    Input/Output are tuple[tensor] that match the caffe2 net's external_input/output.
-    """
-
-    _ids = count(0)
-
-    def __init__(self, predict_net, init_net):
-        logger.info(f"Initializing ProtobufModel for: {predict_net.name} ...")
-        super().__init__()
-        assert isinstance(predict_net, caffe2_pb2.NetDef)
-        assert isinstance(init_net, caffe2_pb2.NetDef)
-        # create unique temporary workspace for each instance
-        self.ws_name = "__tmp_ProtobufModel_{}__".format(next(self._ids))
-        self.net = core.Net(predict_net)
-
-        logger.info("Running init_net once to fill the parameters ...")
-        with ScopedWS(self.ws_name, is_reset=True, is_cleanup=False) as ws:
-            ws.RunNetOnce(init_net)
-            uninitialized_external_input = []
-            for blob in self.net.Proto().external_input:
-                if blob not in ws.Blobs():
-                    uninitialized_external_input.append(blob)
-                    ws.CreateBlob(blob)
-            ws.CreateNet(self.net)
-
-        self._error_msgs = set()
-        self._input_blobs = uninitialized_external_input
-
-    def _infer_output_devices(self, inputs):
-        """
-        Returns:
-            list[str]: list of device for each external output
-        """
-
-        def _get_device_type(torch_tensor):
-            assert torch_tensor.device.type in ["cpu", "cuda"]
-            assert torch_tensor.device.index == 0
-            return torch_tensor.device.type
-
-        predict_net = self.net.Proto()
-        input_device_types = {
-            (name, 0): _get_device_type(tensor) for name, tensor in zip(self._input_blobs, inputs)
-        }
-        device_type_map = infer_device_type(
-            predict_net, known_status=input_device_types, device_name_style="pytorch"
-        )
-        ssa, versions = core.get_ssa(predict_net)
-        versioned_outputs = [(name, versions[name]) for name in predict_net.external_output]
-        output_devices = [device_type_map[outp] for outp in versioned_outputs]
-        return output_devices
-
-    def forward(self, inputs):
-        """
-        Args:
-            inputs (tuple[torch.Tensor])
-
-        Returns:
-            tuple[torch.Tensor]
-        """
-        assert len(inputs) == len(self._input_blobs), (
-            f"Length of inputs ({len(inputs)}) "
-            f"doesn't match the required input blobs: {self._input_blobs}"
-        )
-
-        with ScopedWS(self.ws_name, is_reset=False, is_cleanup=False) as ws:
-            for b, tensor in zip(self._input_blobs, inputs):
-                ws.FeedBlob(b, tensor)
-
-            try:
-                ws.RunNet(self.net.Proto().name)
-            except RuntimeError as e:
-                if not str(e) in self._error_msgs:
-                    self._error_msgs.add(str(e))
-                    logger.warning("Encountered new RuntimeError: \n{}".format(str(e)))
-                logger.warning("Catch the error and use partial results.")
-
-            c2_outputs = [ws.FetchBlob(b) for b in self.net.Proto().external_output]
-            # Remove outputs of current run, this is necessary in order to
-            # prevent fetching the result from previous run if the model fails
-            # in the middle.
-            for b in self.net.Proto().external_output:
-                # Needs to create uninitialized blob to make the net runable.
-                # This is "equivalent" to: ws.RemoveBlob(b) then ws.CreateBlob(b),
-                # but there'no such API.
-                ws.FeedBlob(b, f"{b}, a C++ native class of type nullptr (uninitialized).")
-
-        # Cast output to torch.Tensor on the desired device
-        output_devices = (
-            self._infer_output_devices(inputs)
-            if any(t.device.type != "cpu" for t in inputs)
-            else ["cpu" for _ in self.net.Proto().external_output]
-        )
-
-        outputs = []
-        for name, c2_output, device in zip(
-            self.net.Proto().external_output, c2_outputs, output_devices
-        ):
-            if not isinstance(c2_output, np.ndarray):
-                raise RuntimeError(
-                    "Invalid output for blob {}, received: {}".format(name, c2_output)
-                )
-            outputs.append(torch.tensor(c2_output).to(device=device))
-        return tuple(outputs)
-
-
-class ProtobufDetectionModel(torch.nn.Module):
-    """
-    A class works just like a pytorch meta arch in terms of inference, but running
-    caffe2 model under the hood.
-    """
-
-    def __init__(self, predict_net, init_net, *, convert_outputs=None):
-        """
-        Args:
-            predict_net, init_net (core.Net): caffe2 nets
-            convert_outptus (callable): a function that converts caffe2
-                outputs to the same format of the original pytorch model.
-                By default, use the one defined in the caffe2 meta_arch.
-        """
-        super().__init__()
-        self.protobuf_model = ProtobufModel(predict_net, init_net)
-        self.size_divisibility = get_pb_arg_vali(predict_net, "size_divisibility", 0)
-        self.device = get_pb_arg_vals(predict_net, "device", b"cpu").decode("ascii")
-
-        if convert_outputs is None:
-            meta_arch = get_pb_arg_vals(predict_net, "meta_architecture", b"GeneralizedRCNN")
-            meta_arch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[meta_arch.decode("ascii")]
-            self._convert_outputs = meta_arch.get_outputs_converter(predict_net, init_net)
-        else:
-            self._convert_outputs = convert_outputs
-
-    def _convert_inputs(self, batched_inputs):
-        # currently all models convert inputs in the same way
-        return convert_batched_inputs_to_c2_format(
-            batched_inputs, self.size_divisibility, self.device
-        )
-
-    def forward(self, batched_inputs):
-        c2_inputs = self._convert_inputs(batched_inputs)
-        c2_results = self.protobuf_model(c2_inputs)
-        c2_results = dict(zip(self.protobuf_model.net.Proto().external_output, c2_results))
-        return self._convert_outputs(batched_inputs, c2_inputs, c2_results)

