PushWorld
- A benchmark for manipulation planning with tools and movable obstacles
- 
-
-
- )
-}
\ No newline at end of file
diff --git a/spaces/Bart92/RVC_HF/infer/lib/infer_pack/modules.py b/spaces/Bart92/RVC_HF/infer/lib/infer_pack/modules.py
deleted file mode 100644
index 2201a58bee9b7808d386b3ef9ac2d1f9630e56ef..0000000000000000000000000000000000000000
--- a/spaces/Bart92/RVC_HF/infer/lib/infer_pack/modules.py
+++ /dev/null
@@ -1,521 +0,0 @@
-import copy
-import math
-
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils import remove_weight_norm, weight_norm
-
-from infer.lib.infer_pack import commons
-from infer.lib.infer_pack.commons import get_padding, init_weights
-from infer.lib.infer_pack.transforms import piecewise_rational_quadratic_transform
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(
- self,
- in_channels,
- hidden_channels,
- out_channels,
- kernel_size,
- n_layers,
- p_dropout,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(
- nn.Conv1d(
- in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
- for _ in range(n_layers - 1):
- self.conv_layers.append(
- nn.Conv1d(
- hidden_channels,
- hidden_channels,
- kernel_size,
- padding=kernel_size // 2,
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
-
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size**i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(
- nn.Conv1d(
- channels,
- channels,
- kernel_size,
- groups=channels,
- dilation=dilation,
- padding=padding,
- )
- )
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(
- self,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- p_dropout=0,
- ):
- super(WN, self).__init__()
- assert kernel_size % 2 == 1
- 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.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(
- gin_channels, 2 * hidden_channels * n_layers, 1
- )
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
- for i in range(n_layers):
- dilation = dilation_rate**i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(
- hidden_channels,
- 2 * hidden_channels,
- kernel_size,
- dilation=dilation,
- padding=padding,
- )
- in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:, : self.hidden_channels, :]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:, self.hidden_channels :, :]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]),
- )
- ),
- ]
- )
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- ]
- )
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- ]
- )
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels, 1))
- self.logs = nn.Parameter(torch.zeros(channels, 1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1, 2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False,
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- 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.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=p_dropout,
- gin_channels=gin_channels,
- )
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels] * 2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1, 2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
- def __init__(
- self,
- in_channels,
- filter_channels,
- kernel_size,
- n_layers,
- num_bins=10,
- tail_bound=5.0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
- self.proj = nn.Conv1d(
- filter_channels, self.half_channels * (num_bins * 3 - 1), 1
- )
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
- self.filter_channels
- )
- unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(
- x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails="linear",
- tail_bound=self.tail_bound,
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1, 2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/Benson/text-generation/Examples/Descargar Clicker Hroes Mod Apk.md b/spaces/Benson/text-generation/Examples/Descargar Clicker Hroes Mod Apk.md
deleted file mode 100644
index 790dc4c5613a2f0def055c0bf596bc1ac51f1118..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Clicker Hroes Mod Apk.md
+++ /dev/null
@@ -1,70 +0,0 @@
- 105 ? `px-0` : `pl-1 pr-8 md:pl-16 md:pr-16`,
- `print:pt-0 print:px-0 print:pl-0 print:pr-0`,
- fonts.actionman.className
- )}>
-
- 105 ? `items-start` : `items-center`
- )}>
-
-
-
-
-
-
- {waitABitMore ? `Story is ready, but server is a bit busy!`: 'Generating a new story..'}
- {waitABitMore ? `Please hold tight..` : ''} -
- - {waitABitMore ? `Please hold tight..` : ''} -
-
Descargar Clicker Heroes Mod APK: Una guía para principiantes
-Si usted está buscando un divertido y adictivo juego de ocio que le mantendrá entretenido durante horas, es posible que desee probar Clicker Heroes. Este juego es uno de los juegos de clickers más populares del mercado, con millones de descargas y críticas positivas. Pero lo que si quieres disfrutar del juego sin limitaciones o restricciones? Ahí es donde Clicker Heroes mod apk entra en juego. En este artículo, le diremos todo lo que necesita saber sobre Clicker Heroes, por qué debe descargar su apk mod, y cómo hacerlo de forma segura y fácil.
-descargar clicker héroes mod apk
Download File >>> https://bltlly.com/2v6JzL
-
¿Qué es Clicker Heroes?
-Clicker Heroes es un juego desarrollado por Playsaurus, un estudio especializado en juegos casuales y ociosos. El juego fue lanzado en 2014 para navegadores, y posteriormente portado a dispositivos móviles y consolas. El juego ha recibido varias actualizaciones y expansiones a lo largo de los años, añadiendo nuevas características y contenido.
-El juego de Clicker Heroes
-La jugabilidad de Clicker Heroes es simple y directa. Empiezas tocando la pantalla para matar monstruos y ganar oro. Puedes usar el oro para contratar y actualizar héroes, que te ayudarán a luchar contra más monstruos y jefes. También puedes desbloquear habilidades y habilidades que aumentarán tu daño y velocidad. El juego no tiene fin, ya que siempre puedes avanzar a niveles y mundos más altos, donde te enfrentarás a enemigos y desafíos más fuertes.
-Las características de Clicker Heroes
-Algunas de las características que hacen que Clicker Heroes se destaque de otros juegos de clicker son:
- --
-
- Más de 1000 héroes para recoger y actualizar, cada uno con sus propias habilidades y habilidades únicas. -
- Más de 10.000 niveles para explorar, cada uno con diferentes temas y entornos. -
- Una variedad de modos de juego, como incursiones, clanes, torneos, eventos, misiones, logros y más. -
- Un estilo gráfico vibrante y colorido, con animaciones y efectos suaves. - -
- Una comunidad amigable y activa, con foros, wikis, guías, consejos, trucos y más. -
¿Por qué descargar Clicker Heroes mod apk?
-Aunque Clicker Heroes es un juego gratuito, también tiene algunas compras en la aplicación que pueden mejorar tu experiencia de juego. Por ejemplo, puedes comprar rubíes, que son la moneda premium del juego. Puedes usar rubíes para comprar pieles, cofres, dorados, auto-clickers, timelapses y más. Sin embargo, estas compras pueden ser bastante caras, especialmente si desea obtener los mejores artículos y mejoras. Es por eso que algunos jugadores prefieren descargar Clicker Heroes mod apk en su lugar.
-Los beneficios de Clicker Heroes mod apk
-Clicker Heroes mod apk es una versión modificada del juego original que le da acceso a recursos y características ilimitadas. Algunos de los beneficios de descargar Clicker Heroes mod apk son:
--
-
- Puedes obtener rubíes ilimitados gratis, sin gastar dinero real. -
- Puedes desbloquear todos los héroes y sus mejoras al instante, sin moler o esperar. -
- Puedes saltarte cualquier nivel o jefe que encuentres demasiado duro o aburrido. -
- Puedes personalizar la configuración del juego según tus preferencias. -
- Puedes disfrutar del juego sin anuncios ni interrupciones. -
Los inconvenientes de Clicker Heroes mod apk
-Sin embargo, descargar Clicker Heroes mod apk también tiene algunos inconvenientes que usted debe tener en cuenta. Algunos de los inconvenientes de descargar Clicker Heroes mod apk son:
--
-
- Es posible que se enfrentan a algunos problemas de compatibilidad o rendimiento, como el apk mod no puede ser actualizado o optimizado para su dispositivo. -
- Usted puede encontrar algunos errores o fallos, como el apk mod no puede ser probado o verificado por la calidad. -
- Usted puede arriesgarse a perder su progreso o datos, como el apk mod no puede ser sincronizado o respaldado con su cuenta. -
- Usted puede violar los términos y condiciones del juego, como el apk mod puede ser considerado como trampa o piratería. - -
Cómo descargar e instalar Clicker Heroes mod apk?
-Si usted ha decidido descargar Clicker Heroes mod apk, usted debe seguir estos pasos cuidadosamente para asegurar una instalación segura y exitosa. Antes de proceder, asegúrese de que tiene suficiente espacio de almacenamiento en su dispositivo y que tiene una conexión a Internet estable.
-Paso 1: Encuentra una fuente confiable
-El primer paso es encontrar una fuente confiable que ofrece Clicker Heroes mod apk para descargar. Usted puede buscar en línea para varios sitios web o plataformas que proporcionan este servicio, pero tenga cuidado de los sitios falsos o estafa que pueden engañar a descargar algo más. También puedes consultar las reseñas y valoraciones de otros usuarios para ver si han tenido una buena experiencia con la fuente. Alternativamente, puede utilizar el siguiente enlace para descargar Clicker Heroes mod apk de una fuente de confianza:
-]? \d* )? :
- .* (?: ft | filetype | syn | syntax ) = ( [^:\s]+ )
-''', re.VERBOSE)
-
-
-def get_filetype_from_line(l):
- m = modeline_re.search(l)
- if m:
- return m.group(1)
-
-
-def get_filetype_from_buffer(buf, max_lines=5):
- """
- Scan the buffer for modelines and return filetype if one is found.
- """
- lines = buf.splitlines()
- for l in lines[-1:-max_lines-1:-1]:
- ret = get_filetype_from_line(l)
- if ret:
- return ret
- for i in range(max_lines, -1, -1):
- if i < len(lines):
- ret = get_filetype_from_line(lines[i])
- if ret:
- return ret
-
- return None
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/utils.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/utils.py
deleted file mode 100644
index 61407eba5c591176c8aed5c361e068e3a1337dee..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/s3transfer/utils.py
+++ /dev/null
@@ -1,802 +0,0 @@
-# Copyright 2016 Amazon.com, Inc. or its affiliates. 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. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file 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.
-import functools
-import logging
-import math
-import os
-import random
-import socket
-import stat
-import string
-import threading
-from collections import defaultdict
-
-from botocore.exceptions import IncompleteReadError, ReadTimeoutError
-
-from s3transfer.compat import SOCKET_ERROR, fallocate, rename_file
-
-MAX_PARTS = 10000
-# The maximum file size you can upload via S3 per request.
-# See: http://docs.aws.amazon.com/AmazonS3/latest/dev/UploadingObjects.html
-# and: http://docs.aws.amazon.com/AmazonS3/latest/dev/qfacts.html
-MAX_SINGLE_UPLOAD_SIZE = 5 * (1024**3)
-MIN_UPLOAD_CHUNKSIZE = 5 * (1024**2)
-logger = logging.getLogger(__name__)
-
-
-S3_RETRYABLE_DOWNLOAD_ERRORS = (
- socket.timeout,
- SOCKET_ERROR,
- ReadTimeoutError,
- IncompleteReadError,
-)
-
-
-def random_file_extension(num_digits=8):
- return ''.join(random.choice(string.hexdigits) for _ in range(num_digits))
-
-
-def signal_not_transferring(request, operation_name, **kwargs):
- if operation_name in ['PutObject', 'UploadPart'] and hasattr(
- request.body, 'signal_not_transferring'
- ):
- request.body.signal_not_transferring()
-
-
-def signal_transferring(request, operation_name, **kwargs):
- if operation_name in ['PutObject', 'UploadPart'] and hasattr(
- request.body, 'signal_transferring'
- ):
- request.body.signal_transferring()
-
-
-def calculate_num_parts(size, part_size):
- return int(math.ceil(size / float(part_size)))
-
-
-def calculate_range_parameter(
- part_size, part_index, num_parts, total_size=None
-):
- """Calculate the range parameter for multipart downloads/copies
-
- :type part_size: int
- :param part_size: The size of the part
-
- :type part_index: int
- :param part_index: The index for which this parts starts. This index starts
- at zero
-
- :type num_parts: int
- :param num_parts: The total number of parts in the transfer
-
- :returns: The value to use for Range parameter on downloads or
- the CopySourceRange parameter for copies
- """
- # Used to calculate the Range parameter
- start_range = part_index * part_size
- if part_index == num_parts - 1:
- end_range = ''
- if total_size is not None:
- end_range = str(total_size - 1)
- else:
- end_range = start_range + part_size - 1
- range_param = f'bytes={start_range}-{end_range}'
- return range_param
-
-
-def get_callbacks(transfer_future, callback_type):
- """Retrieves callbacks from a subscriber
-
- :type transfer_future: s3transfer.futures.TransferFuture
- :param transfer_future: The transfer future the subscriber is associated
- to.
-
- :type callback_type: str
- :param callback_type: The type of callback to retrieve from the subscriber.
- Valid types include:
- * 'queued'
- * 'progress'
- * 'done'
-
- :returns: A list of callbacks for the type specified. All callbacks are
- preinjected with the transfer future.
- """
- callbacks = []
- for subscriber in transfer_future.meta.call_args.subscribers:
- callback_name = 'on_' + callback_type
- if hasattr(subscriber, callback_name):
- callbacks.append(
- functools.partial(
- getattr(subscriber, callback_name), future=transfer_future
- )
- )
- return callbacks
-
-
-def invoke_progress_callbacks(callbacks, bytes_transferred):
- """Calls all progress callbacks
-
- :param callbacks: A list of progress callbacks to invoke
- :param bytes_transferred: The number of bytes transferred. This is passed
- to the callbacks. If no bytes were transferred the callbacks will not
- be invoked because no progress was achieved. It is also possible
- to receive a negative amount which comes from retrying a transfer
- request.
- """
- # Only invoke the callbacks if bytes were actually transferred.
- if bytes_transferred:
- for callback in callbacks:
- callback(bytes_transferred=bytes_transferred)
-
-
-def get_filtered_dict(original_dict, whitelisted_keys):
- """Gets a dictionary filtered by whitelisted keys
-
- :param original_dict: The original dictionary of arguments to source keys
- and values.
- :param whitelisted_key: A list of keys to include in the filtered
- dictionary.
-
- :returns: A dictionary containing key/values from the original dictionary
- whose key was included in the whitelist
- """
- filtered_dict = {}
- for key, value in original_dict.items():
- if key in whitelisted_keys:
- filtered_dict[key] = value
- return filtered_dict
-
-
-class CallArgs:
- def __init__(self, **kwargs):
- """A class that records call arguments
-
- The call arguments must be passed as keyword arguments. It will set
- each keyword argument as an attribute of the object along with its
- associated value.
- """
- for arg, value in kwargs.items():
- setattr(self, arg, value)
-
-
-class FunctionContainer:
- """An object that contains a function and any args or kwargs to call it
-
- When called the provided function will be called with provided args
- and kwargs.
- """
-
- def __init__(self, func, *args, **kwargs):
- self._func = func
- self._args = args
- self._kwargs = kwargs
-
- def __repr__(self):
- return 'Function: {} with args {} and kwargs {}'.format(
- self._func, self._args, self._kwargs
- )
-
- def __call__(self):
- return self._func(*self._args, **self._kwargs)
-
-
-class CountCallbackInvoker:
- """An abstraction to invoke a callback when a shared count reaches zero
-
- :param callback: Callback invoke when finalized count reaches zero
- """
-
- def __init__(self, callback):
- self._lock = threading.Lock()
- self._callback = callback
- self._count = 0
- self._is_finalized = False
-
- @property
- def current_count(self):
- with self._lock:
- return self._count
-
- def increment(self):
- """Increment the count by one"""
- with self._lock:
- if self._is_finalized:
- raise RuntimeError(
- 'Counter has been finalized it can no longer be '
- 'incremented.'
- )
- self._count += 1
-
- def decrement(self):
- """Decrement the count by one"""
- with self._lock:
- if self._count == 0:
- raise RuntimeError(
- 'Counter is at zero. It cannot dip below zero'
- )
- self._count -= 1
- if self._is_finalized and self._count == 0:
- self._callback()
-
- def finalize(self):
- """Finalize the counter
-
- Once finalized, the counter never be incremented and the callback
- can be invoked once the count reaches zero
- """
- with self._lock:
- self._is_finalized = True
- if self._count == 0:
- self._callback()
-
-
-class OSUtils:
- _MAX_FILENAME_LEN = 255
-
- def get_file_size(self, filename):
- return os.path.getsize(filename)
-
- def open_file_chunk_reader(self, filename, start_byte, size, callbacks):
- return ReadFileChunk.from_filename(
- filename, start_byte, size, callbacks, enable_callbacks=False
- )
-
- def open_file_chunk_reader_from_fileobj(
- self,
- fileobj,
- chunk_size,
- full_file_size,
- callbacks,
- close_callbacks=None,
- ):
- return ReadFileChunk(
- fileobj,
- chunk_size,
- full_file_size,
- callbacks=callbacks,
- enable_callbacks=False,
- close_callbacks=close_callbacks,
- )
-
- def open(self, filename, mode):
- return open(filename, mode)
-
- def remove_file(self, filename):
- """Remove a file, noop if file does not exist."""
- # Unlike os.remove, if the file does not exist,
- # then this method does nothing.
- try:
- os.remove(filename)
- except OSError:
- pass
-
- def rename_file(self, current_filename, new_filename):
- rename_file(current_filename, new_filename)
-
- def is_special_file(cls, filename):
- """Checks to see if a file is a special UNIX file.
-
- It checks if the file is a character special device, block special
- device, FIFO, or socket.
-
- :param filename: Name of the file
-
- :returns: True if the file is a special file. False, if is not.
- """
- # If it does not exist, it must be a new file so it cannot be
- # a special file.
- if not os.path.exists(filename):
- return False
- mode = os.stat(filename).st_mode
- # Character special device.
- if stat.S_ISCHR(mode):
- return True
- # Block special device
- if stat.S_ISBLK(mode):
- return True
- # Named pipe / FIFO
- if stat.S_ISFIFO(mode):
- return True
- # Socket.
- if stat.S_ISSOCK(mode):
- return True
- return False
-
- def get_temp_filename(self, filename):
- suffix = os.extsep + random_file_extension()
- path = os.path.dirname(filename)
- name = os.path.basename(filename)
- temp_filename = name[: self._MAX_FILENAME_LEN - len(suffix)] + suffix
- return os.path.join(path, temp_filename)
-
- def allocate(self, filename, size):
- try:
- with self.open(filename, 'wb') as f:
- fallocate(f, size)
- except OSError:
- self.remove_file(filename)
- raise
-
-
-class DeferredOpenFile:
- def __init__(self, filename, start_byte=0, mode='rb', open_function=open):
- """A class that defers the opening of a file till needed
-
- This is useful for deferring opening of a file till it is needed
- in a separate thread, as there is a limit of how many open files
- there can be in a single thread for most operating systems. The
- file gets opened in the following methods: ``read()``, ``seek()``,
- and ``__enter__()``
-
- :type filename: str
- :param filename: The name of the file to open
-
- :type start_byte: int
- :param start_byte: The byte to seek to when the file is opened.
-
- :type mode: str
- :param mode: The mode to use to open the file
-
- :type open_function: function
- :param open_function: The function to use to open the file
- """
- self._filename = filename
- self._fileobj = None
- self._start_byte = start_byte
- self._mode = mode
- self._open_function = open_function
-
- def _open_if_needed(self):
- if self._fileobj is None:
- self._fileobj = self._open_function(self._filename, self._mode)
- if self._start_byte != 0:
- self._fileobj.seek(self._start_byte)
-
- @property
- def name(self):
- return self._filename
-
- def read(self, amount=None):
- self._open_if_needed()
- return self._fileobj.read(amount)
-
- def write(self, data):
- self._open_if_needed()
- self._fileobj.write(data)
-
- def seek(self, where, whence=0):
- self._open_if_needed()
- self._fileobj.seek(where, whence)
-
- def tell(self):
- if self._fileobj is None:
- return self._start_byte
- return self._fileobj.tell()
-
- def close(self):
- if self._fileobj:
- self._fileobj.close()
-
- def __enter__(self):
- self._open_if_needed()
- return self
-
- def __exit__(self, *args, **kwargs):
- self.close()
-
-
-class ReadFileChunk:
- def __init__(
- self,
- fileobj,
- chunk_size,
- full_file_size,
- callbacks=None,
- enable_callbacks=True,
- close_callbacks=None,
- ):
- """
-
- Given a file object shown below::
-
- |___________________________________________________|
- 0 | | full_file_size
- |----chunk_size---|
- f.tell()
-
- :type fileobj: file
- :param fileobj: File like object
-
- :type chunk_size: int
- :param chunk_size: The max chunk size to read. Trying to read
- pass the end of the chunk size will behave like you've
- reached the end of the file.
-
- :type full_file_size: int
- :param full_file_size: The entire content length associated
- with ``fileobj``.
-
- :type callbacks: A list of function(amount_read)
- :param callbacks: Called whenever data is read from this object in the
- order provided.
-
- :type enable_callbacks: boolean
- :param enable_callbacks: True if to run callbacks. Otherwise, do not
- run callbacks
-
- :type close_callbacks: A list of function()
- :param close_callbacks: Called when close is called. The function
- should take no arguments.
- """
- self._fileobj = fileobj
- self._start_byte = self._fileobj.tell()
- self._size = self._calculate_file_size(
- self._fileobj,
- requested_size=chunk_size,
- start_byte=self._start_byte,
- actual_file_size=full_file_size,
- )
- # _amount_read represents the position in the chunk and may exceed
- # the chunk size, but won't allow reads out of bounds.
- self._amount_read = 0
- self._callbacks = callbacks
- if callbacks is None:
- self._callbacks = []
- self._callbacks_enabled = enable_callbacks
- self._close_callbacks = close_callbacks
- if close_callbacks is None:
- self._close_callbacks = close_callbacks
-
- @classmethod
- def from_filename(
- cls,
- filename,
- start_byte,
- chunk_size,
- callbacks=None,
- enable_callbacks=True,
- ):
- """Convenience factory function to create from a filename.
-
- :type start_byte: int
- :param start_byte: The first byte from which to start reading.
-
- :type chunk_size: int
- :param chunk_size: The max chunk size to read. Trying to read
- pass the end of the chunk size will behave like you've
- reached the end of the file.
-
- :type full_file_size: int
- :param full_file_size: The entire content length associated
- with ``fileobj``.
-
- :type callbacks: function(amount_read)
- :param callbacks: Called whenever data is read from this object.
-
- :type enable_callbacks: bool
- :param enable_callbacks: Indicate whether to invoke callback
- during read() calls.
-
- :rtype: ``ReadFileChunk``
- :return: A new instance of ``ReadFileChunk``
-
- """
- f = open(filename, 'rb')
- f.seek(start_byte)
- file_size = os.fstat(f.fileno()).st_size
- return cls(f, chunk_size, file_size, callbacks, enable_callbacks)
-
- def _calculate_file_size(
- self, fileobj, requested_size, start_byte, actual_file_size
- ):
- max_chunk_size = actual_file_size - start_byte
- return min(max_chunk_size, requested_size)
-
- def read(self, amount=None):
- amount_left = max(self._size - self._amount_read, 0)
- if amount is None:
- amount_to_read = amount_left
- else:
- amount_to_read = min(amount_left, amount)
- data = self._fileobj.read(amount_to_read)
- self._amount_read += len(data)
- if self._callbacks is not None and self._callbacks_enabled:
- invoke_progress_callbacks(self._callbacks, len(data))
- return data
-
- def signal_transferring(self):
- self.enable_callback()
- if hasattr(self._fileobj, 'signal_transferring'):
- self._fileobj.signal_transferring()
-
- def signal_not_transferring(self):
- self.disable_callback()
- if hasattr(self._fileobj, 'signal_not_transferring'):
- self._fileobj.signal_not_transferring()
-
- def enable_callback(self):
- self._callbacks_enabled = True
-
- def disable_callback(self):
- self._callbacks_enabled = False
-
- def seek(self, where, whence=0):
- if whence not in (0, 1, 2):
- # Mimic io's error for invalid whence values
- raise ValueError(f"invalid whence ({whence}, should be 0, 1 or 2)")
-
- # Recalculate where based on chunk attributes so seek from file
- # start (whence=0) is always used
- where += self._start_byte
- if whence == 1:
- where += self._amount_read
- elif whence == 2:
- where += self._size
-
- self._fileobj.seek(max(where, self._start_byte))
- if self._callbacks is not None and self._callbacks_enabled:
- # To also rewind the callback() for an accurate progress report
- bounded_where = max(min(where - self._start_byte, self._size), 0)
- bounded_amount_read = min(self._amount_read, self._size)
- amount = bounded_where - bounded_amount_read
- invoke_progress_callbacks(
- self._callbacks, bytes_transferred=amount
- )
- self._amount_read = max(where - self._start_byte, 0)
-
- def close(self):
- if self._close_callbacks is not None and self._callbacks_enabled:
- for callback in self._close_callbacks:
- callback()
- self._fileobj.close()
-
- def tell(self):
- return self._amount_read
-
- def __len__(self):
- # __len__ is defined because requests will try to determine the length
- # of the stream to set a content length. In the normal case
- # of the file it will just stat the file, but we need to change that
- # behavior. By providing a __len__, requests will use that instead
- # of stat'ing the file.
- return self._size
-
- def __enter__(self):
- return self
-
- def __exit__(self, *args, **kwargs):
- self.close()
-
- def __iter__(self):
- # This is a workaround for http://bugs.python.org/issue17575
- # Basically httplib will try to iterate over the contents, even
- # if its a file like object. This wasn't noticed because we've
- # already exhausted the stream so iterating over the file immediately
- # stops, which is what we're simulating here.
- return iter([])
-
-
-class StreamReaderProgress:
- """Wrapper for a read only stream that adds progress callbacks."""
-
- def __init__(self, stream, callbacks=None):
- self._stream = stream
- self._callbacks = callbacks
- if callbacks is None:
- self._callbacks = []
-
- def read(self, *args, **kwargs):
- value = self._stream.read(*args, **kwargs)
- invoke_progress_callbacks(self._callbacks, len(value))
- return value
-
-
-class NoResourcesAvailable(Exception):
- pass
-
-
-class TaskSemaphore:
- def __init__(self, count):
- """A semaphore for the purpose of limiting the number of tasks
-
- :param count: The size of semaphore
- """
- self._semaphore = threading.Semaphore(count)
-
- def acquire(self, tag, blocking=True):
- """Acquire the semaphore
-
- :param tag: A tag identifying what is acquiring the semaphore. Note
- that this is not really needed to directly use this class but is
- needed for API compatibility with the SlidingWindowSemaphore
- implementation.
- :param block: If True, block until it can be acquired. If False,
- do not block and raise an exception if cannot be acquired.
-
- :returns: A token (can be None) to use when releasing the semaphore
- """
- logger.debug("Acquiring %s", tag)
- if not self._semaphore.acquire(blocking):
- raise NoResourcesAvailable("Cannot acquire tag '%s'" % tag)
-
- def release(self, tag, acquire_token):
- """Release the semaphore
-
- :param tag: A tag identifying what is releasing the semaphore
- :param acquire_token: The token returned from when the semaphore was
- acquired. Note that this is not really needed to directly use this
- class but is needed for API compatibility with the
- SlidingWindowSemaphore implementation.
- """
- logger.debug(f"Releasing acquire {tag}/{acquire_token}")
- self._semaphore.release()
-
-
-class SlidingWindowSemaphore(TaskSemaphore):
- """A semaphore used to coordinate sequential resource access.
-
- This class is similar to the stdlib BoundedSemaphore:
-
- * It's initialized with a count.
- * Each call to ``acquire()`` decrements the counter.
- * If the count is at zero, then ``acquire()`` will either block until the
- count increases, or if ``blocking=False``, then it will raise
- a NoResourcesAvailable exception indicating that it failed to acquire the
- semaphore.
-
- The main difference is that this semaphore is used to limit
- access to a resource that requires sequential access. For example,
- if I want to access resource R that has 20 subresources R_0 - R_19,
- this semaphore can also enforce that you only have a max range of
- 10 at any given point in time. You must also specify a tag name
- when you acquire the semaphore. The sliding window semantics apply
- on a per tag basis. The internal count will only be incremented
- when the minimum sequence number for a tag is released.
-
- """
-
- def __init__(self, count):
- self._count = count
- # Dict[tag, next_sequence_number].
- self._tag_sequences = defaultdict(int)
- self._lowest_sequence = {}
- self._lock = threading.Lock()
- self._condition = threading.Condition(self._lock)
- # Dict[tag, List[sequence_number]]
- self._pending_release = {}
-
- def current_count(self):
- with self._lock:
- return self._count
-
- def acquire(self, tag, blocking=True):
- logger.debug("Acquiring %s", tag)
- self._condition.acquire()
- try:
- if self._count == 0:
- if not blocking:
- raise NoResourcesAvailable("Cannot acquire tag '%s'" % tag)
- else:
- while self._count == 0:
- self._condition.wait()
- # self._count is no longer zero.
- # First, check if this is the first time we're seeing this tag.
- sequence_number = self._tag_sequences[tag]
- if sequence_number == 0:
- # First time seeing the tag, so record we're at 0.
- self._lowest_sequence[tag] = sequence_number
- self._tag_sequences[tag] += 1
- self._count -= 1
- return sequence_number
- finally:
- self._condition.release()
-
- def release(self, tag, acquire_token):
- sequence_number = acquire_token
- logger.debug("Releasing acquire %s/%s", tag, sequence_number)
- self._condition.acquire()
- try:
- if tag not in self._tag_sequences:
- raise ValueError("Attempted to release unknown tag: %s" % tag)
- max_sequence = self._tag_sequences[tag]
- if self._lowest_sequence[tag] == sequence_number:
- # We can immediately process this request and free up
- # resources.
- self._lowest_sequence[tag] += 1
- self._count += 1
- self._condition.notify()
- queued = self._pending_release.get(tag, [])
- while queued:
- if self._lowest_sequence[tag] == queued[-1]:
- queued.pop()
- self._lowest_sequence[tag] += 1
- self._count += 1
- else:
- break
- elif self._lowest_sequence[tag] < sequence_number < max_sequence:
- # We can't do anything right now because we're still waiting
- # for the min sequence for the tag to be released. We have
- # to queue this for pending release.
- self._pending_release.setdefault(tag, []).append(
- sequence_number
- )
- self._pending_release[tag].sort(reverse=True)
- else:
- raise ValueError(
- "Attempted to release unknown sequence number "
- "%s for tag: %s" % (sequence_number, tag)
- )
- finally:
- self._condition.release()
-
-
-class ChunksizeAdjuster:
- def __init__(
- self,
- max_size=MAX_SINGLE_UPLOAD_SIZE,
- min_size=MIN_UPLOAD_CHUNKSIZE,
- max_parts=MAX_PARTS,
- ):
- self.max_size = max_size
- self.min_size = min_size
- self.max_parts = max_parts
-
- def adjust_chunksize(self, current_chunksize, file_size=None):
- """Get a chunksize close to current that fits within all S3 limits.
-
- :type current_chunksize: int
- :param current_chunksize: The currently configured chunksize.
-
- :type file_size: int or None
- :param file_size: The size of the file to upload. This might be None
- if the object being transferred has an unknown size.
-
- :returns: A valid chunksize that fits within configured limits.
- """
- chunksize = current_chunksize
- if file_size is not None:
- chunksize = self._adjust_for_max_parts(chunksize, file_size)
- return self._adjust_for_chunksize_limits(chunksize)
-
- def _adjust_for_chunksize_limits(self, current_chunksize):
- if current_chunksize > self.max_size:
- logger.debug(
- "Chunksize greater than maximum chunksize. "
- "Setting to %s from %s." % (self.max_size, current_chunksize)
- )
- return self.max_size
- elif current_chunksize < self.min_size:
- logger.debug(
- "Chunksize less than minimum chunksize. "
- "Setting to %s from %s." % (self.min_size, current_chunksize)
- )
- return self.min_size
- else:
- return current_chunksize
-
- def _adjust_for_max_parts(self, current_chunksize, file_size):
- chunksize = current_chunksize
- num_parts = int(math.ceil(file_size / float(chunksize)))
-
- while num_parts > self.max_parts:
- chunksize *= 2
- num_parts = int(math.ceil(file_size / float(chunksize)))
-
- if chunksize != current_chunksize:
- logger.debug(
- "Chunksize would result in the number of parts exceeding the "
- "maximum. Setting to %s from %s."
- % (chunksize, current_chunksize)
- )
-
- return chunksize
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/grouped_batch_sampler.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/grouped_batch_sampler.py
deleted file mode 100644
index 138e106136083383d9f8729f1da930804463b297..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/data/samplers/grouped_batch_sampler.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import numpy as np
-from torch.utils.data.sampler import BatchSampler, Sampler
-
-
-class GroupedBatchSampler(BatchSampler):
- """
- Wraps another sampler to yield a mini-batch of indices.
- It enforces that the batch only contain elements from the same group.
- It also tries to provide mini-batches which follows an ordering which is
- as close as possible to the ordering from the original sampler.
- """
-
- def __init__(self, sampler, group_ids, batch_size):
- """
- Args:
- sampler (Sampler): Base sampler.
- group_ids (list[int]): If the sampler produces indices in range [0, N),
- `group_ids` must be a list of `N` ints which contains the group id of each sample.
- The group ids must be a set of integers in the range [0, num_groups).
- batch_size (int): Size of mini-batch.
- """
- if not isinstance(sampler, Sampler):
- raise ValueError(
- "sampler should be an instance of "
- "torch.utils.data.Sampler, but got sampler={}".format(sampler)
- )
- self.sampler = sampler
- self.group_ids = np.asarray(group_ids)
- assert self.group_ids.ndim == 1
- self.batch_size = batch_size
- groups = np.unique(self.group_ids).tolist()
-
- # buffer the indices of each group until batch size is reached
- self.buffer_per_group = {k: [] for k in groups}
-
- def __iter__(self):
- for idx in self.sampler:
- group_id = self.group_ids[idx]
- group_buffer = self.buffer_per_group[group_id]
- group_buffer.append(idx)
- if len(group_buffer) == self.batch_size:
- yield group_buffer[:] # yield a copy of the list
- del group_buffer[:]
-
- def __len__(self):
- raise NotImplementedError("len() of GroupedBatchSampler is not well-defined.")
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/proposal_utils.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/proposal_utils.py
deleted file mode 100644
index d4af90525ba07eb8d313460ee2c3f468fe367cff..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/detectron2/modeling/proposal_generator/proposal_utils.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import math
-import torch
-
-from detectron2.structures import Instances
-
-
-def add_ground_truth_to_proposals(gt_boxes, proposals):
- """
- Call `add_ground_truth_to_proposals_single_image` for all images.
-
- Args:
- gt_boxes(list[Boxes]): list of N elements. Element i is a Boxes
- representing the gound-truth for image i.
- proposals (list[Instances]): list of N elements. Element i is a Instances
- representing the proposals for image i.
-
- Returns:
- list[Instances]: list of N Instances. Each is the proposals for the image,
- with field "proposal_boxes" and "objectness_logits".
- """
- assert gt_boxes is not None
-
- assert len(proposals) == len(gt_boxes)
- if len(proposals) == 0:
- return proposals
-
- return [
- add_ground_truth_to_proposals_single_image(gt_boxes_i, proposals_i)
- for gt_boxes_i, proposals_i in zip(gt_boxes, proposals)
- ]
-
-
-def add_ground_truth_to_proposals_single_image(gt_boxes, proposals):
- """
- Augment `proposals` with ground-truth boxes from `gt_boxes`.
-
- Args:
- Same as `add_ground_truth_to_proposals`, but with gt_boxes and proposals
- per image.
-
- Returns:
- Same as `add_ground_truth_to_proposals`, but for only one image.
- """
- device = proposals.objectness_logits.device
- # Concatenating gt_boxes with proposals requires them to have the same fields
- # Assign all ground-truth boxes an objectness logit corresponding to P(object) \approx 1.
- gt_logit_value = math.log((1.0 - 1e-10) / (1 - (1.0 - 1e-10)))
-
- gt_logits = gt_logit_value * torch.ones(len(gt_boxes), device=device)
- gt_proposal = Instances(proposals.image_size)
-
- gt_proposal.proposal_boxes = gt_boxes
- gt_proposal.objectness_logits = gt_logits
- new_proposals = Instances.cat([proposals, gt_proposal])
-
- return new_proposals
diff --git a/spaces/CVPR/GFPGAN-example/gfpgan/train.py b/spaces/CVPR/GFPGAN-example/gfpgan/train.py
deleted file mode 100644
index fe5f1f909ae15a8d830ef65dcb43436d4f4ee7ae..0000000000000000000000000000000000000000
--- a/spaces/CVPR/GFPGAN-example/gfpgan/train.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# flake8: noqa
-import os.path as osp
-from basicsr.train import train_pipeline
-
-import gfpgan.archs
-import gfpgan.data
-import gfpgan.models
-
-if __name__ == '__main__':
- root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
- train_pipeline(root_path)
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_union.py b/spaces/CVPR/LIVE/pybind11/tests/test_union.py
deleted file mode 100644
index 2a2c12fb4836a0f2194e06af7781216d78ed0ba2..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_union.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# -*- coding: utf-8 -*-
-from pybind11_tests import union_ as m
-
-
-def test_union():
- instance = m.TestUnion()
-
- instance.as_uint = 10
- assert instance.as_int == 10
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/swap_ranges.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/swap_ranges.h
deleted file mode 100644
index 78769715c5b3f07ce74ddc3807369c8692af2426..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/generic/swap_ranges.h
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * 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.
- */
-
-
-#pragma once
-
-#include Title Generated:> {summary} Original Title:> {title} Title Generated:> {summary}
- Apply Instruct Pix2Pix Diffusion to a video
- You may also like: Thank you for visiting our web server. Download File ✏ https://tinurli.com/2uwjRt DOWNLOAD • https://tinurli.com/2uwkCv If you are looking for a thrilling and realistic first-person shooter game for your Android device, you might want to check out Modern Combat 2: Black Pegasus APK. This is a sequel to the popular Modern Combat: Sandstorm game, developed and published by Gameloft in 2010. It features new environments, updated graphics, and more robust multiplayer modes. DOWNLOAD ✓ https://urlca.com/2uOd2S Modern Combat 2: Black Pegasus APK takes you to the heart of a global modern conflict, where you can play as one of three different characters and complete 12 missions across various locations. You can also join online battles with up to 10 players, level up through 72 ranks, and unlock new weapons and perks. The game has been praised for its awesome graphics, stunning sound, captivating gameplay, and online multiplayer features. 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The game has an online multiplayer mode that lets you join or create matches with up to 10 players across four different modes: Team Deathmatch, Capture The Flag, Bomb Squad, and Zone Control. You can also chat with your teammates using voice or text messages. To play online, tap the multiplayer button on the main menu and select the mode you want. You can then choose to join an existing match or create your own. Modern Combat 2: Black Pegasus APK is a great game that offers a lot of fun and excitement for FPS fans. However, it also has some drawbacks that you should be aware of before downloading it. Here are some of the pros and cons of Modern Combat 2: Black Pegasus APK: Modern Combat 2: Black Pegasus APK is one of the best FPS games for Android devices. It offers a thrilling and realistic experience that will keep you entertained for hours. It has awesome graphics, sound, gameplay, and multiplayer features that make it stand out from other games in the genre. It is also free to download and play, which is a great deal for such a high-quality game. However, the game also has some drawbacks that you should consider before downloading it. It has some bugs, ads, compatibility issues, and a large file size that may affect your enjoyment of the game. It may also drain your battery and overheat your device, so you should play it with caution. Overall, we recommend Modern Combat 2: Black Pegasus APK to anyone who loves FPS games and wants to have a blast on their Android device. It is a game that will challenge your skills and immerse you in a modern warfare scenario. You can download it from the links provided in this article and start playing right away. Have fun! Here are some of the frequently asked questions and their answers about Modern Combat 2: Black Pegasus APK: Yes, Modern Combat 2: Black Pegasus APK is safe to download and install on your Android device. It does not contain any viruses, malware, or spyware that can harm your device or data. However, you should always download it from trusted sources and scan it with an antivirus app before installing it. Yes, Modern Combat 2: Black Pegasus APK is legal to download and play on your Android device. It is not a pirated or cracked version of the game, but a modified version that allows you to play it for free. However, you should be aware that downloading and playing modified games may violate the terms and conditions of the original developers and publishers, and may result in legal actions against you. To update Modern Combat 2: Black Pegasus APK, you need to download and install the latest version of the APK and OBB files from the links provided in this article. You do not need to uninstall the previous version of the game, as the new version will overwrite it automatically. However, you should always back up your game data before updating it, as you may lose your progress or settings. 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-
- )
-}
diff --git a/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/Fengbinbin/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich 版本信息 {pkg.version}
- 站点域名
- copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
- {host}
-
-
- """)
-
- with gr.Row():
- with gr.Column():
- model_name = gr.Dropdown(
- label="Model",
- choices=get_model_list(),
- value="dreamlike-art/dreamlike-photoreal-2.0",
-
- )
- prompt = gr.Textbox(label='Prompt')
- run_button = gr.Button(label='Run')
- with gr.Accordion('Advanced options', open=False):
- watermark = gr.Radio(["Picsart AI Research", "Text2Video-Zero",
- "None"], label="Watermark", value='Picsart AI Research')
-
- if on_huggingspace:
- video_length = gr.Slider(
- label="Video length", minimum=8, maximum=16, step=1)
- else:
- video_length = gr.Number(
- label="Video length", value=8, precision=0)
-
- n_prompt = gr.Textbox(
- label="Optional Negative Prompt", value='')
- seed = gr.Slider(label='Seed',
- info="-1 for random seed on each run. Otherwise, the seed will be fixed.",
- minimum=-1,
- maximum=65536,
- value=0,
- step=1)
-
- motion_field_strength_x = gr.Slider(
- label='Global Translation $\\delta_{x}$', minimum=-20, maximum=20,
- value=12,
- step=1)
- motion_field_strength_y = gr.Slider(
- label='Global Translation $\\delta_{y}$', minimum=-20, maximum=20,
- value=12,
- step=1)
-
- t0 = gr.Slider(label="Timestep t0", minimum=0,
- maximum=47, value=44, step=1,
- info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1 ",
- )
- t1 = gr.Slider(label="Timestep t1", minimum=1,
- info="Perform DDPM steps from t0 to t1. The larger the gap between t0 and t1, the more variance between the frames. Ensure t0 < t1",
- maximum=48, value=47, step=1)
- chunk_size = gr.Slider(
- label="Chunk size", minimum=2, maximum=16, value=2, step=1, visible=not on_huggingspace,
- info="Number of frames processed at once. Reduce for lower memory usage."
- )
- merging_ratio = gr.Slider(
- label="Merging ratio", minimum=0.0, maximum=0.9, step=0.1, value=0.0, visible=not on_huggingspace,
- info="Ratio of how many tokens are merged. The higher the more compression (less memory and faster inference)."
- )
-
- with gr.Column():
- result = gr.Video(label="Generated Video")
-
- inputs = [
- prompt,
- model_name,
- motion_field_strength_x,
- motion_field_strength_y,
- t0,
- t1,
- n_prompt,
- chunk_size,
- video_length,
- watermark,
- merging_ratio,
- seed,
- ]
-
- gr.Examples(examples=examples,
- inputs=inputs,
- outputs=result,
- fn=model.process_text2video,
- run_on_click=False,
- cache_examples=on_huggingspace,
- )
-
- run_button.click(fn=model.process_text2video,
- inputs=inputs,
- outputs=result,)
- return demo
diff --git a/spaces/PierreCugnet/airline-sentiment-analysis/README.md b/spaces/PierreCugnet/airline-sentiment-analysis/README.md
deleted file mode 100644
index bd1471226d66df14bccd35e5abfb08bc64e5cc97..0000000000000000000000000000000000000000
--- a/spaces/PierreCugnet/airline-sentiment-analysis/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Airline Sentiment Analysis
-emoji: 📈
-colorFrom: indigo
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/stage_trainer.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/stage_trainer.py
deleted file mode 100644
index 5b5b9b3776c78edd2f3e1dadf1b205da0a23ef82..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/engine/stage_trainer.py
+++ /dev/null
@@ -1,184 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import datetime
-import logging
-import time
-
-import torch
-import torch.distributed as dist
-
-from maskrcnn_benchmark.utils.comm import get_world_size
-from maskrcnn_benchmark.utils.metric_logger import MetricLogger
-
-
-def reduce_loss_dict(all_loss_dict):
- """
- Reduce the loss dictionary from all processes so that process with rank
- 0 has the averaged results. Returns a dict with the same fields as
- loss_dict, after reduction.
- """
- world_size = get_world_size()
- with torch.no_grad():
- loss_names = []
- all_losses = []
- for loss_dict in all_loss_dict:
- for k in sorted(loss_dict.keys()):
- loss_names.append(k)
- all_losses.append(loss_dict[k])
- all_losses = torch.stack(all_losses, dim=0)
- if world_size > 1:
- dist.reduce(all_losses, dst=0)
- if dist.get_rank() == 0:
- # only main process gets accumulated, so only divide by
- # world_size in this case
- all_losses /= world_size
-
- reduced_losses = {}
- for k, v in zip(loss_names, all_losses):
- if k not in reduced_losses:
- reduced_losses[k] = v / len(all_loss_dict)
- reduced_losses[k] += v / len(all_loss_dict)
-
- return reduced_losses
-
-
-def do_train(
- model,
- data_loader,
- optimizer,
- scheduler,
- checkpointer,
- device,
- checkpoint_period,
- arguments,
-):
- logger = logging.getLogger("maskrcnn_benchmark.trainer")
- logger.info("Start training")
- meters = MetricLogger(delimiter=" ")
- epoch_per_stage = arguments['epoch_per_stage']
- max_iter = sum(len(stage_loader) * epoch_per_stage[si] for si, stage_loader in enumerate(data_loader))
- max_iter += epoch_per_stage[-1] * min(len(stage_loader) for stage_loader in data_loader)
- model.train()
- start_training_time = time.time()
- end = time.time()
-
- for stage_i, stage_loader in enumerate(data_loader):
- for ep in range(epoch_per_stage[stage_i]):
- start_iter = arguments["iteration"]
- for iteration, (images, targets, _) in enumerate(stage_loader, start_iter):
- data_time = time.time() - end
- iteration = iteration + 1
- arguments["iteration"] = iteration
-
- scheduler[stage_i].step()
-
- all_stage_loss_dict = []
- images = images.to(device)
- targets = [target.to(device) for target in targets]
- loss_dict = model(images, targets, stage_i)
- all_stage_loss_dict.append(loss_dict)
-
- losses = sum(loss for loss_dict in all_stage_loss_dict for loss in loss_dict.values())
-
- # reduce losses over all GPUs for logging purposes
- loss_dict_reduced = reduce_loss_dict(all_stage_loss_dict)
- losses_reduced = sum(loss for loss in loss_dict_reduced.values())
- meters.update(loss=losses_reduced, **loss_dict_reduced)
-
- optimizer.zero_grad()
- losses.backward()
- optimizer.step()
-
- batch_time = time.time() - end
- end = time.time()
- meters.update(time=batch_time, data=data_time)
-
- eta_seconds = meters.time.global_avg * (max_iter - iteration)
- eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-
- if iteration % 20 == 0 or iteration == max_iter:
- logger.info(
- meters.delimiter.join(
- [
- "eta: {eta}",
- "iter: {iter}",
- "{meters}",
- "lr: {lr:.6f}",
- "max mem: {memory:.0f}",
- ]
- ).format(
- eta=eta_string,
- iter=iteration,
- meters=str(meters),
- lr=optimizer.param_groups[0]["lr"],
- memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
- )
- )
- if iteration % checkpoint_period == 0:
- checkpointer.save("model_{:07d}".format(iteration), **arguments)
- if iteration == max_iter:
- checkpointer.save("model_final", **arguments)
-
- for ep in range(epoch_per_stage[-1]):
- start_iter = arguments["iteration"]
- for iteration, stage_loader in enumerate(zip(*data_loader), start_iter):
- data_time = time.time() - end
- iteration = iteration + 1
- arguments["iteration"] = iteration
-
- scheduler[-1].step()
-
- all_task_loss_dict = []
- for stage_i, (images, targets, _) in enumerate(stage_loader):
- images = images.to(device)
- targets = [target.to(device) for target in targets]
- loss_dict = model(images, targets, stage_i)
- all_task_loss_dict.append(loss_dict)
-
- losses = sum(loss for loss_dict in all_task_loss_dict for loss in loss_dict.values())
-
- # reduce losses over all GPUs for logging purposes
- loss_dict_reduced = reduce_loss_dict(all_task_loss_dict)
- losses_reduced = sum(loss for loss in loss_dict_reduced.values())
- meters.update(loss=losses_reduced, **loss_dict_reduced)
-
- optimizer.zero_grad()
- losses.backward()
- optimizer.step()
-
- batch_time = time.time() - end
- end = time.time()
- meters.update(time=batch_time, data=data_time)
-
- eta_seconds = meters.time.global_avg * (max_iter - iteration)
- eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
-
- if iteration % 20 == 0 or iteration == max_iter:
- logger.info(
- meters.delimiter.join(
- [
- "eta: {eta}",
- "iter: {iter}",
- "{meters}",
- "lr: {lr:.6f}",
- "max mem: {memory:.0f}",
- ]
- ).format(
- eta=eta_string,
- iter=iteration,
- meters=str(meters),
- lr=optimizer.param_groups[0]["lr"],
- memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
- )
- )
- if iteration % checkpoint_period == 0:
- checkpointer.save("model_{:07d}".format(iteration), **arguments)
- if iteration == max_iter:
- checkpointer.save("model_final", **arguments)
-
- total_training_time = time.time() - start_training_time
- total_time_str = str(datetime.timedelta(seconds=total_training_time))
- logger.info(
- "Total training time: {} ({:.4f} s / it)".format(
- total_time_str, total_training_time / (max_iter)
- )
- )
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/cli/cmdoptions.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/cli/cmdoptions.py
deleted file mode 100644
index b4e2560dea29ee1f4b88b5a734960f551d611720..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_internal/cli/cmdoptions.py
+++ /dev/null
@@ -1,1049 +0,0 @@
-"""
-shared options and groups
-
-The principle here is to define options once, but *not* instantiate them
-globally. One reason being that options with action='append' can carry state
-between parses. pip parses general options twice internally, and shouldn't
-pass on state. To be consistent, all options will follow this design.
-"""
-
-# The following comment should be removed at some point in the future.
-# mypy: strict-optional=False
-
-import importlib.util
-import logging
-import os
-import textwrap
-from functools import partial
-from optparse import SUPPRESS_HELP, Option, OptionGroup, OptionParser, Values
-from textwrap import dedent
-from typing import Any, Callable, Dict, Optional, Tuple
-
-from pip._vendor.packaging.utils import canonicalize_name
-
-from pip._internal.cli.parser import ConfigOptionParser
-from pip._internal.exceptions import CommandError
-from pip._internal.locations import USER_CACHE_DIR, get_src_prefix
-from pip._internal.models.format_control import FormatControl
-from pip._internal.models.index import PyPI
-from pip._internal.models.target_python import TargetPython
-from pip._internal.utils.hashes import STRONG_HASHES
-from pip._internal.utils.misc import strtobool
-
-logger = logging.getLogger(__name__)
-
-
-def raise_option_error(parser: OptionParser, option: Option, msg: str) -> None:
- """
- Raise an option parsing error using parser.error().
-
- Args:
- parser: an OptionParser instance.
- option: an Option instance.
- msg: the error text.
- """
- msg = f"{option} error: {msg}"
- msg = textwrap.fill(" ".join(msg.split()))
- parser.error(msg)
-
-
-def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
- """
- Return an OptionGroup object
- group -- assumed to be dict with 'name' and 'options' keys
- parser -- an optparse Parser
- """
- option_group = OptionGroup(parser, group["name"])
- for option in group["options"]:
- option_group.add_option(option())
- return option_group
-
-
-def check_dist_restriction(options: Values, check_target: bool = False) -> None:
- """Function for determining if custom platform options are allowed.
-
- :param options: The OptionParser options.
- :param check_target: Whether or not to check if --target is being used.
- """
- dist_restriction_set = any(
- [
- options.python_version,
- options.platforms,
- options.abis,
- options.implementation,
- ]
- )
-
- binary_only = FormatControl(set(), {":all:"})
- sdist_dependencies_allowed = (
- options.format_control != binary_only and not options.ignore_dependencies
- )
-
- # Installations or downloads using dist restrictions must not combine
- # source distributions and dist-specific wheels, as they are not
- # guaranteed to be locally compatible.
- if dist_restriction_set and sdist_dependencies_allowed:
- raise CommandError(
- "When restricting platform and interpreter constraints using "
- "--python-version, --platform, --abi, or --implementation, "
- "either --no-deps must be set, or --only-binary=:all: must be "
- "set and --no-binary must not be set (or must be set to "
- ":none:)."
- )
-
- if check_target:
- if dist_restriction_set and not options.target_dir:
- raise CommandError(
- "Can not use any platform or abi specific options unless "
- "installing via '--target'"
- )
-
-
-def _path_option_check(option: Option, opt: str, value: str) -> str:
- return os.path.expanduser(value)
-
-
-def _package_name_option_check(option: Option, opt: str, value: str) -> str:
- return canonicalize_name(value)
-
-
-class PipOption(Option):
- TYPES = Option.TYPES + ("path", "package_name")
- TYPE_CHECKER = Option.TYPE_CHECKER.copy()
- TYPE_CHECKER["package_name"] = _package_name_option_check
- TYPE_CHECKER["path"] = _path_option_check
-
-
-###########
-# options #
-###########
-
-help_: Callable[..., Option] = partial(
- Option,
- "-h",
- "--help",
- dest="help",
- action="help",
- help="Show help.",
-)
-
-debug_mode: Callable[..., Option] = partial(
- Option,
- "--debug",
- dest="debug_mode",
- action="store_true",
- default=False,
- help=(
- "Let unhandled exceptions propagate outside the main subroutine, "
- "instead of logging them to stderr."
- ),
-)
-
-isolated_mode: Callable[..., Option] = partial(
- Option,
- "--isolated",
- dest="isolated_mode",
- action="store_true",
- default=False,
- help=(
- "Run pip in an isolated mode, ignoring environment variables and user "
- "configuration."
- ),
-)
-
-require_virtualenv: Callable[..., Option] = partial(
- Option,
- "--require-virtualenv",
- "--require-venv",
- dest="require_venv",
- action="store_true",
- default=False,
- help=(
- "Allow pip to only run in a virtual environment; "
- "exit with an error otherwise."
- ),
-)
-
-python: Callable[..., Option] = partial(
- Option,
- "--python",
- dest="python",
- help="Run pip with the specified Python interpreter.",
-)
-
-verbose: Callable[..., Option] = partial(
- Option,
- "-v",
- "--verbose",
- dest="verbose",
- action="count",
- default=0,
- help="Give more output. Option is additive, and can be used up to 3 times.",
-)
-
-no_color: Callable[..., Option] = partial(
- Option,
- "--no-color",
- dest="no_color",
- action="store_true",
- default=False,
- help="Suppress colored output.",
-)
-
-version: Callable[..., Option] = partial(
- Option,
- "-V",
- "--version",
- dest="version",
- action="store_true",
- help="Show version and exit.",
-)
-
-quiet: Callable[..., Option] = partial(
- Option,
- "-q",
- "--quiet",
- dest="quiet",
- action="count",
- default=0,
- help=(
- "Give less output. Option is additive, and can be used up to 3"
- " times (corresponding to WARNING, ERROR, and CRITICAL logging"
- " levels)."
- ),
-)
-
-progress_bar: Callable[..., Option] = partial(
- Option,
- "--progress-bar",
- dest="progress_bar",
- type="choice",
- choices=["on", "off"],
- default="on",
- help="Specify whether the progress bar should be used [on, off] (default: on)",
-)
-
-log: Callable[..., Option] = partial(
- PipOption,
- "--log",
- "--log-file",
- "--local-log",
- dest="log",
- metavar="path",
- type="path",
- help="Path to a verbose appending log.",
-)
-
-no_input: Callable[..., Option] = partial(
- Option,
- # Don't ask for input
- "--no-input",
- dest="no_input",
- action="store_true",
- default=False,
- help="Disable prompting for input.",
-)
-
-proxy: Callable[..., Option] = partial(
- Option,
- "--proxy",
- dest="proxy",
- type="str",
- default="",
- help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
-)
-
-retries: Callable[..., Option] = partial(
- Option,
- "--retries",
- dest="retries",
- type="int",
- default=5,
- help="Maximum number of retries each connection should attempt "
- "(default %default times).",
-)
-
-timeout: Callable[..., Option] = partial(
- Option,
- "--timeout",
- "--default-timeout",
- metavar="sec",
- dest="timeout",
- type="float",
- default=15,
- help="Set the socket timeout (default %default seconds).",
-)
-
-
-def exists_action() -> Option:
- return Option(
- # Option when path already exist
- "--exists-action",
- dest="exists_action",
- type="choice",
- choices=["s", "i", "w", "b", "a"],
- default=[],
- action="append",
- metavar="action",
- help="Default action when a path already exists: "
- "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
- )
-
-
-cert: Callable[..., Option] = partial(
- PipOption,
- "--cert",
- dest="cert",
- type="path",
- metavar="path",
- help=(
- "Path to PEM-encoded CA certificate bundle. "
- "If provided, overrides the default. "
- "See 'SSL Certificate Verification' in pip documentation "
- "for more information."
- ),
-)
-
-client_cert: Callable[..., Option] = partial(
- PipOption,
- "--client-cert",
- dest="client_cert",
- type="path",
- default=None,
- metavar="path",
- help="Path to SSL client certificate, a single file containing the "
- "private key and the certificate in PEM format.",
-)
-
-index_url: Callable[..., Option] = partial(
- Option,
- "-i",
- "--index-url",
- "--pypi-url",
- dest="index_url",
- metavar="URL",
- default=PyPI.simple_url,
- help="Base URL of the Python Package Index (default %default). "
- "This should point to a repository compliant with PEP 503 "
- "(the simple repository API) or a local directory laid out "
- "in the same format.",
-)
-
-
-def extra_index_url() -> Option:
- return Option(
- "--extra-index-url",
- dest="extra_index_urls",
- metavar="URL",
- action="append",
- default=[],
- help="Extra URLs of package indexes to use in addition to "
- "--index-url. Should follow the same rules as "
- "--index-url.",
- )
-
-
-no_index: Callable[..., Option] = partial(
- Option,
- "--no-index",
- dest="no_index",
- action="store_true",
- default=False,
- help="Ignore package index (only looking at --find-links URLs instead).",
-)
-
-
-def find_links() -> Option:
- return Option(
- "-f",
- "--find-links",
- dest="find_links",
- action="append",
- default=[],
- metavar="url",
- help="If a URL or path to an html file, then parse for links to "
- "archives such as sdist (.tar.gz) or wheel (.whl) files. "
- "If a local path or file:// URL that's a directory, "
- "then look for archives in the directory listing. "
- "Links to VCS project URLs are not supported.",
- )
-
-
-def trusted_host() -> Option:
- return Option(
- "--trusted-host",
- dest="trusted_hosts",
- action="append",
- metavar="HOSTNAME",
- default=[],
- help="Mark this host or host:port pair as trusted, even though it "
- "does not have valid or any HTTPS.",
- )
-
-
-def constraints() -> Option:
- return Option(
- "-c",
- "--constraint",
- dest="constraints",
- action="append",
- default=[],
- metavar="file",
- help="Constrain versions using the given constraints file. "
- "This option can be used multiple times.",
- )
-
-
-def requirements() -> Option:
- return Option(
- "-r",
- "--requirement",
- dest="requirements",
- action="append",
- default=[],
- metavar="file",
- help="Install from the given requirements file. "
- "This option can be used multiple times.",
- )
-
-
-def editable() -> Option:
- return Option(
- "-e",
- "--editable",
- dest="editables",
- action="append",
- default=[],
- metavar="path/url",
- help=(
- "Install a project in editable mode (i.e. setuptools "
- '"develop mode") from a local project path or a VCS url.'
- ),
- )
-
-
-def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
- value = os.path.abspath(value)
- setattr(parser.values, option.dest, value)
-
-
-src: Callable[..., Option] = partial(
- PipOption,
- "--src",
- "--source",
- "--source-dir",
- "--source-directory",
- dest="src_dir",
- type="path",
- metavar="dir",
- default=get_src_prefix(),
- action="callback",
- callback=_handle_src,
- help="Directory to check out editable projects into. "
- 'The default in a virtualenv is "
- Description: Simply input any textual prompt to generate videos right away and unleash your creativity and imagination! You can also select from the examples below. For performance purposes, our current preview release allows to generate up to 16 frames, which can be configured in the Advanced Options.
-
-
Selected EMG Bin
')
- outputs.append(gr.Plot(show_label=False))
-
- gr.Markdown("# Outputs:")
-
- outputs.append(gr.Markdown("## Mean Euclidean Distance = ... mm"))
- with gr.Row():
- with gr.Column():
- gr.Markdown('EMG Driven
')
- outputs.append(gr.Image(show_label=False))
- with gr.Column():
- gr.Markdown('Camera Driven
')
- outputs.append(gr.Image(show_label=False))
-
- inputs[1].change(fn=run_ai, inputs=inputs, outputs=outputs)
-
-top_div.launch()
diff --git a/spaces/Rbrq/DeticChatGPT/detic/modeling/roi_heads/detic_roi_heads.py b/spaces/Rbrq/DeticChatGPT/detic/modeling/roi_heads/detic_roi_heads.py
deleted file mode 100644
index c87559359e0516443a43ed327110ec55fa4fa307..0000000000000000000000000000000000000000
--- a/spaces/Rbrq/DeticChatGPT/detic/modeling/roi_heads/detic_roi_heads.py
+++ /dev/null
@@ -1,271 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import numpy as np
-import json
-import math
-import torch
-from torch import nn
-from torch.autograd.function import Function
-from typing import Dict, List, Optional, Tuple, Union
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import ShapeSpec
-from detectron2.layers import batched_nms
-from detectron2.structures import Boxes, Instances, pairwise_iou
-from detectron2.utils.events import get_event_storage
-
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads.fast_rcnn import fast_rcnn_inference
-from detectron2.modeling.roi_heads.roi_heads import ROI_HEADS_REGISTRY, StandardROIHeads
-from detectron2.modeling.roi_heads.cascade_rcnn import CascadeROIHeads, _ScaleGradient
-from detectron2.modeling.roi_heads.box_head import build_box_head
-from .detic_fast_rcnn import DeticFastRCNNOutputLayers
-from ..debug import debug_second_stage
-
-from torch.cuda.amp import autocast
-
-@ROI_HEADS_REGISTRY.register()
-class DeticCascadeROIHeads(CascadeROIHeads):
- @configurable
- def __init__(
- self,
- *,
- mult_proposal_score: bool = False,
- with_image_labels: bool = False,
- add_image_box: bool = False,
- image_box_size: float = 1.0,
- ws_num_props: int = 512,
- add_feature_to_prop: bool = False,
- mask_weight: float = 1.0,
- one_class_per_proposal: bool = False,
- **kwargs,
- ):
- super().__init__(**kwargs)
- self.mult_proposal_score = mult_proposal_score
- self.with_image_labels = with_image_labels
- self.add_image_box = add_image_box
- self.image_box_size = image_box_size
- self.ws_num_props = ws_num_props
- self.add_feature_to_prop = add_feature_to_prop
- self.mask_weight = mask_weight
- self.one_class_per_proposal = one_class_per_proposal
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- ret = super().from_config(cfg, input_shape)
- ret.update({
- 'mult_proposal_score': cfg.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE,
- 'with_image_labels': cfg.WITH_IMAGE_LABELS,
- 'add_image_box': cfg.MODEL.ROI_BOX_HEAD.ADD_IMAGE_BOX,
- 'image_box_size': cfg.MODEL.ROI_BOX_HEAD.IMAGE_BOX_SIZE,
- 'ws_num_props': cfg.MODEL.ROI_BOX_HEAD.WS_NUM_PROPS,
- 'add_feature_to_prop': cfg.MODEL.ROI_BOX_HEAD.ADD_FEATURE_TO_PROP,
- 'mask_weight': cfg.MODEL.ROI_HEADS.MASK_WEIGHT,
- 'one_class_per_proposal': cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL,
- })
- return ret
-
-
- @classmethod
- def _init_box_head(self, cfg, input_shape):
- ret = super()._init_box_head(cfg, input_shape)
- del ret['box_predictors']
- cascade_bbox_reg_weights = cfg.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS
- box_predictors = []
- for box_head, bbox_reg_weights in zip(ret['box_heads'], \
- cascade_bbox_reg_weights):
- box_predictors.append(
- DeticFastRCNNOutputLayers(
- cfg, box_head.output_shape,
- box2box_transform=Box2BoxTransform(weights=bbox_reg_weights)
- ))
- ret['box_predictors'] = box_predictors
- return ret
-
-
- def _forward_box(self, features, proposals, targets=None,
- ann_type='box', classifier_info=(None,None,None)):
- """
- Add mult proposal scores at testing
- Add ann_type
- """
- if (not self.training) and self.mult_proposal_score:
- if len(proposals) > 0 and proposals[0].has('scores'):
- proposal_scores = [p.get('scores') for p in proposals]
- else:
- proposal_scores = [p.get('objectness_logits') for p in proposals]
-
- features = [features[f] for f in self.box_in_features]
- head_outputs = [] # (predictor, predictions, proposals)
- prev_pred_boxes = None
- image_sizes = [x.image_size for x in proposals]
-
- for k in range(self.num_cascade_stages):
- if k > 0:
- proposals = self._create_proposals_from_boxes(
- prev_pred_boxes, image_sizes,
- logits=[p.objectness_logits for p in proposals])
- if self.training and ann_type in ['box']:
- proposals = self._match_and_label_boxes(
- proposals, k, targets)
- predictions = self._run_stage(features, proposals, k,
- classifier_info=classifier_info)
- prev_pred_boxes = self.box_predictor[k].predict_boxes(
- (predictions[0], predictions[1]), proposals)
- head_outputs.append((self.box_predictor[k], predictions, proposals))
-
- if self.training:
- losses = {}
- storage = get_event_storage()
- for stage, (predictor, predictions, proposals) in enumerate(head_outputs):
- with storage.name_scope("stage{}".format(stage)):
- if ann_type != 'box':
- stage_losses = {}
- if ann_type in ['image', 'caption', 'captiontag']:
- image_labels = [x._pos_category_ids for x in targets]
- weak_losses = predictor.image_label_losses(
- predictions, proposals, image_labels,
- classifier_info=classifier_info,
- ann_type=ann_type)
- stage_losses.update(weak_losses)
- else: # supervised
- stage_losses = predictor.losses(
- (predictions[0], predictions[1]), proposals,
- classifier_info=classifier_info)
- if self.with_image_labels:
- stage_losses['image_loss'] = \
- predictions[0].new_zeros([1])[0]
- losses.update({k + "_stage{}".format(stage): v \
- for k, v in stage_losses.items()})
- return losses
- else:
- # Each is a list[Tensor] of length #image. Each tensor is Ri x (K+1)
- scores_per_stage = [h[0].predict_probs(h[1], h[2]) for h in head_outputs]
- scores = [
- sum(list(scores_per_image)) * (1.0 / self.num_cascade_stages)
- for scores_per_image in zip(*scores_per_stage)
- ]
- if self.mult_proposal_score:
- scores = [(s * ps[:, None]) ** 0.5 \
- for s, ps in zip(scores, proposal_scores)]
- if self.one_class_per_proposal:
- scores = [s * (s == s[:, :-1].max(dim=1)[0][:, None]).float() for s in scores]
- predictor, predictions, proposals = head_outputs[-1]
- boxes = predictor.predict_boxes(
- (predictions[0], predictions[1]), proposals)
- pred_instances, _ = fast_rcnn_inference(
- boxes,
- scores,
- image_sizes,
- predictor.test_score_thresh,
- predictor.test_nms_thresh,
- predictor.test_topk_per_image,
- )
- return pred_instances
-
-
- def forward(self, images, features, proposals, targets=None,
- ann_type='box', classifier_info=(None,None,None)):
- '''
- enable debug and image labels
- classifier_info is shared across the batch
- '''
- if self.training:
- if ann_type in ['box', 'prop', 'proptag']:
- proposals = self.label_and_sample_proposals(
- proposals, targets)
- else:
- proposals = self.get_top_proposals(proposals)
-
- losses = self._forward_box(features, proposals, targets, \
- ann_type=ann_type, classifier_info=classifier_info)
- if ann_type == 'box' and targets[0].has('gt_masks'):
- mask_losses = self._forward_mask(features, proposals)
- losses.update({k: v * self.mask_weight \
- for k, v in mask_losses.items()})
- losses.update(self._forward_keypoint(features, proposals))
- else:
- losses.update(self._get_empty_mask_loss(
- features, proposals,
- device=proposals[0].objectness_logits.device))
- return proposals, losses
- else:
- pred_instances = self._forward_box(
- features, proposals, classifier_info=classifier_info)
- pred_instances = self.forward_with_given_boxes(features, pred_instances)
- return pred_instances, {}
-
-
- def get_top_proposals(self, proposals):
- for i in range(len(proposals)):
- proposals[i].proposal_boxes.clip(proposals[i].image_size)
- proposals = [p[:self.ws_num_props] for p in proposals]
- for i, p in enumerate(proposals):
- p.proposal_boxes.tensor = p.proposal_boxes.tensor.detach()
- if self.add_image_box:
- proposals[i] = self._add_image_box(p)
- return proposals
-
-
- def _add_image_box(self, p):
- image_box = Instances(p.image_size)
- n = 1
- h, w = p.image_size
- f = self.image_box_size
- image_box.proposal_boxes = Boxes(
- p.proposal_boxes.tensor.new_tensor(
- [w * (1. - f) / 2.,
- h * (1. - f) / 2.,
- w * (1. - (1. - f) / 2.),
- h * (1. - (1. - f) / 2.)]
- ).view(n, 4))
- image_box.objectness_logits = p.objectness_logits.new_ones(n)
- return Instances.cat([p, image_box])
-
-
- def _get_empty_mask_loss(self, features, proposals, device):
- if self.mask_on:
- return {'loss_mask': torch.zeros(
- (1, ), device=device, dtype=torch.float32)[0]}
- else:
- return {}
-
-
- def _create_proposals_from_boxes(self, boxes, image_sizes, logits):
- """
- Add objectness_logits
- """
- boxes = [Boxes(b.detach()) for b in boxes]
- proposals = []
- for boxes_per_image, image_size, logit in zip(
- boxes, image_sizes, logits):
- boxes_per_image.clip(image_size)
- if self.training:
- inds = boxes_per_image.nonempty()
- boxes_per_image = boxes_per_image[inds]
- logit = logit[inds]
- prop = Instances(image_size)
- prop.proposal_boxes = boxes_per_image
- prop.objectness_logits = logit
- proposals.append(prop)
- return proposals
-
-
- def _run_stage(self, features, proposals, stage, \
- classifier_info=(None,None,None)):
- """
- Support classifier_info and add_feature_to_prop
- """
- pool_boxes = [x.proposal_boxes for x in proposals]
- box_features = self.box_pooler(features, pool_boxes)
- box_features = _ScaleGradient.apply(box_features, 1.0 / self.num_cascade_stages)
- box_features = self.box_head[stage](box_features)
- if self.add_feature_to_prop:
- feats_per_image = box_features.split(
- [len(p) for p in proposals], dim=0)
- for feat, p in zip(feats_per_image, proposals):
- p.feat = feat
- return self.box_predictor[stage](
- box_features,
- classifier_info=classifier_info)
diff --git a/spaces/Realcat/image-matching-webui/third_party/SuperGluePretrainedNetwork/models/matching.py b/spaces/Realcat/image-matching-webui/third_party/SuperGluePretrainedNetwork/models/matching.py
deleted file mode 100644
index c5c0eda3337d021464eb6283e57b7412c08afb03..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/SuperGluePretrainedNetwork/models/matching.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# %BANNER_BEGIN%
-# ---------------------------------------------------------------------
-# %COPYRIGHT_BEGIN%
-#
-# Magic Leap, Inc. ("COMPANY") CONFIDENTIAL
-#
-# Unpublished Copyright (c) 2020
-# Magic Leap, Inc., All Rights Reserved.
-#
-# NOTICE: All information contained herein is, and remains the property
-# of COMPANY. The intellectual and technical concepts contained herein
-# are proprietary to COMPANY and may be covered by U.S. and Foreign
-# Patents, patents in process, and are protected by trade secret or
-# copyright law. Dissemination of this information or reproduction of
-# this material is strictly forbidden unless prior written permission is
-# obtained from COMPANY. Access to the source code contained herein is
-# hereby forbidden to anyone except current COMPANY employees, managers
-# or contractors who have executed Confidentiality and Non-disclosure
-# agreements explicitly covering such access.
-#
-# The copyright notice above does not evidence any actual or intended
-# publication or disclosure of this source code, which includes
-# information that is confidential and/or proprietary, and is a trade
-# secret, of COMPANY. ANY REPRODUCTION, MODIFICATION, DISTRIBUTION,
-# PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR THROUGH USE OF THIS
-# SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS
-# STRICTLY PROHIBITED, AND IN VIOLATION OF APPLICABLE LAWS AND
-# INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE
-# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS
-# TO REPRODUCE, DISCLOSE OR DISTRIBUTE ITS CONTENTS, OR TO MANUFACTURE,
-# USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.
-#
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-# ----------------------------------------------------------------------
-# %AUTHORS_BEGIN%
-#
-# Originating Authors: Paul-Edouard Sarlin
-#
-# %AUTHORS_END%
-# --------------------------------------------------------------------*/
-# %BANNER_END%
-
-import torch
-
-from .superpoint import SuperPoint
-from .superglue import SuperGlue
-
-
-class Matching(torch.nn.Module):
- """Image Matching Frontend (SuperPoint + SuperGlue)"""
-
- def __init__(self, config={}):
- super().__init__()
- self.superpoint = SuperPoint(config.get("superpoint", {}))
- self.superglue = SuperGlue(config.get("superglue", {}))
-
- def forward(self, data):
- """Run SuperPoint (optionally) and SuperGlue
- SuperPoint is skipped if ['keypoints0', 'keypoints1'] exist in input
- Args:
- data: dictionary with minimal keys: ['image0', 'image1']
- """
- pred = {}
-
- # Extract SuperPoint (keypoints, scores, descriptors) if not provided
- if "keypoints0" not in data:
- pred0 = self.superpoint({"image": data["image0"]})
- pred = {**pred, **{k + "0": v for k, v in pred0.items()}}
- if "keypoints1" not in data:
- pred1 = self.superpoint({"image": data["image1"]})
- pred = {**pred, **{k + "1": v for k, v in pred1.items()}}
-
- # Batch all features
- # We should either have i) one image per batch, or
- # ii) the same number of local features for all images in the batch.
- data = {**data, **pred}
-
- for k in data:
- if isinstance(data[k], (list, tuple)):
- data[k] = torch.stack(data[k])
-
- # Perform the matching
- pred = {**pred, **self.superglue(data)}
-
- return pred
diff --git a/spaces/Riksarkivet/htr_demo/src/htr_pipeline/utils/pipeline_inferencer.py b/spaces/Riksarkivet/htr_demo/src/htr_pipeline/utils/pipeline_inferencer.py
deleted file mode 100644
index 39a1c15c4d158b5ab46852d05badc1adadcaa08d..0000000000000000000000000000000000000000
--- a/spaces/Riksarkivet/htr_demo/src/htr_pipeline/utils/pipeline_inferencer.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import gradio as gr
-from tqdm import tqdm
-
-from src.htr_pipeline.utils.process_segmask import SegMaskHelper
-from src.htr_pipeline.utils.xml_helper import XMLHelper
-
-terminate = False
-
-# TODO check why region is so slow to start.. Is their error with loading the model?
-
-
-class PipelineInferencer:
- def __init__(self, process_seg_mask: SegMaskHelper, xml_helper: XMLHelper):
- self.process_seg_mask = process_seg_mask
- self.xml_helper = xml_helper
-
- def image_to_page_xml(
- self,
- image,
- htr_tool_transcriber_model_dropdown,
- pred_score_threshold_regions,
- pred_score_threshold_lines,
- containments_threshold,
- inferencer,
- ):
- # temporary solutions.. for trocr..
- self.htr_tool_transcriber_model_dropdown = htr_tool_transcriber_model_dropdown
-
- template_data = self.xml_helper.prepare_template_data(self.xml_helper.xml_file_name, image)
- template_data["textRegions"] = self._process_regions(
- image, inferencer, pred_score_threshold_regions, pred_score_threshold_lines, containments_threshold
- )
-
- return self.xml_helper.render(template_data)
-
- def _process_regions(
- self,
- image,
- inferencer,
- pred_score_threshold_regions,
- pred_score_threshold_lines,
- containments_threshold,
- htr_threshold=0.6,
- ):
- global terminate
-
- _, regions_cropped_ordered, reg_polygons_ordered, reg_masks_ordered = inferencer.predict_regions(
- image,
- pred_score_threshold=pred_score_threshold_regions,
- containments_threshold=containments_threshold,
- visualize=False,
- )
- gr.Info(f"Found {len(regions_cropped_ordered)} Regions to parse")
- region_data_list = []
- for i, data in tqdm(enumerate(zip(regions_cropped_ordered, reg_polygons_ordered, reg_masks_ordered))):
- if terminate:
- break
- region_data = self._create_region_data(
- data, i, inferencer, pred_score_threshold_lines, containments_threshold, htr_threshold
- )
- if region_data:
- region_data_list.append(region_data)
-
- return region_data_list
-
- def _create_region_data(
- self, data, index, inferencer, pred_score_threshold_lines, containments_threshold, htr_threshold
- ):
- text_region, reg_pol, mask = data
- region_data = {"id": f"region_{index}", "boundary": reg_pol}
-
- text_lines, htr_scores = self._process_lines(
- text_region,
- inferencer,
- pred_score_threshold_lines,
- containments_threshold,
- mask,
- region_data["id"],
- htr_threshold,
- )
-
- if not text_lines:
- return None
-
- region_data["textLines"] = text_lines
- mean_htr_score = sum(htr_scores) / len(htr_scores) if htr_scores else 0
-
- return region_data if mean_htr_score > htr_threshold + 0.1 else None
-
- def _process_lines(
- self, text_region, inferencer, pred_score_threshold, containments_threshold, mask, region_id, htr_threshold=0.6
- ):
- _, lines_cropped_ordered, line_polygons_ordered = inferencer.predict_lines(
- text_region, pred_score_threshold, containments_threshold, visualize=False, custom_track=False
- )
-
- if not lines_cropped_ordered:
- return None, []
-
- line_polygons_ordered_trans = self.process_seg_mask._translate_line_coords(mask, line_polygons_ordered)
-
- text_lines = []
- htr_scores = []
-
- id_number = region_id.split("_")[1]
- total_lines_len = len(lines_cropped_ordered)
-
- gr.Info(f" Region {id_number}, found {total_lines_len} lines to parse and transcribe.")
-
- global terminate
-
- for index, (line, line_pol) in enumerate(zip(lines_cropped_ordered, line_polygons_ordered_trans)):
- if terminate:
- break
- line_data, htr_score = self._create_line_data(line, line_pol, index, region_id, inferencer, htr_threshold)
-
- if line_data:
- text_lines.append(line_data)
- htr_scores.append(htr_score)
-
- remaining_lines = total_lines_len - index - 1
-
- if (index + 1) % 10 == 0 and remaining_lines > 5: # +1 because index starts at 0
- gr.Info(
- f"Region {id_number}, parsed {index + 1} lines. Still {remaining_lines} lines left to transcribe."
- )
-
- return text_lines, htr_scores
-
- def _create_line_data(self, line, line_pol, index, region_id, inferencer, htr_threshold):
- line_data = {"id": f"line_{region_id}_{index}", "boundary": line_pol}
-
- # temporary solution..
- if self.htr_tool_transcriber_model_dropdown == "Riksarkivet/satrn_htr":
- transcribed_text, htr_score = inferencer.transcribe(line)
- else:
- transcribed_text, htr_score = inferencer.transcribe_different_model(
- line, self.htr_tool_transcriber_model_dropdown
- )
-
- line_data["unicode"] = self.xml_helper.escape_xml_chars(transcribed_text)
- line_data["pred_score"] = round(htr_score, 4)
-
- return line_data if htr_score > htr_threshold else None, htr_score
-
-
-if __name__ == "__main__":
- pass
diff --git a/spaces/Rinox06/webui/app.py b/spaces/Rinox06/webui/app.py
deleted file mode 100644
index 46634a8b73e78222880763b1e15a1e86cae79df4..0000000000000000000000000000000000000000
--- a/spaces/Rinox06/webui/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
- os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
- os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-
-os.system(f"git clone -b v1.5 https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/ show_progress=False,/ show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-os.system(f"sed -i -e 's/ outputs=\[/queue=False, &/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/ queue=False, / /g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-if "IS_SHARED_UI" in os.environ:
- os.system(f"rm -rfv /home/user/app/stable-diffusion-webui/scripts/")
-
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
- os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-
- os.system(f"wget -q https://huggingface.co/ckpt/anything-v3-vae-swapped/resolve/main/anything-v3-vae-swapped.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/anything-v3-vae-swapped.ckpt")
- os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
- os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
- os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
-
- os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
- # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
- os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-
- # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
- os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
- os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
- os.system(f"git clone https://github.com/camenduru/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-
- # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
- os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
- #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
- #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
- os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
- #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
- #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
- os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
- #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
- #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
- #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-
- #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
- #os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-
- #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
- #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-
- os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.ckpt")
- os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v2-1_768-ema-pruned.yaml")
-
- os.system(f"wget -q {os.getenv('MODEL_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME')}")
- os.system(f"wget -q {os.getenv('VAE_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('VAE_NAME')}")
- os.system(f"wget -q {os.getenv('YAML_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('YAML_NAME')}")
- os.system(f"wget -q {os.getenv('EMBED_LINK')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('EMBED_NAME')}")
-
- os.system(f"wget -q {os.getenv('EMBED_LINK1')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('EMBED_NAME1')}")
- os.system(f"wget -q {os.getenv('EMBED_LINK2')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('EMBED_NAME2')}")
- os.system(f"wget -q {os.getenv('EMBED_LINK3')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('EMBED_NAME3')}")
- os.system(f"wget -q {os.getenv('EMBED_LINK4')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('EMBED_NAME4')}")
-
- os.system(f"wget -q {os.getenv('MODEL_LINK1')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME1')}")
- os.system(f"wget -q {os.getenv('MODEL_LINK2')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME2')}")
- os.system(f"wget -q {os.getenv('MODEL_LINK3')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME3')}")
- os.system(f"wget -q {os.getenv('MODEL_LINK4')} -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/{os.getenv('MODEL_NAME4')}")
-
- os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test")
-
\ No newline at end of file
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/evaluation/metrics.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/evaluation/metrics.py
deleted file mode 100644
index 16c7dd47cadd53cf1caaa194e28a343f2aacc599..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/core/evaluation/metrics.py
+++ /dev/null
@@ -1,326 +0,0 @@
-from collections import OrderedDict
-
-import annotator.uniformer.mmcv as mmcv
-import numpy as np
-import torch
-
-
-def f_score(precision, recall, beta=1):
- """calcuate the f-score value.
-
- Args:
- precision (float | torch.Tensor): The precision value.
- recall (float | torch.Tensor): The recall value.
- beta (int): Determines the weight of recall in the combined score.
- Default: False.
-
- Returns:
- [torch.tensor]: The f-score value.
- """
- score = (1 + beta**2) * (precision * recall) / (
- (beta**2 * precision) + recall)
- return score
-
-
-def intersect_and_union(pred_label,
- label,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate intersection and Union.
-
- Args:
- pred_label (ndarray | str): Prediction segmentation map
- or predict result filename.
- label (ndarray | str): Ground truth segmentation map
- or label filename.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- label_map (dict): Mapping old labels to new labels. The parameter will
- work only when label is str. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. The parameter will
- work only when label is str. Default: False.
-
- Returns:
- torch.Tensor: The intersection of prediction and ground truth
- histogram on all classes.
- torch.Tensor: The union of prediction and ground truth histogram on
- all classes.
- torch.Tensor: The prediction histogram on all classes.
- torch.Tensor: The ground truth histogram on all classes.
- """
-
- if isinstance(pred_label, str):
- pred_label = torch.from_numpy(np.load(pred_label))
- else:
- pred_label = torch.from_numpy((pred_label))
-
- if isinstance(label, str):
- label = torch.from_numpy(
- mmcv.imread(label, flag='unchanged', backend='pillow'))
- else:
- label = torch.from_numpy(label)
-
- if label_map is not None:
- for old_id, new_id in label_map.items():
- label[label == old_id] = new_id
- if reduce_zero_label:
- label[label == 0] = 255
- label = label - 1
- label[label == 254] = 255
-
- mask = (label != ignore_index)
- pred_label = pred_label[mask]
- label = label[mask]
-
- intersect = pred_label[pred_label == label]
- area_intersect = torch.histc(
- intersect.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_pred_label = torch.histc(
- pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_label = torch.histc(
- label.float(), bins=(num_classes), min=0, max=num_classes - 1)
- area_union = area_pred_label + area_label - area_intersect
- return area_intersect, area_union, area_pred_label, area_label
-
-
-def total_intersect_and_union(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Total Intersection and Union.
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- ndarray: The intersection of prediction and ground truth histogram
- on all classes.
- ndarray: The union of prediction and ground truth histogram on all
- classes.
- ndarray: The prediction histogram on all classes.
- ndarray: The ground truth histogram on all classes.
- """
- num_imgs = len(results)
- assert len(gt_seg_maps) == num_imgs
- total_area_intersect = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_union = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_pred_label = torch.zeros((num_classes, ), dtype=torch.float64)
- total_area_label = torch.zeros((num_classes, ), dtype=torch.float64)
- for i in range(num_imgs):
- area_intersect, area_union, area_pred_label, area_label = \
- intersect_and_union(
- results[i], gt_seg_maps[i], num_classes, ignore_index,
- label_map, reduce_zero_label)
- total_area_intersect += area_intersect
- total_area_union += area_union
- total_area_pred_label += area_pred_label
- total_area_label += area_label
- return total_area_intersect, total_area_union, total_area_pred_label, \
- total_area_label
-
-
-def mean_iou(results,
- gt_seg_maps,
- num_classes,
- ignore_index,
- nan_to_num=None,
- label_map=dict(),
- reduce_zero_label=False):
- """Calculate Mean Intersection and Union (mIoU)
-
- Args:
- results (list[ndarray] | list[str]): List of prediction segmentation
- maps or list of prediction result filenames.