spaces/CVPR/regionclip-demo/detectron2/utils/collect_env.py

@@ -1,211 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import importlib
-import numpy as np
-import os
-import re
-import subprocess
-import sys
-from collections import defaultdict
-import PIL
-import torch
-import torchvision
-from tabulate import tabulate
-
-__all__ = ["collect_env_info"]
-
-
-def collect_torch_env():
-    try:
-        import torch.__config__
-
-        return torch.__config__.show()
-    except ImportError:
-        # compatible with older versions of pytorch
-        from torch.utils.collect_env import get_pretty_env_info
-
-        return get_pretty_env_info()
-
-
-def get_env_module():
-    var_name = "DETECTRON2_ENV_MODULE"
-    return var_name, os.environ.get(var_name, "<not set>")
-
-
-def detect_compute_compatibility(CUDA_HOME, so_file):
-    try:
-        cuobjdump = os.path.join(CUDA_HOME, "bin", "cuobjdump")
-        if os.path.isfile(cuobjdump):
-            output = subprocess.check_output(
-                "'{}' --list-elf '{}'".format(cuobjdump, so_file), shell=True
-            )
-            output = output.decode("utf-8").strip().split("\n")
-            arch = []
-            for line in output:
-                line = re.findall(r"\.sm_([0-9]*)\.", line)[0]
-                arch.append(".".join(line))
-            arch = sorted(set(arch))
-            return ", ".join(arch)
-        else:
-            return so_file + "; cannot find cuobjdump"
-    except Exception:
-        # unhandled failure
-        return so_file
-
-
-def collect_env_info():
-    has_gpu = torch.cuda.is_available()  # true for both CUDA & ROCM
-    torch_version = torch.__version__
-
-    # NOTE that CUDA_HOME/ROCM_HOME could be None even when CUDA runtime libs are functional
-    from torch.utils.cpp_extension import CUDA_HOME, ROCM_HOME
-
-    has_rocm = False
-    if (getattr(torch.version, "hip", None) is not None) and (ROCM_HOME is not None):
-        has_rocm = True
-    has_cuda = has_gpu and (not has_rocm)
-
-    data = []
-    data.append(("sys.platform", sys.platform))  # check-template.yml depends on it
-    data.append(("Python", sys.version.replace("\n", "")))
-    data.append(("numpy", np.__version__))
-
-    try:
-        import detectron2  # noqa
-
-        data.append(
-            ("detectron2", detectron2.__version__ + " @" + os.path.dirname(detectron2.__file__))
-        )
-    except ImportError:
-        data.append(("detectron2", "failed to import"))
-    except AttributeError:
-        data.append(("detectron2", "imported a wrong installation"))
-
-    try:
-        import detectron2._C as _C
-    except ImportError as e:
-        data.append(("detectron2._C", f"not built correctly: {e}"))
-
-        # print system compilers when extension fails to build
-        if sys.platform != "win32":  # don't know what to do for windows
-            try:
-                # this is how torch/utils/cpp_extensions.py choose compiler
-                cxx = os.environ.get("CXX", "c++")
-                cxx = subprocess.check_output("'{}' --version".format(cxx), shell=True)
-                cxx = cxx.decode("utf-8").strip().split("\n")[0]
-            except subprocess.SubprocessError:
-                cxx = "Not found"
-            data.append(("Compiler ($CXX)", cxx))
-
-            if has_cuda and CUDA_HOME is not None:
-                try:
-                    nvcc = os.path.join(CUDA_HOME, "bin", "nvcc")
-                    nvcc = subprocess.check_output("'{}' -V".format(nvcc), shell=True)
-                    nvcc = nvcc.decode("utf-8").strip().split("\n")[-1]
-                except subprocess.SubprocessError:
-                    nvcc = "Not found"
-                data.append(("CUDA compiler", nvcc))
-        if has_cuda and sys.platform != "win32":
-            try:
-                so_file = importlib.util.find_spec("detectron2._C").origin
-            except (ImportError, AttributeError):
-                pass
-            else:
-                data.append(
-                    ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, so_file))
-                )
-    else:
-        # print compilers that are used to build extension
-        data.append(("Compiler", _C.get_compiler_version()))
-        data.append(("CUDA compiler", _C.get_cuda_version()))  # cuda or hip
-        if has_cuda and getattr(_C, "has_cuda", lambda: True)():
-            data.append(
-                ("detectron2 arch flags", detect_compute_compatibility(CUDA_HOME, _C.__file__))
-            )
-
-    data.append(get_env_module())
-    data.append(("PyTorch", torch_version + " @" + os.path.dirname(torch.__file__)))
-    data.append(("PyTorch debug build", torch.version.debug))
-
-    data.append(("GPU available", has_gpu))
-    if has_gpu:
-        devices = defaultdict(list)
-        for k in range(torch.cuda.device_count()):
-            cap = ".".join((str(x) for x in torch.cuda.get_device_capability(k)))
-            name = torch.cuda.get_device_name(k) + f" (arch={cap})"
-            devices[name].append(str(k))
-        for name, devids in devices.items():
-            data.append(("GPU " + ",".join(devids), name))
-
-        if has_rocm:
-            msg = " - invalid!" if not (ROCM_HOME and os.path.isdir(ROCM_HOME)) else ""
-            data.append(("ROCM_HOME", str(ROCM_HOME) + msg))
-        else:
-            msg = " - invalid!" if not (CUDA_HOME and os.path.isdir(CUDA_HOME)) else ""
-            data.append(("CUDA_HOME", str(CUDA_HOME) + msg))
-
-            cuda_arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)
-            if cuda_arch_list:
-                data.append(("TORCH_CUDA_ARCH_LIST", cuda_arch_list))
-    data.append(("Pillow", PIL.__version__))
-
-    try:
-        data.append(
-            (
-                "torchvision",
-                str(torchvision.__version__) + " @" + os.path.dirname(torchvision.__file__),
-            )
-        )
-        if has_cuda:
-            try:
-                torchvision_C = importlib.util.find_spec("torchvision._C").origin
-                msg = detect_compute_compatibility(CUDA_HOME, torchvision_C)
-                data.append(("torchvision arch flags", msg))
-            except (ImportError, AttributeError):
-                data.append(("torchvision._C", "Not found"))
-    except AttributeError:
-        data.append(("torchvision", "unknown"))
-
-    try:
-        import fvcore
-
-        data.append(("fvcore", fvcore.__version__))
-    except (ImportError, AttributeError):
-        pass
-
-    try:
-        import iopath
-
-        data.append(("iopath", iopath.__version__))
-    except (ImportError, AttributeError):
-        pass
-
-    try:
-        import cv2
-
-        data.append(("cv2", cv2.__version__))
-    except (ImportError, AttributeError):
-        data.append(("cv2", "Not found"))
-    env_str = tabulate(data) + "\n"
-    env_str += collect_torch_env()
-    return env_str
-
-
-if __name__ == "__main__":
-    try:
-        from detectron2.utils.collect_env import collect_env_info as f
-
-        print(f())
-    except ImportError:
-        print(collect_env_info())
-
-    if torch.cuda.is_available():
-        for k in range(torch.cuda.device_count()):
-            device = f"cuda:{k}"
-            try:
-                x = torch.tensor([1, 2.0], dtype=torch.float32)
-                x = x.to(device)
-            except Exception as e:
-                print(
-                    f"Unable to copy tensor to device={device}: {e}. "
-                    "Your CUDA environment is broken."
-                )

spaces/Caoyunkang/Segment-Any-Anomaly/GroundingDINO/groundingdino/models/GroundingDINO/__init__.py

@@ -1,15 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Conditional DETR
-# Copyright (c) 2021 Microsoft. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copied from DETR (https://github.com/facebookresearch/detr)
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-# ------------------------------------------------------------------------
-
-from .groundingdino import build_groundingdino

spaces/Cherrycreamco/webui/oh-no.py

@@ -1,14 +0,0 @@
-import gradio as gr
-
-block = gr.Blocks()
-
-def run():
-  with block:
-    gr.Markdown(
-    """
-    <p>oh no 😐 something wrong with the 🤗 hugging face servers 😐 hopefully, it will be fixed soon</p>
-    """)
-    block.launch(server_name="0.0.0.0", server_port=7860)
-
-if __name__ == "__main__":
-    run()