- gt_seg_maps (list[ndarray] | list[str]): list of ground truth
- segmentation maps or list of label filenames.
- num_classes (int): Number of categories.
- ignore_index (int): Index that will be ignored in evaluation.
- nan_to_num (int, optional): If specified, NaN values will be replaced
- by the numbers defined by the user. Default: None.
- label_map (dict): Mapping old labels to new labels. Default: dict().
- reduce_zero_label (bool): Wether ignore zero label. Default: False.
-
- Returns:
- dict[str, float | ndarray]:
-
', "\n").replace('\\"', '"').replace('
-
-"""
-
-article = """
-
-
-
-
-
- Pix2Pix Video
-
-
-
-
-"""
-
-with gr.Blocks(css='style.css') as demo:
- with gr.Column(elem_id="col-container"):
- gr.HTML(title)
- with gr.Row():
- with gr.Column():
- prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=False, elem_id="prompt-in")
- video_inp = gr.Video(label="Video source", source="upload", type="filepath", include_audio=False, elem_id="input-vid")
- with gr.Row():
- seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456)
- trim_in = gr.Slider(label="Cut video at (s)", minimun=1, maximum=3, step=1, value=1)
- with gr.Column():
- gr.HTML("""
-
-
-
-
-
-
- 
'
- f''
- )
- with gr.Row():
- with gr.Column():
- if args.files:
- vc_input = gr.Textbox(label="Input audio path")
- else:
- vc_input = gr.Audio(label="Input audio"+' (less than 5 minutes 30 seconds)' if limitation else '')
- vc_transpose = gr.Number(label="Transpose", value=0)
- vc_f0method = gr.Radio(
- label="Pitch extraction algorithm, PM is fast but Harvest is better for low frequencies",
- choices=["pm", "harvest"],
- value="pm",
- interactive=True,
- )
- vc_index_ratio = gr.Slider(
- minimum=0,
- maximum=1,
- label="Retrieval feature ratio",
- value=0.6,
- interactive=True,
- )
- tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
- tts_text = gr.Textbox(visible=False,label="TTS text (600 words limitation)" if limitation else "TTS text")
- tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
- vc_submit = gr.Button("Generate", variant="primary")
- with gr.Column():
- vc_output1 = gr.Textbox(label="Output Message")
- vc_output2 = gr.Audio(label="Output Audio")
- vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output1, vc_output2])
- tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
- app.queue(concurrency_count=1, max_size=20, api_open=args.api).launch(share=args.share)
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/sd_disable_initialization.py b/spaces/aodianyun/stable-diffusion-webui/modules/sd_disable_initialization.py
deleted file mode 100644
index 50e4c180fc74988ec697e4cef2773bd2a785bccf..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/sd_disable_initialization.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import ldm.modules.encoders.modules
-import open_clip
-import torch
-import transformers.utils.hub
-
-
-class DisableInitialization:
- """
- When an object of this class enters a `with` block, it starts:
- - preventing torch's layer initialization functions from working
- - changes CLIP and OpenCLIP to not download model weights
- - changes CLIP to not make requests to check if there is a new version of a file you already have
-
- When it leaves the block, it reverts everything to how it was before.
-
- Use it like this:
- ```
- with DisableInitialization():
- do_things()
- ```
- """
-
- def __init__(self, disable_clip=True):
- self.replaced = []
- self.disable_clip = disable_clip
-
- def replace(self, obj, field, func):
- original = getattr(obj, field, None)
- if original is None:
- return None
-
- self.replaced.append((obj, field, original))
- setattr(obj, field, func)
-
- return original
-
- def __enter__(self):
- def do_nothing(*args, **kwargs):
- pass
-
- def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
- return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
-
- def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
- res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
- res.name_or_path = pretrained_model_name_or_path
- return res
-
- def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
- args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
- return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
-
- def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
-
- # this file is always 404, prevent making request
- if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
- return None
-
- try:
- res = original(url, *args, local_files_only=True, **kwargs)
- if res is None:
- res = original(url, *args, local_files_only=False, **kwargs)
- return res
- except Exception as e:
- return original(url, *args, local_files_only=False, **kwargs)
-
- def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
- return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
-
- def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
- return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
-
- def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
- return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
-
- self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
- self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
- self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
-
- if self.disable_clip:
- self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
- self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
- self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
- self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
- self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
- self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- for obj, field, original in self.replaced:
- setattr(obj, field, original)
-
- self.replaced.clear()
-
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_overflow.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_overflow.py
deleted file mode 100644
index 01c447198f1bc0ebd4f330299d3387c9911fd344..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_overflow.py
+++ /dev/null
@@ -1,399 +0,0 @@
-import os
-import random
-import unittest
-from copy import deepcopy
-
-import torch
-
-from tests import get_tests_output_path
-from TTS.tts.configs.overflow_config import OverflowConfig
-from TTS.tts.layers.overflow.common_layers import Encoder, Outputnet, OverflowUtils
-from TTS.tts.layers.overflow.decoder import Decoder
-from TTS.tts.layers.overflow.neural_hmm import EmissionModel, NeuralHMM, TransitionModel
-from TTS.tts.models.overflow import Overflow
-from TTS.tts.utils.helpers import sequence_mask
-from TTS.utils.audio import AudioProcessor
-
-# pylint: disable=unused-variable
-
-torch.manual_seed(1)
-use_cuda = torch.cuda.is_available()
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-config_global = OverflowConfig(num_chars=24)
-ap = AudioProcessor.init_from_config(config_global)
-
-config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-parameter_path = os.path.join(get_tests_output_path(), "lj_parameters.pt")
-
-torch.save({"mean": -5.5138, "std": 2.0636, "init_transition_prob": 0.3212}, parameter_path)
-
-
-def _create_inputs(batch_size=8):
- max_len_t, max_len_m = random.randint(25, 50), random.randint(50, 80)
- input_dummy = torch.randint(0, 24, (batch_size, max_len_t)).long().to(device)
- input_lengths = torch.randint(20, max_len_t, (batch_size,)).long().to(device).sort(descending=True)[0]
- input_lengths[0] = max_len_t
- input_dummy = input_dummy * sequence_mask(input_lengths)
- mel_spec = torch.randn(batch_size, max_len_m, config_global.audio["num_mels"]).to(device)
- mel_lengths = torch.randint(40, max_len_m, (batch_size,)).long().to(device).sort(descending=True)[0]
- mel_lengths[0] = max_len_m
- mel_spec = mel_spec * sequence_mask(mel_lengths).unsqueeze(2)
- return input_dummy, input_lengths, mel_spec, mel_lengths
-
-
-def get_model(config=None):
- if config is None:
- config = config_global
- config.mel_statistics_parameter_path = parameter_path
- model = Overflow(config)
- model = model.to(device)
- return model
-
-
-def reset_all_weights(model):
- """
- refs:
- - https://discuss.pytorch.org/t/how-to-re-set-alll-parameters-in-a-network/20819/6
- - https://stackoverflow.com/questions/63627997/reset-parameters-of-a-neural-network-in-pytorch
- - https://pytorch.org/docs/stable/generated/torch.nn.Module.html
- """
-
- @torch.no_grad()
- def weight_reset(m):
- # - check if the current module has reset_parameters & if it's callabed called it on m
- reset_parameters = getattr(m, "reset_parameters", None)
- if callable(reset_parameters):
- m.reset_parameters()
-
- # Applies fn recursively to every submodule see: https://pytorch.org/docs/stable/generated/torch.nn.Module.html
- model.apply(fn=weight_reset)
-
-
-class TestOverflow(unittest.TestCase):
- def test_forward(self):
- model = get_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- outputs = model(input_dummy, input_lengths, mel_spec, mel_lengths)
- self.assertEqual(outputs["log_probs"].shape, (input_dummy.shape[0],))
- self.assertEqual(model.state_per_phone * max(input_lengths), outputs["alignments"].shape[2])
-
- def test_inference(self):
- model = get_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- output_dict = model.inference(input_dummy)
- self.assertEqual(output_dict["model_outputs"].shape[2], config_global.out_channels)
-
- def test_init_from_config(self):
- config = deepcopy(config_global)
- config.mel_statistics_parameter_path = parameter_path
- config.prenet_dim = 256
- model = Overflow.init_from_config(config_global)
- self.assertEqual(model.prenet_dim, config.prenet_dim)
-
-
-class TestOverflowEncoder(unittest.TestCase):
- @staticmethod
- def get_encoder(state_per_phone):
- config = deepcopy(config_global)
- config.state_per_phone = state_per_phone
- config.num_chars = 24
- return Encoder(config.num_chars, config.state_per_phone, config.prenet_dim, config.encoder_n_convolutions).to(
- device
- )
-
- def test_forward_with_state_per_phone_multiplication(self):
- for s_p_p in [1, 2, 3]:
- input_dummy, input_lengths, _, _ = _create_inputs()
- model = self.get_encoder(s_p_p)
- x, x_len = model(input_dummy, input_lengths)
- self.assertEqual(x.shape[1], input_dummy.shape[1] * s_p_p)
-
- def test_inference_with_state_per_phone_multiplication(self):
- for s_p_p in [1, 2, 3]:
- input_dummy, input_lengths, _, _ = _create_inputs()
- model = self.get_encoder(s_p_p)
- x, x_len = model.inference(input_dummy, input_lengths)
- self.assertEqual(x.shape[1], input_dummy.shape[1] * s_p_p)
-
-
-class TestOverflowUtils(unittest.TestCase):
- def test_logsumexp(self):
- a = torch.randn(10) # random numbers
- self.assertTrue(torch.eq(torch.logsumexp(a, dim=0), OverflowUtils.logsumexp(a, dim=0)).all())
-
- a = torch.zeros(10) # all zeros
- self.assertTrue(torch.eq(torch.logsumexp(a, dim=0), OverflowUtils.logsumexp(a, dim=0)).all())
-
- a = torch.ones(10) # all ones
- self.assertTrue(torch.eq(torch.logsumexp(a, dim=0), OverflowUtils.logsumexp(a, dim=0)).all())
-
-
-class TestOverflowDecoder(unittest.TestCase):
- @staticmethod
- def _get_decoder(num_flow_blocks_dec=None, hidden_channels_dec=None, reset_weights=True):
- config = deepcopy(config_global)
- config.num_flow_blocks_dec = (
- num_flow_blocks_dec if num_flow_blocks_dec is not None else config.num_flow_blocks_dec
- )
- config.hidden_channels_dec = (
- hidden_channels_dec if hidden_channels_dec is not None else config.hidden_channels_dec
- )
- config.dropout_p_dec = 0.0 # turn off dropout to check invertibility
- decoder = Decoder(
- config.out_channels,
- config.hidden_channels_dec,
- config.kernel_size_dec,
- config.dilation_rate,
- config.num_flow_blocks_dec,
- config.num_block_layers,
- config.dropout_p_dec,
- config.num_splits,
- config.num_squeeze,
- config.sigmoid_scale,
- config.c_in_channels,
- ).to(device)
- if reset_weights:
- reset_all_weights(decoder)
- return decoder
-
- def test_decoder_forward_backward(self):
- for num_flow_blocks_dec in [8, None]:
- for hidden_channels_dec in [100, None]:
- decoder = self._get_decoder(num_flow_blocks_dec, hidden_channels_dec)
- _, _, mel_spec, mel_lengths = _create_inputs()
- z, z_len, _ = decoder(mel_spec.transpose(1, 2), mel_lengths)
- mel_spec_, mel_lengths_, _ = decoder(z, z_len, reverse=True)
- mask = sequence_mask(z_len).unsqueeze(1)
- mel_spec = mel_spec[:, : z.shape[2], :].transpose(1, 2) * mask
- z = z * mask
- self.assertTrue(
- torch.isclose(mel_spec, mel_spec_, atol=1e-2).all(),
- f"num_flow_blocks_dec={num_flow_blocks_dec}, hidden_channels_dec={hidden_channels_dec}",
- )
-
-
-class TestNeuralHMM(unittest.TestCase):
- @staticmethod
- def _get_neural_hmm(deterministic_transition=None):
- config = deepcopy(config_global)
- neural_hmm = NeuralHMM(
- config.out_channels,
- config.ar_order,
- config.deterministic_transition if deterministic_transition is None else deterministic_transition,
- config.encoder_in_out_features,
- config.prenet_type,
- config.prenet_dim,
- config.prenet_n_layers,
- config.prenet_dropout,
- config.prenet_dropout_at_inference,
- config.memory_rnn_dim,
- config.outputnet_size,
- config.flat_start_params,
- config.std_floor,
- ).to(device)
- return neural_hmm
-
- @staticmethod
- def _get_emission_model():
- return EmissionModel().to(device)
-
- @staticmethod
- def _get_transition_model():
- return TransitionModel().to(device)
-
- @staticmethod
- def _get_embedded_input():
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- input_dummy = torch.nn.Embedding(config_global.num_chars, config_global.encoder_in_out_features).to(device)(
- input_dummy
- )
- return input_dummy, input_lengths, mel_spec, mel_lengths
-
- def test_neural_hmm_forward(self):
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- neural_hmm = self._get_neural_hmm()
- log_prob, log_alpha_scaled, transition_matrix, means = neural_hmm(
- input_dummy, input_lengths, mel_spec.transpose(1, 2), mel_lengths
- )
- self.assertEqual(log_prob.shape, (input_dummy.shape[0],))
- self.assertEqual(log_alpha_scaled.shape, transition_matrix.shape)
-
- def test_mask_lengths(self):
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- neural_hmm = self._get_neural_hmm()
- log_prob, log_alpha_scaled, transition_matrix, means = neural_hmm(
- input_dummy, input_lengths, mel_spec.transpose(1, 2), mel_lengths
- )
- log_c = torch.randn(mel_spec.shape[0], mel_spec.shape[1], device=device)
- log_c, log_alpha_scaled = neural_hmm._mask_lengths( # pylint: disable=protected-access
- mel_lengths, log_c, log_alpha_scaled
- )
- assertions = []
- for i in range(mel_spec.shape[0]):
- assertions.append(log_c[i, mel_lengths[i] :].sum() == 0.0)
- self.assertTrue(all(assertions), "Incorrect masking")
- assertions = []
- for i in range(mel_spec.shape[0]):
- assertions.append(log_alpha_scaled[i, mel_lengths[i] :, : input_lengths[i]].sum() == 0.0)
- self.assertTrue(all(assertions), "Incorrect masking")
-
- def test_process_ar_timestep(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
-
- h_post_prenet, c_post_prenet = model._init_lstm_states( # pylint: disable=protected-access
- input_dummy.shape[0], config_global.memory_rnn_dim, mel_spec
- )
- h_post_prenet, c_post_prenet = model._process_ar_timestep( # pylint: disable=protected-access
- 1,
- mel_spec,
- h_post_prenet,
- c_post_prenet,
- )
-
- self.assertEqual(h_post_prenet.shape, (input_dummy.shape[0], config_global.memory_rnn_dim))
- self.assertEqual(c_post_prenet.shape, (input_dummy.shape[0], config_global.memory_rnn_dim))
-
- def test_add_go_token(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
-
- out = model._add_go_token(mel_spec) # pylint: disable=protected-access
- self.assertEqual(out.shape, mel_spec.shape)
- self.assertTrue((out[:, 1:] == mel_spec[:, :-1]).all(), "Go token not appended properly")
-
- def test_forward_algorithm_variables(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
-
- (
- log_c,
- log_alpha_scaled,
- transition_matrix,
- _,
- ) = model._initialize_forward_algorithm_variables( # pylint: disable=protected-access
- mel_spec, input_dummy.shape[1] * config_global.state_per_phone
- )
-
- self.assertEqual(log_c.shape, (mel_spec.shape[0], mel_spec.shape[1]))
- self.assertEqual(
- log_alpha_scaled.shape,
- (
- mel_spec.shape[0],
- mel_spec.shape[1],
- input_dummy.shape[1] * config_global.state_per_phone,
- ),
- )
- self.assertEqual(
- transition_matrix.shape,
- (mel_spec.shape[0], mel_spec.shape[1], input_dummy.shape[1] * config_global.state_per_phone),
- )
-
- def test_get_absorption_state_scaling_factor(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- input_lengths = input_lengths * config_global.state_per_phone
- (
- log_c,
- log_alpha_scaled,
- transition_matrix,
- _,
- ) = model._initialize_forward_algorithm_variables( # pylint: disable=protected-access
- mel_spec, input_dummy.shape[1] * config_global.state_per_phone
- )
- log_alpha_scaled = torch.rand_like(log_alpha_scaled).clamp(1e-3)
- transition_matrix = torch.randn_like(transition_matrix).sigmoid().log()
- sum_final_log_c = model.get_absorption_state_scaling_factor(
- mel_lengths, log_alpha_scaled, input_lengths, transition_matrix
- )
-
- text_mask = ~sequence_mask(input_lengths)
- transition_prob_mask = ~model.get_mask_for_last_item(input_lengths, device=input_lengths.device)
-
- outputs = []
-
- for i in range(input_dummy.shape[0]):
- last_log_alpha_scaled = log_alpha_scaled[i, mel_lengths[i] - 1].masked_fill(text_mask[i], -float("inf"))
- log_last_transition_probability = OverflowUtils.log_clamped(
- torch.sigmoid(transition_matrix[i, mel_lengths[i] - 1])
- ).masked_fill(transition_prob_mask[i], -float("inf"))
- outputs.append(last_log_alpha_scaled + log_last_transition_probability)
-
- sum_final_log_c_computed = torch.logsumexp(torch.stack(outputs), dim=1)
-
- self.assertTrue(torch.isclose(sum_final_log_c_computed, sum_final_log_c).all())
-
- def test_inference(self):
- model = self._get_neural_hmm()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- for temp in [0.334, 0.667, 1.0]:
- outputs = model.inference(
- input_dummy, input_lengths, temp, config_global.max_sampling_time, config_global.duration_threshold
- )
- self.assertEqual(outputs["hmm_outputs"].shape[-1], outputs["input_parameters"][0][0][0].shape[-1])
- self.assertEqual(
- outputs["output_parameters"][0][0][0].shape[-1], outputs["input_parameters"][0][0][0].shape[-1]
- )
- self.assertEqual(len(outputs["alignments"]), input_dummy.shape[0])
-
- def test_emission_model(self):
- model = self._get_emission_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- x_t = torch.randn(input_dummy.shape[0], config_global.out_channels).to(device)
- means = torch.randn(input_dummy.shape[0], input_dummy.shape[1], config_global.out_channels).to(device)
- std = torch.rand_like(means).to(device).clamp_(1e-3) # std should be positive
- out = model(x_t, means, std, input_lengths)
- self.assertEqual(out.shape, (input_dummy.shape[0], input_dummy.shape[1]))
-
- # testing sampling
- for temp in [0, 0.334, 0.667]:
- out = model.sample(means, std, 0)
- self.assertEqual(out.shape, means.shape)
- if temp == 0:
- self.assertTrue(torch.isclose(out, means).all())
-
- def test_transition_model(self):
- model = self._get_transition_model()
- input_dummy, input_lengths, mel_spec, mel_lengths = self._get_embedded_input()
- prev_t_log_scaled_alph = torch.randn(input_dummy.shape[0], input_lengths.max()).to(device)
- transition_vector = torch.randn(input_lengths.max()).to(device)
- out = model(prev_t_log_scaled_alph, transition_vector, input_lengths)
- self.assertEqual(out.shape, (input_dummy.shape[0], input_lengths.max()))
-
-
-class TestOverflowOutputNet(unittest.TestCase):
- @staticmethod
- def _get_outputnet():
- config = deepcopy(config_global)
- outputnet = Outputnet(
- config.encoder_in_out_features,
- config.memory_rnn_dim,
- config.out_channels,
- config.outputnet_size,
- config.flat_start_params,
- config.std_floor,
- ).to(device)
- return outputnet
-
- @staticmethod
- def _get_embedded_input():
- input_dummy, input_lengths, mel_spec, mel_lengths = _create_inputs()
- input_dummy = torch.nn.Embedding(config_global.num_chars, config_global.encoder_in_out_features).to(device)(
- input_dummy
- )
- one_timestep_frame = torch.randn(input_dummy.shape[0], config_global.memory_rnn_dim).to(device)
- return input_dummy, one_timestep_frame
-
- def test_outputnet_forward_with_flat_start(self):
- model = self._get_outputnet()
- input_dummy, one_timestep_frame = self._get_embedded_input()
- mean, std, transition_vector = model(one_timestep_frame, input_dummy)
- self.assertTrue(torch.isclose(mean, torch.tensor(model.flat_start_params["mean"] * 1.0)).all())
- self.assertTrue(torch.isclose(std, torch.tensor(model.flat_start_params["std"] * 1.0)).all())
- self.assertTrue(
- torch.isclose(
- transition_vector.sigmoid(), torch.tensor(model.flat_start_params["transition_p"] * 1.0)
- ).all()
- )
diff --git a/spaces/artificialguybr/video-dubbing/whisper/tests/test_transcribe.py b/spaces/artificialguybr/video-dubbing/whisper/tests/test_transcribe.py
deleted file mode 100644
index e4f8fd0f77d8af9c7327b00c82379a2cf1a2446c..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/whisper/tests/test_transcribe.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-
-import pytest
-import torch
-
-import whisper
-from whisper.tokenizer import get_tokenizer
-
-
-@pytest.mark.parametrize("model_name", whisper.available_models())
-def test_transcribe(model_name: str):
- device = "cuda" if torch.cuda.is_available() else "cpu"
- model = whisper.load_model(model_name).to(device)
- audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac")
-
- language = "en" if model_name.endswith(".en") else None
- result = model.transcribe(
- audio_path, language=language, temperature=0.0, word_timestamps=True
- )
- assert result["language"] == "en"
- assert result["text"] == "".join([s["text"] for s in result["segments"]])
-
- transcription = result["text"].lower()
- assert "my fellow americans" in transcription
- assert "your country" in transcription
- assert "do for you" in transcription
-
- tokenizer = get_tokenizer(model.is_multilingual)
- all_tokens = [t for s in result["segments"] for t in s["tokens"]]
- assert tokenizer.decode(all_tokens) == result["text"]
- assert tokenizer.decode_with_timestamps(all_tokens).startswith("<|0.00|>")
-
- timing_checked = False
- for segment in result["segments"]:
- for timing in segment["words"]:
- assert timing["start"] < timing["end"]
- if timing["word"].strip(" ,") == "Americans":
- assert timing["start"] <= 1.8
- assert timing["end"] >= 1.8
- timing_checked = True
-
- assert timing_checked
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Runtime/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Runtime/__init__.py
deleted file mode 100644
index fa81adaff68e06d8e915a6afa375f62f7e5a8fad..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Runtime/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# empty file
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_with_log_scale.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_with_log_scale.py
deleted file mode 100644
index b45fcd92ebd0a07a46e87a4afac896a85ea235d8..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/line_with_log_scale.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""
-Line Chart with Logarithmic Scale
----------------------------------
-How to make a line chart on a `Logarithmic scale {title}\n'+
- (f'Model author: Reina' if author else "")+
- (f'' if cover else "")+
- '
'.join(note_history[0].split(' ')[2:])]
- history = history[1:]
- return inputs, note_history, history
-
-def add_note_to_history(note, note_history):# good example of non async since we wait around til we know it went okay.
- note_history.append(note)
- note_history = ' '.join(note_history)
- return [note_history]
-
-title = "💬ChatBack🧠💾"
-description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions.
- Current Best SOTA Chatbot: https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+ChatBack%21+Are+you+ready+to+rock%3F """
-
-def get_base(filename):
- basedir = os.path.dirname(__file__)
- print(basedir)
- #loadPath = basedir + "\\" + filename # works on windows
- loadPath = basedir + filename
- print(loadPath)
- return loadPath
-
-def chat(message, history):
- history = history or []
- if history:
- history_useful = [' '.join([str(a[0])+' '+str(a[1]) for a in history])]
- else:
- history_useful = []
-
- history_useful = add_note_to_history(message, history_useful)
- inputs = tokenizer(history_useful, return_tensors="pt")
- inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
- reply_ids = model.generate(**inputs)
- response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
- history_useful = add_note_to_history(response, history_useful)
- list_history = history_useful[0].split(' ')
- history.append((list_history[-2], list_history[-1]))
-
- df=pd.DataFrame()
-
- if UseMemory:
- outputfileName = 'ChatbotMemory01-24-2023-05-07-PM.csv'
- df = store_message(message, response, outputfileName) # Save to dataset
- basedir = get_base(outputfileName)
-
- return history, df, basedir
-
-
-with gr.Blocks() as demo:
- gr.Markdown(""
-
-examples = [
-#['PleaseRepeatLouder.jpg',['ja']],['ProhibitedInWhiteHouse.JPG',['en']],['BeautyIsTruthTruthisBeauty.JPG',['en']],
-['20-Books.jpg',['en']],['COVID.png',['en']],['chinese.jpg',['ch_sim', 'en']],['japanese.jpg',['ja', 'en']],['Hindi.jpeg',['hi', 'en']]
-]
-
-css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
-choices = [
- "ch_sim",
- "ch_tra",
- "de",
- "en",
- "es",
- "ja",
- "hi",
- "ru"
-]
-gr.Interface(
- inference,
- [gr.inputs.Image(type='file', label='Input'),gr.inputs.CheckboxGroup(choices, type="value", default=['en'], label='language')],
- [gr.outputs.Image(type='file', label='Output'), gr.outputs.Dataframe(headers=['text', 'confidence'])],
- title=title,
- description=description,
- article=article,
- examples=examples,
- css=css,
- enable_queue=True
- ).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/aymm/Task-Exploration-Hate-Speech/posts/context.py b/spaces/aymm/Task-Exploration-Hate-Speech/posts/context.py
deleted file mode 100644
index 1c728281aed6423b5d4841f6d554577811fe8346..0000000000000000000000000000000000000000
--- a/spaces/aymm/Task-Exploration-Hate-Speech/posts/context.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import streamlit as st
-
-title = "Hate Speech Detection in Automatic Content Moderation"
-description = "The history and development of hate speech detection as a modeling task"
-date = "2022-01-26"
-thumbnail = "images/huggingface_logo.png"
-
-def run_article():
- st.markdown("""
- # What is Automatic Content Moderation?
-
- This is where the history of automatic content moderation (ACM) will go.
-
- # The Landscape of ACM
-
- This is where the current platforms and approaches with go.
-
- # Current Challenges
-
- This is where the discussion of current challenges, examples from media, and value tensions will go.
-
- So what does all this mean for conceptualizing this real world problem as a machine learning task? First we'll look at the data, then at the models.
- """)
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/app.py b/spaces/azusarang/so-vits-svc-models-ba_P/app.py
deleted file mode 100644
index f26dda8ad5866048eb95268a84ffe23eafba6932..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/app.py
+++ /dev/null
@@ -1,376 +0,0 @@
-# -*- coding: utf-8 -*-
-import traceback
-import torch
-from scipy.io import wavfile
-import edge_tts
-import subprocess
-import gradio as gr
-import gradio.processing_utils as gr_pu
-import io
-import os
-import logging
-import time
-from pathlib import Path
-import re
-import json
-import argparse
-
-import librosa
-import matplotlib.pyplot as plt
-import numpy as np
-import soundfile
-
-from inference import infer_tool
-from inference import slicer
-from inference.infer_tool import Svc
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('multipart').setLevel(logging.WARNING)
-
-model = None
-spk = None
-debug = False
-
-
-class HParams():
- def __init__(self, **kwargs):
- for k, v in kwargs.items():
- if type(v) == dict:
- v = HParams(**v)
- self[k] = v
-
- def keys(self):
- return self.__dict__.keys()
-
- def items(self):
- return self.__dict__.items()
-
- def values(self):
- return self.__dict__.values()
-
- def __len__(self):
- return len(self.__dict__)
-
- def __getitem__(self, key):
- return getattr(self, key)
-
- def __setitem__(self, key, value):
- return setattr(self, key, value)
-
- def __contains__(self, key):
- return key in self.__dict__
-
- def __repr__(self):
- return self.__dict__.__repr__()
-
-
-def get_hparams_from_file(config_path):
- with open(config_path, "r", encoding="utf-8") as f:
- data = f.read()
- config = json.loads(data)
-
- hparams = HParams(**config)
- return hparams
-
-
-def vc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold):
- try:
- if input_audio is None:
- raise gr.Error("你需要上传音频")
- if model is None:
- raise gr.Error("你需要指定模型")
- sampling_rate, audio = input_audio
- # print(audio.shape,sampling_rate)
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- temp_path = "temp.wav"
- soundfile.write(temp_path, audio, sampling_rate, format="wav")
- _audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,
- pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold)
- model.clear_empty()
- os.remove(temp_path)
- # 构建保存文件的路径，并保存到results文件夹内
- try:
- timestamp = str(int(time.time()))
- filename = sid + "_" + timestamp + ".wav"
- # output_file = os.path.join("./results", filename)
- # soundfile.write(output_file, _audio, model.target_sample, format="wav")
- soundfile.write('/tmp/'+filename, _audio,
- model.target_sample, format="wav")
- # return f"推理成功，音频文件保存为results/{filename}", (model.target_sample, _audio)
- return f"推理成功，音频文件保存为{filename}", (model.target_sample, _audio)
- except Exception as e:
- if debug:
- traceback.print_exc()
- return f"文件保存失败，请手动保存", (model.target_sample, _audio)
- except Exception as e:
- if debug:
- traceback.print_exc()
- raise gr.Error(e)
-
-
-def tts_func(_text, _rate, _voice):
- # 使用edge-tts把文字转成音频
- # voice = "zh-CN-XiaoyiNeural"#女性，较高音
- # voice = "zh-CN-YunxiNeural"#男性
- voice = "zh-CN-YunxiNeural" # 男性
- if (_voice == "女"):
- voice = "zh-CN-XiaoyiNeural"
- output_file = "/tmp/"+_text[0:10]+".wav"
- # communicate = edge_tts.Communicate(_text, voice)
- # await communicate.save(output_file)
- if _rate >= 0:
- ratestr = "+{:.0%}".format(_rate)
- elif _rate < 0:
- ratestr = "{:.0%}".format(_rate) # 减号自带
-
- p = subprocess.Popen("edge-tts " +
- " --text "+_text +
- " --write-media "+output_file +
- " --voice "+voice +
- " --rate="+ratestr, shell=True,
- stdout=subprocess.PIPE,
- stdin=subprocess.PIPE)
- p.wait()
- return output_file
-
-
-def text_clear(text):
- return re.sub(r"[\n\,\(\) ]", "", text)
-
-
-def vc_fn2(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold):
- # 使用edge-tts把文字转成音频
- text2tts = text_clear(text2tts)
- output_file = tts_func(text2tts, tts_rate, tts_voice)
-
- # 调整采样率
- sr2 = 44100
- wav, sr = librosa.load(output_file)
- wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2)
- save_path2 = text2tts[0:10]+"_44k"+".wav"
- wavfile.write(save_path2, sr2,
- (wav2 * np.iinfo(np.int16).max).astype(np.int16)
- )
-
- # 读取音频
- sample_rate, data = gr_pu.audio_from_file(save_path2)
- vc_input = (sample_rate, data)
-
- a, b = vc_fn(sid, vc_input, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale,
- pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold)
- os.remove(output_file)
- os.remove(save_path2)
- return a, b
-
-
-models_info = [
- {
- "description": """
- 这个模型包含碧蓝档案的141名角色。\n\n
- Space采用CPU推理，速度极慢，建议下载模型本地GPU推理。\n\n
- """,
- "model_path": "./G_387200.pth",
- "config_path": "./config.json",
- }
-]
-
-model_inferall = []
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--share", action="store_true",
- default=False, help="share gradio app")
- # 一定要设置的部分
- parser.add_argument('-cl', '--clip', type=float,
- default=0, help='音频强制切片，默认0为自动切片，单位为秒/s')
- parser.add_argument('-n', '--clean_names', type=str, nargs='+',
- default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下')
- parser.add_argument('-t', '--trans', type=int, nargs='+',
- default=[0], help='音高调整，支持正负（半音）')
- parser.add_argument('-s', '--spk_list', type=str,
- nargs='+', default=['nen'], help='合成目标说话人名称')
-
- # 可选项部分
- parser.add_argument('-a', '--auto_predict_f0', action='store_true',
- default=False, help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调')
- parser.add_argument('-cm', '--cluster_model_path', type=str,
- default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填')
- parser.add_argument('-cr', '--cluster_infer_ratio', type=float,
- default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则默认0即可')
- parser.add_argument('-lg', '--linear_gradient', type=float, default=0,
- help='两段音频切片的交叉淡入长度，如果强制切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒')
- parser.add_argument('-f0p', '--f0_predictor', type=str, default="pm",
- help='选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)')
- parser.add_argument('-eh', '--enhance', action='store_true', default=False,
- help='是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭')
- parser.add_argument('-shd', '--shallow_diffusion', action='store_true',
- default=False, help='是否使用浅层扩散，使用后可解决一部分电音问题，默认关闭，该选项打开时，NSF_HIFIGAN增强器将会被禁止')
-
- # 浅扩散设置
- parser.add_argument('-dm', '--diffusion_model_path', type=str,
- default="logs/44k/diffusion/model_0.pt", help='扩散模型路径')
- parser.add_argument('-dc', '--diffusion_config_path', type=str,
- default="logs/44k/diffusion/config.yaml", help='扩散模型配置文件路径')
- parser.add_argument('-ks', '--k_step', type=int,
- default=100, help='扩散步数，越大越接近扩散模型的结果，默认100')
- parser.add_argument('-od', '--only_diffusion', action='store_true',
- default=False, help='纯扩散模式，该模式不会加载sovits模型，以扩散模型推理')
-
- # 不用动的部分
- parser.add_argument('-sd', '--slice_db', type=int,
- default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50')
- parser.add_argument('-d', '--device', type=str,
- default=None, help='推理设备，None则为自动选择cpu和gpu')
- parser.add_argument('-ns', '--noice_scale', type=float,
- default=0.4, help='噪音级别，会影响咬字和音质，较为玄学')
- parser.add_argument('-p', '--pad_seconds', type=float, default=0.5,
- help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现')
- parser.add_argument('-wf', '--wav_format', type=str,
- default='flac', help='音频输出格式')
- parser.add_argument('-lgr', '--linear_gradient_retain', type=float,
- default=0.75, help='自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭')
- parser.add_argument('-eak', '--enhancer_adaptive_key',
- type=int, default=0, help='使增强器适应更高的音域(单位为半音数)|默认为0')
- parser.add_argument('-ft', '--f0_filter_threshold', type=float, default=0.05,
- help='F0过滤阈值，只有使用crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音')
- args = parser.parse_args()
- categories = ["Blue Archive"]
- others = {
- "PCR vits-fast-fineturning": "https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-pcr",
- "Blue Archive vits-fast-fineturning": "https://huggingface.co/spaces/FrankZxShen/vits-fast-fineturning-models-ba",
- }
- for info in models_info:
- config_path = info['config_path']
- model_path = info['model_path']
- description = info['description']
- clean_names = args.clean_names
- trans = args.trans
- spk_list = list(get_hparams_from_file(config_path).spk.keys())
- slice_db = args.slice_db
- wav_format = args.wav_format
- auto_predict_f0 = args.auto_predict_f0
- cluster_infer_ratio = args.cluster_infer_ratio
- noice_scale = args.noice_scale
- pad_seconds = args.pad_seconds
- clip = args.clip
- lg = args.linear_gradient
- lgr = args.linear_gradient_retain
- f0p = args.f0_predictor
- enhance = args.enhance
- enhancer_adaptive_key = args.enhancer_adaptive_key
- cr_threshold = args.f0_filter_threshold
- diffusion_model_path = args.diffusion_model_path
- diffusion_config_path = args.diffusion_config_path
- k_step = args.k_step
- only_diffusion = args.only_diffusion
- shallow_diffusion = args.shallow_diffusion
-
- model = Svc(model_path, config_path, args.device, args.cluster_model_path, enhance,
- diffusion_model_path, diffusion_config_path, shallow_diffusion, only_diffusion)
-
- model_inferall.append((description, spk_list, model))
-
- app = gr.Blocks()
- with app:
- gr.Markdown(
- "#
")
-
- with gr.Row():
- t1 = gr.Textbox(lines=1, default="", label="Chat Text:")
- b1 = gr.Button("Respond and Retrieve Messages")
-
- with gr.Row(): # inputs and buttons
- s1 = gr.State([])
- df1 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
- with gr.Row(): # inputs and buttons
- file = gr.File(label="File")
- s2 = gr.Markdown()
-
- b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1, file])
-
-demo.launch(debug=True, show_error=True)
\ No newline at end of file
diff --git a/spaces/awacke1/Image-to-Multilingual-OCR/backupapp.py b/spaces/awacke1/Image-to-Multilingual-OCR/backupapp.py
deleted file mode 100644
index 7ff0c4409d1d18607c4e33b856a6eea611e94edf..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Image-to-Multilingual-OCR/backupapp.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import pandas as pd
-import PIL
-from PIL import Image
-from PIL import ImageDraw
-import gradio as gr
-import torch
-import easyocr
-
-#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/BeautyIsTruthTruthisBeauty.JPG', 'BeautyIsTruthTruthisBeauty.JPG')
-#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/PleaseRepeatLouder.jpg', 'PleaseRepeatLouder.jpg')
-#torch.hub.download_url_to_file('https://github.com/AaronCWacker/Yggdrasil/blob/main/images/ProhibitedInWhiteHouse.JPG', 'ProhibitedInWhiteHouse.JPG')
-
-torch.hub.download_url_to_file('https://raw.githubusercontent.com/AaronCWacker/Yggdrasil/master/images/20-Books.jpg','20-Books.jpg')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/english.png', 'COVID.png')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/chinese.jpg', 'chinese.jpg')
-torch.hub.download_url_to_file('https://github.com/JaidedAI/EasyOCR/raw/master/examples/japanese.jpg', 'japanese.jpg')
-torch.hub.download_url_to_file('https://i.imgur.com/mwQFd7G.jpeg', 'Hindi.jpeg')
-
-def draw_boxes(image, bounds, color='yellow', width=2):
- draw = ImageDraw.Draw(image)
- for bound in bounds:
- p0, p1, p2, p3 = bound[0]
- draw.line([*p0, *p1, *p2, *p3, *p0], fill=color, width=width)
- return image
-
-def inference(img, lang):
- reader = easyocr.Reader(lang)
- bounds = reader.readtext(img.name)
- im = PIL.Image.open(img.name)
- draw_boxes(im, bounds)
- im.save('result.jpg')
- return ['result.jpg', pd.DataFrame(bounds).iloc[: , 1:]]
-
-title = '🖼️Image to Multilingual OCR👁️Gradio'
-description = 'Multilingual OCR which works conveniently on all devices in multiple languages.'
-article = "Click to Go
-
- 
- Welcome!
-
- 
-
-
-## Installation
-Install Detectron2 following [the instructions](https://detectron2.readthedocs.io/tutorials/install.html).
-To use cityscapes, prepare data follow the [tutorial](https://detectron2.readthedocs.io/tutorials/builtin_datasets.html#expected-dataset-structure-for-cityscapes).
-
-## Training
-
-To train a model with 8 GPUs run:
-```bash
-cd /path/to/detectron2/projects/Panoptic-DeepLab
-python train_net.py --config-file configs/Cityscapes-PanopticSegmentation/panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024_dsconv.yaml --num-gpus 8
-```
-
-## Evaluation
-
-Model evaluation can be done similarly:
-```bash
-cd /path/to/detectron2/projects/Panoptic-DeepLab
-python train_net.py --config-file configs/Cityscapes-PanopticSegmentation/panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024_dsconv.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint
-```
-
-## Benchmark network speed
-
-If you want to benchmark the network speed without post-processing, you can run the evaluation script with `MODEL.PANOPTIC_DEEPLAB.BENCHMARK_NETWORK_SPEED True`:
-```bash
-cd /path/to/detectron2/projects/Panoptic-DeepLab
-python train_net.py --config-file configs/Cityscapes-PanopticSegmentation/panoptic_deeplab_R_52_os16_mg124_poly_90k_bs32_crop_512_1024_dsconv.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint MODEL.PANOPTIC_DEEPLAB.BENCHMARK_NETWORK_SPEED True
-```
-
-## Cityscapes Panoptic Segmentation
-Cityscapes models are trained with ImageNet pretraining.
-
-
-
-
-
-
-Note:
-- [R52](https://dl.fbaipublicfiles.com/detectron2/DeepLab/R-52.pkl): a ResNet-50 with its first 7x7 convolution replaced by 3 3x3 convolutions. This modification has been used in most semantic segmentation papers. We pre-train this backbone on ImageNet using the default recipe of [pytorch examples](https://github.com/pytorch/examples/tree/master/imagenet).
-- DC5 means using dilated convolution in `res5`.
-- We use a smaller training crop size (512x1024) than the original paper (1025x2049), we find using larger crop size (1024x2048) could further improve PQ by 1.5% but also degrades AP by 3%.
-- The implementation with regular Conv2d in ASPP and head is much heavier head than the original paper.
-- This implementation does not include optimized post-processing code needed for deployment. Post-processing the network
- outputs now takes similar amount of time to the network itself. Please refer to speed in the
- original paper for comparison.
-- DSConv refers to using DepthwiseSeparableConv2d in ASPP and decoder. The implementation with DSConv is identical to the original paper.
-
-## COCO Panoptic Segmentation
-COCO models are trained with ImageNet pretraining on 16 V100s.
-
-Method
-Backbone
-Output
-
resolutionPQ
-SQ
-RQ
-mIoU
-AP
-Memory (M)
-model id
-download
-
- Panoptic-DeepLab
-R50-DC5
-1024×2048
- 58.6
- 80.9
- 71.2
- 75.9
- 29.8
- 8668
- -
-model | metrics
-Panoptic-DeepLab
-R52-DC5
-1024×2048
- 60.3
- 81.5
- 72.9
- 78.2
- 33.2
- 9682
- 30841561
-model | metrics
-Panoptic-DeepLab (DSConv)
-R52-DC5
-1024×2048
- 60.3
- 81.0
- 73.2
- 78.7
- 32.1
- 10466
- 33148034
-model | metrics
-
-
-
-
-
-Note:
-- [R52](https://dl.fbaipublicfiles.com/detectron2/DeepLab/R-52.pkl): a ResNet-50 with its first 7x7 convolution replaced by 3 3x3 convolutions. This modification has been used in most semantic segmentation papers. We pre-train this backbone on ImageNet using the default recipe of [pytorch examples](https://github.com/pytorch/examples/tree/master/imagenet).
-- DC5 means using dilated convolution in `res5`.
-- This reproduced number matches the original paper (35.5 vs. 35.1 PQ).
-- This implementation does not include optimized post-processing code needed for deployment. Post-processing the network
- outputs now takes more time than the network itself. Please refer to speed in the original paper for comparison.
-- DSConv refers to using DepthwiseSeparableConv2d in ASPP and decoder.
-
-## Citing Panoptic-DeepLab
-
-If you use Panoptic-DeepLab, please use the following BibTeX entry.
-
-* CVPR 2020 paper:
-
-```
-@inproceedings{cheng2020panoptic,
- title={Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation},
- author={Cheng, Bowen and Collins, Maxwell D and Zhu, Yukun and Liu, Ting and Huang, Thomas S and Adam, Hartwig and Chen, Liang-Chieh},
- booktitle={CVPR},
- year={2020}
-}
-```
-
-* ICCV 2019 COCO-Mapillary workshp challenge report:
-
-```
-@inproceedings{cheng2019panoptic,
- title={Panoptic-DeepLab},
- author={Cheng, Bowen and Collins, Maxwell D and Zhu, Yukun and Liu, Ting and Huang, Thomas S and Adam, Hartwig and Chen, Liang-Chieh},
- booktitle={ICCV COCO + Mapillary Joint Recognition Challenge Workshop},
- year={2019}
-}
-```
diff --git a/spaces/cfwef/gpt/Dockerfile b/spaces/cfwef/gpt/Dockerfile
deleted file mode 100644
index 564392c933342f77731be47faa417bb8906067bc..0000000000000000000000000000000000000000
--- a/spaces/cfwef/gpt/Dockerfile
+++ /dev/null
@@ -1,13 +0,0 @@
-FROM python:3.11
-
-RUN echo '[global]' > /etc/pip.conf && \
- echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
- echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
-
-RUN pip3 install gradio requests[socks] mdtex2html
-
-COPY . /gpt
-WORKDIR /gpt
-
-
-CMD ["python3", "main.py"]
\ No newline at end of file
diff --git a/spaces/chendl/compositional_test/multimodal/open_flamingo/src/flamingo_lm.py b/spaces/chendl/compositional_test/multimodal/open_flamingo/src/flamingo_lm.py
deleted file mode 100644
index f3c1a5f981f95fa22219c73bdcd288165317d13c..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/open_flamingo/src/flamingo_lm.py
+++ /dev/null
@@ -1,173 +0,0 @@
-import random
-import torch
-import torch.nn as nn
-import numpy as np
-
-from .helpers import GatedCrossAttentionBlock
-from .utils import getattr_recursive, setattr_recursive
-
-
-class FlamingoLayer(nn.Module):
- def __init__(self, decoder_layer):
- super().__init__()
- self.decoder_layer = decoder_layer
- self.vis_x = None
- self.image_nums = None
- self.image_start_index_list = None
- self.media_locations = None
- self.add_visual_token = False
- self.input_ids = None
-
- def is_conditioned(self) -> bool:
- """Check whether the layer is conditioned."""
- return self.vis_x is not None
-
- # Used this great idea from this implementation of Flamingo (https://github.com/dhansmair/flamingo-mini/)
- def condition_vis_x(self, vis_x, image_nums=None, image_start_index_list=None, num_beams=None, visual_tokens=None, data_list=None):
- self.vis_x = vis_x
- self.image_nums = image_nums
- self.image_start_index_list = image_start_index_list
- self.num_beams = num_beams
- self.visual_tokens = visual_tokens
- self.data_list = data_list
- self.input_ids = None
-
-
- def condition_media_locations(self, media_locations):
- self.media_locations = media_locations
-
- def condition_attend_previous(self, attend_previous):
- self.attend_previous = attend_previous
-
- def forward(
- self,
- hidden_states, # alignment with hugging face name
- attention_mask=None,
- **decoder_layer_kwargs,
- ):
- if self.media_locations is None:
- raise ValueError("media_locations must be conditioned before forward pass")
-
- if self.vis_x is not None:
- if self.training:
- single_length = self.vis_x.shape[-2]
- image_nums = self.image_nums
- image_start_index_list = self.image_start_index_list
- image_nums = [0] + np.cumsum(image_nums).tolist()
- for i, (image_num_begin, image_num_end, start_indices) in enumerate(zip(image_nums[:-1], image_nums[1:], image_start_index_list)):
- for index in start_indices:
- if image_num_begin < image_num_end:
- hidden_states[i, index:index+single_length] = self.vis_x[image_num_begin]
- image_num_begin += 1
-
- if self.visual_tokens is not None and len(self.visual_tokens) != 0:
- for i, (x, y) in enumerate(self.data_list):
- if len(self.visual_tokens[i].shape) > 1:
- # print(self.visual_tokens[i].shape[0], "embedding")
- hidden_states[x, y+1-self.visual_tokens[i].shape[0]:y+1] = self.visual_tokens[i]
- else:
- # print(self.visual_tokens[i].shape[0], "embedding")
- hidden_states[x, y] = self.visual_tokens[i]
-
- elif not self.training:
- if (
- ("past_key_value" in decoder_layer_kwargs and decoder_layer_kwargs["past_key_value"] is None) or
- ("layer_past" in decoder_layer_kwargs and decoder_layer_kwargs["layer_past"] is None)
- ):
- single_length = self.vis_x.shape[-2]
- image_nums = self.image_nums
- image_start_index_list = self.image_start_index_list
- image_nums = [0] + np.cumsum(image_nums).tolist()
- for i, (image_num_begin, image_num_end, start_indices) in enumerate(zip(image_nums[:-1], image_nums[1:], image_start_index_list)):
- for index in start_indices:
- if image_num_begin < image_num_end:
- hidden_states[i, index:index+single_length] = self.vis_x[image_num_begin]
- image_num_begin += 1
- if self.visual_tokens is not None and len(self.visual_tokens) != 0:
- for i, (x, y) in enumerate(self.data_list):
- # import pdb; pdb.set_trace()
- # print(x, y, self.visual_tokens[i].shape)
- if len(self.visual_tokens[i].shape) > 1:
- # print(self.visual_tokens[i].shape[0], "embedding")
- hidden_states[x, y+1-self.visual_tokens[i].shape[0]:y+1] = self.visual_tokens[i]
- else:
- # print(self.visual_tokens[i].shape[0], "embedding")
- hidden_states[x, y] = self.visual_tokens[i]
- hidden_states = self.decoder_layer(
- hidden_states, attention_mask=attention_mask, **decoder_layer_kwargs
- )
- return hidden_states
-
-
-class FlamingoLMMixin(nn.Module):
- """
- Mixin to add cross-attention layers to a language model.
- """
-
- def set_decoder_layers_attr_name(self, decoder_layers_attr_name):
- self.decoder_layers_attr_name = decoder_layers_attr_name
-
- def _get_decoder_layers(self):
- return getattr_recursive(self, self.decoder_layers_attr_name)
-
- def _set_decoder_layers(self, value):
- setattr_recursive(self, self.decoder_layers_attr_name, value)
-
- def init_flamingo(
- self,
- media_token_id,
- use_media_placement_augmentation,
- ):
- """
- Initialize Flamingo by adding a new gated cross attn to the decoder. Store the media token id for computing the media locations.
- """
- self._set_decoder_layers(
- nn.ModuleList(
- [FlamingoLayer(decoder_layer) for decoder_layer in self._get_decoder_layers()]
- )
- )
- self.media_token_id = media_token_id
- self.use_media_placement_augmentation = use_media_placement_augmentation
- self.initialized_flamingo = True
-
- def forward(self, *input, **kwargs):
- """Condition the Flamingo layers on the media locations before forward()"""
- if not self.initialized_flamingo:
- raise ValueError(
- "Flamingo layers are not initialized. Please call `init_flamingo` first."
- )
-
- input_ids = kwargs["input_ids"] if "input_ids" in kwargs else input[0]
- media_locations = input_ids == self.media_token_id
- attend_previous = (
- (random.random() < 0.5) if self.use_media_placement_augmentation else True
- )
-
- if (
- "gpt2" in self.__class__.__name__.lower()
- or "codegen" in self.__class__.__name__.lower()
- ):
- for layer in self.transformer.h:
- layer.condition_media_locations(media_locations)
- layer.condition_attend_previous(attend_previous)
- elif "gptneox" in self.__class__.__name__.lower():
- for layer in self.gpt_neox.layers:
- layer.condition_media_locations(media_locations)
- layer.condition_attend_previous(attend_previous)
- else:
- for layer in self.get_decoder().layers:
- layer.condition_media_locations(media_locations)
- layer.condition_attend_previous(attend_previous)
- return super().forward(
- *input, **kwargs
- ) # Call the other parent's forward method
-
- def is_conditioned(self) -> bool:
- """Check whether all decoder layers are already conditioned."""
- return all(l.is_conditioned() for l in self._get_decoder_layers())
-
- def clear_conditioned_layers(self):
- for layer in self._get_decoder_layers():
- layer.condition_vis_x(None)
- layer.condition_media_locations(None)
- layer.condition_attend_previous(None)
diff --git a/spaces/chendl/compositional_test/transformers/examples/research_projects/rag/use_own_knowledge_dataset.py b/spaces/chendl/compositional_test/transformers/examples/research_projects/rag/use_own_knowledge_dataset.py
deleted file mode 100644
index 84d7c854975f1156d313731cf539bd719019d7c6..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/examples/research_projects/rag/use_own_knowledge_dataset.py
+++ /dev/null
@@ -1,208 +0,0 @@
-import logging
-import os
-from dataclasses import dataclass, field
-from functools import partial
-from pathlib import Path
-from tempfile import TemporaryDirectory
-from typing import List, Optional
-
-import faiss
-import torch
-from datasets import Features, Sequence, Value, load_dataset
-
-from transformers import (
- DPRContextEncoder,
- DPRContextEncoderTokenizerFast,
- HfArgumentParser,
- RagRetriever,
- RagSequenceForGeneration,
- RagTokenizer,
-)
-
-
-logger = logging.getLogger(__name__)
-torch.set_grad_enabled(False)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def split_text(text: str, n=100, character=" ") -> List[str]:
- """Split the text every ``n``-th occurrence of ``character``"""
- text = text.split(character)
- return [character.join(text[i : i + n]).strip() for i in range(0, len(text), n)]
-
-
-def split_documents(documents: dict) -> dict:
- """Split documents into passages"""
- titles, texts = [], []
- for title, text in zip(documents["title"], documents["text"]):
- if text is not None:
- for passage in split_text(text):
- titles.append(title if title is not None else "")
- texts.append(passage)
- return {"title": titles, "text": texts}
-
-
-def embed(documents: dict, ctx_encoder: DPRContextEncoder, ctx_tokenizer: DPRContextEncoderTokenizerFast) -> dict:
- """Compute the DPR embeddings of document passages"""
- input_ids = ctx_tokenizer(
- documents["title"], documents["text"], truncation=True, padding="longest", return_tensors="pt"
- )["input_ids"]
- embeddings = ctx_encoder(input_ids.to(device=device), return_dict=True).pooler_output
- return {"embeddings": embeddings.detach().cpu().numpy()}
-
-
-def main(
- rag_example_args: "RagExampleArguments",
- processing_args: "ProcessingArguments",
- index_hnsw_args: "IndexHnswArguments",
-):
- ######################################
- logger.info("Step 1 - Create the dataset")
- ######################################
-
- # The dataset needed for RAG must have three columns:
- # - title (string): title of the document
- # - text (string): text of a passage of the document
- # - embeddings (array of dimension d): DPR representation of the passage
-
- # Let's say you have documents in tab-separated csv files with columns "title" and "text"
- assert os.path.isfile(rag_example_args.csv_path), "Please provide a valid path to a csv file"
-
- # You can load a Dataset object this way
- dataset = load_dataset(
- "csv", data_files=[rag_example_args.csv_path], split="train", delimiter="\t", column_names=["title", "text"]
- )
-
- # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
-
- # Then split the documents into passages of 100 words
- dataset = dataset.map(split_documents, batched=True, num_proc=processing_args.num_proc)
-
- # And compute the embeddings
- ctx_encoder = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=device)
- ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
- new_features = Features(
- {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))}
- ) # optional, save as float32 instead of float64 to save space
- dataset = dataset.map(
- partial(embed, ctx_encoder=ctx_encoder, ctx_tokenizer=ctx_tokenizer),
- batched=True,
- batch_size=processing_args.batch_size,
- features=new_features,
- )
-
- # And finally save your dataset
- passages_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset")
- dataset.save_to_disk(passages_path)
- # from datasets import load_from_disk
- # dataset = load_from_disk(passages_path) # to reload the dataset
-
- ######################################
- logger.info("Step 2 - Index the dataset")
- ######################################
-
- # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
- index = faiss.IndexHNSWFlat(index_hnsw_args.d, index_hnsw_args.m, faiss.METRIC_INNER_PRODUCT)
- dataset.add_faiss_index("embeddings", custom_index=index)
-
- # And save the index
- index_path = os.path.join(rag_example_args.output_dir, "my_knowledge_dataset_hnsw_index.faiss")
- dataset.get_index("embeddings").save(index_path)
- # dataset.load_faiss_index("embeddings", index_path) # to reload the index
-
- ######################################
- logger.info("Step 3 - Load RAG")
- ######################################
-
- # Easy way to load the model
- retriever = RagRetriever.from_pretrained(
- rag_example_args.rag_model_name, index_name="custom", indexed_dataset=dataset
- )
- model = RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name, retriever=retriever)
- tokenizer = RagTokenizer.from_pretrained(rag_example_args.rag_model_name)
-
- # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
- # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)
-
- ######################################
- logger.info("Step 4 - Have fun")
- ######################################
-
- question = rag_example_args.question or "What does Moses' rod turn into ?"
- input_ids = tokenizer.question_encoder(question, return_tensors="pt")["input_ids"]
- generated = model.generate(input_ids)
- generated_string = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
- logger.info("Q: " + question)
- logger.info("A: " + generated_string)
-
-
-@dataclass
-class RagExampleArguments:
- csv_path: str = field(
- default=str(Path(__file__).parent / "test_data" / "my_knowledge_dataset.csv"),
- metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"},
- )
- question: Optional[str] = field(
- default=None,
- metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."},
- )
- rag_model_name: str = field(
- default="facebook/rag-sequence-nq",
- metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"},
- )
- dpr_ctx_encoder_model_name: str = field(
- default="facebook/dpr-ctx_encoder-multiset-base",
- metadata={
- "help": (
- "The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"
- " 'facebook/dpr-ctx_encoder-multiset-base'"
- )
- },
- )
- output_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Path to a directory where the dataset passages and the index will be saved"},
- )
-
-
-@dataclass
-class ProcessingArguments:
- num_proc: Optional[int] = field(
- default=None,
- metadata={
- "help": "The number of processes to use to split the documents into passages. Default is single process."
- },
- )
- batch_size: int = field(
- default=16,
- metadata={
- "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
- },
- )
-
-
-@dataclass
-class IndexHnswArguments:
- d: int = field(
- default=768,
- metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."},
- )
- m: int = field(
- default=128,
- metadata={
- "help": (
- "The number of bi-directional links created for every new element during the HNSW index construction."
- )
- },
- )
-
-
-if __name__ == "__main__":
- logging.basicConfig(level=logging.WARNING)
- logger.setLevel(logging.INFO)
-
- parser = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
- rag_example_args, processing_args, index_hnsw_args = parser.parse_args_into_dataclasses()
- with TemporaryDirectory() as tmp_dir:
- rag_example_args.output_dir = rag_example_args.output_dir or tmp_dir
- main(rag_example_args, processing_args, index_hnsw_args)
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/driver/types.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/driver/types.py
deleted file mode 100644
index 015e162fbea9c8c5c4f93b4759b6dafab462ad1b..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/driver/types.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Sequence, Any
-
-Matrix = Sequence[Sequence[Any]]
-
-
-class Closable(ABC):
- @abstractmethod
- def close(self):
- pass
-
-
-class ByteSource(Closable):
- last_message = None
-
- @abstractmethod
- def read_leb128(self) -> int:
- pass
-
- @abstractmethod
- def read_leb128_str(self) -> str:
- pass
-
- @abstractmethod
- def read_uint64(self) -> int:
- pass
-
- @abstractmethod
- def read_bytes(self, sz: int) -> bytes:
- pass
-
- @abstractmethod
- def read_str_col(self, num_rows: int, encoding: str, nullable: bool = False, null_obj: Any = None):
- pass
-
- @abstractmethod
- def read_bytes_col(self, sz: int, num_rows: int):
- pass
-
- @abstractmethod
- def read_fixed_str_col(self, sz: int, num_rows: int, encoding: str):
- pass
-
- @abstractmethod
- def read_array(self, array_type: str, num_rows: int):
- pass
-
- @abstractmethod
- def read_byte(self) -> int:
- pass
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/parser/_parser.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/parser/_parser.py
deleted file mode 100644
index 37d1663b2f72447800d9a553929e3de932244289..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/dateutil/parser/_parser.py
+++ /dev/null
@@ -1,1613 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-This module offers a generic date/time string parser which is able to parse
-most known formats to represent a date and/or time.
-
-This module attempts to be forgiving with regards to unlikely input formats,
-returning a datetime object even for dates which are ambiguous. If an element
-of a date/time stamp is omitted, the following rules are applied:
-
-- If AM or PM is left unspecified, a 24-hour clock is assumed, however, an hour
- on a 12-hour clock (``0 <= hour <= 12``) *must* be specified if AM or PM is
- specified.
-- If a time zone is omitted, a timezone-naive datetime is returned.
-
-If any other elements are missing, they are taken from the
-:class:`datetime.datetime` object passed to the parameter ``default``. If this
-results in a day number exceeding the valid number of days per month, the
-value falls back to the end of the month.
-
-Additional resources about date/time string formats can be found below:
-
-- `A summary of the international standard date and time notation
- Method
-Backbone
-Output
-
resolutionPQ
-SQ
-RQ
-Box AP
-Mask AP
-Memory (M)
-model id
-download
-
- Panoptic-DeepLab (DSConv)
-R52-DC5
-640×640
- 35.5
- 77.3
- 44.7
- 18.6
- 19.7
-
- 246448865
-model | metrics
-I Do I Die Dyos Ko Day Full PORTABLE Movie Download
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/IBM USB Serial Parallel Adapter Driver 22P9035 Troubleshooting Tips and FAQs.md b/spaces/cihyFjudo/fairness-paper-search/IBM USB Serial Parallel Adapter Driver 22P9035 Troubleshooting Tips and FAQs.md
deleted file mode 100644
index 45712e0cb42613d2ad7b64ad72e6bfb3608c47e1..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/IBM USB Serial Parallel Adapter Driver 22P9035 Troubleshooting Tips and FAQs.md
+++ /dev/null
@@ -1,6 +0,0 @@
-ibm-usb-serial-parallel-adapter-driver-22p9035
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/clip-italian/clip-italian-demo/image2text.py b/spaces/clip-italian/clip-italian-demo/image2text.py
deleted file mode 100644
index 7db4d4ed4d65b77130ba5b62d7f8ed156be17f9f..0000000000000000000000000000000000000000
--- a/spaces/clip-italian/clip-italian-demo/image2text.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import streamlit as st
-from text2image import get_model, get_tokenizer, get_image_transform
-from utils import text_encoder, image_encoder
-from PIL import Image
-from jax import numpy as jnp
-from io import BytesIO
-import pandas as pd
-import requests
-import jax
-import gc
-
-headers = {
- "User-Agent":
- "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
-}
-
-
-def app():
- st.title("From Image to Text")
- st.markdown(
- """
-
- ### 👋 Ciao!
-
- Here you can find the captions or the labels that are most related to a given image. It is a zero-shot
- image classification task!
-
- 🤌 Italian mode on! 🤌
-
- For example, try typing "gatto" (cat) in the space for label1 and "cane" (dog) in the space for label2 and click
- "classify"!
-
- """
- )
-
- image_url = st.text_input(
- "You can input the URL of an image",
- value="https://upload.wikimedia.org/wikipedia/commons/thumb/8/88/Ragdoll%2C_blue_mitted.JPG/1280px-Ragdoll%2C_blue_mitted.JPG",
- )
-
- MAX_CAP = 4
-
- col1, col2 = st.columns([0.75, 0.25])
-
- with col2:
- captions_count = st.selectbox(
- "Number of labels", options=range(1, MAX_CAP + 1), index=1
- )
- compute = st.button("CLASSIFY")
-
- with col1:
- captions = list()
- for idx in range(min(MAX_CAP, captions_count)):
- captions.append(st.text_input(f"Insert label {idx+1}"))
-
- if compute:
- captions = [c for c in captions if c != ""]
-
- if not captions or not image_url:
- st.error("Please choose one image and at least one label")
- else:
- with st.spinner("Computing..."):
- model = get_model()
- tokenizer = get_tokenizer()
-
- text_embeds = list()
- for i, c in enumerate(captions):
- text_embeds.extend(text_encoder(c, model, tokenizer)[0])
-
- text_embeds = jnp.array(text_embeds)
- response = requests.get(image_url, headers=headers, stream=True)
- image = Image.open(BytesIO(response.content)).convert("RGB")
- transform = get_image_transform(model.config.vision_config.image_size)
- image_embed, _ = image_encoder(transform(image), model)
-
- # we could have a softmax here
- cos_similarities = jax.nn.softmax(
- jnp.matmul(image_embed, text_embeds.T)
- )
-
- chart_data = pd.Series(cos_similarities[0], index=captions)
-
- col1, col2 = st.columns(2)
- with col1:
- st.bar_chart(chart_data)
-
- with col2:
- st.image(image, use_column_width=True)
- gc.collect()
-
- elif image_url:
- response = requests.get(image_url, headers=headers, stream=True)
- image = Image.open(BytesIO(response.content)).convert("RGB")
- st.image(image)
diff --git a/spaces/cloixai/stable-diffusion-webui-cpu/README.md b/spaces/cloixai/stable-diffusion-webui-cpu/README.md
deleted file mode 100644
index 8c0c714325c21001caaf9bb4aaa0da5e8e769b51..0000000000000000000000000000000000000000
--- a/spaces/cloixai/stable-diffusion-webui-cpu/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Stable Diffusion Webui on Cpu
-emoji: 🏃
-colorFrom: pink
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.0
-app_file: app.py
-pinned: false
-python_version: 3.10.6
-duplicated_from: DreamSunny/stable-diffusion-webui-cpu
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/data.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/data.py
deleted file mode 100644
index 28e66bfab5764fe58e19fb339b2cdf8ad9d510b4..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/data.py
+++ /dev/null
@@ -1,299 +0,0 @@
-import json
-import os
-import random
-import hashlib
-import warnings
-
-import pandas as pd
-from toolz import curried
-from typing import Callable
-
-from .core import sanitize_dataframe
-from .core import sanitize_geo_interface
-from .deprecation import AltairDeprecationWarning
-from .plugin_registry import PluginRegistry
-
-
-# ==============================================================================
-# Data transformer registry
-# ==============================================================================
-DataTransformerType = Callable
-
-
-class DataTransformerRegistry(PluginRegistry[DataTransformerType]):
- _global_settings = {"consolidate_datasets": True}
-
- @property
- def consolidate_datasets(self):
- return self._global_settings["consolidate_datasets"]
-
- @consolidate_datasets.setter
- def consolidate_datasets(self, value):
- self._global_settings["consolidate_datasets"] = value
-
-
-# ==============================================================================
-# Data model transformers
-#
-# A data model transformer is a pure function that takes a dict or DataFrame
-# and returns a transformed version of a dict or DataFrame. The dict objects
-# will be the Data portion of the VegaLite schema. The idea is that user can
-# pipe a sequence of these data transformers together to prepare the data before
-# it hits the renderer.
-#
-# In this version of Altair, renderers only deal with the dict form of a
-# VegaLite spec, after the Data model has been put into a schema compliant
-# form.
-#
-# A data model transformer has the following type signature:
-# DataModelType = Union[dict, pd.DataFrame]
-# DataModelTransformerType = Callable[[DataModelType, KwArgs], DataModelType]
-# ==============================================================================
-
-
-class MaxRowsError(Exception):
- """Raised when a data model has too many rows."""
-
- pass
-
-
-@curried.curry
-def limit_rows(data, max_rows=5000):
- """Raise MaxRowsError if the data model has more than max_rows.
-
- If max_rows is None, then do not perform any check.
- """
- check_data_type(data)
- if hasattr(data, "__geo_interface__"):
- if data.__geo_interface__["type"] == "FeatureCollection":
- values = data.__geo_interface__["features"]
- else:
- values = data.__geo_interface__
- elif isinstance(data, pd.DataFrame):
- values = data
- elif isinstance(data, dict):
- if "values" in data:
- values = data["values"]
- else:
- return data
- elif hasattr(data, "__dataframe__"):
- values = data
- if max_rows is not None and len(values) > max_rows:
- raise MaxRowsError(
- "The number of rows in your dataset is greater "
- f"than the maximum allowed ({max_rows}).\n\n"
- "See https://altair-viz.github.io/user_guide/large_datasets.html "
- "for information on how to plot large datasets, "
- "including how to install third-party data management tools and, "
- "in the right circumstance, disable the restriction"
- )
- return data
-
-
-@curried.curry
-def sample(data, n=None, frac=None):
- """Reduce the size of the data model by sampling without replacement."""
- check_data_type(data)
- if isinstance(data, pd.DataFrame):
- return data.sample(n=n, frac=frac)
- elif isinstance(data, dict):
- if "values" in data:
- values = data["values"]
- n = n if n else int(frac * len(values))
- values = random.sample(values, n)
- return {"values": values}
- elif hasattr(data, "__dataframe__"):
- # experimental interchange dataframe support
- pi = import_pyarrow_interchange()
- pa_table = pi.from_dataframe(data)
- n = n if n else int(frac * len(pa_table))
- indices = random.sample(range(len(pa_table)), n)
- return pa_table.take(indices)
-
-
-@curried.curry
-def to_json(
- data,
- prefix="altair-data",
- extension="json",
- filename="{prefix}-{hash}.{extension}",
- urlpath="",
-):
- """
- Write the data model to a .json file and return a url based data model.
- """
- data_json = _data_to_json_string(data)
- data_hash = _compute_data_hash(data_json)
- filename = filename.format(prefix=prefix, hash=data_hash, extension=extension)
- with open(filename, "w") as f:
- f.write(data_json)
- return {"url": os.path.join(urlpath, filename), "format": {"type": "json"}}
-
-
-@curried.curry
-def to_csv(
- data,
- prefix="altair-data",
- extension="csv",
- filename="{prefix}-{hash}.{extension}",
- urlpath="",
-):
- """Write the data model to a .csv file and return a url based data model."""
- data_csv = _data_to_csv_string(data)
- data_hash = _compute_data_hash(data_csv)
- filename = filename.format(prefix=prefix, hash=data_hash, extension=extension)
- with open(filename, "w") as f:
- f.write(data_csv)
- return {"url": os.path.join(urlpath, filename), "format": {"type": "csv"}}
-
-
-@curried.curry
-def to_values(data):
- """Replace a DataFrame by a data model with values."""
- check_data_type(data)
- if hasattr(data, "__geo_interface__"):
- if isinstance(data, pd.DataFrame):
- data = sanitize_dataframe(data)
- data = sanitize_geo_interface(data.__geo_interface__)
- return {"values": data}
- elif isinstance(data, pd.DataFrame):
- data = sanitize_dataframe(data)
- return {"values": data.to_dict(orient="records")}
- elif isinstance(data, dict):
- if "values" not in data:
- raise KeyError("values expected in data dict, but not present.")
- return data
- elif hasattr(data, "__dataframe__"):
- # experimental interchange dataframe support
- pi = import_pyarrow_interchange()
- pa_table = pi.from_dataframe(data)
- return {"values": pa_table.to_pylist()}
-
-
-def check_data_type(data):
- """Raise if the data is not a dict or DataFrame."""
- if not isinstance(data, (dict, pd.DataFrame)) and not any(
- hasattr(data, attr) for attr in ["__geo_interface__", "__dataframe__"]
- ):
- raise TypeError(
- "Expected dict, DataFrame or a __geo_interface__ attribute, got: {}".format(
- type(data)
- )
- )
-
-
-# ==============================================================================
-# Private utilities
-# ==============================================================================
-
-
-def _compute_data_hash(data_str):
- return hashlib.md5(data_str.encode()).hexdigest()
-
-
-def _data_to_json_string(data):
- """Return a JSON string representation of the input data"""
- check_data_type(data)
- if hasattr(data, "__geo_interface__"):
- if isinstance(data, pd.DataFrame):
- data = sanitize_dataframe(data)
- data = sanitize_geo_interface(data.__geo_interface__)
- return json.dumps(data)
- elif isinstance(data, pd.DataFrame):
- data = sanitize_dataframe(data)
- return data.to_json(orient="records", double_precision=15)
- elif isinstance(data, dict):
- if "values" not in data:
- raise KeyError("values expected in data dict, but not present.")
- return json.dumps(data["values"], sort_keys=True)
- elif hasattr(data, "__dataframe__"):
- # experimental interchange dataframe support
- pi = import_pyarrow_interchange()
- pa_table = pi.from_dataframe(data)
- return json.dumps(pa_table.to_pylist())
- else:
- raise NotImplementedError(
- "to_json only works with data expressed as " "a DataFrame or as a dict"
- )
-
-
-def _data_to_csv_string(data):
- """return a CSV string representation of the input data"""
- check_data_type(data)
- if hasattr(data, "__geo_interface__"):
- raise NotImplementedError(
- "to_csv does not work with data that "
- "contains the __geo_interface__ attribute"
- )
- elif isinstance(data, pd.DataFrame):
- data = sanitize_dataframe(data)
- return data.to_csv(index=False)
- elif isinstance(data, dict):
- if "values" not in data:
- raise KeyError("values expected in data dict, but not present")
- return pd.DataFrame.from_dict(data["values"]).to_csv(index=False)
- elif hasattr(data, "__dataframe__"):
- # experimental interchange dataframe support
- pi = import_pyarrow_interchange()
- import pyarrow as pa
- import pyarrow.csv as pa_csv
-
- pa_table = pi.from_dataframe(data)
- csv_buffer = pa.BufferOutputStream()
- pa_csv.write_csv(pa_table, csv_buffer)
- return csv_buffer.getvalue().to_pybytes().decode()
- else:
- raise NotImplementedError(
- "to_csv only works with data expressed as " "a DataFrame or as a dict"
- )
-
-
-def pipe(data, *funcs):
- """
- Pipe a value through a sequence of functions
-
- Deprecated: use toolz.curried.pipe() instead.
- """
- warnings.warn(
- "alt.pipe() is deprecated, and will be removed in a future release. "
- "Use toolz.curried.pipe() instead.",
- AltairDeprecationWarning,
- stacklevel=1,
- )
- return curried.pipe(data, *funcs)
-
-
-def curry(*args, **kwargs):
- """Curry a callable function
-
- Deprecated: use toolz.curried.curry() instead.
- """
- warnings.warn(
- "alt.curry() is deprecated, and will be removed in a future release. "
- "Use toolz.curried.curry() instead.",
- AltairDeprecationWarning,
- stacklevel=1,
- )
- return curried.curry(*args, **kwargs)
-
-
-def import_pyarrow_interchange():
- import pkg_resources
-
- try:
- pkg_resources.require("pyarrow>=11.0.0")
- # The package is installed and meets the minimum version requirement
- import pyarrow.interchange as pi
-
- return pi
- except pkg_resources.DistributionNotFound as err:
- # The package is not installed
- raise ImportError(
- "Usage of the DataFrame Interchange Protocol requires the package 'pyarrow', but it is not installed."
- ) from err
- except pkg_resources.VersionConflict as err:
- # The package is installed but does not meet the minimum version requirement
- raise ImportError(
- "The installed version of 'pyarrow' does not meet the minimum requirement of version 11.0.0. "
- "Please update 'pyarrow' to use the DataFrame Interchange Protocol."
- ) from err
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/vector.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/vector.py
deleted file mode 100644
index 666ff15cf8be0a0c17de4f86b74584d2bb27244f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/vector.py
+++ /dev/null
@@ -1,148 +0,0 @@
-from numbers import Number
-import math
-import operator
-import warnings
-
-
-__all__ = ["Vector"]
-
-
-class Vector(tuple):
-
- """A math-like vector.
-
- Represents an n-dimensional numeric vector. ``Vector`` objects support
- vector addition and subtraction, scalar multiplication and division,
- negation, rounding, and comparison tests.
- """
-
- __slots__ = ()
-
- def __new__(cls, values, keep=False):
- if keep is not False:
- warnings.warn(
- "the 'keep' argument has been deprecated",
- DeprecationWarning,
- )
- if type(values) == Vector:
- # No need to create a new object
- return values
- return super().__new__(cls, values)
-
- def __repr__(self):
- return f"{self.__class__.__name__}({super().__repr__()})"
-
- def _vectorOp(self, other, op):
- if isinstance(other, Vector):
- assert len(self) == len(other)
- return self.__class__(op(a, b) for a, b in zip(self, other))
- if isinstance(other, Number):
- return self.__class__(op(v, other) for v in self)
- raise NotImplementedError()
-
- def _scalarOp(self, other, op):
- if isinstance(other, Number):
- return self.__class__(op(v, other) for v in self)
- raise NotImplementedError()
-
- def _unaryOp(self, op):
- return self.__class__(op(v) for v in self)
-
- def __add__(self, other):
- return self._vectorOp(other, operator.add)
-
- __radd__ = __add__
-
- def __sub__(self, other):
- return self._vectorOp(other, operator.sub)
-
- def __rsub__(self, other):
- return self._vectorOp(other, _operator_rsub)
-
- def __mul__(self, other):
- return self._scalarOp(other, operator.mul)
-
- __rmul__ = __mul__
-
- def __truediv__(self, other):
- return self._scalarOp(other, operator.truediv)
-
- def __rtruediv__(self, other):
- return self._scalarOp(other, _operator_rtruediv)
-
- def __pos__(self):
- return self._unaryOp(operator.pos)
-
- def __neg__(self):
- return self._unaryOp(operator.neg)
-
- def __round__(self, *, round=round):
- return self._unaryOp(round)
-
- def __eq__(self, other):
- if isinstance(other, list):
- # bw compat Vector([1, 2, 3]) == [1, 2, 3]
- other = tuple(other)
- return super().__eq__(other)
-
- def __ne__(self, other):
- return not self.__eq__(other)
-
- def __bool__(self):
- return any(self)
-
- __nonzero__ = __bool__
-
- def __abs__(self):
- return math.sqrt(sum(x * x for x in self))
-
- def length(self):
- """Return the length of the vector. Equivalent to abs(vector)."""
- return abs(self)
-
- def normalized(self):
- """Return the normalized vector of the vector."""
- return self / abs(self)
-
- def dot(self, other):
- """Performs vector dot product, returning the sum of
- ``a[0] * b[0], a[1] * b[1], ...``"""
- assert len(self) == len(other)
- return sum(a * b for a, b in zip(self, other))
-
- # Deprecated methods/properties
-
- def toInt(self):
- warnings.warn(
- "the 'toInt' method has been deprecated, use round(vector) instead",
- DeprecationWarning,
- )
- return self.__round__()
-
- @property
- def values(self):
- warnings.warn(
- "the 'values' attribute has been deprecated, use "
- "the vector object itself instead",
- DeprecationWarning,
- )
- return list(self)
-
- @values.setter
- def values(self, values):
- raise AttributeError(
- "can't set attribute, the 'values' attribute has been deprecated",
- )
-
- def isclose(self, other: "Vector", **kwargs) -> bool:
- """Return True if the vector is close to another Vector."""
- assert len(self) == len(other)
- return all(math.isclose(a, b, **kwargs) for a, b in zip(self, other))
-
-
-def _operator_rsub(a, b):
- return operator.sub(b, a)
-
-
-def _operator_rtruediv(a, b):
- return operator.truediv(b, a)
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/bmvaudio.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/bmvaudio.c
deleted file mode 100644
index fc211732bbb148bf0eb65eba239fcf986e0f507e..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/bmvaudio.c
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * Discworld II BMV audio decoder
- * Copyright (c) 2011 Konstantin Shishkov
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg 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.
- *
- * FFmpeg 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 FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/channel_layout.h"
-#include "libavutil/common.h"
-
-#include "avcodec.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-static const int bmv_aud_mults[16] = {
- 16512, 8256, 4128, 2064, 1032, 516, 258, 192, 129, 88, 64, 56, 48, 40, 36, 32
-};
-
-static av_cold int bmv_aud_decode_init(AVCodecContext *avctx)
-{
- av_channel_layout_uninit(&avctx->ch_layout);
- avctx->ch_layout = (AVChannelLayout)AV_CHANNEL_LAYOUT_STEREO;
- avctx->sample_fmt = AV_SAMPLE_FMT_S16;
-
- return 0;
-}
-
-static int bmv_aud_decode_frame(AVCodecContext *avctx, AVFrame *frame,
- int *got_frame_ptr, AVPacket *avpkt)
-{
- const uint8_t *buf = avpkt->data;
- int buf_size = avpkt->size;
- int blocks = 0, total_blocks, i;
- int ret;
- int16_t *output_samples;
- int scale[2];
-
- total_blocks = *buf++;
- if (buf_size < total_blocks * 65 + 1) {
- av_log(avctx, AV_LOG_ERROR, "expected %d bytes, got %d\n",
- total_blocks * 65 + 1, buf_size);
- return AVERROR_INVALIDDATA;
- }
-
- /* get output buffer */
- frame->nb_samples = total_blocks * 32;
- if ((ret = ff_get_buffer(avctx, frame, 0)) < 0)
- return ret;
- output_samples = (int16_t *)frame->data[0];
-
- for (blocks = 0; blocks < total_blocks; blocks++) {
- uint8_t code = *buf++;
- code = (code >> 1) | (code << 7);
- scale[0] = bmv_aud_mults[code & 0xF];
- scale[1] = bmv_aud_mults[code >> 4];
- for (i = 0; i < 32; i++) {
- *output_samples++ = av_clip_int16((scale[0] * (int8_t)*buf++) >> 5);
- *output_samples++ = av_clip_int16((scale[1] * (int8_t)*buf++) >> 5);
- }
- }
-
- *got_frame_ptr = 1;
-
- return buf_size;
-}
-
-const FFCodec ff_bmv_audio_decoder = {
- .p.name = "bmv_audio",
- CODEC_LONG_NAME("Discworld II BMV audio"),
- .p.type = AVMEDIA_TYPE_AUDIO,
- .p.id = AV_CODEC_ID_BMV_AUDIO,
- .init = bmv_aud_decode_init,
- FF_CODEC_DECODE_CB(bmv_aud_decode_frame),
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_CHANNEL_CONF,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hpeldsp.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hpeldsp.c
deleted file mode 100644
index 1ec76e7a45cabae149ffd2ba9c360764a5d178c5..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/hpeldsp.c
+++ /dev/null
@@ -1,373 +0,0 @@
-/*
- * Half-pel DSP functions.
- * Copyright (c) 2000, 2001 Fabrice Bellard
- * Copyright (c) 2002-2004 Michael Niedermayer
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Euro Truck Simulator 2 and Challenge Yourself with Different Cargo Types.md b/spaces/congsaPfin/Manga-OCR/logs/Download Euro Truck Simulator 2 and Challenge Yourself with Different Cargo Types.md deleted file mode 100644 index 5606f459b8a8c711e1bbd9951bf8fcef922bf8f8..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Download Euro Truck Simulator 2 and Challenge Yourself with Different Cargo Types.md +++ /dev/null @@ -1,139 +0,0 @@ -
-
How to Download the Latest Version of Euro Truck Simulator 2
-Have you ever dreamed of becoming a truck driver and traveling across Europe? If so, you might want to check out Euro Truck Simulator 2, a realistic and immersive truck driving simulator that lets you explore dozens of cities, deliver various cargoes, and customize your own fleet of trucks. In this article, we will show you how to download the latest version of Euro Truck Simulator 2, what you need to run it, and what you can expect from this amazing game.
-Requirements and Compatibility
-Before you download Euro Truck Simulator 2, you should make sure that your computer meets the minimum or recommended system requirements for the game. Here are the specifications you need to know:
-download the latest version of euro truck simulator 2
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| Minimum | -Recommended | -
|---|---|
| OS: Windows 7 | -OS: Windows 10 | -
| Processor: Dual core CPU 2.4 GHz | -Processor: Quad core CPU 3.0 GHz | -
| Memory: 4 GB RAM | -Memory: 6 GB RAM | -
| Graphics: GeForce GTS 450-class (Intel HD 4000) | -Graphics: GeForce GTX 760-class (2 GB) | -
| Storage: 10 GB available space | -Storage: 10 GB available space | -
| Sound Card: Any | -Sound Card: Any | -
You should also make sure that your computer has DirectX version 11 installed, as well as a compatible keyboard and mouse. If you want to use a controller or a steering wheel, you should check if they are supported by the game.