spaces/CrucibleAI/ControlNetMediaPipeFaceSD21/cldm/hack.py

@@ -1,111 +0,0 @@
-import torch
-import einops
-
-import ldm.modules.encoders.modules
-import ldm.modules.attention
-
-from transformers import logging
-from ldm.modules.attention import default
-
-
-def disable_verbosity():
-    logging.set_verbosity_error()
-    print('logging improved.')
-    return
-
-
-def enable_sliced_attention():
-    ldm.modules.attention.CrossAttention.forward = _hacked_sliced_attentin_forward
-    print('Enabled sliced_attention.')
-    return
-
-
-def hack_everything(clip_skip=0):
-    disable_verbosity()
-    ldm.modules.encoders.modules.FrozenCLIPEmbedder.forward = _hacked_clip_forward
-    ldm.modules.encoders.modules.FrozenCLIPEmbedder.clip_skip = clip_skip
-    print('Enabled clip hacks.')
-    return
-
-
-# Written by Lvmin
-def _hacked_clip_forward(self, text):
-    PAD = self.tokenizer.pad_token_id
-    EOS = self.tokenizer.eos_token_id
-    BOS = self.tokenizer.bos_token_id
-
-    def tokenize(t):
-        return self.tokenizer(t, truncation=False, add_special_tokens=False)["input_ids"]
-
-    def transformer_encode(t):
-        if self.clip_skip > 1:
-            rt = self.transformer(input_ids=t, output_hidden_states=True)
-            return self.transformer.text_model.final_layer_norm(rt.hidden_states[-self.clip_skip])
-        else:
-            return self.transformer(input_ids=t, output_hidden_states=False).last_hidden_state
-
-    def split(x):
-        return x[75 * 0: 75 * 1], x[75 * 1: 75 * 2], x[75 * 2: 75 * 3]
-
-    def pad(x, p, i):
-        return x[:i] if len(x) >= i else x + [p] * (i - len(x))
-
-    raw_tokens_list = tokenize(text)
-    tokens_list = []
-
-    for raw_tokens in raw_tokens_list:
-        raw_tokens_123 = split(raw_tokens)
-        raw_tokens_123 = [[BOS] + raw_tokens_i + [EOS] for raw_tokens_i in raw_tokens_123]
-        raw_tokens_123 = [pad(raw_tokens_i, PAD, 77) for raw_tokens_i in raw_tokens_123]
-        tokens_list.append(raw_tokens_123)
-
-    tokens_list = torch.IntTensor(tokens_list).to(self.device)
-
-    feed = einops.rearrange(tokens_list, 'b f i -> (b f) i')
-    y = transformer_encode(feed)
-    z = einops.rearrange(y, '(b f) i c -> b (f i) c', f=3)
-
-    return z
-
-
-# Stolen from https://github.com/basujindal/stable-diffusion/blob/main/optimizedSD/splitAttention.py
-def _hacked_sliced_attentin_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-    k = self.to_k(context)
-    v = self.to_v(context)
-    del context, x
-
-    q, k, v = map(lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    limit = k.shape[0]
-    att_step = 1
-    q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
-    k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
-    v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))
-
-    q_chunks.reverse()
-    k_chunks.reverse()
-    v_chunks.reverse()
-    sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    del k, q, v
-    for i in range(0, limit, att_step):
-        q_buffer = q_chunks.pop()
-        k_buffer = k_chunks.pop()
-        v_buffer = v_chunks.pop()
-        sim_buffer = torch.einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
-
-        del k_buffer, q_buffer
-        # attention, what we cannot get enough of, by chunks
-
-        sim_buffer = sim_buffer.softmax(dim=-1)
-
-        sim_buffer = torch.einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
-        del v_buffer
-        sim[i:i + att_step, :, :] = sim_buffer
-
-        del sim_buffer
-    sim = einops.rearrange(sim, '(b h) n d -> b n (h d)', h=h)
-    return self.to_out(sim)

spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/model_serialization.py

@@ -1,80 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from collections import OrderedDict
-import logging
-
-import torch
-
-from maskrcnn_benchmark.utils.imports import import_file
-
-
-def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
-    """
-    Strategy: suppose that the models that we will create will have prefixes appended
-    to each of its keys, for example due to an extra level of nesting that the original
-    pre-trained weights from ImageNet won't contain. For example, model.state_dict()
-    might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
-    res2.conv1.weight. We thus want to match both parameters together.
-    For that, we look for each model weight, look among all loaded keys if there is one
-    that is a suffix of the current weight name, and use it if that's the case.
-    If multiple matches exist, take the one with longest size
-    of the corresponding name. For example, for the same model as before, the pretrained
-    weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
-    we want to match backbone[0].body.conv1.weight to conv1.weight, and
-    backbone[0].body.res2.conv1.weight to res2.conv1.weight.
-    """
-    current_keys = sorted(list(model_state_dict.keys()))
-    loaded_keys = sorted(list(loaded_state_dict.keys()))
-    # get a matrix of string matches, where each (i, j) entry correspond to the size of the
-    # loaded_key string, if it matches
-    match_matrix = [
-        len(j) if i.endswith(j) else 0 for i in current_keys for j in loaded_keys
-    ]
-    match_matrix = torch.as_tensor(match_matrix).view(
-        len(current_keys), len(loaded_keys)
-    )
-    max_match_size, idxs = match_matrix.max(1)
-    # remove indices that correspond to no-match
-    idxs[max_match_size == 0] = -1
-
-    # used for logging
-    max_size = max([len(key) for key in current_keys]) if current_keys else 1
-    max_size_loaded = max([len(key) for key in loaded_keys]) if loaded_keys else 1
-    log_str_template = "{: <{}} loaded from {: <{}} of shape {}"
-    logger = logging.getLogger(__name__)
-    for idx_new, idx_old in enumerate(idxs.tolist()):
-        if idx_old == -1:
-            continue
-        key = current_keys[idx_new]
-        key_old = loaded_keys[idx_old]
-        model_state_dict[key] = loaded_state_dict[key_old]
-        logger.info(
-            log_str_template.format(
-                key,
-                max_size,
-                key_old,
-                max_size_loaded,
-                tuple(loaded_state_dict[key_old].shape),
-            )
-        )
-
-
-def strip_prefix_if_present(state_dict, prefix):
-    keys = sorted(state_dict.keys())
-    if not all(key.startswith(prefix) for key in keys):
-        return state_dict
-    stripped_state_dict = OrderedDict()
-    for key, value in state_dict.items():
-        stripped_state_dict[key.replace(prefix, "")] = value
-    return stripped_state_dict
-
-
-def load_state_dict(model, loaded_state_dict):
-    model_state_dict = model.state_dict()
-    # if the state_dict comes from a model that was wrapped in a
-    # DataParallel or DistributedDataParallel during serialization,
-    # remove the "module" prefix before performing the matching
-    loaded_state_dict = strip_prefix_if_present(loaded_state_dict, prefix="module.")
-    align_and_update_state_dicts(model_state_dict, loaded_state_dict)
-
-    # use strict loading
-    model.load_state_dict(model_state_dict)