-Download Options
-There are several ways to download Euro Truck Simulator 2, depending on your preference and budget. Here are some of the most popular options:
--
-
- Steam: Steam is a digital distribution platform that offers a wide range of games, including Euro Truck Simulator 2. You can buy the game for $19.99 or get it as part of a bundle with other DLCs and expansions. Steam also allows you to download updates automatically, access online features, and join the Steam community. -
- Euro Truck Simulator 2 Website: You can also buy the game directly from the official website of Euro Truck Simulator 2. You can choose from different payment methods, such as credit card, PayPal, or Paysafecard. You will receive a product key that you can use to activate the game on Steam or download it from their servers. -
- Other Online Stores: There are also other online stores that sell Euro Truck Simulator 2, such as Humble Bundle, GOG.com, or Amazon. You should compare the prices and reviews before buying from these sources, as they may vary in quality and reliability. -
- Demo Version: If you are not sure if you want to buy the game or not, you can try the demo version first. The demo version allows you to play for free for one hour, with limited features and content. You can download the demo version from Steam or from the official website. -
- Pirated Version: We do not recommend downloading a pirated version of Euro Truck Simulator 2, as it is illegal, unsafe, and unethical. Pirated versions may contain viruses, malware, or spyware that can harm your computer or compromise your privacy. Pirated versions may also have bugs, glitches, or errors that can ruin your gaming experience. Pirated versions may also prevent you from accessing online features, such as multiplayer, mods, or achievements. Pirated versions may also get you banned from Steam or other platforms. Therefore, we strongly advise you to buy the game legally and support the developers who worked hard to create it. -
Installation and Setup
-Once you have downloaded Euro Truck Simulator 2, you need to install and launch it on your computer. The installation and setup process may vary depending on the source of your download, but here are some general steps you can follow:
--
-
- Run the installer: Locate the installer file on your computer and double-click on it to start the installation process. You may need to agree to the terms and conditions, choose a destination folder, and select the components you want to install. -
- Activate the game: If you bought the game from Steam or another online store, you may need to activate the game with a product key. You can find the product key in your email confirmation, in your Steam library, or in your online account. Enter the product key when prompted and verify it online. -
- Launch the game: After the installation is complete, you can launch the game from your desktop shortcut, from your Start menu, or from your Steam library. You may need to create a profile, choose a language, and adjust the settings before you start playing. -
Features and Benefits
-Euro Truck Simulator 2 is more than just a driving simulator. It is a game that offers many features and benefits that will make you feel like a real trucker. Here are some of the things you can do and enjoy in Euro Truck Simulator 2:
--
-
- Explore Europe: Euro Truck Simulator 2 covers more than 70 cities in 13 countries across Europe, including Germany, France, Italy, Spain, Poland, and more. You can drive on realistic roads, highways, bridges, tunnels, and landmarks that reflect the diversity and beauty of the continent. -
- Deliver cargoes: Euro Truck Simulator 2 gives you hundreds of different cargoes to transport, ranging from food and furniture to chemicals and machinery. You can choose from various contracts and jobs that suit your preferences and skills. You can also earn money and reputation by completing deliveries on time and without damage. -
- Customize your trucks: Euro Truck Simulator 2 allows you to buy, upgrade, and personalize your own fleet of trucks. You can choose from over 40 licensed truck models from brands such as Volvo, Scania, MAN, Mercedes-Benz, and more. You can also modify your trucks with various parts and accessories, such as engines, transmissions, chassis, paint jobs, lights, horns, and more. -
- Grow your business: Euro Truck Simulator 2 lets you start your own trucking company and expand it across Europe. You can hire drivers, buy garages, manage finances, and compete with other companies. You can also join online events and challenges that reward you with exclusive rewards and achievements. -
- Enjoy the community: Euro Truck Simulator 2 has a large and active community of fans and modders who create and share new content for the game. You can download and install mods that add new maps, trucks, cargoes, traffic, weather, sounds, and more. You can also join the multiplayer mode and drive with other players online. -
Tips and Tricks
-Euro Truck Simulator 2 is a fun and challenging game that requires skill and strategy. Here are some tips and tricks that can help you improve your gameplay and performance:
--
-
- Plan your route: Before you accept a job, you should check the map and plan your route carefully. You should consider the distance, the time limit, the tolls, the traffic, the weather, and the road conditions. You should also avoid taking shortcuts or detours that may lead you to dead ends or restricted areas. -
- Follow the rules: Euro Truck Simulator 2 simulates the real driving laws and regulations of each country. You should follow the speed limits, obey the traffic signs and signals, use the indicators, respect the right of way, and avoid collisions and violations. You should also rest and refuel regularly to avoid fatigue and damage. -
- Save your game: Euro Truck Simulator 2 allows you to save your game at any time, except when you are in a cutscene or a loading screen. You should save your game frequently, especially before and after a delivery, to avoid losing your progress or having to repeat a job. You can also use the quick save feature by pressing F5 on your keyboard. -
- Use the options: Euro Truck Simulator 2 offers many options and settings that you can customize to suit your preferences and needs. You can adjust the graphics, the sound, the controls, the gameplay, and the difficulty. You can also enable or disable various features, such as automatic parking, traffic offenses, rain probability, fatigue simulation, and more. -
- Have fun: Euro Truck Simulator 2 is a game that allows you to have fun and enjoy yourself. You can listen to music, watch videos, chat with other players, take screenshots, record videos, and share your experiences with others. You can also explore new places, discover hidden secrets, and try new things. -
Conclusion
-Euro Truck Simulator 2 is a game that will give you hours of entertainment and satisfaction. It is a game that will let you experience the thrill and challenge of truck driving in Europe. It is a game that will let you customize your trucks, grow your business, and join a community. It is a game that you should download and play today.
-FAQs
-Here are some frequently asked questions about Euro Truck Simulator 2:
--
-
- Q: How do I update Euro Truck Simulator 2 to the latest version? -A: If you bought the game from Steam or another online store, you should receive updates automatically when they are available. If you bought the game from the official website or another source, you should download and install the updates manually from their website. -
- Q: How do I get more money in Euro Truck Simulator 2? -A: There are several ways to get more money in Euro Truck Simulator 2. You can complete more deliveries, take higher-paying jobs, use bank loans, sell or trade-in your trucks, hire more drivers, or use cheats or mods. -
- Q: How do I get more XP in Euro Truck Simulator 2? -A: There are several ways to get more XP in Euro Truck Simulator 2. You can complete more deliveries, take longer-distance jobs, deliver fragile or valuable cargoes, park your trailer manually, drive in adverse weather conditions, or use skills or mods. -
- Q: How do I change the camera view in Euro Truck Simulator 2? -A: You can change the camera view in Euro Truck Simulator 2 by pressing the number keys on your keyboard. The default keys are 1 for interior view, 2 for exterior view, 3 for left mirror view, 4 for right mirror view, 5 for bumper view, 6 for roof view, and 9 for free camera view. You can also use the mouse wheel to zoom in or out. -
- Q: How do I use mods in Euro Truck Simulator 2? -A: You can use mods in Euro Truck Simulator 2 by downloading them from various websites, such as Steam Workshop, ETS2 Mods, or ModLand. You should check the compatibility and quality of the mods before installing them. You should also backup your game files and save games before using mods. To install mods, you need to copy the mod files to the "mod" folder in your Euro Truck Simulator 2 directory. To activate mods, you need to go to the "Mod Manager" in the game menu and enable the mods you want to use. -
- Q: How do I join the multiplayer mode in Euro Truck Simulator 2? -A: You can join the multiplayer mode in Euro Truck Simulator 2 by downloading and installing a mod called TruckersMP. TruckersMP is a mod that allows you to play online with other players on dedicated servers. You can find more information and instructions on how to join TruckersMP on their website. -
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Dream League Soccer 2020 Mod Apk 3.040 The Ultimate Soccer Experience with Unlimited Money.md b/spaces/congsaPfin/Manga-OCR/logs/Dream League Soccer 2020 Mod Apk 3.040 The Ultimate Soccer Experience with Unlimited Money.md deleted file mode 100644 index 59ba6a1b989daecbb9615a00b1add83d677fecf5..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Dream League Soccer 2020 Mod Apk 3.040 The Ultimate Soccer Experience with Unlimited Money.md +++ /dev/null @@ -1,68 +0,0 @@ - -
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-If you are a fan of soccer games, you might have heard of Dream League Soccer 2020, one of the most popular and realistic soccer games on mobile devices. But did you know that there is a modded version of this game that gives you unlimited money, coins, gems, and more? In this article, we will tell you everything you need to know about Dream League Soccer 2020 Mod Apk, including what it is, why you should download it, what features it offers, how to download and install it, and how it compares to the original version. So, let's get started!
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- Enjoy playing with millions of other players online. You can also chat, join groups, and make friends on Roblox. -
- Download and install Roblox Studio from the Google Play Store. This is a free app that allows you to create and publish games on Roblox. -
- Launch Roblox Studio and sign in with your Roblox account. -
- Choose a template or start from scratch to create your own game. You can use the built-in tools and features to design your game world and add interactivity. -
- Use the playtest mode to test your game and make sure it works properly. -
- Publish your game to Roblox and share it with others. You can also monetize your game by selling items, game passes, or premium access. -
- You can access modded versions of Roblox with extra features and functions. For example, you can get a mega menu with over 60 features such as unlimited robux, fly mode, teleportation, speed hack, god mode, invisibility, and more. -
- You can enjoy a fast and secure download process with no viruses or malware. 5play is a trusted website that scans all the files before uploading them. You don't need to worry about any harm to your device or data. -
- You can use a free and easy to use website with no registration or subscription required. 5play is a user-friendly website that allows you to download apps and games with just a few clicks. You don't need to sign up or pay anything to use it. -
- Go to the official website of Undawn and sign up for the beta test by choosing your platform (iOS or Android) and entering a valid email address. You will receive a confirmation email with a link to download the apk file. Alternatively, you can use one of the links from the web search results . -
- Download the apk file to your device. Make sure you have enough storage space and a stable internet connection. -
- Enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. -
- Locate the apk file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to finish. -
- Launch the game from your app drawer or home screen and enjoy playing Undawn! -
- Adjust the graphics settings according to your device's capabilities. You can choose from low, medium, high, or ultra settings. You can also enable or disable features such as shadows, anti-aliasing, or HDR. -
- Use headphones or earphones to enjoy the immersive sound effects and music of the game. You can also adjust the volume and sound quality in the settings menu. -
- Use the auto-aim feature to help you aim and shoot more accurately. You can also customize the sensitivity, layout, and size of the controls in the settings menu. -
- Use the chat feature to communicate with your teammates or other players. You can use text or voice chat, as well as emojis and stickers. You can also mute or block players who are annoying or abusive. -
- Use the map feature to navigate the open world and find points of interest, such as missions, resources, enemies, or allies. You can also mark locations or enemies for your teammates to see. -
- The open-world exploration, where you can roam freely in different locations and scavenge for resources, weapons, and equipment. -
- The survival aspect, where you need to manage your health, hunger, thirst, stamina, and inventory. -
- The crafting system, where you can create items, weapons, equipment, shelters, and traps using the resources you find. -
- The combat system, where you can use different weapons, skills, and tactics to fight against zombies and other enemies. -
- The multiplayer mode, where you can cooperate or compete with other players online. -
- The survivors, who are ordinary people who have managed to survive the disaster and are trying to rebuild their lives in this new world. -
- The raiders, who are ruthless bandits who prey on other survivors and steal their resources and territory. -
- The infected, who are humans who have been turned into zombies by the virus and are driven by their hunger for flesh. -
- The mutants, who are creatures that have been mutated by the virus or radiation and have developed new abilities and behaviors. -
- The rebels, who are former soldiers who have rebelled against their corrupt government and are fighting for freedom and justice. -
- The survival mode, where you need to survive as long as possible in a randomly generated map with limited resources and enemies. -
- The raid mode, where you need to
- The raid mode, where you need to attack or defend a base with your teammates or other players online. -
- The quest mode, where you need to complete various objectives and tasks given by NPCs or factions. -
- The event mode, where you need to participate in special events that occur randomly in the game world. -
- Experience points, which can help you level up and unlock new skills and perks. -
- Resources, which can help you craft items, weapons, equipment, shelters, and traps. -
- Weapons and equipment, which can help you improve your combat performance and survival chances. -
- Currency, which can help you buy or sell items, weapons, equipment, or services from NPCs or other players. -
- Reputation, which can help you gain favor or hostility from different factions and characters. -
- When will Undawn be officially released? -
- Is Undawn free to play? -
- Is Undawn safe to download and install? -
- How can I report bugs or glitches in Undawn? -
- How can I get more information about Undawn? -
- Cardigans have more coat patterns that can be obvious (e.g. brindle, blue merle), but they also share common coats (e.g. red, sable), which causes them to look very similar. -
- Cardigans are often stockier and heavier (which can be hard to tell via a photo). -
- Pembrokes (especially US-standard AKC) have docked tails, cardigans do not. However, this dataset has tailed-pembrokes. -
- Personality! Pembrokes can be louder, more vocal, and also have less energy (generally speaking). -
- For the whole notebook which also includes the model training, refer here. -
- I used a subset of the Stanford Dogs Dataset, which is available here. -
- This whole project is part of my own experimentation, following Lesson 2 of the FastAI ML course. -
- Faraz Ahmed as Faraz Khan (Alpha) -
- Qasim Khan as Kashif Kirmani (Bravo) -
- Abdullah Mahmood as Gulsher Khan (Charlie) -
- Ayub Khoso as Brigadier Faraz -
- Shahnaz Khwaja as Shehnaz -
- Kiran Butt as Shahnaz's friend -
- Maria Wasti as Shahnaz's sister -
- Samina Ahmed as Shahnaz's mother -
- Mehboob Alam as Gulsher's father -
- Seemi Raheel as Gulsher's mother -
- Entertainment: You can enjoy various live streaming content from hosts around the world, who showcase their talents, skills, personalities, and even their bodies. You can watch anything from singing and dancing to gaming and chatting, depending on your preferences and moods. You can also browse through different categories and filters to find the content that suits you best. -
- Income: You can earn money by becoming a host or sending gifts to your favorite hosts. If you have something interesting or attractive to offer, you can create your own live show and attract viewers who will support you with gifts or tips. Alternatively, you can also show your appreciation to the hosts that you like by sending them gifts or coins that they can exchange for real money. -
- Socialization: You can chat and interact with hosts and other users, make new friends and connections, and even find romance or love. You can send messages, emojis, stickers, voice notes, or video calls to communicate with others. You can also join groups or communities that share your interests or hobbies. -
- Security: Beware of viruses and malware that may harm your device or steal your data. Since this app is not available on the official Google Play Store or App Store, you have to download it from third-party sources that may not be trustworthy or secure. Therefore, you need to be careful when choosing where to download the app, and check the app's details, ratings, reviews, and permissions before installing it. You should also use a reliable antivirus software to scan the app and your device regularly. -
- Legality: Be aware of the laws and regulations regarding online content and gambling in Indonesia. Since this app contains some adult or explicit content, as well as a feature that allows you to play a slot game and win real money, it may violate some of the rules or norms that govern the Indonesian internet space. Therefore, you need to be cautious and discreet when using the app, and avoid any legal troubles or penalties that may arise from your activities. -
- Morality: Be responsible and respectful when using the app, avoid exposing yourself or others to inappropriate or harmful content. Since this app gives you a lot of freedom and flexibility to create or consume live streaming content, you may encounter some situations or scenarios that are not suitable for your age, values, or beliefs. Therefore, you need to be mindful and respectful of yourself and others, and avoid any content that may offend, harm, or exploit anyone. -
- Step 1: Find a reliable source for downloading the app, such as JalanTikus or Dafunda Download. These are some of the websites that offer APK live bar bar Indonesia for free and without any registration or verification. You can access these websites from your browser or use a VPN service if they are blocked by your internet provider. -
- Step 2: Check the app's details, ratings, reviews, and permissions before downloading it. You can see the app's name, size, version, developer, category, description, screenshots, ratings, reviews, and permissions on the website. You should read them carefully and make sure that they match your expectations and needs. You should also compare them with other sources or apps to verify their authenticity and quality. -
- Step 3: Install the app on your device and follow the instructions to create an account and start using it. You can download the app by clicking on the download button or scanning the QR code on the website. You may need to enable the installation of apps from unknown sources on your device settings. After downloading the app, you can open it and follow the instructions to create an account with your phone number or email address. You can also choose your username, password, avatar, gender, and birthday. Then, you can start exploring the app and enjoy its features. -
- Wish Live: A global live streaming platform with hosts from various countries and genres. You can watch live shows from singers, dancers, gamers, comedians, models, and more. You can also chat with them and send them gifts or coins. You can also become a host yourself and earn money from your fans. -
- Para Me: A social networking app that allows you to meet and chat with people from different cultures and backgrounds. You can join various chat rooms based on your interests or preferences, such as music, movies, sports, travel, etc. You can also send voice messages, video calls, gifts, or stickers to express yourself. -
- TATA Live: A live streaming app that offers both adult and non-adult content, as well as a slot game feature that lets you win real money. You can watch live shows from sexy or cute hosts who will entertain you with their charm and skills. You can also play a slot game with them and win cash prizes or gifts. -
- Q: What is APK live bar bar Indonesia? -
- A: APK live bar bar Indonesia is an app that allows you to watch or host live shows that feature various content, from music and dance to games and chat. -
- Q: How can I download APK live bar bar Indonesia? -
- A: You can download APK live bar bar Indonesia from third-party websites, such as JalanTikus or Dafunda Download. You need to check the app's details, ratings, reviews, and permissions before downloading it. You also need to enable the installation of apps from unknown sources on your device settings. -
- Q: What are the benefits of using APK live bar bar Indonesia? -
- A: You can enjoy various live streaming content from hosts around the world, earn money by becoming a host or sending gifts to your favorite hosts, and chat and interact with hosts and other users. -
- Q: What are the risks of using APK live bar bar Indonesia? -
- A: You may encounter viruses and malware that may harm your device or steal your data, legal troubles or penalties that may arise from your online content or gambling activities, and inappropriate or harmful content that may offend, harm, or exploit yourself or others. -
- Q: What are some alternatives to APK live bar bar Indonesia? -
- A: You can try other apps that offer similar or better live streaming experiences, such as Wish Live, Para Me, or TATA Live. -
- Over 100 levels to play -
- Over 20 tanks to choose from -
- Over 10 weapons to equip -
- Realistic physics and graphics -
- Simple and intuitive controls -
- Online leaderboards and achievements -
- You can fire your weapons without worrying about running out of ammo -
- You can choose any tank and weapon you want without unlocking them -
- You can customize your tank with any skin and sticker you like -
- You can play without ads or internet connection -
- You can have more fun and challenge in the game -
- Go to https://an1.com/109 and download the Tank Heroes mod apk an1 file -
- Go to your device settings and enable the installation of apps from unknown sources -
- Locate the downloaded file and tap on it to install it -
- Launch the game and enjoy -
- Is Tank Heroes mod apk an1 safe to download? -
- Do I need to root or jailbreak my device to use Tank Heroes mod apk an1? -
- Will I get banned from the game if I use Tank Heroes mod apk an1? -
- Can I update Tank Heroes mod apk an1? -
- Can I play Tank Heroes mod apk an1 with my friends? -
- Go to [APKCombo] on your browser. -
- Type "Vice Online Mod APK" in the search bar and hit enter. -
- Select the latest version of Vice Online Mod APK from the results. -
- Click on the "Download APK" button and wait for the download to complete. -
- Go to your device settings and look for "Security" or "Privacy" options. -
- Find and tap on "Unknown sources" or "Install unknown apps" option. -
- Toggle on or allow this option to enable it. -
- Locate the downloaded APK file on your device storage or file manager. -
- Tap on the APK file and follow the instructions on the screen to install it. -
- Once the installation is complete, tap on "Open" or find the game icon on your home screen or app drawer. -
- Launch the game and enjoy playing with unlimited money, mega menu, no ads, and other features. -
- Is Vice Online Mod APK safe to use? -
- Do I need to root my device to use Vice Online Mod APK? -
- Will I get banned for using Vice Online Mod APK? -
- Can I play online with other players using Vice Online Mod APK? -
- Can I update Vice Online Mod APK? -
- You can watch your favourite shows and movies anytime, anywhere. -
- You can watch on up to six devices per account, and register up to four devices at once. -
- You can download shows and movies to watch offline, or stream them online. -
- You can watch live TV on the go, or catch up on demand. -
- You can pause, rewind, and fast-forward live TV. -
- You can use the app as a remote control for your Sky Q box. -
- You need a Sky TV subscription and a Sky ID. -
- You need a compatible device. For Windows, you need Windows 7 or higher. For Mac, you need Mac OS X 10.11 or higher. -
- You need a broadband connection of at least 2 Mbps for streaming and downloading. -
- You need at least 5 GB of free space on your device for downloading. -
- To watch live TV, go to the "Live TV" tab and select a channel. You can also use the TV guide to see what's on and set reminders. -
- To watch on demand, go to the "On Demand" tab and browse by genre, channel, or A-Z. You can also use the search function to find what you want. -
- To download shows and movies, go to the "Downloads" tab and select what you want to download. You can also manage your downloads and delete them when you are done. -
- To watch offline, go to the "Downloads" tab and select what you want to watch. You can watch offline for up to 30 days after downloading. -
- To change your settings, go to the "Settings" tab and adjust your preferences. You can change your video quality, parental controls, subtitles, and more. -
- To register or unregister a device, go to https://skyid.sky.com/manage/devices and sign in with your Sky ID. You can register up to four devices at once and swap one device per month. -
- Q: How much does Sky Go cost? -
- A: Sky Go is included at no extra cost to your Sky TV subscription. You don't need to pay anything extra to use it. -
- Q: How many devices can I use with Sky Go? -
- A: You can use up to six devices per account with Sky Go. However, you can only register up to four devices at once and swap one device per month. -
- Q: What channels can I watch with Sky Go? -
- A: You can watch over 100 channels with Sky Go, depending on your Sky TV package. Some of the channels include Sky Atlantic, Sky Cinema, Sky Sports, and more. -
- Q: Can I watch Sky Go abroad? -
- A: Yes, you can watch Sky Go abroad in any of the EU countries. However, you may not be able to access some content due to licensing restrictions. -
- Q: Can I watch Sky Go on a smart TV? -
- A: No, you cannot watch Sky Go directly on a smart TV. However, you can use an HDMI cable or a casting device to connect your PC or laptop to your smart TV and watch it that way. 401be4b1e0
-
-
\ No newline at end of file diff --git a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py b/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py deleted file mode 100644 index c8340c723fad8e07e2fc62daaa3912487498814b..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/Video-Matting-Anything/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py +++ /dev/null @@ -1,221 +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. -# ------------------------------------------------------------------------ - -""" -Backbone modules. -""" - -from typing import Dict, List - -import torch -import torch.nn.functional as F -import torchvision -from torch import nn -from torchvision.models._utils import IntermediateLayerGetter - -from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process - -from .position_encoding import build_position_encoding -from .swin_transformer import build_swin_transformer - - -class FrozenBatchNorm2d(torch.nn.Module): - """ - BatchNorm2d where the batch statistics and the affine parameters are fixed. - - Copy-paste from torchvision.misc.ops with added eps before rqsrt, - without which any other models than torchvision.models.resnet[18,34,50,101] - produce nans. - """ - - def __init__(self, n): - super(FrozenBatchNorm2d, self).__init__() - self.register_buffer("weight", torch.ones(n)) - self.register_buffer("bias", torch.zeros(n)) - self.register_buffer("running_mean", torch.zeros(n)) - self.register_buffer("running_var", torch.ones(n)) - - def _load_from_state_dict( - self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ): - num_batches_tracked_key = prefix + "num_batches_tracked" - if num_batches_tracked_key in state_dict: - del state_dict[num_batches_tracked_key] - - super(FrozenBatchNorm2d, self)._load_from_state_dict( - state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs - ) - - def forward(self, x): - # move reshapes to the beginning - # to make it fuser-friendly - w = self.weight.reshape(1, -1, 1, 1) - b = self.bias.reshape(1, -1, 1, 1) - rv = self.running_var.reshape(1, -1, 1, 1) - rm = self.running_mean.reshape(1, -1, 1, 1) - eps = 1e-5 - scale = w * (rv + eps).rsqrt() - bias = b - rm * scale - return x * scale + bias - - -class BackboneBase(nn.Module): - def __init__( - self, - backbone: nn.Module, - train_backbone: bool, - num_channels: int, - return_interm_indices: list, - ): - super().__init__() - for name, parameter in backbone.named_parameters(): - if ( - not train_backbone - or "layer2" not in name - and "layer3" not in name - and "layer4" not in name - ): - parameter.requires_grad_(False) - - return_layers = {} - for idx, layer_index in enumerate(return_interm_indices): - return_layers.update( - {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)} - ) - - # if len: - # if use_stage1_feature: - # return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} - # else: - # return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"} - # else: - # return_layers = {'layer4': "0"} - self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) - self.num_channels = num_channels - - def forward(self, tensor_list: NestedTensor): - xs = self.body(tensor_list.tensors) - out: Dict[str, NestedTensor] = {} - for name, x in xs.items(): - m = tensor_list.mask - assert m is not None - mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] - out[name] = NestedTensor(x, mask) - # import ipdb; ipdb.set_trace() - return out - - -class Backbone(BackboneBase): - """ResNet backbone with frozen BatchNorm.""" - - def __init__( - self, - name: str, - train_backbone: bool, - dilation: bool, - return_interm_indices: list, - batch_norm=FrozenBatchNorm2d, - ): - if name in ["resnet18", "resnet34", "resnet50", "resnet101"]: - backbone = getattr(torchvision.models, name)( - replace_stride_with_dilation=[False, False, dilation], - pretrained=is_main_process(), - norm_layer=batch_norm, - ) - else: - raise NotImplementedError("Why you can get here with name {}".format(name)) - # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 - assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available." - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - num_channels_all = [256, 512, 1024, 2048] - num_channels = num_channels_all[4 - len(return_interm_indices) :] - super().__init__(backbone, train_backbone, num_channels, return_interm_indices) - - -class Joiner(nn.Sequential): - def __init__(self, backbone, position_embedding): - super().__init__(backbone, position_embedding) - - def forward(self, tensor_list: NestedTensor): - xs = self[0](tensor_list) - out: List[NestedTensor] = [] - pos = [] - for name, x in xs.items(): - out.append(x) - # position encoding - pos.append(self[1](x).to(x.tensors.dtype)) - - return out, pos - - -def build_backbone(args): - """ - Useful args: - - backbone: backbone name - - lr_backbone: - - dilation - - return_interm_indices: available: [0,1,2,3], [1,2,3], [3] - - backbone_freeze_keywords: - - use_checkpoint: for swin only for now - - """ - position_embedding = build_position_encoding(args) - train_backbone = True - if not train_backbone: - raise ValueError("Please set lr_backbone > 0") - return_interm_indices = args.return_interm_indices - assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]] - args.backbone_freeze_keywords - use_checkpoint = getattr(args, "use_checkpoint", False) - - if args.backbone in ["resnet50", "resnet101"]: - backbone = Backbone( - args.backbone, - train_backbone, - args.dilation, - return_interm_indices, - batch_norm=FrozenBatchNorm2d, - ) - bb_num_channels = backbone.num_channels - elif args.backbone in [ - "swin_T_224_1k", - "swin_B_224_22k", - "swin_B_384_22k", - "swin_L_224_22k", - "swin_L_384_22k", - ]: - pretrain_img_size = int(args.backbone.split("_")[-2]) - backbone = build_swin_transformer( - args.backbone, - pretrain_img_size=pretrain_img_size, - out_indices=tuple(return_interm_indices), - dilation=False, - use_checkpoint=use_checkpoint, - ) - - bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :] - else: - raise NotImplementedError("Unknown backbone {}".format(args.backbone)) - - assert len(bb_num_channels) == len( - return_interm_indices - ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}" - - model = Joiner(backbone, position_embedding) - model.num_channels = bb_num_channels - assert isinstance( - bb_num_channels, List - ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels)) - # import ipdb; ipdb.set_trace() - return model diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/has-symbols/test/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/has-symbols/test/index.js deleted file mode 100644 index 352129ca356c8ce4ba2e5e6b78c092f401e0f2da..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/has-symbols/test/index.js +++ /dev/null @@ -1,22 +0,0 @@ -'use strict'; - -var test = require('tape'); -var hasSymbols = require('../'); -var runSymbolTests = require('./tests'); - -test('interface', function (t) { - t.equal(typeof hasSymbols, 'function', 'is a function'); - t.equal(typeof hasSymbols(), 'boolean', 'returns a boolean'); - t.end(); -}); - -test('Symbols are supported', { skip: !hasSymbols() }, function (t) { - runSymbolTests(t); - t.end(); -}); - -test('Symbols are not supported', { skip: hasSymbols() }, function (t) { - t.equal(typeof Symbol, 'undefined', 'global Symbol is undefined'); - t.equal(typeof Object.getOwnPropertySymbols, 'undefined', 'Object.getOwnPropertySymbols does not exist'); - t.end(); -}); diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/node_modules/debug/README.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/node_modules/debug/README.md deleted file mode 100644 index e9c3e047c2b22aacd54f096af48f918217e06d84..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/node_modules/debug/README.md +++ /dev/null @@ -1,481 +0,0 @@ -# debug -[](https://travis-ci.org/debug-js/debug) [](https://coveralls.io/github/debug-js/debug?branch=master) [](https://visionmedia-community-slackin.now.sh/) [](#backers) -[](#sponsors) - -
-
-A tiny JavaScript debugging utility modelled after Node.js core's debugging
-technique. Works in Node.js and web browsers.
-
-## Installation
-
-```bash
-$ npm install debug
-```
-
-## Usage
-
-`debug` exposes a function; simply pass this function the name of your module, and it will return a decorated version of `console.error` for you to pass debug statements to. This will allow you to toggle the debug output for different parts of your module as well as the module as a whole.
-
-Example [_app.js_](./examples/node/app.js):
-
-```js
-var debug = require('debug')('http')
- , http = require('http')
- , name = 'My App';
-
-// fake app
-
-debug('booting %o', name);
-
-http.createServer(function(req, res){
- debug(req.method + ' ' + req.url);
- res.end('hello\n');
-}).listen(3000, function(){
- debug('listening');
-});
-
-// fake worker of some kind
-
-require('./worker');
-```
-
-Example [_worker.js_](./examples/node/worker.js):
-
-```js
-var a = require('debug')('worker:a')
- , b = require('debug')('worker:b');
-
-function work() {
- a('doing lots of uninteresting work');
- setTimeout(work, Math.random() * 1000);
-}
-
-work();
-
-function workb() {
- b('doing some work');
- setTimeout(workb, Math.random() * 2000);
-}
-
-workb();
-```
-
-The `DEBUG` environment variable is then used to enable these based on space or
-comma-delimited names.
-
-Here are some examples:
-
-
-
-
-
-#### Windows command prompt notes
-
-##### CMD
-
-On Windows the environment variable is set using the `set` command.
-
-```cmd
-set DEBUG=*,-not_this
-```
-
-Example:
-
-```cmd
-set DEBUG=* & node app.js
-```
-
-##### PowerShell (VS Code default)
-
-PowerShell uses different syntax to set environment variables.
-
-```cmd
-$env:DEBUG = "*,-not_this"
-```
-
-Example:
-
-```cmd
-$env:DEBUG='app';node app.js
-```
-
-Then, run the program to be debugged as usual.
-
-npm script example:
-```js
- "windowsDebug": "@powershell -Command $env:DEBUG='*';node app.js",
-```
-
-## Namespace Colors
-
-Every debug instance has a color generated for it based on its namespace name.
-This helps when visually parsing the debug output to identify which debug instance
-a debug line belongs to.
-
-#### Node.js
-
-In Node.js, colors are enabled when stderr is a TTY. You also _should_ install
-the [`supports-color`](https://npmjs.org/supports-color) module alongside debug,
-otherwise debug will only use a small handful of basic colors.
-
-
-
-#### Web Browser
-
-Colors are also enabled on "Web Inspectors" that understand the `%c` formatting
-option. These are WebKit web inspectors, Firefox ([since version
-31](https://hacks.mozilla.org/2014/05/editable-box-model-multiple-selection-sublime-text-keys-much-more-firefox-developer-tools-episode-31/))
-and the Firebug plugin for Firefox (any version).
-
-
-
-
-## Millisecond diff
-
-When actively developing an application it can be useful to see when the time spent between one `debug()` call and the next. Suppose for example you invoke `debug()` before requesting a resource, and after as well, the "+NNNms" will show you how much time was spent between calls.
-
-
-
-When stdout is not a TTY, `Date#toISOString()` is used, making it more useful for logging the debug information as shown below:
-
-
-
-
-## Conventions
-
-If you're using this in one or more of your libraries, you _should_ use the name of your library so that developers may toggle debugging as desired without guessing names. If you have more than one debuggers you _should_ prefix them with your library name and use ":" to separate features. For example "bodyParser" from Connect would then be "connect:bodyParser". If you append a "*" to the end of your name, it will always be enabled regardless of the setting of the DEBUG environment variable. You can then use it for normal output as well as debug output.
-
-## Wildcards
-
-The `*` character may be used as a wildcard. Suppose for example your library has
-debuggers named "connect:bodyParser", "connect:compress", "connect:session",
-instead of listing all three with
-`DEBUG=connect:bodyParser,connect:compress,connect:session`, you may simply do
-`DEBUG=connect:*`, or to run everything using this module simply use `DEBUG=*`.
-
-You can also exclude specific debuggers by prefixing them with a "-" character.
-For example, `DEBUG=*,-connect:*` would include all debuggers except those
-starting with "connect:".
-
-## Environment Variables
-
-When running through Node.js, you can set a few environment variables that will
-change the behavior of the debug logging:
-
-| Name | Purpose |
-|-----------|-------------------------------------------------|
-| `DEBUG` | Enables/disables specific debugging namespaces. |
-| `DEBUG_HIDE_DATE` | Hide date from debug output (non-TTY). |
-| `DEBUG_COLORS`| Whether or not to use colors in the debug output. |
-| `DEBUG_DEPTH` | Object inspection depth. |
-| `DEBUG_SHOW_HIDDEN` | Shows hidden properties on inspected objects. |
-
-
-__Note:__ The environment variables beginning with `DEBUG_` end up being
-converted into an Options object that gets used with `%o`/`%O` formatters.
-See the Node.js documentation for
-[`util.inspect()`](https://nodejs.org/api/util.html#util_util_inspect_object_options)
-for the complete list.
-
-## Formatters
-
-Debug uses [printf-style](https://wikipedia.org/wiki/Printf_format_string) formatting.
-Below are the officially supported formatters:
-
-| Formatter | Representation |
-|-----------|----------------|
-| `%O` | Pretty-print an Object on multiple lines. |
-| `%o` | Pretty-print an Object all on a single line. |
-| `%s` | String. |
-| `%d` | Number (both integer and float). |
-| `%j` | JSON. Replaced with the string '[Circular]' if the argument contains circular references. |
-| `%%` | Single percent sign ('%'). This does not consume an argument. |
-
-
-### Custom formatters
-
-You can add custom formatters by extending the `debug.formatters` object.
-For example, if you wanted to add support for rendering a Buffer as hex with
-`%h`, you could do something like:
-
-```js
-const createDebug = require('debug')
-createDebug.formatters.h = (v) => {
- return v.toString('hex')
-}
-
-// …elsewhere
-const debug = createDebug('foo')
-debug('this is hex: %h', new Buffer('hello world'))
-// foo this is hex: 68656c6c6f20776f726c6421 +0ms
-```
-
-
-## Browser Support
-
-You can build a browser-ready script using [browserify](https://github.com/substack/node-browserify),
-or just use the [browserify-as-a-service](https://wzrd.in/) [build](https://wzrd.in/standalone/debug@latest),
-if you don't want to build it yourself.
-
-Debug's enable state is currently persisted by `localStorage`.
-Consider the situation shown below where you have `worker:a` and `worker:b`,
-and wish to debug both. You can enable this using `localStorage.debug`:
-
-```js
-localStorage.debug = 'worker:*'
-```
-
-And then refresh the page.
-
-```js
-a = debug('worker:a');
-b = debug('worker:b');
-
-setInterval(function(){
- a('doing some work');
-}, 1000);
-
-setInterval(function(){
- b('doing some work');
-}, 1200);
-```
-
-In Chromium-based web browsers (e.g. Brave, Chrome, and Electron), the JavaScript console will—by default—only show messages logged by `debug` if the "Verbose" log level is _enabled_.
-
-
-
-## Output streams
-
- By default `debug` will log to stderr, however this can be configured per-namespace by overriding the `log` method:
-
-Example [_stdout.js_](./examples/node/stdout.js):
-
-```js
-var debug = require('debug');
-var error = debug('app:error');
-
-// by default stderr is used
-error('goes to stderr!');
-
-var log = debug('app:log');
-// set this namespace to log via console.log
-log.log = console.log.bind(console); // don't forget to bind to console!
-log('goes to stdout');
-error('still goes to stderr!');
-
-// set all output to go via console.info
-// overrides all per-namespace log settings
-debug.log = console.info.bind(console);
-error('now goes to stdout via console.info');
-log('still goes to stdout, but via console.info now');
-```
-
-## Extend
-You can simply extend debugger
-```js
-const log = require('debug')('auth');
-
-//creates new debug instance with extended namespace
-const logSign = log.extend('sign');
-const logLogin = log.extend('login');
-
-log('hello'); // auth hello
-logSign('hello'); //auth:sign hello
-logLogin('hello'); //auth:login hello
-```
-
-## Set dynamically
-
-You can also enable debug dynamically by calling the `enable()` method :
-
-```js
-let debug = require('debug');
-
-console.log(1, debug.enabled('test'));
-
-debug.enable('test');
-console.log(2, debug.enabled('test'));
-
-debug.disable();
-console.log(3, debug.enabled('test'));
-
-```
-
-print :
-```
-1 false
-2 true
-3 false
-```
-
-Usage :
-`enable(namespaces)`
-`namespaces` can include modes separated by a colon and wildcards.
-
-Note that calling `enable()` completely overrides previously set DEBUG variable :
-
-```
-$ DEBUG=foo node -e 'var dbg = require("debug"); dbg.enable("bar"); console.log(dbg.enabled("foo"))'
-=> false
-```
-
-`disable()`
-
-Will disable all namespaces. The functions returns the namespaces currently
-enabled (and skipped). This can be useful if you want to disable debugging
-temporarily without knowing what was enabled to begin with.
-
-For example:
-
-```js
-let debug = require('debug');
-debug.enable('foo:*,-foo:bar');
-let namespaces = debug.disable();
-debug.enable(namespaces);
-```
-
-Note: There is no guarantee that the string will be identical to the initial
-enable string, but semantically they will be identical.
-
-## Checking whether a debug target is enabled
-
-After you've created a debug instance, you can determine whether or not it is
-enabled by checking the `enabled` property:
-
-```javascript
-const debug = require('debug')('http');
-
-if (debug.enabled) {
- // do stuff...
-}
-```
-
-You can also manually toggle this property to force the debug instance to be
-enabled or disabled.
-
-## Usage in child processes
-
-Due to the way `debug` detects if the output is a TTY or not, colors are not shown in child processes when `stderr` is piped. A solution is to pass the `DEBUG_COLORS=1` environment variable to the child process.
-For example:
-
-```javascript
-worker = fork(WORKER_WRAP_PATH, [workerPath], {
- stdio: [
- /* stdin: */ 0,
- /* stdout: */ 'pipe',
- /* stderr: */ 'pipe',
- 'ipc',
- ],
- env: Object.assign({}, process.env, {
- DEBUG_COLORS: 1 // without this settings, colors won't be shown
- }),
-});
-
-worker.stderr.pipe(process.stderr, { end: false });
-```
-
-
-## Authors
-
- - TJ Holowaychuk
- - Nathan Rajlich
- - Andrew Rhyne
- - Josh Junon
-
-## Backers
-
-Support us with a monthly donation and help us continue our activities. [[Become a backer](https://opencollective.com/debug#backer)]
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-## Sponsors
-
-Become a sponsor and get your logo on our README on Github with a link to your site. [[Become a sponsor](https://opencollective.com/debug#sponsor)]
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-## License
-
-(The MIT License)
-
-Copyright (c) 2014-2017 TJ Holowaychuk <tj@vision-media.ca>
-Copyright (c) 2018-2021 Josh Junon
-
-Permission is hereby granted, free of charge, to any person obtaining
-a copy of this software and associated documentation files (the
-'Software'), to deal in the Software without restriction, including
-without limitation the rights to use, copy, modify, merge, publish,
-distribute, sublicense, and/or sell copies of the Software, and to
-permit persons to whom the Software is furnished to do so, subject to
-the following conditions:
-
-The above copyright notice and this permission notice shall be
-included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
-EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
-IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
-CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
-TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
-SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/spaces/firefighter/TransDis-CreativityAutoAssessment/README.md b/spaces/firefighter/TransDis-CreativityAutoAssessment/README.md
deleted file mode 100644
index 75a24342eb2802c5e170bb4010ddcbfe74a1eebe..0000000000000000000000000000000000000000
--- a/spaces/firefighter/TransDis-CreativityAutoAssessment/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: TransDis-CreativityAutoAssessment
-emoji: 💡
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/firsk/ai_otto/app.py b/spaces/firsk/ai_otto/app.py
deleted file mode 100644
index 398f52bbc898d4074c6b27c9179015508b9b92ca..0000000000000000000000000000000000000000
--- a/spaces/firsk/ai_otto/app.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# flake8: noqa: E402
-
-import sys, os
-import logging
-
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-logging.basicConfig(
- level=logging.INFO, format="| %(name)s | %(levelname)s | %(message)s"
-)
-
-logger = logging.getLogger(__name__)
-
-import torch
-import argparse
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import cleaned_text_to_sequence, get_bert
-from text.cleaner import clean_text
-import gradio as gr
-import webbrowser
-import numpy as np
-
-net_g = None
-
-if sys.platform == "darwin" and torch.backends.mps.is_available():
- device = "mps"
- os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-else:
- device = "cuda"
-
-
-def get_text(text, language_str, hps):
- norm_text, phone, tone, word2ph = clean_text(text, language_str)
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-
- if hps.data.add_blank:
- phone = commons.intersperse(phone, 0)
- tone = commons.intersperse(tone, 0)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert = get_bert(norm_text, word2ph, language_str, device)
- del word2ph
- assert bert.shape[-1] == len(phone), phone
-
- if language_str == "ZH":
- bert = bert
- ja_bert = torch.zeros(768, len(phone))
- elif language_str == "JP":
- ja_bert = bert
- bert = torch.zeros(1024, len(phone))
- else:
- bert = torch.zeros(1024, len(phone))
- ja_bert = torch.zeros(768, len(phone))
-
- assert bert.shape[-1] == len(
- phone
- ), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
-
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
- return bert, ja_bert, phone, tone, language
-
-
-def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
- global net_g
- bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
- with torch.no_grad():
- x_tst = phones.to(device).unsqueeze(0)
- tones = tones.to(device).unsqueeze(0)
- lang_ids = lang_ids.to(device).unsqueeze(0)
- bert = bert.to(device).unsqueeze(0)
- ja_bert = ja_bert.to(device).unsqueeze(0)
- x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
- del phones
- speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
- audio = (
- net_g.infer(
- x_tst,
- x_tst_lengths,
- speakers,
- tones,
- lang_ids,
- bert,
- ja_bert,
- sdp_ratio=sdp_ratio,
- noise_scale=noise_scale,
- noise_scale_w=noise_scale_w,
- length_scale=length_scale,
- )[0][0, 0]
- .data.cpu()
- .float()
- .numpy()
- )
- del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
- torch.cuda.empty_cache()
- return audio
-
-
-def tts_fn(
- text, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale, language
-):
- slices = text.split("|")
- audio_list = []
- with torch.no_grad():
- for slice in slices:
- audio = infer(
- slice,
- sdp_ratio=sdp_ratio,
- noise_scale=noise_scale,
- noise_scale_w=noise_scale_w,
- length_scale=length_scale,
- sid=speaker,
- language=language,
- )
- audio_list.append(audio)
- silence = np.zeros(hps.data.sampling_rate) # 生成1秒的静音
- audio_list.append(silence) # 将静音添加到列表中
- audio_concat = np.concatenate(audio_list)
- return "Success", (hps.data.sampling_rate, audio_concat)
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "-m", "--model", default="./logs/OUTPUT_MODEL/G_159000.pth", help="path of your model"
- )
- parser.add_argument(
- "-c",
- "--config",
- default="./configs/config.json",
- help="path of your config file",
- )
- parser.add_argument(
- "--share", default=True, help="make link public", action="store_true"
- )
- parser.add_argument(
- "-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
- )
-
- args = parser.parse_args()
- if args.debug:
- logger.info("Enable DEBUG-LEVEL log")
- logging.basicConfig(level=logging.DEBUG)
- hps = utils.get_hparams_from_file(args.config)
-
- device = (
- "cuda:0"
- if torch.cuda.is_available()
- else (
- "mps"
- if sys.platform == "darwin" and torch.backends.mps.is_available()
- else "cpu"
- )
- )
- net_g = SynthesizerTrn(
- len(symbols),
- hps.data.filter_length // 2 + 1,
- hps.train.segment_size // hps.data.hop_length,
- n_speakers=hps.data.n_speakers,
- **hps.model,
- ).to(device)
- _ = net_g.eval()
-
- _ = utils.load_checkpoint(args.model, net_g, None, skip_optimizer=True)
-
- speaker_ids = hps.data.spk2id
- speakers = list(speaker_ids.keys())
- languages = ["ZH", "JP"]
- with gr.Blocks() as app:
- with gr.Row():
- with gr.Column():
- text = gr.TextArea(
- label="Text",
- placeholder="Input Text Here",
- value="年仅三岁的国王毫无畏惧",
- )
- speaker = gr.Dropdown(
- choices=speakers, value=speakers[0], label="Speaker"
- )
- sdp_ratio = gr.Slider(
- minimum=0, maximum=1, value=0.2, step=0.1, label="SDP Ratio"
- )
- noise_scale = gr.Slider(
- minimum=0.1, maximum=2, value=0.6, step=0.1, label="Noise Scale"
- )
- noise_scale_w = gr.Slider(
- minimum=0.1, maximum=2, value=0.8, step=0.1, label="Noise Scale W"
- )
- length_scale = gr.Slider(
- minimum=0.1, maximum=2, value=1, step=0.1, label="Length Scale"
- )
- language = gr.Dropdown(
- choices=languages, value=languages[0], label="Language"
- )
- btn = gr.Button("Generate!", variant="primary")
- with gr.Column():
- text_output = gr.Textbox(label="Message")
- audio_output = gr.Audio(label="Output Audio")
-
- btn.click(
- tts_fn,
- inputs=[
- text,
- speaker,
- sdp_ratio,
- noise_scale,
- noise_scale_w,
- length_scale,
- language,
- ],
- outputs=[text_output, audio_output],
- )
-
- webbrowser.open("http://127.0.0.1:7860")
- app.launch(share=args.share)
diff --git a/spaces/flax-community/spanish-image-captioning/sections/usage.md b/spaces/flax-community/spanish-image-captioning/sections/usage.md
deleted file mode 100644
index 7fd5c2cf140f99031a3cef1a4b1684c43aea4651..0000000000000000000000000000000000000000
--- a/spaces/flax-community/spanish-image-captioning/sections/usage.md
+++ /dev/null
@@ -1,7 +0,0 @@
-- This demo loads the `FlaxCLIPVisionMarianMT` present in the `model` directory of this repository. The checkpoint is loaded from `ckpt/ckpt-23999` which is pre-trained checkpoint with 24k steps. 100 random validation set examples are present in the `references.tsv` with respective images in the `images` directory.
-
-- We provide `English Translation` of the generated caption and reference captions for users who are not well-acquainted with Spanish. This is done using `mtranslate` to keep things flexible enough and needs internet connection as it uses the Google Translate API. We will also add the original captions soon.
-
-- The sidebar contains generation parameters such as `Number of Beams`, `Top-P`, `Temperature` which will be used when generating the caption.
-
-- Clicking on `Generate Caption` will generate the caption in Spanish.
\ No newline at end of file
diff --git a/spaces/flowers-team/SocialAISchool/scripts/tensorboard_aggregator.py b/spaces/flowers-team/SocialAISchool/scripts/tensorboard_aggregator.py
deleted file mode 100644
index d4002c231bce5dfc1a61108241d64053f687e220..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/scripts/tensorboard_aggregator.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import os
-import sys
-import argparse
-import shutil
-from collections import defaultdict
-
-import numpy as np
-import tensorflow as tf
-from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
-
-
-def tabulate_events(exp_path):
-
- seeds = [s for s in os.listdir(exp_path) if "combined" not in s]
- summary_iterators = [EventAccumulator(os.path.join(exp_path, dname)).Reload() for dname in seeds]
-
- tags = summary_iterators[0].Tags()['scalars']
- for it in summary_iterators:
- assert it.Tags()['scalars'] == tags
-
- out = defaultdict(list)
- for tag in tags:
- for events in zip(*[acc.Scalars(tag) for acc in summary_iterators]):
- assert len(set(e.step for e in events)) == 1
-
- out[tag].append([e.value for e in events])
-
- return out
-
-
-def create_histogram_summary(tag, values, bins=1000):
- # Convert to a numpy array
- values = np.array(values)
-
- # Create histogram using numpy
- counts, bin_edges = np.histogram(values, bins=bins)
-
- # Fill fields of histogram proto
- hist = tf.HistogramProto()
- hist.min = float(np.min(values))
- hist.max = float(np.max(values))
- hist.num = int(np.prod(values.shape))
- hist.sum = float(np.sum(values))
- hist.sum_squares = float(np.sum(values**2))
-
- # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
- # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
- # Thus, we drop the start of the first bin
- bin_edges = bin_edges[1:]
-
- # Add bin edges and counts
- for edge in bin_edges:
- hist.bucket_limit.append(edge)
- for c in counts:
- hist.bucket.append(c)
-
- # Create and write Summary
- return tf.Summary.Value(tag=tag, histo=hist)
-
-
-def create_parsed_histogram_summary(tag, values, bins=1000):
- # Convert to a numpy array
-
- # Create histogram using numpy
- counts, bin_edges = np.histogram(values, bins=bins)
-
- # Fill fields of histogram proto
- hist = tf.HistogramProto()
- hist.min = float(np.min(values))
- hist.max = float(np.max(values))
- hist.num = int(np.prod(values.shape))
- hist.sum = float(np.sum(values))
- hist.sum_squares = float(np.sum(values**2))
-
- # Requires equal number as bins, where the first goes from -DBL_MAX to bin_edges[1]
- # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/summary.proto#L30
- # Thus, we drop the start of the first bin
- bin_edges = bin_edges[1:]
-
- # Add bin edges and counts
- for edge in bin_edges:
- hist.bucket_limit.append(edge)
- for c in counts:
- hist.bucket.append(c)
-
- # Create and write Summary
- return tf.Summary.Value(tag=tag, histo=hist)
-
-
-def write_combined_events(exp_path, d_combined, dname='combined', mean_var_tags=()):
-
- fpath = os.path.join(exp_path, dname)
- if os.path.isdir(fpath):
- shutil.rmtree(fpath)
- assert not os.path.isdir(fpath)
-
- writer = tf.summary.FileWriter(fpath)
-
-
- tags, values = zip(*d_combined.items())
-
- cap = min([len(v) for v in values])
- values = [v[:cap] for v in values]
-
- timestep_mean = np.array(values).mean(axis=-1)
- timestep_var = np.array(values).var(axis=-1)
- timesteps = timestep_mean[tags.index("frames")]
-
- for tag, means, vars in zip(tags, timestep_mean, timestep_var):
- for i, mean, var in zip(timesteps, means, vars):
- summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=mean)])
- writer.add_summary(summary, global_step=i)
- writer.flush()
-
- if tag in mean_var_tags:
- values = np.array([mean - var, mean, mean + var])
-
- summary = tf.Summary(value=[
- create_histogram_summary(tag=tag+"_var", values=values)
- ])
- writer.add_summary(summary, global_step=i)
- writer.flush()
-
-
-if __name__ == "__main__":
- if len(sys.argv) > 1:
- dpath = sys.argv[1]
- else:
- raise ValueError("Specify dir")
-
- parser = argparse.ArgumentParser()
-
- parser.add_argument('--experiments', nargs='+', help='experiment directories to aggregate', required=True)
-
- parser.add_argument('--mean-var-tags', nargs='+', help='tags to create mean-var histograms from', required=False, default=["return_mean"])
-
- args = parser.parse_args()
-
- for exp_path in args.experiments:
- d = tabulate_events(exp_path)
- write_combined_events(exp_path, d, mean_var_tags=args.mean_var_tags)
\ No newline at end of file
diff --git a/spaces/fluffyfluff/multiple-pdf-chat/README.md b/spaces/fluffyfluff/multiple-pdf-chat/README.md
deleted file mode 100644
index f77ed40df8d8504c5aee1e0f68c37ea0c2a0ffac..0000000000000000000000000000000000000000
--- a/spaces/fluffyfluff/multiple-pdf-chat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Multiple Pdf Chat
-emoji: 🚀
-colorFrom: green
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/foduucom/stockmarket-future-prediction/README.md b/spaces/foduucom/stockmarket-future-prediction/README.md
deleted file mode 100644
index 84ea7c870d967b54e12e4a6efe2bdf146d22c6f0..0000000000000000000000000000000000000000
--- a/spaces/foduucom/stockmarket-future-prediction/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stockmarket Future Prediction
-emoji: 🔥
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.45.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/gbharti/fastai-model-deploy/.ipynb_checkpoints/README-checkpoint.md b/spaces/gbharti/fastai-model-deploy/.ipynb_checkpoints/README-checkpoint.md
deleted file mode 100644
index 4e9e3c0ceae4ef49b3f5c1814058ee40036ef7c9..0000000000000000000000000000000000000000
--- a/spaces/gbharti/fastai-model-deploy/.ipynb_checkpoints/README-checkpoint.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Fastai Model Reply
-emoji: ⚡
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py
deleted file mode 100644
index 60437756ceedf06055ec349df69a25465738d3f0..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/runner/hooks/momentum_updater.py
+++ /dev/null
@@ -1,493 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import annotator.uniformer.mmcv as mmcv
-from .hook import HOOKS, Hook
-from .lr_updater import annealing_cos, annealing_linear, format_param
-
-
-class MomentumUpdaterHook(Hook):
-
- def __init__(self,
- by_epoch=True,
- warmup=None,
- warmup_iters=0,
- warmup_ratio=0.9):
- # validate the "warmup" argument
- if warmup is not None:
- if warmup not in ['constant', 'linear', 'exp']:
- raise ValueError(
- f'"{warmup}" is not a supported type for warming up, valid'
- ' types are "constant" and "linear"')
- if warmup is not None:
- assert warmup_iters > 0, \
- '"warmup_iters" must be a positive integer'
- assert 0 < warmup_ratio <= 1.0, \
- '"warmup_momentum" must be in range (0,1]'
-
- self.by_epoch = by_epoch
- self.warmup = warmup
- self.warmup_iters = warmup_iters
- self.warmup_ratio = warmup_ratio
-
- self.base_momentum = [] # initial momentum for all param groups
- self.regular_momentum = [
- ] # expected momentum if no warming up is performed
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, base_momentum):
- raise NotImplementedError
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k in runner.optimizer.keys():
- _momentum_group = [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum[k]
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- return [
- self.get_momentum(runner, _base_momentum)
- for _base_momentum in self.base_momentum
- ]
-
- def get_warmup_momentum(self, cur_iters):
-
- def _get_warmup_momentum(cur_iters, regular_momentum):
- if self.warmup == 'constant':
- warmup_momentum = [
- _momentum / self.warmup_ratio
- for _momentum in self.regular_momentum
- ]
- elif self.warmup == 'linear':
- k = (1 - cur_iters / self.warmup_iters) * (1 -
- self.warmup_ratio)
- warmup_momentum = [
- _momentum / (1 - k) for _momentum in self.regular_mom
- ]
- elif self.warmup == 'exp':
- k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters)
- warmup_momentum = [
- _momentum / k for _momentum in self.regular_mom
- ]
- return warmup_momentum
-
- if isinstance(self.regular_momentum, dict):
- momentum_groups = {}
- for key, regular_momentum in self.regular_momentum.items():
- momentum_groups[key] = _get_warmup_momentum(
- cur_iters, regular_momentum)
- return momentum_groups
- else:
- return _get_warmup_momentum(cur_iters, self.regular_momentum)
-
- def before_run(self, runner):
- # NOTE: when resuming from a checkpoint,
- # if 'initial_momentum' is not saved,
- # it will be set according to the optimizer params
- if isinstance(runner.optimizer, dict):
- self.base_momentum = {}
- for k, optim in runner.optimizer.items():
- for group in optim.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- _base_momentum = [
- group['initial_momentum'] for group in optim.param_groups
- ]
- self.base_momentum.update({k: _base_momentum})
- else:
- for group in runner.optimizer.param_groups:
- if 'momentum' in group.keys():
- group.setdefault('initial_momentum', group['momentum'])
- else:
- group.setdefault('initial_momentum', group['betas'][0])
- self.base_momentum = [
- group['initial_momentum']
- for group in runner.optimizer.param_groups
- ]
-
- def before_train_epoch(self, runner):
- if not self.by_epoch:
- return
- self.regular_mom = self.get_regular_momentum(runner)
- self._set_momentum(runner, self.regular_mom)
-
- def before_train_iter(self, runner):
- cur_iter = runner.iter
- if not self.by_epoch:
- self.regular_mom = self.get_regular_momentum(runner)
- if self.warmup is None or cur_iter >= self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
- elif self.by_epoch:
- if self.warmup is None or cur_iter > self.warmup_iters:
- return
- elif cur_iter == self.warmup_iters:
- self._set_momentum(runner, self.regular_mom)
- else:
- warmup_momentum = self.get_warmup_momentum(cur_iter)
- self._set_momentum(runner, warmup_momentum)
-
-
-@HOOKS.register_module()
-class StepMomentumUpdaterHook(MomentumUpdaterHook):
- """Step momentum scheduler with min value clipping.
-
- Args:
- step (int | list[int]): Step to decay the momentum. If an int value is
- given, regard it as the decay interval. If a list is given, decay
- momentum at these steps.
- gamma (float, optional): Decay momentum ratio. Default: 0.5.
- min_momentum (float, optional): Minimum momentum value to keep. If
- momentum after decay is lower than this value, it will be clipped
- accordingly. If None is given, we don't perform lr clipping.
- Default: None.
- """
-
- def __init__(self, step, gamma=0.5, min_momentum=None, **kwargs):
- if isinstance(step, list):
- assert mmcv.is_list_of(step, int)
- assert all([s > 0 for s in step])
- elif isinstance(step, int):
- assert step > 0
- else:
- raise TypeError('"step" must be a list or integer')
- self.step = step
- self.gamma = gamma
- self.min_momentum = min_momentum
- super(StepMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- progress = runner.epoch if self.by_epoch else runner.iter
-
- # calculate exponential term
- if isinstance(self.step, int):
- exp = progress // self.step
- else:
- exp = len(self.step)
- for i, s in enumerate(self.step):
- if progress < s:
- exp = i
- break
-
- momentum = base_momentum * (self.gamma**exp)
- if self.min_momentum is not None:
- # clip to a minimum value
- momentum = max(momentum, self.min_momentum)
- return momentum
-
-
-@HOOKS.register_module()
-class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook):
-
- def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs):
- assert (min_momentum is None) ^ (min_momentum_ratio is None)
- self.min_momentum = min_momentum
- self.min_momentum_ratio = min_momentum_ratio
- super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs)
-
- def get_momentum(self, runner, base_momentum):
- if self.by_epoch:
- progress = runner.epoch
- max_progress = runner.max_epochs
- else:
- progress = runner.iter
- max_progress = runner.max_iters
- if self.min_momentum_ratio is not None:
- target_momentum = base_momentum * self.min_momentum_ratio
- else:
- target_momentum = self.min_momentum
- return annealing_cos(base_momentum, target_momentum,
- progress / max_progress)
-
-
-@HOOKS.register_module()
-class CyclicMomentumUpdaterHook(MomentumUpdaterHook):
- """Cyclic momentum Scheduler.
-
- Implement the cyclical momentum scheduler policy described in
- https://arxiv.org/pdf/1708.07120.pdf
-
- This momentum scheduler usually used together with the CyclicLRUpdater
- to improve the performance in the 3D detection area.
-
- Attributes:
- target_ratio (tuple[float]): Relative ratio of the lowest momentum and
- the highest momentum to the initial momentum.
- cyclic_times (int): Number of cycles during training
- step_ratio_up (float): The ratio of the increasing process of momentum
- in the total cycle.
- by_epoch (bool): Whether to update momentum by epoch.
- """
-
- def __init__(self,
- by_epoch=False,
- target_ratio=(0.85 / 0.95, 1),
- cyclic_times=1,
- step_ratio_up=0.4,
- **kwargs):
- if isinstance(target_ratio, float):
- target_ratio = (target_ratio, target_ratio / 1e5)
- elif isinstance(target_ratio, tuple):
- target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \
- if len(target_ratio) == 1 else target_ratio
- else:
- raise ValueError('target_ratio should be either float '
- f'or tuple, got {type(target_ratio)}')
-
- assert len(target_ratio) == 2, \
- '"target_ratio" must be list or tuple of two floats'
- assert 0 <= step_ratio_up < 1.0, \
- '"step_ratio_up" must be in range [0,1)'
-
- self.target_ratio = target_ratio
- self.cyclic_times = cyclic_times
- self.step_ratio_up = step_ratio_up
- self.momentum_phases = [] # init momentum_phases
- # currently only support by_epoch=False
- assert not by_epoch, \
- 'currently only support "by_epoch" = False'
- super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs)
-
- def before_run(self, runner):
- super(CyclicMomentumUpdaterHook, self).before_run(runner)
- # initiate momentum_phases
- # total momentum_phases are separated as up and down
- max_iter_per_phase = runner.max_iters // self.cyclic_times
- iter_up_phase = int(self.step_ratio_up * max_iter_per_phase)
- self.momentum_phases.append(
- [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]])
- self.momentum_phases.append([
- iter_up_phase, max_iter_per_phase, max_iter_per_phase,
- self.target_ratio[0], self.target_ratio[1]
- ])
-
- def get_momentum(self, runner, base_momentum):
- curr_iter = runner.iter
- for (start_iter, end_iter, max_iter_per_phase, start_ratio,
- end_ratio) in self.momentum_phases:
- curr_iter %= max_iter_per_phase
- if start_iter <= curr_iter < end_iter:
- progress = curr_iter - start_iter
- return annealing_cos(base_momentum * start_ratio,
- base_momentum * end_ratio,
- progress / (end_iter - start_iter))
-
-
-@HOOKS.register_module()
-class OneCycleMomentumUpdaterHook(MomentumUpdaterHook):
- """OneCycle momentum Scheduler.
-
- This momentum scheduler usually used together with the OneCycleLrUpdater
- to improve the performance.
-
- Args:
- base_momentum (float or list): Lower momentum boundaries in the cycle
- for each parameter group. Note that momentum is cycled inversely
- to learning rate; at the peak of a cycle, momentum is
- 'base_momentum' and learning rate is 'max_lr'.
- Default: 0.85
- max_momentum (float or list): Upper momentum boundaries in the cycle
- for each parameter group. Functionally,
- it defines the cycle amplitude (max_momentum - base_momentum).
- Note that momentum is cycled inversely
- to learning rate; at the start of a cycle, momentum is
- 'max_momentum' and learning rate is 'base_lr'
- Default: 0.95
- pct_start (float): The percentage of the cycle (in number of steps)
- spent increasing the learning rate.
- Default: 0.3
- anneal_strategy (str): {'cos', 'linear'}
- Specifies the annealing strategy: 'cos' for cosine annealing,
- 'linear' for linear annealing.
- Default: 'cos'
- three_phase (bool): If three_phase is True, use a third phase of the
- schedule to annihilate the learning rate according to
- final_div_factor instead of modifying the second phase (the first
- two phases will be symmetrical about the step indicated by
- pct_start).
- Default: False
- """
-
- def __init__(self,
- base_momentum=0.85,
- max_momentum=0.95,
- pct_start=0.3,
- anneal_strategy='cos',
- three_phase=False,
- **kwargs):
- # validate by_epoch, currently only support by_epoch=False
- if 'by_epoch' not in kwargs:
- kwargs['by_epoch'] = False
- else:
- assert not kwargs['by_epoch'], \
- 'currently only support "by_epoch" = False'
- if not isinstance(base_momentum, (float, list, dict)):
- raise ValueError('base_momentum must be the type among of float,'
- 'list or dict.')
- self._base_momentum = base_momentum
- if not isinstance(max_momentum, (float, list, dict)):
- raise ValueError('max_momentum must be the type among of float,'
- 'list or dict.')
- self._max_momentum = max_momentum
- # validate pct_start
- if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
- raise ValueError('Expected float between 0 and 1 pct_start, but '
- f'got {pct_start}')
- self.pct_start = pct_start
- # validate anneal_strategy
- if anneal_strategy not in ['cos', 'linear']:
- raise ValueError('anneal_strategy must by one of "cos" or '
- f'"linear", instead got {anneal_strategy}')
- elif anneal_strategy == 'cos':
- self.anneal_func = annealing_cos
- elif anneal_strategy == 'linear':
- self.anneal_func = annealing_linear
- self.three_phase = three_phase
- self.momentum_phases = [] # init momentum_phases
- super(OneCycleMomentumUpdaterHook, self).__init__(**kwargs)
-
- def before_run(self, runner):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(
- optim.param_groups, _base_momentum, _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
- else:
- optim = runner.optimizer
- if ('momentum' not in optim.defaults
- and 'betas' not in optim.defaults):
- raise ValueError('optimizer must support momentum with'
- 'option enabled')
- self.use_beta1 = 'betas' in optim.defaults
- k = type(optim).__name__
- _base_momentum = format_param(k, optim, self._base_momentum)
- _max_momentum = format_param(k, optim, self._max_momentum)
- for group, b_momentum, m_momentum in zip(optim.param_groups,
- _base_momentum,
- _max_momentum):
- if self.use_beta1:
- _, beta2 = group['betas']
- group['betas'] = (m_momentum, beta2)
- else:
- group['momentum'] = m_momentum
- group['base_momentum'] = b_momentum
- group['max_momentum'] = m_momentum
-
- if self.three_phase:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter':
- float(2 * self.pct_start * runner.max_iters) - 2,
- 'start_momentum':
- 'base_momentum',
- 'end_momentum':
- 'max_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'max_momentum',
- 'end_momentum': 'max_momentum'
- })
- else:
- self.momentum_phases.append({
- 'end_iter':
- float(self.pct_start * runner.max_iters) - 1,
- 'start_momentum':
- 'max_momentum',
- 'end_momentum':
- 'base_momentum'
- })
- self.momentum_phases.append({
- 'end_iter': runner.max_iters - 1,
- 'start_momentum': 'base_momentum',
- 'end_momentum': 'max_momentum'
- })
-
- def _set_momentum(self, runner, momentum_groups):
- if isinstance(runner.optimizer, dict):
- for k, optim in runner.optimizer.items():
- for param_group, mom in zip(optim.param_groups,
- momentum_groups[k]):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
- else:
- for param_group, mom in zip(runner.optimizer.param_groups,
- momentum_groups):
- if 'momentum' in param_group.keys():
- param_group['momentum'] = mom
- elif 'betas' in param_group.keys():
- param_group['betas'] = (mom, param_group['betas'][1])
-
- def get_momentum(self, runner, param_group):
- curr_iter = runner.iter
- start_iter = 0
- for i, phase in enumerate(self.momentum_phases):
- end_iter = phase['end_iter']
- if curr_iter <= end_iter or i == len(self.momentum_phases) - 1:
- pct = (curr_iter - start_iter) / (end_iter - start_iter)
- momentum = self.anneal_func(
- param_group[phase['start_momentum']],
- param_group[phase['end_momentum']], pct)
- break
- start_iter = end_iter
- return momentum
-
- def get_regular_momentum(self, runner):
- if isinstance(runner.optimizer, dict):
- momentum_groups = {}
- for k, optim in runner.optimizer.items():
- _momentum_group = [
- self.get_momentum(runner, param_group)
- for param_group in optim.param_groups
- ]
- momentum_groups.update({k: _momentum_group})
- return momentum_groups
- else:
- momentum_groups = []
- for param_group in runner.optimizer.param_groups:
- momentum_groups.append(self.get_momentum(runner, param_group))
- return momentum_groups
diff --git "a/spaces/giswqs/Streamlit/pages/2_\360\237\217\240_U.S._Housing.py" "b/spaces/giswqs/Streamlit/pages/2_\360\237\217\240_U.S._Housing.py"
deleted file mode 100644
index 0c2e4996d01c41d196b93308c9532305ee89cf43..0000000000000000000000000000000000000000
--- "a/spaces/giswqs/Streamlit/pages/2_\360\237\217\240_U.S._Housing.py"
+++ /dev/null
@@ -1,482 +0,0 @@
-import datetime
-import os
-import pathlib
-import requests
-import zipfile
-import pandas as pd
-import pydeck as pdk
-import geopandas as gpd
-import streamlit as st
-import leafmap.colormaps as cm
-from leafmap.common import hex_to_rgb
-
-st.set_page_config(layout="wide")
-
-st.sidebar.info(
- """
- - Web App URL:
Value: {" - + selected_col - + "}
Date: " - + selected_period - + "", - "style": {"backgroundColor": "steelblue", "color": "white"}, - } - - layers = [geojson] - if show_nodata: - layers.append(geojson_null) - - r = pdk.Deck( - layers=layers, - initial_view_state=initial_view_state, - map_style="light", - tooltip=tooltip, - ) - - row3_col1, row3_col2 = st.columns([6, 1]) - - with row3_col1: - st.pydeck_chart(r) - with row3_col2: - st.write( - cm.create_colormap( - palette, - label=selected_col.replace("_", " ").title(), - width=0.2, - height=3, - orientation="vertical", - vmin=min_value, - vmax=max_value, - font_size=10, - ) - ) - row4_col1, row4_col2, row4_col3 = st.columns([1, 2, 3]) - with row4_col1: - show_data = st.checkbox("Show raw data") - with row4_col2: - show_cols = st.multiselect("Select columns", data_cols) - with row4_col3: - show_colormaps = st.checkbox("Preview all color palettes") - if show_colormaps: - st.write(cm.plot_colormaps(return_fig=True)) - if show_data: - if scale == "National": - st.dataframe(gdf[["NAME", "GEOID"] + show_cols]) - elif scale == "State": - st.dataframe(gdf[["NAME", "STUSPS"] + show_cols]) - elif scale == "County": - st.dataframe(gdf[["NAME", "STATEFP", "COUNTYFP"] + show_cols]) - elif scale == "Metro": - st.dataframe(gdf[["NAME", "CBSAFP"] + show_cols]) - elif scale == "Zip": - st.dataframe(gdf[["GEOID10"] + show_cols]) - - -app() diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Dell Precision M4600 Enable 11.md b/spaces/gotiQspiryo/whisper-ui/examples/Dell Precision M4600 Enable 11.md deleted file mode 100644 index c1fc0fc179c34e4fa408088be412e58754bfd4f4..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/Dell Precision M4600 Enable 11.md +++ /dev/null @@ -1,5 +0,0 @@ - -This is supper. I did not know that Bluetooth can be enabled or disabled from Bios setup utility. I found the check box for Bluetooth unchecked. I ticked it and immediately the computer restarted, dell support assistant suggested driver update which I did and my Bluetooth worked immediately. Prior to this I had tried windows as well as dell troubleshooter which both showed I was running the lates drivers. Windows showed that Bluetooth is not available on this device while dell said the machine is out of warranty hence they cannot assist. Thanks man.
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\ No newline at end of file diff --git a/spaces/gwang-kim/DATID-3D/eg3d/viz/__init__.py b/spaces/gwang-kim/DATID-3D/eg3d/viz/__init__.py deleted file mode 100644 index dfebd04f47e6f6b1b44984c14c23b57d56f72240..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/eg3d/viz/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: LicenseRef-NvidiaProprietary -# -# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -# property and proprietary rights in and to this material, related -# documentation and any modifications thereto. Any use, reproduction, -# disclosure or distribution of this material and related documentation -# without an express license agreement from NVIDIA CORPORATION or -# its affiliates is strictly prohibited. - -# empty diff --git a/spaces/gyrojeff/YuzuMarker.FontDetection/Dockerfile b/spaces/gyrojeff/YuzuMarker.FontDetection/Dockerfile deleted file mode 100644 index 94952257f3d04051ff43b081da2620c93629dc6d..0000000000000000000000000000000000000000 --- a/spaces/gyrojeff/YuzuMarker.FontDetection/Dockerfile +++ /dev/null @@ -1,20 +0,0 @@ -FROM docker.io/jeffersonqin/yuzumarker.fontdetection.huggingfacespace.base:latest - -RUN useradd -m -u 1000 user -USER user -ENV HOME=/home/user \ - PATH=/home/user/.local/bin:$PATH - -WORKDIR $HOME/app -USER root -RUN mv /workspace/font_demo_cache.bin $HOME/app/font_demo_cache.bin -RUN mv /workspace/demo_fonts $HOME/app/demo_fonts - -USER user -COPY --chown=user detector $HOME/app/detector -COPY --chown=user font_dataset $HOME/app/font_dataset -COPY --chown=user utils $HOME/app/utils -COPY --chown=user configs $HOME/app/configs -COPY --chown=user demo.py $HOME/app/demo.py - -CMD ["python", "demo.py", "-d", "-1", "-c", "huggingface://gyrojeff/YuzuMarker.FontDetection/name=4x-epoch=18-step=368676.ckpt", "-m", "resnet50", "-z", "512", "-p", "7860", "-a", "0.0.0.0"] diff --git a/spaces/haakohu/deep_privacy2/dp2/detection/models/__init__.py b/spaces/haakohu/deep_privacy2/dp2/detection/models/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py deleted file mode 100644 index b0af8d130af70b22791535c67f9dcf34baf5e528..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/evaluation/flickr/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .flickr_eval import FlickrEvaluator diff --git a/spaces/haseeb-heaven/AutoBard-Coder/codes/generated_code.c b/spaces/haseeb-heaven/AutoBard-Coder/codes/generated_code.c deleted file mode 100644 index bdcfde68b1460da1c6c5cc12347d4234fb17c6bf..0000000000000000000000000000000000000000 --- a/spaces/haseeb-heaven/AutoBard-Coder/codes/generated_code.c +++ /dev/null @@ -1,15 +0,0 @@ -#include- -int main() { - int number = 10; // Hardcode the input value - int factorial = 1; - - for (int i = 1; i <= number; i++) { - factorial *= i; - } - - printf("The factorial of %d is: %d -", number, factorial); - - return 0; -} \ No newline at end of file diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/dev/run_instant_tests.sh b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/dev/run_instant_tests.sh deleted file mode 100644 index 2c51de649262e7371fb173210c8edc377e8177e0..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/dev/run_instant_tests.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -e -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -BIN="python tools/train_net.py" -OUTPUT="instant_test_output" -NUM_GPUS=2 - -CFG_LIST=( "${@:1}" ) -if [ ${#CFG_LIST[@]} -eq 0 ]; then - CFG_LIST=( ./configs/quick_schedules/*instant_test.yaml ) -fi - -echo "========================================================================" -echo "Configs to run:" -echo "${CFG_LIST[@]}" -echo "========================================================================" - -for cfg in "${CFG_LIST[@]}"; do - echo "========================================================================" - echo "Running $cfg ..." - echo "========================================================================" - $BIN --num-gpus $NUM_GPUS --config-file "$cfg" \ - SOLVER.IMS_PER_BATCH $(($NUM_GPUS * 2)) \ - OUTPUT_DIR "$OUTPUT" - rm -rf "$OUTPUT" -done - diff --git a/spaces/hasibzunair/fifa-tryon-demo/u2net_human_seg_test.py b/spaces/hasibzunair/fifa-tryon-demo/u2net_human_seg_test.py deleted file mode 100644 index ade5fc040e6ea18663659e1818723c436ca48d5e..0000000000000000000000000000000000000000 --- a/spaces/hasibzunair/fifa-tryon-demo/u2net_human_seg_test.py +++ /dev/null @@ -1,117 +0,0 @@ -import os -from skimage import io, transform -import torch -import torchvision -from torch.autograd import Variable -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import Dataset, DataLoader -from torchvision import transforms#, utils -# import torch.optim as optim - -import numpy as np -from PIL import Image -import glob - -from data_loader import RescaleT -from data_loader import ToTensor -from data_loader import ToTensorLab -from data_loader import SalObjDataset - -from model import U2NET # full size version 173.6 MB - -# normalize the predicted SOD probability map -def normPRED(d): - ma = torch.max(d) - mi = torch.min(d) - - dn = (d-mi)/(ma-mi) - - return dn - -def save_output(image_name,pred,d_dir): - - predict = pred - predict = predict.squeeze() - predict_np = predict.cpu().data.numpy() - - im = Image.fromarray(predict_np*255).convert('RGB') - img_name = image_name.split(os.sep)[-1] - image = io.imread(image_name) - imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR) - - pb_np = np.array(imo) - - aaa = img_name.split(".") - bbb = aaa[0:-1] - imidx = bbb[0] - for i in range(1,len(bbb)): - imidx = imidx + "." + bbb[i] - - imo.save(d_dir+imidx+'.png') - -def main(): - - # --------- 1. get image path and name --------- - model_name='u2net' - - - image_dir = os.path.join(os.getcwd(), 'test_data', 'test_human_images') - prediction_dir = os.path.join(os.getcwd(), 'test_data', 'test_human_images' + '_results' + os.sep) - model_dir = os.path.join(os.getcwd(), 'saved_models', model_name+'_human_seg', model_name + '_human_seg.pth') - - img_name_list = glob.glob(image_dir + os.sep + '*') - print(img_name_list) - - # --------- 2. dataloader --------- - #1. dataloader - test_salobj_dataset = SalObjDataset(img_name_list = img_name_list, - lbl_name_list = [], - transform=transforms.Compose([RescaleT(320), - ToTensorLab(flag=0)]) - ) - test_salobj_dataloader = DataLoader(test_salobj_dataset, - batch_size=1, - shuffle=False, - num_workers=1) - - # --------- 3. model define --------- - if(model_name=='u2net'): - print("...load U2NET---173.6 MB") - net = U2NET(3,1) - - if torch.cuda.is_available(): - net.load_state_dict(torch.load(model_dir)) - net.cuda() - else: - net.load_state_dict(torch.load(model_dir, map_location='cpu')) - net.eval() - - # --------- 4. inference for each image --------- - for i_test, data_test in enumerate(test_salobj_dataloader): - - print("inferencing:",img_name_list[i_test].split(os.sep)[-1]) - - inputs_test = data_test['image'] - inputs_test = inputs_test.type(torch.FloatTensor) - - if torch.cuda.is_available(): - inputs_test = Variable(inputs_test.cuda()) - else: - inputs_test = Variable(inputs_test) - - d1,d2,d3,d4,d5,d6,d7= net(inputs_test) - - # normalization - pred = d1[:,0,:,:] - pred = normPRED(pred) - - # save results to test_results folder - if not os.path.exists(prediction_dir): - os.makedirs(prediction_dir, exist_ok=True) - save_output(img_name_list[i_test],pred,prediction_dir) - - del d1,d2,d3,d4,d5,d6,d7 - -if __name__ == "__main__": - main() diff --git a/spaces/hekbobo/bingo/src/components/ui/input.tsx b/spaces/hekbobo/bingo/src/components/ui/input.tsx deleted file mode 100644 index 684a857f3d769b78818fb13de1abaebfb09ca79c..0000000000000000000000000000000000000000 --- a/spaces/hekbobo/bingo/src/components/ui/input.tsx +++ /dev/null @@ -1,25 +0,0 @@ -import * as React from 'react' - -import { cn } from '@/lib/utils' - -export interface InputProps - extends React.InputHTMLAttributes {} - -const Input = React.forwardRef )', - secondary: 'rgb(var(--color-secondary) / )', - 'primary-background': 'rgb(var(--primary-background) / )', - 'primary-text': 'rgb(var(--primary-text) / )', - 'secondary-text': 'rgb(var(--secondary-text) / )', - 'light-text': 'rgb(var(--light-text) / )', - 'primary-border': 'rgb(var(--primary-border) / )', - }, - keyframes: { - slideDownAndFade: { - from: { opacity: 0, transform: 'translateY(-2px)' }, - to: { opacity: 1, transform: 'translateY(0)' }, - }, - slideLeftAndFade: { - from: { opacity: 0, transform: 'translateX(2px)' }, - to: { opacity: 1, transform: 'translateX(0)' }, - }, - slideUpAndFade: { - from: { opacity: 0, transform: 'translateY(2px)' }, - to: { opacity: 1, transform: 'translateY(0)' }, - }, - slideRightAndFade: { - from: { opacity: 0, transform: 'translateX(2px)' }, - to: { opacity: 1, transform: 'translateX(0)' }, - }, - }, - animation: { - slideDownAndFade: 'slideDownAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)', - slideLeftAndFade: 'slideLeftAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)', - slideUpAndFade: 'slideUpAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)', - slideRightAndFade: 'slideRightAndFade 400ms cubic-bezier(0.16, 1, 0.3, 1)', - }, - }, - }, - plugins: [require('@headlessui/tailwindcss'), require('tailwind-scrollbar')], -} diff --git a/spaces/huazhao/DeepDanbooru_string/app.py b/spaces/huazhao/DeepDanbooru_string/app.py deleted file mode 100644 index 49019837c9207cc68cb37be0342f3bc44fd0decb..0000000000000000000000000000000000000000 --- a/spaces/huazhao/DeepDanbooru_string/app.py +++ /dev/null @@ -1,185 +0,0 @@ -#!/usr/bin/env python - -from __future__ import annotations - -import argparse -import functools -import os -import html -import pathlib -import tarfile - -import deepdanbooru as dd -import gradio as gr -import huggingface_hub -import numpy as np -import PIL.Image -import tensorflow as tf -import piexif -import piexif.helper - -TITLE = 'DeepDanbooru String' - -TOKEN = os.environ['TOKEN'] -MODEL_REPO = 'CikeyQI/DeepDanbooru_string' -MODEL_FILENAME = 'model-resnet_custom_v3.h5' -LABEL_FILENAME = 'tags.txt' - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument('--score-slider-step', type=float, default=0.05) - parser.add_argument('--score-threshold', type=float, default=0.5) - parser.add_argument('--theme', type=str, default='dark-grass') - parser.add_argument('--live', action='store_true') - parser.add_argument('--share', action='store_true') - parser.add_argument('--port', type=int) - parser.add_argument('--disable-queue', - dest='enable_queue', - action='store_false') - parser.add_argument('--allow-flagging', type=str, default='never') - return parser.parse_args() - - -def load_sample_image_paths() -> list[pathlib.Path]: - image_dir = pathlib.Path('images') - if not image_dir.exists(): - dataset_repo = 'hysts/sample-images-TADNE' - path = huggingface_hub.hf_hub_download(dataset_repo, - 'images.tar.gz', - repo_type='dataset', - use_auth_token=TOKEN) - with tarfile.open(path) as f: - f.extractall() - return sorted(image_dir.glob('*')) - - -def load_model() -> tf.keras.Model: - path = huggingface_hub.hf_hub_download(MODEL_REPO, - MODEL_FILENAME, - use_auth_token=TOKEN) - model = tf.keras.models.load_model(path) - return model - - -def load_labels() -> list[str]: - path = huggingface_hub.hf_hub_download(MODEL_REPO, - LABEL_FILENAME, - use_auth_token=TOKEN) - with open(path) as f: - labels = [line.strip() for line in f.readlines()] - return labels - -def plaintext_to_html(text): - text = " " + "
" - return text - -def predict(image: PIL.Image.Image, score_threshold: float, - model: tf.keras.Model, labels: list[str]) -> dict[str, float]: - rawimage = image - _, height, width, _ = model.input_shape - image = np.asarray(image) - image = tf.image.resize(image, - size=(height, width), - method=tf.image.ResizeMethod.AREA, - preserve_aspect_ratio=True) - image = image.numpy() - image = dd.image.transform_and_pad_image(image, width, height) - image = image / 255. - probs = model.predict(image[None, ...])[0] - probs = probs.astype(float) - res = dict() - for prob, label in zip(probs.tolist(), labels): - if prob < score_threshold: - continue - res[label] = prob - b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True)) - a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)') - c = ', '.join(list(b.keys())) - - items = rawimage.info - geninfo = '' - - if "exif" in rawimage.info: - exif = piexif.load(rawimage.info["exif"]) - exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') - try: - exif_comment = piexif.helper.UserComment.load(exif_comment) - except ValueError: - exif_comment = exif_comment.decode('utf8', errors="ignore") - - items['exif comment'] = exif_comment - geninfo = exif_comment - - for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', - 'loop', 'background', 'timestamp', 'duration']: - items.pop(field, None) - - geninfo = items.get('parameters', geninfo) - - info = f""" -
\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "PNG Info
-""" - for key, text in items.items(): - info += f""" ---""".strip()+"\n" - - if len(info) == 0: - message = "Nothing found in the image." - info = f"{plaintext_to_html(str(key))}
-{plaintext_to_html(str(text))}
-" - - return (a,c,res,info) - - -def main(): - args = parse_args() - model = load_model() - labels = load_labels() - - func = functools.partial(predict, model=model, labels=labels) - func = functools.update_wrapper(func, predict) - - gr.Interface( - func, - [ - gr.inputs.Image(type='pil', label='Input'), - gr.inputs.Slider(0, - 1, - step=args.score_slider_step, - default=args.score_threshold, - label='Score Threshold'), - ], - [ - gr.outputs.Textbox(label='Output (string)'), - gr.outputs.Textbox(label='Output (raw string)'), - gr.outputs.Label(label='Output (label)'), - gr.outputs.HTML() - ], - examples=[ - ['miku.jpg',0.5], - ['miku2.jpg',0.5] - ], - title=TITLE, - description=''' -Demo for [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) with "ready to copy" prompt and a prompt analyzer. - -Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru) - -PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) - ''', - theme=args.theme, - allow_flagging=args.allow_flagging, - live=args.live, - ).launch( - enable_queue=args.enable_queue, - server_port=args.port, - share=args.share, - ) - - -if __name__ == '__main__': - main() diff --git a/spaces/hysts/BLIP-Diffusion/utils.py b/spaces/hysts/BLIP-Diffusion/utils.py deleted file mode 100644 index d52f35753c27096932e2a4f899154e40f7c2d5c5..0000000000000000000000000000000000000000 --- a/spaces/hysts/BLIP-Diffusion/utils.py +++ /dev/null @@ -1,11 +0,0 @@ -import random - -import numpy as np - -MAX_SEED = np.iinfo(np.int32).max - - -def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: - if randomize_seed: - seed = random.randint(0, MAX_SEED) - return seed diff --git a/spaces/hyxue/HiFiFace-inference-demo/models/model_blocks.py b/spaces/hyxue/HiFiFace-inference-demo/models/model_blocks.py deleted file mode 100644 index 533307f91dd4368029d69106b8860ceeca8f4d5e..0000000000000000000000000000000000000000 --- a/spaces/hyxue/HiFiFace-inference-demo/models/model_blocks.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F - - -class ResBlock(nn.Module): - def __init__(self, in_channel, out_channel, down_sample=False, up_sample=False, norm=True): - super(ResBlock, self).__init__() - - main_module_list = [] - if norm: - main_module_list += [ - nn.InstanceNorm2d(in_channel), - nn.LeakyReLU(0.2, inplace=True), - nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=1, padding=1), - ] - else: - main_module_list += [ - nn.LeakyReLU(0.2, inplace=True), - nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=1, padding=1), - ] - if down_sample: - main_module_list.append(nn.AvgPool2d(kernel_size=2)) - elif up_sample: - main_module_list.append(nn.Upsample(scale_factor=2, mode="bilinear")) - if norm: - main_module_list += [ - nn.InstanceNorm2d(out_channel), - nn.LeakyReLU(0.2, inplace=True), - nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1), - ] - else: - main_module_list += [ - nn.LeakyReLU(0.2, inplace=True), - nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1), - ] - self.main_path = nn.Sequential(*main_module_list) - - side_module_list = [nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0)] - if down_sample: - side_module_list.append(nn.AvgPool2d(kernel_size=2)) - elif up_sample: - side_module_list.append(nn.Upsample(scale_factor=2, mode="bilinear")) - self.side_path = nn.Sequential(*side_module_list) - - def forward(self, x): - x1 = self.main_path(x) - x2 = self.side_path(x) - return x1 + x2 - - -class AdaIn(nn.Module): - def __init__(self, in_channel, vector_size): - super(AdaIn, self).__init__() - self.eps = 1e-5 - self.std_style_fc = nn.Linear(vector_size, in_channel) - self.mean_style_fc = nn.Linear(vector_size, in_channel) - - def forward(self, x, style_vector): - std_style = self.std_style_fc(style_vector) - mean_style = self.mean_style_fc(style_vector) - - std_style = std_style.unsqueeze(-1).unsqueeze(-1) - mean_style = mean_style.unsqueeze(-1).unsqueeze(-1) - - x = F.instance_norm(x) - x = std_style * x + mean_style - return x - - -class AdaInResBlock(nn.Module): - def __init__(self, in_channel, out_channel, up_sample=False): - super(AdaInResBlock, self).__init__() - self.vector_size = 257 + 512 - self.up_sample = up_sample - - self.adain1 = AdaIn(in_channel, self.vector_size) - self.adain2 = AdaIn(out_channel, self.vector_size) - - main_module_list = [] - main_module_list += [ - nn.LeakyReLU(0.2, inplace=True), - nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=1, padding=1), - ] - if up_sample: - main_module_list.append(nn.Upsample(scale_factor=2, mode="bilinear")) - self.main_path1 = nn.Sequential(*main_module_list) - - self.main_path2 = nn.Sequential( - nn.LeakyReLU(0.2, inplace=True), - nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=1, padding=1), - ) - - side_module_list = [nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0)] - if up_sample: - side_module_list.append(nn.Upsample(scale_factor=2, mode="bilinear")) - self.side_path = nn.Sequential(*side_module_list) - - def forward(self, x, id_vector): - x1 = self.adain1(x, id_vector) - x1 = self.main_path1(x1) - x2 = self.side_path(x) - - x1 = self.adain2(x1, id_vector) - x1 = self.main_path2(x1) - - return x1 + x2 - - -class UpSamplingBlock(nn.Module): - def __init__( - self, - ): - super(UpSamplingBlock, self).__init__() - self.net = nn.Sequential(ResBlock(256, 256, up_sample=True), ResBlock(256, 256, up_sample=True)) - self.i_r_net = nn.Sequential(nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 3, 3, 1, 1)) - self.m_r_net = nn.Sequential(nn.Conv2d(256, 1, 3, 1, 1), nn.Sigmoid()) - - def forward(self, x): - x = self.net(x) - i_r = self.i_r_net(x) - m_r = self.m_r_net(x) - return i_r, m_r diff --git a/spaces/hzy123/bingo/src/components/external-link.tsx b/spaces/hzy123/bingo/src/components/external-link.tsx deleted file mode 100644 index 011265f364d5a64a770f4c7e9c65c5ade21d623a..0000000000000000000000000000000000000000 --- a/spaces/hzy123/bingo/src/components/external-link.tsx +++ /dev/null @@ -1,30 +0,0 @@ -export function ExternalLink({ - href, - children -}: { - href: string - children: React.ReactNode -}) { - return ( - - {children} - - - ) -} diff --git a/spaces/inamXcontru/PoeticTTS/Being.Riley..Greg.Lansky..Tushy..Riley.Reid.md b/spaces/inamXcontru/PoeticTTS/Being.Riley..Greg.Lansky..Tushy..Riley.Reid.md deleted file mode 100644 index 092126eb0a75c6bbde9933567c426570dbf95b22..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/Being.Riley..Greg.Lansky..Tushy..Riley.Reid.md +++ /dev/null @@ -1,6 +0,0 @@ -{message}
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-How to Install Autodesk Inventor 2014 Professional on Windows 7 or Windows 8 64-bit
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-If you want to install Autodesk Inventor 2014 Professional on your Windows 7 or Windows 8 64-bit computer, you need to follow these steps:
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- Download the setup files from the Autodesk website or use the installation media provided by your reseller. You can choose between a 32-bit or a 64-bit version, depending on your system requirements. For complex models and large assemblies, it is recommended to use the 64-bit version. -
- Save the setup files in a folder on your computer. If you downloaded the 64-bit version, you will have two parts that need to be extracted together. -
- Double-click on the setup.exe file to start the installation process. You will see a welcome screen with some options. You can choose to install now, download now, or browser download. The install now option will install the software directly from the internet, while the other two options will let you download the software first and then install it later. -
- Choose the install now option and click on next. You will be asked to sign in with your Autodesk account or create one if you don't have one already. This is required for activating and registering your software. -
- After signing in, you will see a list of products and components that are available for installation. You can select or deselect the ones that you want or don't want. Make sure that Autodesk Inventor 2014 Professional and the content libraries are selected. -
- Click on next and review the system requirements and license agreement. If everything is OK, click on install to begin the installation. -
- The installation may take some time depending on your internet speed and system performance. You can monitor the progress and status of the installation on the screen. -
- When the installation is complete, you will see a confirmation message. You can click on finish to exit the installer. -
- You can now launch Autodesk Inventor 2014 Professional from your desktop or start menu. You may need to activate your software online or offline using your serial number and product key. -
Congratulations! You have successfully installed Autodesk Inventor 2014 Professional on your Windows 7 or Windows 8 64-bit computer. You can now start creating amazing mechanical designs and simulations with this powerful software.