spaces/DAMO-NLP-SG/Video-LLaMA/video_llama/models/blip2_outputs.py

@@ -1,111 +0,0 @@
-"""
-Adapted from salesforce@LAVIS. Below is the original copyright:
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-from transformers.modeling_outputs import (
-    ModelOutput,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    CausalLMOutputWithCrossAttentions,
-)
-
-
-@dataclass
-class BlipSimilarity(ModelOutput):
-    sim_i2t: torch.FloatTensor = None
-    sim_t2i: torch.FloatTensor = None
-
-    sim_i2t_m: Optional[torch.FloatTensor] = None
-    sim_t2i_m: Optional[torch.FloatTensor] = None
-
-    sim_i2t_targets: Optional[torch.FloatTensor] = None
-    sim_t2i_targets: Optional[torch.FloatTensor] = None
-
-
-@dataclass
-class BlipIntermediateOutput(ModelOutput):
-    """
-    Data class for intermediate outputs of BLIP models.
-
-    image_embeds (torch.FloatTensor): Image embeddings, shape (batch_size, num_patches, embed_dim).
-    text_embeds (torch.FloatTensor): Text embeddings, shape (batch_size, seq_len, embed_dim).
-
-    image_embeds_m (torch.FloatTensor): Image embeddings from momentum visual encoder, shape (batch_size, num_patches, embed_dim).
-    text_embeds_m (torch.FloatTensor): Text embeddings from momentum text encoder, shape (batch_size, seq_len, embed_dim).
-
-    encoder_output (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder.
-    encoder_output_neg (BaseModelOutputWithPoolingAndCrossAttentions): output from the image-grounded text encoder for negative pairs.
-
-    decoder_output (CausalLMOutputWithCrossAttentions): output from the image-grounded text decoder.
-    decoder_labels (torch.LongTensor): labels for the captioning loss.
-
-    itm_logits (torch.FloatTensor): logits for the image-text matching loss, shape (batch_size * 3, 2).
-    itm_labels (torch.LongTensor): labels for the image-text matching loss, shape (batch_size * 3,)
-
-    """
-
-    # uni-modal features
-    image_embeds: torch.FloatTensor = None
-    text_embeds: Optional[torch.FloatTensor] = None
-
-    image_embeds_m: Optional[torch.FloatTensor] = None
-    text_embeds_m: Optional[torch.FloatTensor] = None
-
-    # intermediate outputs of multimodal encoder
-    encoder_output: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
-    encoder_output_neg: Optional[BaseModelOutputWithPoolingAndCrossAttentions] = None
-
-    itm_logits: Optional[torch.FloatTensor] = None
-    itm_labels: Optional[torch.LongTensor] = None
-
-    # intermediate outputs of multimodal decoder
-    decoder_output: Optional[CausalLMOutputWithCrossAttentions] = None
-    decoder_labels: Optional[torch.LongTensor] = None
-
-
-@dataclass
-class BlipOutput(ModelOutput):
-    # some finetuned models (e.g. BlipVQA) do not compute similarity, thus optional.
-    sims: Optional[BlipSimilarity] = None
-
-    intermediate_output: BlipIntermediateOutput = None
-
-    loss: Optional[torch.FloatTensor] = None
-
-    loss_itc: Optional[torch.FloatTensor] = None
-
-    loss_itm: Optional[torch.FloatTensor] = None
-
-    loss_lm: Optional[torch.FloatTensor] = None
-
-
-@dataclass
-class BlipOutputFeatures(ModelOutput):
-    """
-    Data class of features from BlipFeatureExtractor.
-
-    Args:
-        image_embeds: (torch.FloatTensor) of shape (batch_size, num_patches+1, embed_dim), optional
-        image_features: (torch.FloatTensor) of shape (batch_size, num_patches+1, feature_dim), optional
-        text_embeds: (torch.FloatTensor) of shape (batch_size, sequence_length+1, embed_dim), optional
-        text_features: (torch.FloatTensor) of shape (batch_size, sequence_length+1, feature_dim), optional
-
-        The first embedding or feature is for the [CLS] token.
-
-        Features are obtained by projecting the corresponding embedding into a normalized low-dimensional space.
-    """
-
-    image_embeds: Optional[torch.FloatTensor] = None
-    image_embeds_proj: Optional[torch.FloatTensor] = None
-
-    text_embeds: Optional[torch.FloatTensor] = None
-    text_embeds_proj: Optional[torch.FloatTensor] = None
-
-    multimodal_embeds: Optional[torch.FloatTensor] = None

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/filelock/_util.py

@@ -1,37 +0,0 @@
-from __future__ import annotations
-
-import os
-import stat
-import sys
-from errno import EACCES, EISDIR
-
-
-def raise_on_not_writable_file(filename: str) -> None:
-    """
-    Raise an exception if attempting to open the file for writing would fail.
-    This is done so files that will never be writable can be separated from
-    files that are writable but currently locked
-    :param filename: file to check
-    :raises OSError: as if the file was opened for writing.
-    """
-    try:  # use stat to do exists + can write to check without race condition
-        file_stat = os.stat(filename)  # noqa: PTH116
-    except OSError:
-        return  # swallow does not exist or other errors
-
-    if file_stat.st_mtime != 0:  # if os.stat returns but modification is zero that's an invalid os.stat - ignore it
-        if not (file_stat.st_mode & stat.S_IWUSR):
-            raise PermissionError(EACCES, "Permission denied", filename)
-
-        if stat.S_ISDIR(file_stat.st_mode):
-            if sys.platform == "win32":  # pragma: win32 cover
-                # On Windows, this is PermissionError
-                raise PermissionError(EACCES, "Permission denied", filename)
-            else:  # pragma: win32 no cover # noqa: RET506
-                # On linux / macOS, this is IsADirectoryError
-                raise IsADirectoryError(EISDIR, "Is a directory", filename)
-
-
-__all__ = [
-    "raise_on_not_writable_file",
-]

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/C_P_A_L_.py

@@ -1,297 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod
-
-from fontTools.misc.textTools import bytesjoin, safeEval
-from . import DefaultTable
-import array
-from collections import namedtuple
-import struct
-import sys
-
-
-class table_C_P_A_L_(DefaultTable.DefaultTable):
-
-    NO_NAME_ID = 0xFFFF
-    DEFAULT_PALETTE_TYPE = 0
-
-    def __init__(self, tag=None):
-        DefaultTable.DefaultTable.__init__(self, tag)
-        self.palettes = []
-        self.paletteTypes = []
-        self.paletteLabels = []
-        self.paletteEntryLabels = []
-
-    def decompile(self, data, ttFont):
-        (
-            self.version,
-            self.numPaletteEntries,
-            numPalettes,
-            numColorRecords,
-            goffsetFirstColorRecord,
-        ) = struct.unpack(">HHHHL", data[:12])
-        assert (
-            self.version <= 1
-        ), "Version of CPAL table is higher than I know how to handle"
-        self.palettes = []
-        pos = 12
-        for i in range(numPalettes):
-            startIndex = struct.unpack(">H", data[pos : pos + 2])[0]
-            assert startIndex + self.numPaletteEntries <= numColorRecords
-            pos += 2
-            palette = []
-            ppos = goffsetFirstColorRecord + startIndex * 4
-            for j in range(self.numPaletteEntries):
-                palette.append(Color(*struct.unpack(">BBBB", data[ppos : ppos + 4])))
-                ppos += 4
-            self.palettes.append(palette)
-        if self.version == 0:
-            offsetToPaletteTypeArray = 0
-            offsetToPaletteLabelArray = 0
-            offsetToPaletteEntryLabelArray = 0
-        else:
-            pos = 12 + numPalettes * 2
-            (
-                offsetToPaletteTypeArray,
-                offsetToPaletteLabelArray,
-                offsetToPaletteEntryLabelArray,
-            ) = struct.unpack(">LLL", data[pos : pos + 12])
-        self.paletteTypes = self._decompileUInt32Array(
-            data,
-            offsetToPaletteTypeArray,
-            numPalettes,
-            default=self.DEFAULT_PALETTE_TYPE,
-        )
-        self.paletteLabels = self._decompileUInt16Array(
-            data, offsetToPaletteLabelArray, numPalettes, default=self.NO_NAME_ID
-        )
-        self.paletteEntryLabels = self._decompileUInt16Array(
-            data,
-            offsetToPaletteEntryLabelArray,
-            self.numPaletteEntries,
-            default=self.NO_NAME_ID,
-        )
-
-    def _decompileUInt16Array(self, data, offset, numElements, default=0):
-        if offset == 0:
-            return [default] * numElements
-        result = array.array("H", data[offset : offset + 2 * numElements])
-        if sys.byteorder != "big":
-            result.byteswap()
-        assert len(result) == numElements, result
-        return result.tolist()
-
-    def _decompileUInt32Array(self, data, offset, numElements, default=0):
-        if offset == 0:
-            return [default] * numElements
-        result = array.array("I", data[offset : offset + 4 * numElements])
-        if sys.byteorder != "big":
-            result.byteswap()
-        assert len(result) == numElements, result
-        return result.tolist()
-
-    def compile(self, ttFont):
-        colorRecordIndices, colorRecords = self._compileColorRecords()
-        paletteTypes = self._compilePaletteTypes()
-        paletteLabels = self._compilePaletteLabels()
-        paletteEntryLabels = self._compilePaletteEntryLabels()
-        numColorRecords = len(colorRecords) // 4
-        offsetToFirstColorRecord = 12 + len(colorRecordIndices)
-        if self.version >= 1:
-            offsetToFirstColorRecord += 12
-        header = struct.pack(
-            ">HHHHL",
-            self.version,
-            self.numPaletteEntries,
-            len(self.palettes),
-            numColorRecords,
-            offsetToFirstColorRecord,
-        )
-        if self.version == 0:
-            dataList = [header, colorRecordIndices, colorRecords]
-        else:
-            pos = offsetToFirstColorRecord + len(colorRecords)
-            if len(paletteTypes) == 0:
-                offsetToPaletteTypeArray = 0
-            else:
-                offsetToPaletteTypeArray = pos
-                pos += len(paletteTypes)
-            if len(paletteLabels) == 0:
-                offsetToPaletteLabelArray = 0
-            else:
-                offsetToPaletteLabelArray = pos
-                pos += len(paletteLabels)
-            if len(paletteEntryLabels) == 0:
-                offsetToPaletteEntryLabelArray = 0
-            else:
-                offsetToPaletteEntryLabelArray = pos
-                pos += len(paletteLabels)
-            header1 = struct.pack(
-                ">LLL",
-                offsetToPaletteTypeArray,
-                offsetToPaletteLabelArray,
-                offsetToPaletteEntryLabelArray,
-            )
-            dataList = [
-                header,
-                colorRecordIndices,
-                header1,
-                colorRecords,
-                paletteTypes,
-                paletteLabels,
-                paletteEntryLabels,
-            ]
-        return bytesjoin(dataList)
-
-    def _compilePalette(self, palette):
-        assert len(palette) == self.numPaletteEntries
-        pack = lambda c: struct.pack(">BBBB", c.blue, c.green, c.red, c.alpha)
-        return bytesjoin([pack(color) for color in palette])
-
-    def _compileColorRecords(self):
-        colorRecords, colorRecordIndices, pool = [], [], {}
-        for palette in self.palettes:
-            packedPalette = self._compilePalette(palette)
-            if packedPalette in pool:
-                index = pool[packedPalette]
-            else:
-                index = len(colorRecords)
-                colorRecords.append(packedPalette)
-                pool[packedPalette] = index
-            colorRecordIndices.append(struct.pack(">H", index * self.numPaletteEntries))
-        return bytesjoin(colorRecordIndices), bytesjoin(colorRecords)
-
-    def _compilePaletteTypes(self):
-        if self.version == 0 or not any(self.paletteTypes):
-            return b""
-        assert len(self.paletteTypes) == len(self.palettes)
-        result = bytesjoin([struct.pack(">I", ptype) for ptype in self.paletteTypes])
-        assert len(result) == 4 * len(self.palettes)
-        return result
-
-    def _compilePaletteLabels(self):
-        if self.version == 0 or all(l == self.NO_NAME_ID for l in self.paletteLabels):
-            return b""
-        assert len(self.paletteLabels) == len(self.palettes)
-        result = bytesjoin([struct.pack(">H", label) for label in self.paletteLabels])
-        assert len(result) == 2 * len(self.palettes)
-        return result
-
-    def _compilePaletteEntryLabels(self):
-        if self.version == 0 or all(
-            l == self.NO_NAME_ID for l in self.paletteEntryLabels
-        ):
-            return b""
-        assert len(self.paletteEntryLabels) == self.numPaletteEntries
-        result = bytesjoin(
-            [struct.pack(">H", label) for label in self.paletteEntryLabels]
-        )
-        assert len(result) == 2 * self.numPaletteEntries
-        return result
-
-    def toXML(self, writer, ttFont):
-        numPalettes = len(self.palettes)
-        paletteLabels = {i: nameID for (i, nameID) in enumerate(self.paletteLabels)}
-        paletteTypes = {i: typ for (i, typ) in enumerate(self.paletteTypes)}
-        writer.simpletag("version", value=self.version)
-        writer.newline()
-        writer.simpletag("numPaletteEntries", value=self.numPaletteEntries)
-        writer.newline()
-        for index, palette in enumerate(self.palettes):
-            attrs = {"index": index}
-            paletteType = paletteTypes.get(index, self.DEFAULT_PALETTE_TYPE)
-            paletteLabel = paletteLabels.get(index, self.NO_NAME_ID)
-            if self.version > 0 and paletteLabel != self.NO_NAME_ID:
-                attrs["label"] = paletteLabel
-            if self.version > 0 and paletteType != self.DEFAULT_PALETTE_TYPE:
-                attrs["type"] = paletteType
-            writer.begintag("palette", **attrs)
-            writer.newline()
-            if (
-                self.version > 0
-                and paletteLabel != self.NO_NAME_ID
-                and ttFont
-                and "name" in ttFont
-            ):
-                name = ttFont["name"].getDebugName(paletteLabel)
-                if name is not None:
-                    writer.comment(name)
-                    writer.newline()
-            assert len(palette) == self.numPaletteEntries
-            for cindex, color in enumerate(palette):
-                color.toXML(writer, ttFont, cindex)
-            writer.endtag("palette")
-            writer.newline()
-        if self.version > 0 and not all(
-            l == self.NO_NAME_ID for l in self.paletteEntryLabels
-        ):
-            writer.begintag("paletteEntryLabels")
-            writer.newline()
-            for index, label in enumerate(self.paletteEntryLabels):
-                if label != self.NO_NAME_ID:
-                    writer.simpletag("label", index=index, value=label)
-                    if self.version > 0 and label and ttFont and "name" in ttFont:
-                        name = ttFont["name"].getDebugName(label)
-                        if name is not None:
-                            writer.comment(name)
-                    writer.newline()
-            writer.endtag("paletteEntryLabels")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "palette":
-            self.paletteLabels.append(int(attrs.get("label", self.NO_NAME_ID)))
-            self.paletteTypes.append(int(attrs.get("type", self.DEFAULT_PALETTE_TYPE)))
-            palette = []
-            for element in content:
-                if isinstance(element, str):
-                    continue
-                attrs = element[1]
-                color = Color.fromHex(attrs["value"])
-                palette.append(color)
-            self.palettes.append(palette)
-        elif name == "paletteEntryLabels":
-            colorLabels = {}
-            for element in content:
-                if isinstance(element, str):
-                    continue
-                elementName, elementAttr, _ = element
-                if elementName == "label":
-                    labelIndex = safeEval(elementAttr["index"])
-                    nameID = safeEval(elementAttr["value"])
-                    colorLabels[labelIndex] = nameID
-            self.paletteEntryLabels = [
-                colorLabels.get(i, self.NO_NAME_ID)
-                for i in range(self.numPaletteEntries)
-            ]
-        elif "value" in attrs:
-            value = safeEval(attrs["value"])
-            setattr(self, name, value)
-            if name == "numPaletteEntries":
-                self.paletteEntryLabels = [self.NO_NAME_ID] * self.numPaletteEntries
-
-
-class Color(namedtuple("Color", "blue green red alpha")):
-    def hex(self):
-        return "#%02X%02X%02X%02X" % (self.red, self.green, self.blue, self.alpha)
-
-    def __repr__(self):
-        return self.hex()
-
-    def toXML(self, writer, ttFont, index=None):
-        writer.simpletag("color", value=self.hex(), index=index)
-        writer.newline()
-
-    @classmethod
-    def fromHex(cls, value):
-        if value[0] == "#":
-            value = value[1:]
-        red = int(value[0:2], 16)
-        green = int(value[2:4], 16)
-        blue = int(value[4:6], 16)
-        alpha = int(value[6:8], 16) if len(value) >= 8 else 0xFF
-        return cls(red=red, green=green, blue=blue, alpha=alpha)
-
-    @classmethod
-    def fromRGBA(cls, red, green, blue, alpha):
-        return cls(red=red, green=green, blue=blue, alpha=alpha)

spaces/DaCuteRaccoon/dalle-mini/index.html

@@ -1,295 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta
-      name="viewport"
-      content="width=device-width, initial-scale=1, shrink-to-fit=no, maximum-scale=1"
-    />
-
-    <script>
-      window.__gradio_mode__ = "app";
-      window.gradio_config = {
-        version: "3.0.26\n",
-        mode: "blocks",
-        dev_mode: false,
-        components: [
-          {
-            id: 1,
-            type: "column",
-            props: {
-              type: "column",
-              variant: "default",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 2,
-            type: "markdown",
-            props: {
-              value:
-                '<h1><center>DALL\u00b7E mini by <a href="https://www.craiyon.com/" target="_blank">craiyon.com</a></center></h1>',
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 3,
-            type: "markdown",
-            props: {
-              value:
-                "<center>AI model generating images from any prompt!</center>",
-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-          {
-            id: 4,
-            type: "group",
-            props: { type: "group", visible: true, style: {} },
-          },
-          {
-            id: 5,
-            type: "box",
-            props: { type: "box", visible: true, style: {} },
-          },
-          {
-            id: 6,
-            type: "row",
-            props: {
-              type: "row",
-              visible: true,
-              style: { equal_height: true, mobile_collapse: false },
-            },
-          },
-          {
-            id: 7,
-            type: "textbox",
-            props: {
-              lines: 1,
-              max_lines: 1,
-              value: "",
-              label: "Enter your prompt",
-              show_label: false,
-              name: "textbox",
-              visible: true,
-              elem_id: "prompt",
-              style: { container: false },
-            },
-          },
-          {
-            id: 8,
-            type: "button",
-            props: {
-              value: "Run",
-              variant: "primary",
-              name: "button",
-              visible: true,
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-          {
-            id: 9,
-            type: "gallery",
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-              value: [],
-              label: "Generated images",
-              show_label: false,
-              name: "gallery",
-              visible: true,
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-            },
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-                '<p style=\'text-align: center\'>\nNew model available on \ud83d\udd8d\ufe0f <a href="https://www.craiyon.com/" target="_blank">craiyon.com</a>\n</p>',
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-            type: "markdown",
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-              name: "markdown",
-              visible: true,
-              style: {},
-            },
-          },
-        ],
-        theme: "default",
-        css: ".container { max-width: 800px; margin: auto; }",
-        title: "Gradio",
-        enable_queue: false,
-        layout: {
-          id: 0,
-          children: [
-            {
-              id: 1,
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-              id: 10,
-              children: [{ id: 11 }, { id: 12 }, { id: 13 }, { id: 14 }],
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-        dependencies: [
-          {
-            targets: [8],
-            trigger: "click",
-            inputs: [7],
-            outputs: [9],
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spaces/Dagfinn1962/stablediffusion-members/images.py

@@ -1,22 +0,0 @@
-import glob
-import matplotlib.pyplot as plt
-import matplotlib.image as mpimg
-%matplotlib inline
-
-images = []
-for img_path in sorted(glob.glob('brain.png'), reverse=True):
-    images.append(mpimg.imread(img_path))
-
-images = images[:15] 
-
-plt.figure(figsize=(20,10))
-
-columns = 5
-for i, image in enumerate(images):
-    ax = plt.subplot(len(images) / columns + 1, columns, i + 1)
-    ax.axes.xaxis.set_visible(False)
-    ax.axes.yaxis.set_visible(False)
-    ax.axis('off')
-    plt.imshow(image)
-    gc.collect()
-

spaces/Datasculptor/MusicGen/audiocraft/models/musicgen.py

@@ -1,362 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Main model for using MusicGen. This will combine all the required components
-and provide easy access to the generation API.
-"""
-
-import os
-import typing as tp
-
-import torch
-
-from .encodec import CompressionModel
-from .lm import LMModel
-from .builders import get_debug_compression_model, get_debug_lm_model
-from .loaders import load_compression_model, load_lm_model, HF_MODEL_CHECKPOINTS_MAP
-from ..data.audio_utils import convert_audio
-from ..modules.conditioners import ConditioningAttributes, WavCondition
-from ..utils.autocast import TorchAutocast
-
-
-MelodyList = tp.List[tp.Optional[torch.Tensor]]
-MelodyType = tp.Union[torch.Tensor, MelodyList]
-
-
-class MusicGen:
-    """MusicGen main model with convenient generation API.
-
-    Args:
-        name (str): name of the model.
-        compression_model (CompressionModel): Compression model
-            used to map audio to invertible discrete representations.
-        lm (LMModel): Language model over discrete representations.
-    """
-    def __init__(self, name: str, compression_model: CompressionModel, lm: LMModel,
-                 max_duration: float = 30):
-        self.name = name
-        self.compression_model = compression_model
-        self.lm = lm
-        self.max_duration = max_duration
-        self.device = next(iter(lm.parameters())).device
-        self.generation_params: dict = {}
-        self.set_generation_params(duration=15)  # 15 seconds by default
-        self._progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None
-        if self.device.type == 'cpu':
-            self.autocast = TorchAutocast(enabled=False)
-        else:
-            self.autocast = TorchAutocast(
-                enabled=True, device_type=self.device.type, dtype=torch.float16)
-
-    @property
-    def frame_rate(self) -> int:
-        """Roughly the number of AR steps per seconds."""
-        return self.compression_model.frame_rate
-
-    @property
-    def sample_rate(self) -> int:
-        """Sample rate of the generated audio."""
-        return self.compression_model.sample_rate
-
-    @property
-    def audio_channels(self) -> int:
-        """Audio channels of the generated audio."""
-        return self.compression_model.channels
-
-    @staticmethod
-    def get_pretrained(name: str = 'melody', device=None):
-        """Return pretrained model, we provide four models:
-        - small (300M), text to music, # see: https://huggingface.co/facebook/musicgen-small
-        - medium (1.5B), text to music, # see: https://huggingface.co/facebook/musicgen-medium
-        - melody (1.5B) text to music and text+melody to music, # see: https://huggingface.co/facebook/musicgen-melody
-        - large (3.3B), text to music, # see: https://huggingface.co/facebook/musicgen-large
-        """
-
-        if device is None:
-            if torch.cuda.device_count():
-                device = 'cuda'
-            else:
-                device = 'cpu'
-
-        if name == 'debug':
-            # used only for unit tests
-            compression_model = get_debug_compression_model(device)
-            lm = get_debug_lm_model(device)
-            return MusicGen(name, compression_model, lm)
-
-        if name not in HF_MODEL_CHECKPOINTS_MAP:
-            if not os.path.isfile(name) and not os.path.isdir(name):
-                raise ValueError(
-                    f"{name} is not a valid checkpoint name. "
-                    f"Choose one of {', '.join(HF_MODEL_CHECKPOINTS_MAP.keys())}"
-                )
-
-        cache_dir = os.environ.get('MUSICGEN_ROOT', None)
-        compression_model = load_compression_model(name, device=device, cache_dir=cache_dir)
-        lm = load_lm_model(name, device=device, cache_dir=cache_dir)
-        if name == 'melody':
-            lm.condition_provider.conditioners['self_wav'].match_len_on_eval = True
-
-        return MusicGen(name, compression_model, lm)
-
-    def set_generation_params(self, use_sampling: bool = True, top_k: int = 250,
-                              top_p: float = 0.0, temperature: float = 1.0,
-                              duration: float = 30.0, cfg_coef: float = 3.0,
-                              two_step_cfg: bool = False, extend_stride: float = 18):
-        """Set the generation parameters for MusicGen.
-
-        Args:
-            use_sampling (bool, optional): Use sampling if True, else do argmax decoding. Defaults to True.
-            top_k (int, optional): top_k used for sampling. Defaults to 250.
-            top_p (float, optional): top_p used for sampling, when set to 0 top_k is used. Defaults to 0.0.
-            temperature (float, optional): Softmax temperature parameter. Defaults to 1.0.
-            duration (float, optional): Duration of the generated waveform. Defaults to 30.0.
-            cfg_coef (float, optional): Coefficient used for classifier free guidance. Defaults to 3.0.
-            two_step_cfg (bool, optional): If True, performs 2 forward for Classifier Free Guidance,
-                instead of batching together the two. This has some impact on how things
-                are padded but seems to have little impact in practice.
-            extend_stride: when doing extended generation (i.e. more than 30 seconds), by how much
-                should we extend the audio each time. Larger values will mean less context is
-                preserved, and shorter value will require extra computations.
-        """
-        assert extend_stride < self.max_duration, "Cannot stride by more than max generation duration."
-        self.extend_stride = extend_stride
-        self.duration = duration
-        self.generation_params = {
-            'use_sampling': use_sampling,
-            'temp': temperature,
-            'top_k': top_k,
-            'top_p': top_p,
-            'cfg_coef': cfg_coef,
-            'two_step_cfg': two_step_cfg,
-        }
-
-    def set_custom_progress_callback(self, progress_callback: tp.Optional[tp.Callable[[int, int], None]] = None):
-        """Override the default progress callback."""
-        self._progress_callback = progress_callback
-
-    def generate_unconditional(self, num_samples: int, progress: bool = False) -> torch.Tensor:
-        """Generate samples in an unconditional manner.
-
-        Args:
-            num_samples (int): Number of samples to be generated.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        descriptions: tp.List[tp.Optional[str]] = [None] * num_samples
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-
-    def generate(self, descriptions: tp.List[str], progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on text.
-
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, None)
-        assert prompt_tokens is None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-
-    def generate_with_chroma(self, descriptions: tp.List[str], melody_wavs: MelodyType,
-                             melody_sample_rate: int, progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on text and melody.
-
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            melody_wavs: (torch.Tensor or list of Tensor): A batch of waveforms used as
-                melody conditioning. Should have shape [B, C, T] with B matching the description length,
-                C=1 or 2. It can be [C, T] if there is a single description. It can also be
-                a list of [C, T] tensors.
-            melody_sample_rate: (int): Sample rate of the melody waveforms.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        if isinstance(melody_wavs, torch.Tensor):
-            if melody_wavs.dim() == 2:
-                melody_wavs = melody_wavs[None]
-            if melody_wavs.dim() != 3:
-                raise ValueError("Melody wavs should have a shape [B, C, T].")
-            melody_wavs = list(melody_wavs)
-        else:
-            for melody in melody_wavs:
-                if melody is not None:
-                    assert melody.dim() == 2, "One melody in the list has the wrong number of dims."
-
-        melody_wavs = [
-            convert_audio(wav, melody_sample_rate, self.sample_rate, self.audio_channels)
-            if wav is not None else None
-            for wav in melody_wavs]
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions=descriptions, prompt=None,
-                                                                        melody_wavs=melody_wavs)
-        assert prompt_tokens is None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-
-    def generate_continuation(self, prompt: torch.Tensor, prompt_sample_rate: int,
-                              descriptions: tp.Optional[tp.List[tp.Optional[str]]] = None,
-                              progress: bool = False) -> torch.Tensor:
-        """Generate samples conditioned on audio prompts.
-
-        Args:
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-                Prompt should be [B, C, T], or [C, T] if only one sample is generated.
-            prompt_sample_rate (int): Sampling rate of the given audio waveforms.
-            descriptions (tp.List[str], optional): A list of strings used as text conditioning. Defaults to None.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        """
-        if prompt.dim() == 2:
-            prompt = prompt[None]
-        if prompt.dim() != 3:
-            raise ValueError("prompt should have 3 dimensions: [B, C, T] (C = 1).")
-        prompt = convert_audio(prompt, prompt_sample_rate, self.sample_rate, self.audio_channels)
-        if descriptions is None:
-            descriptions = [None] * len(prompt)
-        attributes, prompt_tokens = self._prepare_tokens_and_attributes(descriptions, prompt)
-        assert prompt_tokens is not None
-        return self._generate_tokens(attributes, prompt_tokens, progress)
-
-    @torch.no_grad()
-    def _prepare_tokens_and_attributes(
-            self,
-            descriptions: tp.Sequence[tp.Optional[str]],
-            prompt: tp.Optional[torch.Tensor],
-            melody_wavs: tp.Optional[MelodyList] = None,
-    ) -> tp.Tuple[tp.List[ConditioningAttributes], tp.Optional[torch.Tensor]]:
-        """Prepare model inputs.
-
-        Args:
-            descriptions (tp.List[str]): A list of strings used as text conditioning.
-            prompt (torch.Tensor): A batch of waveforms used for continuation.
-            melody_wavs (tp.Optional[torch.Tensor], optional): A batch of waveforms
-                used as melody conditioning. Defaults to None.
-        """
-        attributes = [
-            ConditioningAttributes(text={'description': description})
-            for description in descriptions]
-
-        if melody_wavs is None:
-            for attr in attributes:
-                attr.wav['self_wav'] = WavCondition(
-                    torch.zeros((1, 1), device=self.device),
-                    torch.tensor([0], device=self.device),
-                    path='null_wav')  # type: ignore
-        else:
-            if self.name != "melody":
-                raise RuntimeError("This model doesn't support melody conditioning. "
-                                   "Use the `melody` model.")
-            assert len(melody_wavs) == len(descriptions), \
-                f"number of melody wavs must match number of descriptions! " \
-                f"got melody len={len(melody_wavs)}, and descriptions len={len(descriptions)}"
-            for attr, melody in zip(attributes, melody_wavs):
-                if melody is None:
-                    attr.wav['self_wav'] = WavCondition(
-                        torch.zeros((1, 1), device=self.device),
-                        torch.tensor([0], device=self.device),
-                        path='null_wav')  # type: ignore
-                else:
-                    attr.wav['self_wav'] = WavCondition(
-                        melody.to(device=self.device),
-                        torch.tensor([melody.shape[-1]], device=self.device))
-
-        if prompt is not None:
-            if descriptions is not None:
-                assert len(descriptions) == len(prompt), "Prompt and nb. descriptions doesn't match"
-            prompt = prompt.to(self.device)
-            prompt_tokens, scale = self.compression_model.encode(prompt)
-            assert scale is None
-        else:
-            prompt_tokens = None
-        return attributes, prompt_tokens
-
-    def _generate_tokens(self, attributes: tp.List[ConditioningAttributes],
-                         prompt_tokens: tp.Optional[torch.Tensor], progress: bool = False) -> torch.Tensor:
-        """Generate discrete audio tokens given audio prompt and/or conditions.
-
-        Args:
-            attributes (tp.List[ConditioningAttributes]): Conditions used for generation (text/melody).
-            prompt_tokens (tp.Optional[torch.Tensor]): Audio prompt used for continuation.
-            progress (bool, optional): Flag to display progress of the generation process. Defaults to False.
-        Returns:
-            torch.Tensor: Generated audio, of shape [B, C, T], T is defined by the generation params.
-        """
-        total_gen_len = int(self.duration * self.frame_rate)
-        max_prompt_len = int(min(self.duration, self.max_duration) * self.frame_rate)
-        current_gen_offset: int = 0
-
-        def _progress_callback(generated_tokens: int, tokens_to_generate: int):
-            generated_tokens += current_gen_offset
-            if self._progress_callback is not None:
-                # Note that total_gen_len might be quite wrong depending on the
-                # codebook pattern used, but with delay it is almost accurate.
-                self._progress_callback(generated_tokens, total_gen_len)
-            else:
-                print(f'{generated_tokens: 6d} / {total_gen_len: 6d}', end='\r')
-
-        if prompt_tokens is not None:
-            assert max_prompt_len >= prompt_tokens.shape[-1], \
-                "Prompt is longer than audio to generate"
-
-        callback = None
-        if progress:
-            callback = _progress_callback
-
-        if self.duration <= self.max_duration:
-            # generate by sampling from LM, simple case.
-            with self.autocast:
-                gen_tokens = self.lm.generate(
-                    prompt_tokens, attributes,
-                    callback=callback, max_gen_len=total_gen_len, **self.generation_params)
-
-        else:
-            # now this gets a bit messier, we need to handle prompts,
-            # melody conditioning etc.
-            ref_wavs = [attr.wav['self_wav'] for attr in attributes]
-            all_tokens = []
-            if prompt_tokens is None:
-                prompt_length = 0
-            else:
-                all_tokens.append(prompt_tokens)
-                prompt_length = prompt_tokens.shape[-1]
-
-            stride_tokens = int(self.frame_rate * self.extend_stride)
-
-            while current_gen_offset + prompt_length < total_gen_len:
-                time_offset = current_gen_offset / self.frame_rate
-                chunk_duration = min(self.duration - time_offset, self.max_duration)
-                max_gen_len = int(chunk_duration * self.frame_rate)
-                for attr, ref_wav in zip(attributes, ref_wavs):
-                    wav_length = ref_wav.length.item()
-                    if wav_length == 0:
-                        continue
-                    # We will extend the wav periodically if it not long enough.
-                    # we have to do it here rather than in conditioners.py as otherwise
-                    # we wouldn't have the full wav.
-                    initial_position = int(time_offset * self.sample_rate)
-                    wav_target_length = int(self.max_duration * self.sample_rate)
-                    print(initial_position / self.sample_rate, wav_target_length / self.sample_rate)
-                    positions = torch.arange(initial_position,
-                                             initial_position + wav_target_length, device=self.device)
-                    attr.wav['self_wav'] = WavCondition(
-                        ref_wav[0][:, positions % wav_length],
-                        torch.full_like(ref_wav[1], wav_target_length))
-                with self.autocast:
-                    gen_tokens = self.lm.generate(
-                        prompt_tokens, attributes,
-                        callback=callback, max_gen_len=max_gen_len, **self.generation_params)
-                if prompt_tokens is None:
-                    all_tokens.append(gen_tokens)
-                else:
-                    all_tokens.append(gen_tokens[:, :, prompt_tokens.shape[-1]:])
-                prompt_tokens = gen_tokens[:, :, stride_tokens:]
-                prompt_length = prompt_tokens.shape[-1]
-                current_gen_offset += stride_tokens
-
-            gen_tokens = torch.cat(all_tokens, dim=-1)
-
-        # generate audio
-        assert gen_tokens.dim() == 3
-        with torch.no_grad():
-            gen_audio = self.compression_model.decode(gen_tokens, None)
-        return gen_audio

spaces/DeepLabCut/MegaDetector_DeepLabCut/DLC_models/models.py

@@ -1,60 +0,0 @@
-import urllib.request
-import tarfile
-from tqdm import tqdm
-import os
-import yaml
-from ruamel.yaml import YAML
-
-def read_plainconfig(configname):
-    if not os.path.exists(configname):
-        raise FileNotFoundError(
-            f"Config {configname} is not found. Please make sure that the file exists."
-        )
-    with open(configname) as file:
-        return YAML().load(file)
-
-def DownloadModel(modelname, target_dir):
-    """
-    Downloads a DeepLabCut Model Zoo Project
-    """
-    
-    def show_progress(count, block_size, total_size):
-        pbar.update(block_size)
-
-    def tarfilenamecutting(tarf):
-        """' auxfun to extract folder path
-        ie. /xyz-trainsetxyshufflez/
-        """
-        for memberid, member in enumerate(tarf.getmembers()):
-            if memberid == 0:
-                parent = str(member.path)
-                l = len(parent) + 1
-            if member.path.startswith(parent):
-                member.path = member.path[l:]
-                yield member
-
-    neturls = read_plainconfig("./model/pretrained_model_urls.yaml") #FIXME
-    
-    if modelname in neturls.keys():
-        url = neturls[modelname]
-        print(url)
-        response = urllib.request.urlopen(url)
-        print(
-            "Downloading the model from the DeepLabCut server @Harvard -> Go Crimson!!! {}....".format(
-                url
-            )
-        )
-        total_size = int(response.getheader("Content-Length"))
-        pbar = tqdm(unit="B", total=total_size, position=0)
-        filename, _ = urllib.request.urlretrieve(url, reporthook=show_progress)
-        with tarfile.open(filename, mode="r:gz") as tar:
-            tar.extractall(target_dir, members=tarfilenamecutting(tar))
-    else:
-        models = [
-            fn
-            for fn in neturls.keys()
-            if "resnet_" not in fn and "mobilenet_" not in fn
-        ]
-        print("Model does not exist: ", modelname)
-        print("Pick one of the following: ", models)
-    return target_dir

spaces/DollieHell/pisa/README.md

@@ -1,10 +0,0 @@
----
-title: Pisa
-emoji: 🌍
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/DragGan/DragGan-Inversion/training/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty

spaces/ECCV2022/PSG/OpenPSG/configs/_base_/datasets/psg.py

@@ -1,93 +0,0 @@
-# dataset settings
-dataset_type = 'PanopticSceneGraphDataset'
-ann_file = './data/psg/psg.json'
-coco_root = 'data/coco'
-
-img_norm_cfg = dict(mean=[123.675, 116.28, 103.53],
-                    std=[58.395, 57.12, 57.375],
-                    to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='LoadPanopticSceneGraphAnnotations',
-        with_bbox=True,
-        with_rel=True,
-        with_mask=True,
-        with_seg=True,
-    ),
-    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='SegRescale', scale_factor=1 / 4),
-    dict(type='SceneGraphFormatBundle'),
-    dict(
-        type='Collect',
-        keys=[
-            'img',
-            'gt_bboxes',
-            'gt_labels',
-            'gt_rels',
-            'gt_relmaps',
-            'gt_masks',
-            'gt_semantic_seg',
-        ],
-    ),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    # Since the forward process may need gt info, annos must be loaded.
-    dict(type='LoadPanopticSceneGraphAnnotations',
-         with_bbox=True,
-         with_rel=True),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(1333, 800),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            # NOTE: Do not change the img to DC.
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='ToTensor', keys=['gt_bboxes', 'gt_labels']),
-            dict(
-                type='ToDataContainer',
-                fields=(dict(key='gt_bboxes'), dict(key='gt_labels')),
-            ),
-            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-        ],
-    ),
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type=dataset_type,
-        ann_file=ann_file,
-        img_prefix=coco_root,
-        seg_prefix=coco_root,
-        pipeline=train_pipeline,
-        split='train',
-        all_bboxes=True,
-    ),
-    val=dict(
-        type=dataset_type,
-        ann_file=ann_file,
-        img_prefix=coco_root,
-        seg_prefix=coco_root,
-        pipeline=test_pipeline,
-        split='test',
-        all_bboxes=True,
-    ),
-    test=dict(
-        type=dataset_type,
-        ann_file=ann_file,
-        img_prefix=coco_root,
-        seg_prefix=coco_root,
-        pipeline=test_pipeline,
-        split='test',
-        all_bboxes=True,
-    ),
-)