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-Autodesk Inventor 2014 Professional is a comprehensive software that covers all aspects of mechanical design and simulation. You can use it to create parts, assemblies, drawings, presentations, animations, and more. You can also perform various types of analysis, such as stress, motion, thermal, and fluid dynamics.
- -To use Autodesk Inventor 2014 Professional, you need to follow these basic steps:
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- Start a new project or open an existing one. A project is a collection of files and settings that define your design. You can create multiple projects for different purposes and switch between them easily. -
- Create a new file or open an existing one. A file is a document that contains your design data. You can create different types of files, such as part files (.ipt), assembly files (.iam), drawing files (.idw), and presentation files (.ipn). -
- Use the tools and commands in the user interface to create and modify your design. The user interface consists of various elements, such as the ribbon, the browser, the graphics window, the navigation bar, the status bar, and the application menu. You can customize the user interface to suit your preferences and workflow. -
- Save and close your file when you are done. You can also export your file to other formats, such as DWG, PDF, STEP, IGES, STL, and more. -
These are the basic steps for using Autodesk Inventor 2014 Professional. Of course, there are many more features and functions that you can explore and learn as you progress with your design projects.
- -How to Update Autodesk Inventor 2014 Professional
-Autodesk Inventor 2014 Professional is a software that is constantly updated and improved by Autodesk. You can update your software to get the latest enhancements, fixes, and security patches. You can also get access to new features and functionalities that are added in the updates.
-To update Autodesk Inventor 2014 Professional, you need to follow these steps:
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- Check if there are any updates available for your software. You can do this by clicking on the application menu in the upper left corner of the user interface and selecting Check for Updates. You can also visit the Autodesk website or use the Autodesk Desktop App to check for updates. -
- Download and install the updates that are applicable to your software. You may need to close your software and restart your computer during the installation process. -
- Launch your software and verify that the updates have been applied successfully. You can do this by clicking on the application menu and selecting About Autodesk Inventor Professional. You will see the version number and build date of your software. -
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- -Colasoft Capsa 7 is a powerful network analyzer that can help you monitor, troubleshoot and optimize your network performance. It can capture and analyze packets in real time, provide various statistics and reports, and support over 300 network protocols. To use Colasoft Capsa 7, you need to activate it with a serial keygen.
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These are some of the main functions and features of Colasoft Capsa 7 that can help you use it for network analysis. You can learn more about how to use Colasoft Capsa 7 by reading the user manual or watching the video tutorials on the Colasoft website.
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- Go to the Colasoft website and choose the edition of Colasoft Capsa 7 that suits your needs. You can choose from Enterprise, Professional, Standard or Free edition. Each edition has different features and prices. -
- Click the Download button to download the installation file of Colasoft Capsa 7. The file size is about 80 MB. -
- Run the installation file and follow the instructions to install Colasoft Capsa 7 on your computer. The installation process will take a few minutes. -
- After the installation is completed, launch Colasoft Capsa 7 from your desktop or start menu. -
- Enter your serial number or license file to activate Colasoft Capsa 7. You can obtain your serial number or license file from Colasoft webpage or Colasoft support after purchasing a license. -
These are the steps to download and install Colasoft Capsa 7 on your computer. You can now start using Colasoft Capsa 7 for network analysis.
-How to Find Colasoft Capsa 7 Serial Keygen Online
- -If you are looking for a serial keygen for Colasoft Capsa 7, you may be tempted to search for it online. There are many websites that claim to offer free or cracked serial keygens for various software products, including Colasoft Capsa 7. However, you should be aware of the risks and consequences of downloading and using such serial keygens.
- -First of all, downloading and using a serial keygen for Colasoft Capsa 7 is illegal and unethical. You are violating the intellectual property rights of Colasoft and its partners, and you may face legal actions or penalties. You are also depriving Colasoft of its rightful revenue and support, which may affect its ability to develop and improve its products and services.
- -Secondly, downloading and using a serial keygen for Colasoft Capsa 7 is unsafe and unreliable. You may expose your computer and network to malware, viruses, spyware, ransomware, or other malicious programs that may harm your system or data. You may also compromise your network security and privacy by allowing unauthorized access or leakage of your sensitive information. You may also experience errors, crashes, or compatibility issues with Colasoft Capsa 7 or other software products.
- -Thirdly, downloading and using a serial keygen for Colasoft Capsa 7 is ineffective and inefficient. You may not be able to activate Colasoft Capsa 7 successfully or permanently with a serial keygen, as Colasoft may detect and block it. You may also miss out on the latest updates, patches, features, or bug fixes that Colasoft provides for its licensed users. You may also not be able to access the technical support or customer service that Colasoft offers for its products.
- -Therefore, we strongly advise you not to download or use a serial keygen for Colasoft Capsa 7 online. Instead, you should purchase a legitimate license from Colasoft or its authorized resellers. You can choose from different editions of Colasoft Capsa 7, such as Enterprise, Professional, Standard or Free. Each edition has different features and prices. You can compare the editions and find the best one for your needs on the Colasoft website.
- -How to Get a Free Trial of Colasoft Capsa 7
- -If you are interested in trying out Colasoft Capsa 7 before purchasing a license, you can get a free trial of it from the Colasoft website. The free trial allows you to use all the features and functions of Colasoft Capsa 7 Enterprise edition for 15 days without any limitations or restrictions. Here are the steps to get a free trial of Colasoft Capsa 7:
- --
-
- Go to the Colasoft website and click the Free Trial button on the top right corner. -
- Fill in your name and email address and click Submit. -
- Check your email inbox and find the email from Colasoft with the download link and the trial serial number. -
- Click the download link and save the installation file of Colasoft Capsa 7 on your computer. -
- Run the installation file and follow the instructions to install Colasoft Capsa 7 on your computer. -
- Launch Colasoft Capsa 7 from your desktop or start menu. -
- Enter your trial serial number and click Next to activate your free trial. -
These are the steps to get a free trial of Colasoft Capsa 7 on your computer. You can now start using Colasoft Capsa 7 for network analysis for 15 days without any limitations or restrictions.
-Conclusion
- -Colasoft Capsa 7 is a powerful network analyzer that can help you monitor, troubleshoot and optimize your network performance and security. It can capture and analyze packets in real time, provide various statistics and reports, and support over 300 network protocols and applications. To use Colasoft Capsa 7, you need to activate it with a serial keygen.
- -A serial keygen is a program that can generate valid serial numbers for software products. You can use a serial keygen to activate Colasoft Capsa 7 without paying for a license. However, this is illegal and unethical, and may cause security risks for your computer and network. Therefore, we do not recommend using a serial keygen to activate Colasoft Capsa 7.
- -Instead, you should purchase a legitimate license from Colasoft or its authorized resellers. You can choose from different editions of Colasoft Capsa 7, such as Enterprise, Professional, Standard or Free. Each edition has different features and prices. You can compare the editions and find the best one for your needs on the Colasoft website.
- -Once you purchase a license, you will receive a serial number that you can use to activate Colasoft Capsa 7 online or offline. To activate online, you just need to enter the serial number and click Next. To activate offline, you need to generate a license file with the serial number and the machine code of your computer. You can obtain the license file from Colasoft webpage or Colasoft support. Then you need to import the license file and click Next.
- -After activating Colasoft Capsa 7, you can enjoy its full features and benefits for your network analysis tasks. You can also get a free trial of Colasoft Capsa 7 Enterprise edition for 15 days from the Colasoft website. If you are interested in trying out Colasoft Capsa 7 before purchasing a license, you can follow the steps above to get a free trial.
- -We hope this article has helped you understand how to activate and use Colasoft Capsa 7 for network analysis. If you have any questions or feedback, please feel free to contact us or leave a comment below. Thank you for reading!
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\ No newline at end of file diff --git a/spaces/insaafS/AI-Story-Gen/README.md b/spaces/insaafS/AI-Story-Gen/README.md deleted file mode 100644 index a0cb03203afb9e0f1bfc713afe9fc0761bb10806..0000000000000000000000000000000000000000 --- a/spaces/insaafS/AI-Story-Gen/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: AI Story Gen -emoji: 🐢 -colorFrom: green -colorTo: red -sdk: gradio -sdk_version: 3.40.1 -app_file: app.py -pinned: false -license: gpl ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ivotai/VITS-Umamusume-voice-synthesizer/text/korean.py b/spaces/ivotai/VITS-Umamusume-voice-synthesizer/text/korean.py deleted file mode 100644 index edee07429a450c55e3d8e246997faaa1e0b89cc9..0000000000000000000000000000000000000000 --- a/spaces/ivotai/VITS-Umamusume-voice-synthesizer/text/korean.py +++ /dev/null @@ -1,210 +0,0 @@ -import re -from jamo import h2j, j2hcj -import ko_pron - - -# This is a list of Korean classifiers preceded by pure Korean numerals. -_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통' - -# List of (hangul, hangul divided) pairs: -_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ㄳ', 'ㄱㅅ'), - ('ㄵ', 'ㄴㅈ'), - ('ㄶ', 'ㄴㅎ'), - ('ㄺ', 'ㄹㄱ'), - ('ㄻ', 'ㄹㅁ'), - ('ㄼ', 'ㄹㅂ'), - ('ㄽ', 'ㄹㅅ'), - ('ㄾ', 'ㄹㅌ'), - ('ㄿ', 'ㄹㅍ'), - ('ㅀ', 'ㄹㅎ'), - ('ㅄ', 'ㅂㅅ'), - ('ㅘ', 'ㅗㅏ'), - ('ㅙ', 'ㅗㅐ'), - ('ㅚ', 'ㅗㅣ'), - ('ㅝ', 'ㅜㅓ'), - ('ㅞ', 'ㅜㅔ'), - ('ㅟ', 'ㅜㅣ'), - ('ㅢ', 'ㅡㅣ'), - ('ㅑ', 'ㅣㅏ'), - ('ㅒ', 'ㅣㅐ'), - ('ㅕ', 'ㅣㅓ'), - ('ㅖ', 'ㅣㅔ'), - ('ㅛ', 'ㅣㅗ'), - ('ㅠ', 'ㅣㅜ') -]] - -# List of (Latin alphabet, hangul) pairs: -_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('a', '에이'), - ('b', '비'), - ('c', '시'), - ('d', '디'), - ('e', '이'), - ('f', '에프'), - ('g', '지'), - ('h', '에이치'), - ('i', '아이'), - ('j', '제이'), - ('k', '케이'), - ('l', '엘'), - ('m', '엠'), - ('n', '엔'), - ('o', '오'), - ('p', '피'), - ('q', '큐'), - ('r', '아르'), - ('s', '에스'), - ('t', '티'), - ('u', '유'), - ('v', '브이'), - ('w', '더블유'), - ('x', '엑스'), - ('y', '와이'), - ('z', '제트') -]] - -# List of (ipa, lazy ipa) pairs: -_ipa_to_lazy_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('t͡ɕ','ʧ'), - ('d͡ʑ','ʥ'), - ('ɲ','n^'), - ('ɕ','ʃ'), - ('ʷ','w'), - ('ɭ','l`'), - ('ʎ','ɾ'), - ('ɣ','ŋ'), - ('ɰ','ɯ'), - ('ʝ','j'), - ('ʌ','ə'), - ('ɡ','g'), - ('\u031a','#'), - ('\u0348','='), - ('\u031e',''), - ('\u0320',''), - ('\u0339','') -]] - - -def latin_to_hangul(text): - for regex, replacement in _latin_to_hangul: - text = re.sub(regex, replacement, text) - return text - - -def divide_hangul(text): - text = j2hcj(h2j(text)) - for regex, replacement in _hangul_divided: - text = re.sub(regex, replacement, text) - return text - - -def hangul_number(num, sino=True): - '''Reference https://github.com/Kyubyong/g2pK''' - num = re.sub(',', '', num) - - if num == '0': - return '영' - if not sino and num == '20': - return '스무' - - digits = '123456789' - names = '일이삼사오육칠팔구' - digit2name = {d: n for d, n in zip(digits, names)} - - modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉' - decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔' - digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())} - digit2dec = {d: dec for d, dec in zip(digits, decimals.split())} - - spelledout = [] - for i, digit in enumerate(num): - i = len(num) - i - 1 - if sino: - if i == 0: - name = digit2name.get(digit, '') - elif i == 1: - name = digit2name.get(digit, '') + '십' - name = name.replace('일십', '십') - else: - if i == 0: - name = digit2mod.get(digit, '') - elif i == 1: - name = digit2dec.get(digit, '') - if digit == '0': - if i % 4 == 0: - last_three = spelledout[-min(3, len(spelledout)):] - if ''.join(last_three) == '': - spelledout.append('') - continue - else: - spelledout.append('') - continue - if i == 2: - name = digit2name.get(digit, '') + '백' - name = name.replace('일백', '백') - elif i == 3: - name = digit2name.get(digit, '') + '천' - name = name.replace('일천', '천') - elif i == 4: - name = digit2name.get(digit, '') + '만' - name = name.replace('일만', '만') - elif i == 5: - name = digit2name.get(digit, '') + '십' - name = name.replace('일십', '십') - elif i == 6: - name = digit2name.get(digit, '') + '백' - name = name.replace('일백', '백') - elif i == 7: - name = digit2name.get(digit, '') + '천' - name = name.replace('일천', '천') - elif i == 8: - name = digit2name.get(digit, '') + '억' - elif i == 9: - name = digit2name.get(digit, '') + '십' - elif i == 10: - name = digit2name.get(digit, '') + '백' - elif i == 11: - name = digit2name.get(digit, '') + '천' - elif i == 12: - name = digit2name.get(digit, '') + '조' - elif i == 13: - name = digit2name.get(digit, '') + '십' - elif i == 14: - name = digit2name.get(digit, '') + '백' - elif i == 15: - name = digit2name.get(digit, '') + '천' - spelledout.append(name) - return ''.join(elem for elem in spelledout) - - -def number_to_hangul(text): - '''Reference https://github.com/Kyubyong/g2pK''' - tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text)) - for token in tokens: - num, classifier = token - if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers: - spelledout = hangul_number(num, sino=False) - else: - spelledout = hangul_number(num, sino=True) - text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}') - # digit by digit for remaining digits - digits = '0123456789' - names = '영일이삼사오육칠팔구' - for d, n in zip(digits, names): - text = text.replace(d, n) - return text - - -def korean_to_lazy_ipa(text): - text = latin_to_hangul(text) - text = number_to_hangul(text) - text=re.sub('[\uac00-\ud7af]+',lambda x:ko_pron.romanise(x.group(0),'ipa').split('] ~ [')[0],text) - for regex, replacement in _ipa_to_lazy_ipa: - text = re.sub(regex, replacement, text) - return text - - -def korean_to_ipa(text): - text = korean_to_lazy_ipa(text) - return text.replace('ʧ','tʃ').replace('ʥ','dʑ') diff --git a/spaces/ivotai/VITS-Umamusume-voice-synthesizer/text/symbols.py b/spaces/ivotai/VITS-Umamusume-voice-synthesizer/text/symbols.py deleted file mode 100644 index 053a7105f7ce95aa51614f6995399fa2172b3eb2..0000000000000000000000000000000000000000 --- a/spaces/ivotai/VITS-Umamusume-voice-synthesizer/text/symbols.py +++ /dev/null @@ -1,76 +0,0 @@ -''' -Defines the set of symbols used in text input to the model. -''' - -# japanese_cleaners -_pad = '_' -_punctuation = ',.!?-' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ ' - - -'''# japanese_cleaners2 -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ ' -''' - - -'''# korean_cleaners -_pad = '_' -_punctuation = ',.!?…~' -_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ ' -''' - -'''# chinese_cleaners -_pad = '_' -_punctuation = '，。！？—…' -_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ ' -''' - -'''# zh_ja_mixture_cleaners -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ ' -''' - -'''# sanskrit_cleaners -_pad = '_' -_punctuation = '।' -_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ ' -''' - -'''# cjks_cleaners -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ ' -''' - -'''# thai_cleaners -_pad = '_' -_punctuation = '.!? ' -_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์' -''' - -'''# cjke_cleaners2 -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ ' -''' - -'''# shanghainese_cleaners -_pad = '_' -_punctuation = ',.!?…' -_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 ' -''' - -'''# chinese_dialect_cleaners -_pad = '_' -_punctuation = ',.!?~…─' -_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ ' -''' - -# Export all symbols: -symbols = [_pad] + list(_punctuation) + list(_letters) - -# Special symbol ids -SPACE_ID = symbols.index(" ") diff --git a/spaces/jbrinkma/deepmind-pushworld/index.html b/spaces/jbrinkma/deepmind-pushworld/index.html deleted file mode 100644 index e9eeed81cbafd90f3c5417d3c258c89ecd44d09b..0000000000000000000000000000000000000000 --- a/spaces/jbrinkma/deepmind-pushworld/index.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - -PushWorld - - - - - - - - - - - - - -----
- - -- --- -- - - - - - diff --git a/spaces/jeang/ernie_demo_toy/ernie/aggregation_strategies.py b/spaces/jeang/ernie_demo_toy/ernie/aggregation_strategies.py deleted file mode 100644 index d6bb5786adec08dea396e7f57f51aca9224165e3..0000000000000000000000000000000000000000 --- a/spaces/jeang/ernie_demo_toy/ernie/aggregation_strategies.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -from statistics import mean - - -class AggregationStrategy: - def __init__( - self, - method, - max_items=None, - top_items=True, - sorting_class_index=1 - ): - self.method = method - self.max_items = max_items - self.top_items = top_items - self.sorting_class_index = sorting_class_index - - def aggregate(self, softmax_tuples): - softmax_dicts = [] - for softmax_tuple in softmax_tuples: - softmax_dict = {} - for i, probability in enumerate(softmax_tuple): - softmax_dict[i] = probability - softmax_dicts.append(softmax_dict) - - if self.max_items is not None: - softmax_dicts = sorted( - softmax_dicts, - key=lambda x: x[self.sorting_class_index], - reverse=self.top_items - ) - if self.max_items < len(softmax_dicts): - softmax_dicts = softmax_dicts[:self.max_items] - - softmax_list = [] - for key in softmax_dicts[0].keys(): - softmax_list.append(self.method( - [probabilities[key] for probabilities in softmax_dicts])) - softmax_tuple = tuple(softmax_list) - return softmax_tuple - - -class AggregationStrategies: - Mean = AggregationStrategy(method=mean) - MeanTopFiveBinaryClassification = AggregationStrategy( - method=mean, - max_items=5, - top_items=True, - sorting_class_index=1 - ) - MeanTopTenBinaryClassification = AggregationStrategy( - method=mean, - max_items=10, - top_items=True, - sorting_class_index=1 - ) - MeanTopFifteenBinaryClassification = AggregationStrategy( - method=mean, - max_items=15, - top_items=True, - sorting_class_index=1 - ) - MeanTopTwentyBinaryClassification = AggregationStrategy( - method=mean, - max_items=20, - top_items=True, - sorting_class_index=1 - ) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/middleware/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/middleware/__init__.py deleted file mode 100644 index 620296d5ad6ca2cc49eb5d0dc140bcbc3204e9b4..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fastapi/middleware/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from starlette.middleware import Middleware as Middleware diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/feaLib/builder.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/feaLib/builder.py deleted file mode 100644 index 42d1f8f24a720a8cbbaf3f7b7344eb4773ca0f4d..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/feaLib/builder.py +++ /dev/null @@ -1,1706 +0,0 @@ -from fontTools.misc import sstruct -from fontTools.misc.textTools import Tag, tostr, binary2num, safeEval -from fontTools.feaLib.error import FeatureLibError -from fontTools.feaLib.lookupDebugInfo import ( - LookupDebugInfo, - LOOKUP_DEBUG_INFO_KEY, - LOOKUP_DEBUG_ENV_VAR, -) -from fontTools.feaLib.parser import Parser -from fontTools.feaLib.ast import FeatureFile -from fontTools.feaLib.variableScalar import VariableScalar -from fontTools.otlLib import builder as otl -from fontTools.otlLib.maxContextCalc import maxCtxFont -from fontTools.ttLib import newTable, getTableModule -from fontTools.ttLib.tables import otBase, otTables -from fontTools.otlLib.builder import ( - AlternateSubstBuilder, - ChainContextPosBuilder, - ChainContextSubstBuilder, - LigatureSubstBuilder, - MultipleSubstBuilder, - CursivePosBuilder, - MarkBasePosBuilder, - MarkLigPosBuilder, - MarkMarkPosBuilder, - ReverseChainSingleSubstBuilder, - SingleSubstBuilder, - ClassPairPosSubtableBuilder, - PairPosBuilder, - SinglePosBuilder, - ChainContextualRule, -) -from fontTools.otlLib.error import OpenTypeLibError -from fontTools.varLib.varStore import OnlineVarStoreBuilder -from fontTools.varLib.builder import buildVarDevTable -from fontTools.varLib.featureVars import addFeatureVariationsRaw -from fontTools.varLib.models import normalizeValue, piecewiseLinearMap -from collections import defaultdict -import itertools -from io import StringIO -import logging -import warnings -import os - - -log = logging.getLogger(__name__) - - -def addOpenTypeFeatures(font, featurefile, tables=None, debug=False): - """Add features from a file to a font. Note that this replaces any features - currently present. - - Args: - font (feaLib.ttLib.TTFont): The font object. - featurefile: Either a path or file object (in which case we - parse it into an AST), or a pre-parsed AST instance. - tables: If passed, restrict the set of affected tables to those in the - list. - debug: Whether to add source debugging information to the font in the - ``Debg`` table - - """ - builder = Builder(font, featurefile) - builder.build(tables=tables, debug=debug) - - -def addOpenTypeFeaturesFromString( - font, features, filename=None, tables=None, debug=False -): - """Add features from a string to a font. Note that this replaces any - features currently present. - - Args: - font (feaLib.ttLib.TTFont): The font object. - features: A string containing feature code. - filename: The directory containing ``filename`` is used as the root of - relative ``include()`` paths; if ``None`` is provided, the current - directory is assumed. - tables: If passed, restrict the set of affected tables to those in the - list. - debug: Whether to add source debugging information to the font in the - ``Debg`` table - - """ - - featurefile = StringIO(tostr(features)) - if filename: - featurefile.name = filename - addOpenTypeFeatures(font, featurefile, tables=tables, debug=debug) - - -class Builder(object): - supportedTables = frozenset( - Tag(tag) - for tag in [ - "BASE", - "GDEF", - "GPOS", - "GSUB", - "OS/2", - "head", - "hhea", - "name", - "vhea", - "STAT", - ] - ) - - def __init__(self, font, featurefile): - self.font = font - # 'featurefile' can be either a path or file object (in which case we - # parse it into an AST), or a pre-parsed AST instance - if isinstance(featurefile, FeatureFile): - self.parseTree, self.file = featurefile, None - else: - self.parseTree, self.file = None, featurefile - self.glyphMap = font.getReverseGlyphMap() - self.varstorebuilder = None - if "fvar" in font: - self.axes = font["fvar"].axes - self.varstorebuilder = OnlineVarStoreBuilder( - [ax.axisTag for ax in self.axes] - ) - self.default_language_systems_ = set() - self.script_ = None - self.lookupflag_ = 0 - self.lookupflag_markFilterSet_ = None - self.language_systems = set() - self.seen_non_DFLT_script_ = False - self.named_lookups_ = {} - self.cur_lookup_ = None - self.cur_lookup_name_ = None - self.cur_feature_name_ = None - self.lookups_ = [] - self.lookup_locations = {"GSUB": {}, "GPOS": {}} - self.features_ = {} # ('latn', 'DEU ', 'smcp') --> [LookupBuilder*] - self.required_features_ = {} # ('latn', 'DEU ') --> 'scmp' - self.feature_variations_ = {} - # for feature 'aalt' - self.aalt_features_ = [] # [(location, featureName)*], for 'aalt' - self.aalt_location_ = None - self.aalt_alternates_ = {} - # for 'featureNames' - self.featureNames_ = set() - self.featureNames_ids_ = {} - # for 'cvParameters' - self.cv_parameters_ = set() - self.cv_parameters_ids_ = {} - self.cv_num_named_params_ = {} - self.cv_characters_ = defaultdict(list) - # for feature 'size' - self.size_parameters_ = None - # for table 'head' - self.fontRevision_ = None # 2.71 - # for table 'name' - self.names_ = [] - # for table 'BASE' - self.base_horiz_axis_ = None - self.base_vert_axis_ = None - # for table 'GDEF' - self.attachPoints_ = {} # "a" --> {3, 7} - self.ligCaretCoords_ = {} # "f_f_i" --> {300, 600} - self.ligCaretPoints_ = {} # "f_f_i" --> {3, 7} - self.glyphClassDefs_ = {} # "fi" --> (2, (file, line, column)) - self.markAttach_ = {} # "acute" --> (4, (file, line, column)) - self.markAttachClassID_ = {} # frozenset({"acute", "grave"}) --> 4 - self.markFilterSets_ = {} # frozenset({"acute", "grave"}) --> 4 - # for table 'OS/2' - self.os2_ = {} - # for table 'hhea' - self.hhea_ = {} - # for table 'vhea' - self.vhea_ = {} - # for table 'STAT' - self.stat_ = {} - # for conditionsets - self.conditionsets_ = {} - # We will often use exactly the same locations (i.e. the font's masters) - # for a large number of variable scalars. Instead of creating a model - # for each, let's share the models. - self.model_cache = {} - - def build(self, tables=None, debug=False): - if self.parseTree is None: - self.parseTree = Parser(self.file, self.glyphMap).parse() - self.parseTree.build(self) - # by default, build all the supported tables - if tables is None: - tables = self.supportedTables - else: - tables = frozenset(tables) - unsupported = tables - self.supportedTables - if unsupported: - unsupported_string = ", ".join(sorted(unsupported)) - raise NotImplementedError( - "The following tables were requested but are unsupported: " - f"{unsupported_string}." - ) - if "GSUB" in tables: - self.build_feature_aalt_() - if "head" in tables: - self.build_head() - if "hhea" in tables: - self.build_hhea() - if "vhea" in tables: - self.build_vhea() - if "name" in tables: - self.build_name() - if "OS/2" in tables: - self.build_OS_2() - if "STAT" in tables: - self.build_STAT() - for tag in ("GPOS", "GSUB"): - if tag not in tables: - continue - table = self.makeTable(tag) - if self.feature_variations_: - self.makeFeatureVariations(table, tag) - if ( - table.ScriptList.ScriptCount > 0 - or table.FeatureList.FeatureCount > 0 - or table.LookupList.LookupCount > 0 - ): - fontTable = self.font[tag] = newTable(tag) - fontTable.table = table - elif tag in self.font: - del self.font[tag] - if any(tag in self.font for tag in ("GPOS", "GSUB")) and "OS/2" in self.font: - self.font["OS/2"].usMaxContext = maxCtxFont(self.font) - if "GDEF" in tables: - gdef = self.buildGDEF() - if gdef: - self.font["GDEF"] = gdef - elif "GDEF" in self.font: - del self.font["GDEF"] - if "BASE" in tables: - base = self.buildBASE() - if base: - self.font["BASE"] = base - elif "BASE" in self.font: - del self.font["BASE"] - if debug or os.environ.get(LOOKUP_DEBUG_ENV_VAR): - self.buildDebg() - - def get_chained_lookup_(self, location, builder_class): - result = builder_class(self.font, location) - result.lookupflag = self.lookupflag_ - result.markFilterSet = self.lookupflag_markFilterSet_ - self.lookups_.append(result) - return result - - def add_lookup_to_feature_(self, lookup, feature_name): - for script, lang in self.language_systems: - key = (script, lang, feature_name) - self.features_.setdefault(key, []).append(lookup) - - def get_lookup_(self, location, builder_class): - if ( - self.cur_lookup_ - and type(self.cur_lookup_) == builder_class - and self.cur_lookup_.lookupflag == self.lookupflag_ - and self.cur_lookup_.markFilterSet == self.lookupflag_markFilterSet_ - ): - return self.cur_lookup_ - if self.cur_lookup_name_ and self.cur_lookup_: - raise FeatureLibError( - "Within a named lookup block, all rules must be of " - "the same lookup type and flag", - location, - ) - self.cur_lookup_ = builder_class(self.font, location) - self.cur_lookup_.lookupflag = self.lookupflag_ - self.cur_lookup_.markFilterSet = self.lookupflag_markFilterSet_ - self.lookups_.append(self.cur_lookup_) - if self.cur_lookup_name_: - # We are starting a lookup rule inside a named lookup block. - self.named_lookups_[self.cur_lookup_name_] = self.cur_lookup_ - if self.cur_feature_name_: - # We are starting a lookup rule inside a feature. This includes - # lookup rules inside named lookups inside features. - self.add_lookup_to_feature_(self.cur_lookup_, self.cur_feature_name_) - return self.cur_lookup_ - - def build_feature_aalt_(self): - if not self.aalt_features_ and not self.aalt_alternates_: - return - alternates = {g: set(a) for g, a in self.aalt_alternates_.items()} - for location, name in self.aalt_features_ + [(None, "aalt")]: - feature = [ - (script, lang, feature, lookups) - for (script, lang, feature), lookups in self.features_.items() - if feature == name - ] - # "aalt" does not have to specify its own lookups, but it might. - if not feature and name != "aalt": - warnings.warn("%s: Feature %s has not been defined" % (location, name)) - continue - for script, lang, feature, lookups in feature: - for lookuplist in lookups: - if not isinstance(lookuplist, list): - lookuplist = [lookuplist] - for lookup in lookuplist: - for glyph, alts in lookup.getAlternateGlyphs().items(): - alternates.setdefault(glyph, set()).update(alts) - single = { - glyph: list(repl)[0] for glyph, repl in alternates.items() if len(repl) == 1 - } - # TODO: Figure out the glyph alternate ordering used by makeotf. - # https://github.com/fonttools/fonttools/issues/836 - multi = { - glyph: sorted(repl, key=self.font.getGlyphID) - for glyph, repl in alternates.items() - if len(repl) > 1 - } - if not single and not multi: - return - self.features_ = { - (script, lang, feature): lookups - for (script, lang, feature), lookups in self.features_.items() - if feature != "aalt" - } - old_lookups = self.lookups_ - self.lookups_ = [] - self.start_feature(self.aalt_location_, "aalt") - if single: - single_lookup = self.get_lookup_(location, SingleSubstBuilder) - single_lookup.mapping = single - if multi: - multi_lookup = self.get_lookup_(location, AlternateSubstBuilder) - multi_lookup.alternates = multi - self.end_feature() - self.lookups_.extend(old_lookups) - - def build_head(self): - if not self.fontRevision_: - return - table = self.font.get("head") - if not table: # this only happens for unit tests - table = self.font["head"] = newTable("head") - table.decompile(b"\0" * 54, self.font) - table.tableVersion = 1.0 - table.created = table.modified = 3406620153 # 2011-12-13 11:22:33 - table.fontRevision = self.fontRevision_ - - def build_hhea(self): - if not self.hhea_: - return - table = self.font.get("hhea") - if not table: # this only happens for unit tests - table = self.font["hhea"] = newTable("hhea") - table.decompile(b"\0" * 36, self.font) - table.tableVersion = 0x00010000 - if "caretoffset" in self.hhea_: - table.caretOffset = self.hhea_["caretoffset"] - if "ascender" in self.hhea_: - table.ascent = self.hhea_["ascender"] - if "descender" in self.hhea_: - table.descent = self.hhea_["descender"] - if "linegap" in self.hhea_: - table.lineGap = self.hhea_["linegap"] - - def build_vhea(self): - if not self.vhea_: - return - table = self.font.get("vhea") - if not table: # this only happens for unit tests - table = self.font["vhea"] = newTable("vhea") - table.decompile(b"\0" * 36, self.font) - table.tableVersion = 0x00011000 - if "verttypoascender" in self.vhea_: - table.ascent = self.vhea_["verttypoascender"] - if "verttypodescender" in self.vhea_: - table.descent = self.vhea_["verttypodescender"] - if "verttypolinegap" in self.vhea_: - table.lineGap = self.vhea_["verttypolinegap"] - - def get_user_name_id(self, table): - # Try to find first unused font-specific name id - nameIDs = [name.nameID for name in table.names] - for user_name_id in range(256, 32767): - if user_name_id not in nameIDs: - return user_name_id - - def buildFeatureParams(self, tag): - params = None - if tag == "size": - params = otTables.FeatureParamsSize() - ( - params.DesignSize, - params.SubfamilyID, - params.RangeStart, - params.RangeEnd, - ) = self.size_parameters_ - if tag in self.featureNames_ids_: - params.SubfamilyNameID = self.featureNames_ids_[tag] - else: - params.SubfamilyNameID = 0 - elif tag in self.featureNames_: - if not self.featureNames_ids_: - # name table wasn't selected among the tables to build; skip - pass - else: - assert tag in self.featureNames_ids_ - params = otTables.FeatureParamsStylisticSet() - params.Version = 0 - params.UINameID = self.featureNames_ids_[tag] - elif tag in self.cv_parameters_: - params = otTables.FeatureParamsCharacterVariants() - params.Format = 0 - params.FeatUILabelNameID = self.cv_parameters_ids_.get( - (tag, "FeatUILabelNameID"), 0 - ) - params.FeatUITooltipTextNameID = self.cv_parameters_ids_.get( - (tag, "FeatUITooltipTextNameID"), 0 - ) - params.SampleTextNameID = self.cv_parameters_ids_.get( - (tag, "SampleTextNameID"), 0 - ) - params.NumNamedParameters = self.cv_num_named_params_.get(tag, 0) - params.FirstParamUILabelNameID = self.cv_parameters_ids_.get( - (tag, "ParamUILabelNameID_0"), 0 - ) - params.CharCount = len(self.cv_characters_[tag]) - params.Character = self.cv_characters_[tag] - return params - - def build_name(self): - if not self.names_: - return - table = self.font.get("name") - if not table: # this only happens for unit tests - table = self.font["name"] = newTable("name") - table.names = [] - for name in self.names_: - nameID, platformID, platEncID, langID, string = name - # For featureNames block, nameID is 'feature tag' - # For cvParameters blocks, nameID is ('feature tag', 'block name') - if not isinstance(nameID, int): - tag = nameID - if tag in self.featureNames_: - if tag not in self.featureNames_ids_: - self.featureNames_ids_[tag] = self.get_user_name_id(table) - assert self.featureNames_ids_[tag] is not None - nameID = self.featureNames_ids_[tag] - elif tag[0] in self.cv_parameters_: - if tag not in self.cv_parameters_ids_: - self.cv_parameters_ids_[tag] = self.get_user_name_id(table) - assert self.cv_parameters_ids_[tag] is not None - nameID = self.cv_parameters_ids_[tag] - table.setName(string, nameID, platformID, platEncID, langID) - table.names.sort() - - def build_OS_2(self): - if not self.os2_: - return - table = self.font.get("OS/2") - if not table: # this only happens for unit tests - table = self.font["OS/2"] = newTable("OS/2") - data = b"\0" * sstruct.calcsize(getTableModule("OS/2").OS2_format_0) - table.decompile(data, self.font) - version = 0 - if "fstype" in self.os2_: - table.fsType = self.os2_["fstype"] - if "panose" in self.os2_: - panose = getTableModule("OS/2").Panose() - ( - panose.bFamilyType, - panose.bSerifStyle, - panose.bWeight, - panose.bProportion, - panose.bContrast, - panose.bStrokeVariation, - panose.bArmStyle, - panose.bLetterForm, - panose.bMidline, - panose.bXHeight, - ) = self.os2_["panose"] - table.panose = panose - if "typoascender" in self.os2_: - table.sTypoAscender = self.os2_["typoascender"] - if "typodescender" in self.os2_: - table.sTypoDescender = self.os2_["typodescender"] - if "typolinegap" in self.os2_: - table.sTypoLineGap = self.os2_["typolinegap"] - if "winascent" in self.os2_: - table.usWinAscent = self.os2_["winascent"] - if "windescent" in self.os2_: - table.usWinDescent = self.os2_["windescent"] - if "vendor" in self.os2_: - table.achVendID = safeEval("'''" + self.os2_["vendor"] + "'''") - if "weightclass" in self.os2_: - table.usWeightClass = self.os2_["weightclass"] - if "widthclass" in self.os2_: - table.usWidthClass = self.os2_["widthclass"] - if "unicoderange" in self.os2_: - table.setUnicodeRanges(self.os2_["unicoderange"]) - if "codepagerange" in self.os2_: - pages = self.build_codepages_(self.os2_["codepagerange"]) - table.ulCodePageRange1, table.ulCodePageRange2 = pages - version = 1 - if "xheight" in self.os2_: - table.sxHeight = self.os2_["xheight"] - version = 2 - if "capheight" in self.os2_: - table.sCapHeight = self.os2_["capheight"] - version = 2 - if "loweropsize" in self.os2_: - table.usLowerOpticalPointSize = self.os2_["loweropsize"] - version = 5 - if "upperopsize" in self.os2_: - table.usUpperOpticalPointSize = self.os2_["upperopsize"] - version = 5 - - def checkattr(table, attrs): - for attr in attrs: - if not hasattr(table, attr): - setattr(table, attr, 0) - - table.version = max(version, table.version) - # this only happens for unit tests - if version >= 1: - checkattr(table, ("ulCodePageRange1", "ulCodePageRange2")) - if version >= 2: - checkattr( - table, - ( - "sxHeight", - "sCapHeight", - "usDefaultChar", - "usBreakChar", - "usMaxContext", - ), - ) - if version >= 5: - checkattr(table, ("usLowerOpticalPointSize", "usUpperOpticalPointSize")) - - def setElidedFallbackName(self, value, location): - # ElidedFallbackName is a convenience method for setting - # ElidedFallbackNameID so only one can be allowed - for token in ("ElidedFallbackName", "ElidedFallbackNameID"): - if token in self.stat_: - raise FeatureLibError( - f"{token} is already set.", - location, - ) - if isinstance(value, int): - self.stat_["ElidedFallbackNameID"] = value - elif isinstance(value, list): - self.stat_["ElidedFallbackName"] = value - else: - raise AssertionError(value) - - def addDesignAxis(self, designAxis, location): - if "DesignAxes" not in self.stat_: - self.stat_["DesignAxes"] = [] - if designAxis.tag in (r.tag for r in self.stat_["DesignAxes"]): - raise FeatureLibError( - f'DesignAxis already defined for tag "{designAxis.tag}".', - location, - ) - if designAxis.axisOrder in (r.axisOrder for r in self.stat_["DesignAxes"]): - raise FeatureLibError( - f"DesignAxis already defined for axis number {designAxis.axisOrder}.", - location, - ) - self.stat_["DesignAxes"].append(designAxis) - - def addAxisValueRecord(self, axisValueRecord, location): - if "AxisValueRecords" not in self.stat_: - self.stat_["AxisValueRecords"] = [] - # Check for duplicate AxisValueRecords - for record_ in self.stat_["AxisValueRecords"]: - if ( - {n.asFea() for n in record_.names} - == {n.asFea() for n in axisValueRecord.names} - and {n.asFea() for n in record_.locations} - == {n.asFea() for n in axisValueRecord.locations} - and record_.flags == axisValueRecord.flags - ): - raise FeatureLibError( - "An AxisValueRecord with these values is already defined.", - location, - ) - self.stat_["AxisValueRecords"].append(axisValueRecord) - - def build_STAT(self): - if not self.stat_: - return - - axes = self.stat_.get("DesignAxes") - if not axes: - raise FeatureLibError("DesignAxes not defined", None) - axisValueRecords = self.stat_.get("AxisValueRecords") - axisValues = {} - format4_locations = [] - for tag in axes: - axisValues[tag.tag] = [] - if axisValueRecords is not None: - for avr in axisValueRecords: - valuesDict = {} - if avr.flags > 0: - valuesDict["flags"] = avr.flags - if len(avr.locations) == 1: - location = avr.locations[0] - values = location.values - if len(values) == 1: # format1 - valuesDict.update({"value": values[0], "name": avr.names}) - if len(values) == 2: # format3 - valuesDict.update( - { - "value": values[0], - "linkedValue": values[1], - "name": avr.names, - } - ) - if len(values) == 3: # format2 - nominal, minVal, maxVal = values - valuesDict.update( - { - "nominalValue": nominal, - "rangeMinValue": minVal, - "rangeMaxValue": maxVal, - "name": avr.names, - } - ) - axisValues[location.tag].append(valuesDict) - else: - valuesDict.update( - { - "location": {i.tag: i.values[0] for i in avr.locations}, - "name": avr.names, - } - ) - format4_locations.append(valuesDict) - - designAxes = [ - { - "ordering": a.axisOrder, - "tag": a.tag, - "name": a.names, - "values": axisValues[a.tag], - } - for a in axes - ] - - nameTable = self.font.get("name") - if not nameTable: # this only happens for unit tests - nameTable = self.font["name"] = newTable("name") - nameTable.names = [] - - if "ElidedFallbackNameID" in self.stat_: - nameID = self.stat_["ElidedFallbackNameID"] - name = nameTable.getDebugName(nameID) - if not name: - raise FeatureLibError( - f"ElidedFallbackNameID {nameID} points " - "to a nameID that does not exist in the " - '"name" table', - None, - ) - elif "ElidedFallbackName" in self.stat_: - nameID = self.stat_["ElidedFallbackName"] - - otl.buildStatTable( - self.font, - designAxes, - locations=format4_locations, - elidedFallbackName=nameID, - ) - - def build_codepages_(self, pages): - pages2bits = { - 1252: 0, - 1250: 1, - 1251: 2, - 1253: 3, - 1254: 4, - 1255: 5, - 1256: 6, - 1257: 7, - 1258: 8, - 874: 16, - 932: 17, - 936: 18, - 949: 19, - 950: 20, - 1361: 21, - 869: 48, - 866: 49, - 865: 50, - 864: 51, - 863: 52, - 862: 53, - 861: 54, - 860: 55, - 857: 56, - 855: 57, - 852: 58, - 775: 59, - 737: 60, - 708: 61, - 850: 62, - 437: 63, - } - bits = [pages2bits[p] for p in pages if p in pages2bits] - pages = [] - for i in range(2): - pages.append("") - for j in range(i * 32, (i + 1) * 32): - if j in bits: - pages[i] += "1" - else: - pages[i] += "0" - return [binary2num(p[::-1]) for p in pages] - - def buildBASE(self): - if not self.base_horiz_axis_ and not self.base_vert_axis_: - return None - base = otTables.BASE() - base.Version = 0x00010000 - base.HorizAxis = self.buildBASEAxis(self.base_horiz_axis_) - base.VertAxis = self.buildBASEAxis(self.base_vert_axis_) - - result = newTable("BASE") - result.table = base - return result - - def buildBASEAxis(self, axis): - if not axis: - return - bases, scripts = axis - axis = otTables.Axis() - axis.BaseTagList = otTables.BaseTagList() - axis.BaseTagList.BaselineTag = bases - axis.BaseTagList.BaseTagCount = len(bases) - axis.BaseScriptList = otTables.BaseScriptList() - axis.BaseScriptList.BaseScriptRecord = [] - axis.BaseScriptList.BaseScriptCount = len(scripts) - for script in sorted(scripts): - record = otTables.BaseScriptRecord() - record.BaseScriptTag = script[0] - record.BaseScript = otTables.BaseScript() - record.BaseScript.BaseLangSysCount = 0 - record.BaseScript.BaseValues = otTables.BaseValues() - record.BaseScript.BaseValues.DefaultIndex = bases.index(script[1]) - record.BaseScript.BaseValues.BaseCoord = [] - record.BaseScript.BaseValues.BaseCoordCount = len(script[2]) - for c in script[2]: - coord = otTables.BaseCoord() - coord.Format = 1 - coord.Coordinate = c - record.BaseScript.BaseValues.BaseCoord.append(coord) - axis.BaseScriptList.BaseScriptRecord.append(record) - return axis - - def buildGDEF(self): - gdef = otTables.GDEF() - gdef.GlyphClassDef = self.buildGDEFGlyphClassDef_() - gdef.AttachList = otl.buildAttachList(self.attachPoints_, self.glyphMap) - gdef.LigCaretList = otl.buildLigCaretList( - self.ligCaretCoords_, self.ligCaretPoints_, self.glyphMap - ) - gdef.MarkAttachClassDef = self.buildGDEFMarkAttachClassDef_() - gdef.MarkGlyphSetsDef = self.buildGDEFMarkGlyphSetsDef_() - gdef.Version = 0x00010002 if gdef.MarkGlyphSetsDef else 0x00010000 - if self.varstorebuilder: - store = self.varstorebuilder.finish() - if store: - gdef.Version = 0x00010003 - gdef.VarStore = store - varidx_map = store.optimize() - - gdef.remap_device_varidxes(varidx_map) - if "GPOS" in self.font: - self.font["GPOS"].table.remap_device_varidxes(varidx_map) - self.model_cache.clear() - if any( - ( - gdef.GlyphClassDef, - gdef.AttachList, - gdef.LigCaretList, - gdef.MarkAttachClassDef, - gdef.MarkGlyphSetsDef, - ) - ) or hasattr(gdef, "VarStore"): - result = newTable("GDEF") - result.table = gdef - return result - else: - return None - - def buildGDEFGlyphClassDef_(self): - if self.glyphClassDefs_: - classes = {g: c for (g, (c, _)) in self.glyphClassDefs_.items()} - else: - classes = {} - for lookup in self.lookups_: - classes.update(lookup.inferGlyphClasses()) - for markClass in self.parseTree.markClasses.values(): - for markClassDef in markClass.definitions: - for glyph in markClassDef.glyphSet(): - classes[glyph] = 3 - if classes: - result = otTables.GlyphClassDef() - result.classDefs = classes - return result - else: - return None - - def buildGDEFMarkAttachClassDef_(self): - classDefs = {g: c for g, (c, _) in self.markAttach_.items()} - if not classDefs: - return None - result = otTables.MarkAttachClassDef() - result.classDefs = classDefs - return result - - def buildGDEFMarkGlyphSetsDef_(self): - sets = [] - for glyphs, id_ in sorted( - self.markFilterSets_.items(), key=lambda item: item[1] - ): - sets.append(glyphs) - return otl.buildMarkGlyphSetsDef(sets, self.glyphMap) - - def buildDebg(self): - if "Debg" not in self.font: - self.font["Debg"] = newTable("Debg") - self.font["Debg"].data = {} - self.font["Debg"].data[LOOKUP_DEBUG_INFO_KEY] = self.lookup_locations - - def buildLookups_(self, tag): - assert tag in ("GPOS", "GSUB"), tag - for lookup in self.lookups_: - lookup.lookup_index = None - lookups = [] - for lookup in self.lookups_: - if lookup.table != tag: - continue - lookup.lookup_index = len(lookups) - self.lookup_locations[tag][str(lookup.lookup_index)] = LookupDebugInfo( - location=str(lookup.location), - name=self.get_lookup_name_(lookup), - feature=None, - ) - lookups.append(lookup) - try: - otLookups = [l.build() for l in lookups] - except OpenTypeLibError as e: - raise FeatureLibError(str(e), e.location) from e - return otLookups - - def makeTable(self, tag): - table = getattr(otTables, tag, None)() - table.Version = 0x00010000 - table.ScriptList = otTables.ScriptList() - table.ScriptList.ScriptRecord = [] - table.FeatureList = otTables.FeatureList() - table.FeatureList.FeatureRecord = [] - table.LookupList = otTables.LookupList() - table.LookupList.Lookup = self.buildLookups_(tag) - - # Build a table for mapping (tag, lookup_indices) to feature_index. - # For example, ('liga', (2,3,7)) --> 23. - feature_indices = {} - required_feature_indices = {} # ('latn', 'DEU') --> 23 - scripts = {} # 'latn' --> {'DEU': [23, 24]} for feature #23,24 - # Sort the feature table by feature tag: - # https://github.com/fonttools/fonttools/issues/568 - sortFeatureTag = lambda f: (f[0][2], f[0][1], f[0][0], f[1]) - for key, lookups in sorted(self.features_.items(), key=sortFeatureTag): - script, lang, feature_tag = key - # l.lookup_index will be None when a lookup is not needed - # for the table under construction. For example, substitution - # rules will have no lookup_index while building GPOS tables. - lookup_indices = tuple( - [l.lookup_index for l in lookups if l.lookup_index is not None] - ) - - size_feature = tag == "GPOS" and feature_tag == "size" - force_feature = self.any_feature_variations(feature_tag, tag) - if len(lookup_indices) == 0 and not size_feature and not force_feature: - continue - - for ix in lookup_indices: - try: - self.lookup_locations[tag][str(ix)] = self.lookup_locations[tag][ - str(ix) - ]._replace(feature=key) - except KeyError: - warnings.warn( - "feaLib.Builder subclass needs upgrading to " - "stash debug information. See fonttools#2065." - ) - - feature_key = (feature_tag, lookup_indices) - feature_index = feature_indices.get(feature_key) - if feature_index is None: - feature_index = len(table.FeatureList.FeatureRecord) - frec = otTables.FeatureRecord() - frec.FeatureTag = feature_tag - frec.Feature = otTables.Feature() - frec.Feature.FeatureParams = self.buildFeatureParams(feature_tag) - frec.Feature.LookupListIndex = list(lookup_indices) - frec.Feature.LookupCount = len(lookup_indices) - table.FeatureList.FeatureRecord.append(frec) - feature_indices[feature_key] = feature_index - scripts.setdefault(script, {}).setdefault(lang, []).append(feature_index) - if self.required_features_.get((script, lang)) == feature_tag: - required_feature_indices[(script, lang)] = feature_index - - # Build ScriptList. - for script, lang_features in sorted(scripts.items()): - srec = otTables.ScriptRecord() - srec.ScriptTag = script - srec.Script = otTables.Script() - srec.Script.DefaultLangSys = None - srec.Script.LangSysRecord = [] - for lang, feature_indices in sorted(lang_features.items()): - langrec = otTables.LangSysRecord() - langrec.LangSys = otTables.LangSys() - langrec.LangSys.LookupOrder = None - - req_feature_index = required_feature_indices.get((script, lang)) - if req_feature_index is None: - langrec.LangSys.ReqFeatureIndex = 0xFFFF - else: - langrec.LangSys.ReqFeatureIndex = req_feature_index - - langrec.LangSys.FeatureIndex = [ - i for i in feature_indices if i != req_feature_index - ] - langrec.LangSys.FeatureCount = len(langrec.LangSys.FeatureIndex) - - if lang == "dflt": - srec.Script.DefaultLangSys = langrec.LangSys - else: - langrec.LangSysTag = lang - srec.Script.LangSysRecord.append(langrec) - srec.Script.LangSysCount = len(srec.Script.LangSysRecord) - table.ScriptList.ScriptRecord.append(srec) - - table.ScriptList.ScriptCount = len(table.ScriptList.ScriptRecord) - table.FeatureList.FeatureCount = len(table.FeatureList.FeatureRecord) - table.LookupList.LookupCount = len(table.LookupList.Lookup) - return table - - def makeFeatureVariations(self, table, table_tag): - feature_vars = {} - has_any_variations = False - # Sort out which lookups to build, gather their indices - for (_, _, feature_tag), variations in self.feature_variations_.items(): - feature_vars[feature_tag] = [] - for conditionset, builders in variations.items(): - raw_conditionset = self.conditionsets_[conditionset] - indices = [] - for b in builders: - if b.table != table_tag: - continue - assert b.lookup_index is not None - indices.append(b.lookup_index) - has_any_variations = True - feature_vars[feature_tag].append((raw_conditionset, indices)) - - if has_any_variations: - for feature_tag, conditions_and_lookups in feature_vars.items(): - addFeatureVariationsRaw( - self.font, table, conditions_and_lookups, feature_tag - ) - - def any_feature_variations(self, feature_tag, table_tag): - for (_, _, feature), variations in self.feature_variations_.items(): - if feature != feature_tag: - continue - for conditionset, builders in variations.items(): - if any(b.table == table_tag for b in builders): - return True - return False - - def get_lookup_name_(self, lookup): - rev = {v: k for k, v in self.named_lookups_.items()} - if lookup in rev: - return rev[lookup] - return None - - def add_language_system(self, location, script, language): - # OpenType Feature File Specification, section 4.b.i - if script == "DFLT" and language == "dflt" and self.default_language_systems_: - raise FeatureLibError( - 'If "languagesystem DFLT dflt" is present, it must be ' - "the first of the languagesystem statements", - location, - ) - if script == "DFLT": - if self.seen_non_DFLT_script_: - raise FeatureLibError( - 'languagesystems using the "DFLT" script tag must ' - "precede all other languagesystems", - location, - ) - else: - self.seen_non_DFLT_script_ = True - if (script, language) in self.default_language_systems_: - raise FeatureLibError( - '"languagesystem %s %s" has already been specified' - % (script.strip(), language.strip()), - location, - ) - self.default_language_systems_.add((script, language)) - - def get_default_language_systems_(self): - # OpenType Feature File specification, 4.b.i. languagesystem: - # If no "languagesystem" statement is present, then the - # implementation must behave exactly as though the following - # statement were present at the beginning of the feature file: - # languagesystem DFLT dflt; - if self.default_language_systems_: - return frozenset(self.default_language_systems_) - else: - return frozenset({("DFLT", "dflt")}) - - def start_feature(self, location, name): - self.language_systems = self.get_default_language_systems_() - self.script_ = "DFLT" - self.cur_lookup_ = None - self.cur_feature_name_ = name - self.lookupflag_ = 0 - self.lookupflag_markFilterSet_ = None - if name == "aalt": - self.aalt_location_ = location - - def end_feature(self): - assert self.cur_feature_name_ is not None - self.cur_feature_name_ = None - self.language_systems = None - self.cur_lookup_ = None - self.lookupflag_ = 0 - self.lookupflag_markFilterSet_ = None - - def start_lookup_block(self, location, name): - if name in self.named_lookups_: - raise FeatureLibError( - 'Lookup "%s" has already been defined' % name, location - ) - if self.cur_feature_name_ == "aalt": - raise FeatureLibError( - "Lookup blocks cannot be placed inside 'aalt' features; " - "move it out, and then refer to it with a lookup statement", - location, - ) - self.cur_lookup_name_ = name - self.named_lookups_[name] = None - self.cur_lookup_ = None - if self.cur_feature_name_ is None: - self.lookupflag_ = 0 - self.lookupflag_markFilterSet_ = None - - def end_lookup_block(self): - assert self.cur_lookup_name_ is not None - self.cur_lookup_name_ = None - self.cur_lookup_ = None - if self.cur_feature_name_ is None: - self.lookupflag_ = 0 - self.lookupflag_markFilterSet_ = None - - def add_lookup_call(self, lookup_name): - assert lookup_name in self.named_lookups_, lookup_name - self.cur_lookup_ = None - lookup = self.named_lookups_[lookup_name] - if lookup is not None: # skip empty named lookup - self.add_lookup_to_feature_(lookup, self.cur_feature_name_) - - def set_font_revision(self, location, revision): - self.fontRevision_ = revision - - def set_language(self, location, language, include_default, required): - assert len(language) == 4 - if self.cur_feature_name_ in ("aalt", "size"): - raise FeatureLibError( - "Language statements are not allowed " - 'within "feature %s"' % self.cur_feature_name_, - location, - ) - if self.cur_feature_name_ is None: - raise FeatureLibError( - "Language statements are not allowed " - "within standalone lookup blocks", - location, - ) - self.cur_lookup_ = None - - key = (self.script_, language, self.cur_feature_name_) - lookups = self.features_.get((key[0], "dflt", key[2])) - if (language == "dflt" or include_default) and lookups: - self.features_[key] = lookups[:] - else: - self.features_[key] = [] - self.language_systems = frozenset([(self.script_, language)]) - - if required: - key = (self.script_, language) - if key in self.required_features_: - raise FeatureLibError( - "Language %s (script %s) has already " - "specified feature %s as its required feature" - % ( - language.strip(), - self.script_.strip(), - self.required_features_[key].strip(), - ), - location, - ) - self.required_features_[key] = self.cur_feature_name_ - - def getMarkAttachClass_(self, location, glyphs): - glyphs = frozenset(glyphs) - id_ = self.markAttachClassID_.get(glyphs) - if id_ is not None: - return id_ - id_ = len(self.markAttachClassID_) + 1 - self.markAttachClassID_[glyphs] = id_ - for glyph in glyphs: - if glyph in self.markAttach_: - _, loc = self.markAttach_[glyph] - raise FeatureLibError( - "Glyph %s already has been assigned " - "a MarkAttachmentType at %s" % (glyph, loc), - location, - ) - self.markAttach_[glyph] = (id_, location) - return id_ - - def getMarkFilterSet_(self, location, glyphs): - glyphs = frozenset(glyphs) - id_ = self.markFilterSets_.get(glyphs) - if id_ is not None: - return id_ - id_ = len(self.markFilterSets_) - self.markFilterSets_[glyphs] = id_ - return id_ - - def set_lookup_flag(self, location, value, markAttach, markFilter): - value = value & 0xFF - if markAttach: - markAttachClass = self.getMarkAttachClass_(location, markAttach) - value = value | (markAttachClass << 8) - if markFilter: - markFilterSet = self.getMarkFilterSet_(location, markFilter) - value = value | 0x10 - self.lookupflag_markFilterSet_ = markFilterSet - else: - self.lookupflag_markFilterSet_ = None - self.lookupflag_ = value - - def set_script(self, location, script): - if self.cur_feature_name_ in ("aalt", "size"): - raise FeatureLibError( - "Script statements are not allowed " - 'within "feature %s"' % self.cur_feature_name_, - location, - ) - if self.cur_feature_name_ is None: - raise FeatureLibError( - "Script statements are not allowed " "within standalone lookup blocks", - location, - ) - if self.language_systems == {(script, "dflt")}: - # Nothing to do. - return - self.cur_lookup_ = None - self.script_ = script - self.lookupflag_ = 0 - self.lookupflag_markFilterSet_ = None - self.set_language(location, "dflt", include_default=True, required=False) - - def find_lookup_builders_(self, lookups): - """Helper for building chain contextual substitutions - - Given a list of lookup names, finds the LookupBuilder for each name. - If an input name is None, it gets mapped to a None LookupBuilder. - """ - lookup_builders = [] - for lookuplist in lookups: - if lookuplist is not None: - lookup_builders.append( - [self.named_lookups_.get(l.name) for l in lookuplist] - ) - else: - lookup_builders.append(None) - return lookup_builders - - def add_attach_points(self, location, glyphs, contourPoints): - for glyph in glyphs: - self.attachPoints_.setdefault(glyph, set()).update(contourPoints) - - def add_feature_reference(self, location, featureName): - if self.cur_feature_name_ != "aalt": - raise FeatureLibError( - 'Feature references are only allowed inside "feature aalt"', location - ) - self.aalt_features_.append((location, featureName)) - - def add_featureName(self, tag): - self.featureNames_.add(tag) - - def add_cv_parameter(self, tag): - self.cv_parameters_.add(tag) - - def add_to_cv_num_named_params(self, tag): - """Adds new items to ``self.cv_num_named_params_`` - or increments the count of existing items.""" - if tag in self.cv_num_named_params_: - self.cv_num_named_params_[tag] += 1 - else: - self.cv_num_named_params_[tag] = 1 - - def add_cv_character(self, character, tag): - self.cv_characters_[tag].append(character) - - def set_base_axis(self, bases, scripts, vertical): - if vertical: - self.base_vert_axis_ = (bases, scripts) - else: - self.base_horiz_axis_ = (bases, scripts) - - def set_size_parameters( - self, location, DesignSize, SubfamilyID, RangeStart, RangeEnd - ): - if self.cur_feature_name_ != "size": - raise FeatureLibError( - "Parameters statements are not allowed " - 'within "feature %s"' % self.cur_feature_name_, - location, - ) - self.size_parameters_ = [DesignSize, SubfamilyID, RangeStart, RangeEnd] - for script, lang in self.language_systems: - key = (script, lang, self.cur_feature_name_) - self.features_.setdefault(key, []) - - # GSUB rules - - # GSUB 1 - def add_single_subst(self, location, prefix, suffix, mapping, forceChain): - if self.cur_feature_name_ == "aalt": - for from_glyph, to_glyph in mapping.items(): - alts = self.aalt_alternates_.setdefault(from_glyph, set()) - alts.add(to_glyph) - return - if prefix or suffix or forceChain: - self.add_single_subst_chained_(location, prefix, suffix, mapping) - return - lookup = self.get_lookup_(location, SingleSubstBuilder) - for from_glyph, to_glyph in mapping.items(): - if from_glyph in lookup.mapping: - if to_glyph == lookup.mapping[from_glyph]: - log.info( - "Removing duplicate single substitution from glyph" - ' "%s" to "%s" at %s', - from_glyph, - to_glyph, - location, - ) - else: - raise FeatureLibError( - 'Already defined rule for replacing glyph "%s" by "%s"' - % (from_glyph, lookup.mapping[from_glyph]), - location, - ) - lookup.mapping[from_glyph] = to_glyph - - # GSUB 2 - def add_multiple_subst( - self, location, prefix, glyph, suffix, replacements, forceChain=False - ): - if prefix or suffix or forceChain: - chain = self.get_lookup_(location, ChainContextSubstBuilder) - sub = self.get_chained_lookup_(location, MultipleSubstBuilder) - sub.mapping[glyph] = replacements - chain.rules.append(ChainContextualRule(prefix, [{glyph}], suffix, [sub])) - return - lookup = self.get_lookup_(location, MultipleSubstBuilder) - if glyph in lookup.mapping: - if replacements == lookup.mapping[glyph]: - log.info( - "Removing duplicate multiple substitution from glyph" - ' "%s" to %s%s', - glyph, - replacements, - f" at {location}" if location else "", - ) - else: - raise FeatureLibError( - 'Already defined substitution for glyph "%s"' % glyph, location - ) - lookup.mapping[glyph] = replacements - - # GSUB 3 - def add_alternate_subst(self, location, prefix, glyph, suffix, replacement): - if self.cur_feature_name_ == "aalt": - alts = self.aalt_alternates_.setdefault(glyph, set()) - alts.update(replacement) - return - if prefix or suffix: - chain = self.get_lookup_(location, ChainContextSubstBuilder) - lookup = self.get_chained_lookup_(location, AlternateSubstBuilder) - chain.rules.append(ChainContextualRule(prefix, [{glyph}], suffix, [lookup])) - else: - lookup = self.get_lookup_(location, AlternateSubstBuilder) - if glyph in lookup.alternates: - raise FeatureLibError( - 'Already defined alternates for glyph "%s"' % glyph, location - ) - # We allow empty replacement glyphs here. - lookup.alternates[glyph] = replacement - - # GSUB 4 - def add_ligature_subst( - self, location, prefix, glyphs, suffix, replacement, forceChain - ): - if prefix or suffix or forceChain: - chain = self.get_lookup_(location, ChainContextSubstBuilder) - lookup = self.get_chained_lookup_(location, LigatureSubstBuilder) - chain.rules.append(ChainContextualRule(prefix, glyphs, suffix, [lookup])) - else: - lookup = self.get_lookup_(location, LigatureSubstBuilder) - - if not all(glyphs): - raise FeatureLibError("Empty glyph class in substitution", location) - - # OpenType feature file syntax, section 5.d, "Ligature substitution": - # "Since the OpenType specification does not allow ligature - # substitutions to be specified on target sequences that contain - # glyph classes, the implementation software will enumerate - # all specific glyph sequences if glyph classes are detected" - for g in sorted(itertools.product(*glyphs)): - lookup.ligatures[g] = replacement - - # GSUB 5/6 - def add_chain_context_subst(self, location, prefix, glyphs, suffix, lookups): - if not all(glyphs) or not all(prefix) or not all(suffix): - raise FeatureLibError( - "Empty glyph class in contextual substitution", location - ) - lookup = self.get_lookup_(location, ChainContextSubstBuilder) - lookup.rules.append( - ChainContextualRule( - prefix, glyphs, suffix, self.find_lookup_builders_(lookups) - ) - ) - - def add_single_subst_chained_(self, location, prefix, suffix, mapping): - if not mapping or not all(prefix) or not all(suffix): - raise FeatureLibError( - "Empty glyph class in contextual substitution", location - ) - # https://github.com/fonttools/fonttools/issues/512 - # https://github.com/fonttools/fonttools/issues/2150 - chain = self.get_lookup_(location, ChainContextSubstBuilder) - sub = chain.find_chainable_single_subst(mapping) - if sub is None: - sub = self.get_chained_lookup_(location, SingleSubstBuilder) - sub.mapping.update(mapping) - chain.rules.append( - ChainContextualRule(prefix, [list(mapping.keys())], suffix, [sub]) - ) - - # GSUB 8 - def add_reverse_chain_single_subst(self, location, old_prefix, old_suffix, mapping): - if not mapping: - raise FeatureLibError("Empty glyph class in substitution", location) - lookup = self.get_lookup_(location, ReverseChainSingleSubstBuilder) - lookup.rules.append((old_prefix, old_suffix, mapping)) - - # GPOS rules - - # GPOS 1 - def add_single_pos(self, location, prefix, suffix, pos, forceChain): - if prefix or suffix or forceChain: - self.add_single_pos_chained_(location, prefix, suffix, pos) - else: - lookup = self.get_lookup_(location, SinglePosBuilder) - for glyphs, value in pos: - if not glyphs: - raise FeatureLibError( - "Empty glyph class in positioning rule", location - ) - otValueRecord = self.makeOpenTypeValueRecord( - location, value, pairPosContext=False - ) - for glyph in glyphs: - try: - lookup.add_pos(location, glyph, otValueRecord) - except OpenTypeLibError as e: - raise FeatureLibError(str(e), e.location) from e - - # GPOS 2 - def add_class_pair_pos(self, location, glyphclass1, value1, glyphclass2, value2): - if not glyphclass1 or not glyphclass2: - raise FeatureLibError("Empty glyph class in positioning rule", location) - lookup = self.get_lookup_(location, PairPosBuilder) - v1 = self.makeOpenTypeValueRecord(location, value1, pairPosContext=True) - v2 = self.makeOpenTypeValueRecord(location, value2, pairPosContext=True) - lookup.addClassPair(location, glyphclass1, v1, glyphclass2, v2) - - def add_specific_pair_pos(self, location, glyph1, value1, glyph2, value2): - if not glyph1 or not glyph2: - raise FeatureLibError("Empty glyph class in positioning rule", location) - lookup = self.get_lookup_(location, PairPosBuilder) - v1 = self.makeOpenTypeValueRecord(location, value1, pairPosContext=True) - v2 = self.makeOpenTypeValueRecord(location, value2, pairPosContext=True) - lookup.addGlyphPair(location, glyph1, v1, glyph2, v2) - - # GPOS 3 - def add_cursive_pos(self, location, glyphclass, entryAnchor, exitAnchor): - if not glyphclass: - raise FeatureLibError("Empty glyph class in positioning rule", location) - lookup = self.get_lookup_(location, CursivePosBuilder) - lookup.add_attachment( - location, - glyphclass, - self.makeOpenTypeAnchor(location, entryAnchor), - self.makeOpenTypeAnchor(location, exitAnchor), - ) - - # GPOS 4 - def add_mark_base_pos(self, location, bases, marks): - builder = self.get_lookup_(location, MarkBasePosBuilder) - self.add_marks_(location, builder, marks) - if not bases: - raise FeatureLibError("Empty glyph class in positioning rule", location) - for baseAnchor, markClass in marks: - otBaseAnchor = self.makeOpenTypeAnchor(location, baseAnchor) - for base in bases: - builder.bases.setdefault(base, {})[markClass.name] = otBaseAnchor - - # GPOS 5 - def add_mark_lig_pos(self, location, ligatures, components): - builder = self.get_lookup_(location, MarkLigPosBuilder) - componentAnchors = [] - if not ligatures: - raise FeatureLibError("Empty glyph class in positioning rule", location) - for marks in components: - anchors = {} - self.add_marks_(location, builder, marks) - for ligAnchor, markClass in marks: - anchors[markClass.name] = self.makeOpenTypeAnchor(location, ligAnchor) - componentAnchors.append(anchors) - for glyph in ligatures: - builder.ligatures[glyph] = componentAnchors - - # GPOS 6 - def add_mark_mark_pos(self, location, baseMarks, marks): - builder = self.get_lookup_(location, MarkMarkPosBuilder) - self.add_marks_(location, builder, marks) - if not baseMarks: - raise FeatureLibError("Empty glyph class in positioning rule", location) - for baseAnchor, markClass in marks: - otBaseAnchor = self.makeOpenTypeAnchor(location, baseAnchor) - for baseMark in baseMarks: - builder.baseMarks.setdefault(baseMark, {})[ - markClass.name - ] = otBaseAnchor - - # GPOS 7/8 - def add_chain_context_pos(self, location, prefix, glyphs, suffix, lookups): - if not all(glyphs) or not all(prefix) or not all(suffix): - raise FeatureLibError( - "Empty glyph class in contextual positioning rule", location - ) - lookup = self.get_lookup_(location, ChainContextPosBuilder) - lookup.rules.append( - ChainContextualRule( - prefix, glyphs, suffix, self.find_lookup_builders_(lookups) - ) - ) - - def add_single_pos_chained_(self, location, prefix, suffix, pos): - if not pos or not all(prefix) or not all(suffix): - raise FeatureLibError( - "Empty glyph class in contextual positioning rule", location - ) - # https://github.com/fonttools/fonttools/issues/514 - chain = self.get_lookup_(location, ChainContextPosBuilder) - targets = [] - for _, _, _, lookups in chain.rules: - targets.extend(lookups) - subs = [] - for glyphs, value in pos: - if value is None: - subs.append(None) - continue - otValue = self.makeOpenTypeValueRecord( - location, value, pairPosContext=False - ) - sub = chain.find_chainable_single_pos(targets, glyphs, otValue) - if sub is None: - sub = self.get_chained_lookup_(location, SinglePosBuilder) - targets.append(sub) - for glyph in glyphs: - sub.add_pos(location, glyph, otValue) - subs.append(sub) - assert len(pos) == len(subs), (pos, subs) - chain.rules.append( - ChainContextualRule(prefix, [g for g, v in pos], suffix, subs) - ) - - def add_marks_(self, location, lookupBuilder, marks): - """Helper for add_mark_{base,liga,mark}_pos.""" - for _, markClass in marks: - for markClassDef in markClass.definitions: - for mark in markClassDef.glyphs.glyphSet(): - if mark not in lookupBuilder.marks: - otMarkAnchor = self.makeOpenTypeAnchor( - location, markClassDef.anchor - ) - lookupBuilder.marks[mark] = (markClass.name, otMarkAnchor) - else: - existingMarkClass = lookupBuilder.marks[mark][0] - if markClass.name != existingMarkClass: - raise FeatureLibError( - "Glyph %s cannot be in both @%s and @%s" - % (mark, existingMarkClass, markClass.name), - location, - ) - - def add_subtable_break(self, location): - self.cur_lookup_.add_subtable_break(location) - - def setGlyphClass_(self, location, glyph, glyphClass): - oldClass, oldLocation = self.glyphClassDefs_.get(glyph, (None, None)) - if oldClass and oldClass != glyphClass: - raise FeatureLibError( - "Glyph %s was assigned to a different class at %s" - % (glyph, oldLocation), - location, - ) - self.glyphClassDefs_[glyph] = (glyphClass, location) - - def add_glyphClassDef( - self, location, baseGlyphs, ligatureGlyphs, markGlyphs, componentGlyphs - ): - for glyph in baseGlyphs: - self.setGlyphClass_(location, glyph, 1) - for glyph in ligatureGlyphs: - self.setGlyphClass_(location, glyph, 2) - for glyph in markGlyphs: - self.setGlyphClass_(location, glyph, 3) - for glyph in componentGlyphs: - self.setGlyphClass_(location, glyph, 4) - - def add_ligatureCaretByIndex_(self, location, glyphs, carets): - for glyph in glyphs: - if glyph not in self.ligCaretPoints_: - self.ligCaretPoints_[glyph] = carets - - def makeLigCaret(self, location, caret): - if not isinstance(caret, VariableScalar): - return caret - default, device = self.makeVariablePos(location, caret) - if device is not None: - return (default, device) - return default - - def add_ligatureCaretByPos_(self, location, glyphs, carets): - carets = [self.makeLigCaret(location, caret) for caret in carets] - for glyph in glyphs: - if glyph not in self.ligCaretCoords_: - self.ligCaretCoords_[glyph] = carets - - def add_name_record(self, location, nameID, platformID, platEncID, langID, string): - self.names_.append([nameID, platformID, platEncID, langID, string]) - - def add_os2_field(self, key, value): - self.os2_[key] = value - - def add_hhea_field(self, key, value): - self.hhea_[key] = value - - def add_vhea_field(self, key, value): - self.vhea_[key] = value - - def add_conditionset(self, location, key, value): - if "fvar" not in self.font: - raise FeatureLibError( - "Cannot add feature variations to a font without an 'fvar' table", - location, - ) - - # Normalize - axisMap = { - axis.axisTag: (axis.minValue, axis.defaultValue, axis.maxValue) - for axis in self.axes - } - - value = { - tag: ( - normalizeValue(bottom, axisMap[tag]), - normalizeValue(top, axisMap[tag]), - ) - for tag, (bottom, top) in value.items() - } - - # NOTE: This might result in rounding errors (off-by-ones) compared to - # rules in Designspace files, since we're working with what's in the - # `avar` table rather than the original values. - if "avar" in self.font: - mapping = self.font["avar"].segments - value = { - axis: tuple( - piecewiseLinearMap(v, mapping[axis]) if axis in mapping else v - for v in condition_range - ) - for axis, condition_range in value.items() - } - - self.conditionsets_[key] = value - - def makeVariablePos(self, location, varscalar): - if not self.varstorebuilder: - raise FeatureLibError( - "Can't define a variable scalar in a non-variable font", location - ) - - varscalar.axes = self.axes - if not varscalar.does_vary: - return varscalar.default, None - - default, index = varscalar.add_to_variation_store( - self.varstorebuilder, self.model_cache, self.font.get("avar") - ) - - device = None - if index is not None and index != 0xFFFFFFFF: - device = buildVarDevTable(index) - - return default, device - - def makeOpenTypeAnchor(self, location, anchor): - """ast.Anchor --> otTables.Anchor""" - if anchor is None: - return None - variable = False - deviceX, deviceY = None, None - if anchor.xDeviceTable is not None: - deviceX = otl.buildDevice(dict(anchor.xDeviceTable)) - if anchor.yDeviceTable is not None: - deviceY = otl.buildDevice(dict(anchor.yDeviceTable)) - for dim in ("x", "y"): - varscalar = getattr(anchor, dim) - if not isinstance(varscalar, VariableScalar): - continue - if getattr(anchor, dim + "DeviceTable") is not None: - raise FeatureLibError( - "Can't define a device coordinate and variable scalar", location - ) - default, device = self.makeVariablePos(location, varscalar) - setattr(anchor, dim, default) - if device is not None: - if dim == "x": - deviceX = device - else: - deviceY = device - variable = True - - otlanchor = otl.buildAnchor( - anchor.x, anchor.y, anchor.contourpoint, deviceX, deviceY - ) - if variable: - otlanchor.Format = 3 - return otlanchor - - _VALUEREC_ATTRS = { - name[0].lower() + name[1:]: (name, isDevice) - for _, name, isDevice, _ in otBase.valueRecordFormat - if not name.startswith("Reserved") - } - - def makeOpenTypeValueRecord(self, location, v, pairPosContext): - """ast.ValueRecord --> otBase.ValueRecord""" - if not v: - return None - - vr = {} - for astName, (otName, isDevice) in self._VALUEREC_ATTRS.items(): - val = getattr(v, astName, None) - if not val: - continue - if isDevice: - vr[otName] = otl.buildDevice(dict(val)) - elif isinstance(val, VariableScalar): - otDeviceName = otName[0:4] + "Device" - feaDeviceName = otDeviceName[0].lower() + otDeviceName[1:] - if getattr(v, feaDeviceName): - raise FeatureLibError( - "Can't define a device coordinate and variable scalar", location - ) - vr[otName], device = self.makeVariablePos(location, val) - if device is not None: - vr[otDeviceName] = device - else: - vr[otName] = val - - if pairPosContext and not vr: - vr = {"YAdvance": 0} if v.vertical else {"XAdvance": 0} - valRec = otl.buildValue(vr) - return valRec diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/unicodedata/Scripts.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/unicodedata/Scripts.py deleted file mode 100644 index 68bb91b396d62b03a8bfd650c64ce0b7375e1e48..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/unicodedata/Scripts.py +++ /dev/null @@ -1,3509 +0,0 @@ -# -*- coding: utf-8 -*- -# -# NOTE: This file was auto-generated with MetaTools/buildUCD.py. -# Source: https://unicode.org/Public/UNIDATA/Scripts.txt -# License: http://unicode.org/copyright.html#License -# -# Scripts-15.0.0.txt -# Date: 2022-04-26, 23:15:02 GMT -# © 2022 Unicode®, Inc. -# Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries. -# For terms of use, see https://www.unicode.org/terms_of_use.html -# -# Unicode Character Database -# For documentation, see https://www.unicode.org/reports/tr44/ -# For more information, see: -# UAX #24, Unicode Script Property: https://www.unicode.org/reports/tr24/ -# Especially the sections: -# https://www.unicode.org/reports/tr24/#Assignment_Script_Values -# https://www.unicode.org/reports/tr24/#Assignment_ScriptX_Values -# - - -RANGES = [ - 0x0000, # .. 0x0040 ; Common - 0x0041, # .. 0x005A ; Latin - 0x005B, # .. 0x0060 ; Common - 0x0061, # .. 0x007A ; Latin - 0x007B, # .. 0x00A9 ; Common - 0x00AA, # .. 0x00AA ; Latin - 0x00AB, # .. 0x00B9 ; Common - 0x00BA, # .. 0x00BA ; Latin - 0x00BB, # .. 0x00BF ; Common - 0x00C0, # .. 0x00D6 ; Latin - 0x00D7, # .. 0x00D7 ; Common - 0x00D8, # .. 0x00F6 ; Latin - 0x00F7, # .. 0x00F7 ; Common - 0x00F8, # .. 0x02B8 ; Latin - 0x02B9, # .. 0x02DF ; Common - 0x02E0, # .. 0x02E4 ; Latin - 0x02E5, # .. 0x02E9 ; Common - 0x02EA, # .. 0x02EB ; Bopomofo - 0x02EC, # .. 0x02FF ; Common - 0x0300, # .. 0x036F ; Inherited - 0x0370, # .. 0x0373 ; Greek - 0x0374, # .. 0x0374 ; Common - 0x0375, # .. 0x0377 ; Greek - 0x0378, # .. 0x0379 ; Unknown - 0x037A, # .. 0x037D ; Greek - 0x037E, # .. 0x037E ; Common - 0x037F, # .. 0x037F ; Greek - 0x0380, # .. 0x0383 ; Unknown - 0x0384, # .. 0x0384 ; Greek - 0x0385, # .. 0x0385 ; Common - 0x0386, # .. 0x0386 ; Greek - 0x0387, # .. 0x0387 ; Common - 0x0388, # .. 0x038A ; Greek - 0x038B, # .. 0x038B ; Unknown - 0x038C, # .. 0x038C ; Greek - 0x038D, # .. 0x038D ; Unknown - 0x038E, # .. 0x03A1 ; Greek - 0x03A2, # .. 0x03A2 ; Unknown - 0x03A3, # .. 0x03E1 ; Greek - 0x03E2, # .. 0x03EF ; Coptic - 0x03F0, # .. 0x03FF ; Greek - 0x0400, # .. 0x0484 ; Cyrillic - 0x0485, # .. 0x0486 ; Inherited - 0x0487, # .. 0x052F ; Cyrillic - 0x0530, # .. 0x0530 ; Unknown - 0x0531, # .. 0x0556 ; Armenian - 0x0557, # .. 0x0558 ; Unknown - 0x0559, # .. 0x058A ; Armenian - 0x058B, # .. 0x058C ; Unknown - 0x058D, # .. 0x058F ; Armenian - 0x0590, # .. 0x0590 ; Unknown - 0x0591, # .. 0x05C7 ; Hebrew - 0x05C8, # .. 0x05CF ; Unknown - 0x05D0, # .. 0x05EA ; Hebrew - 0x05EB, # .. 0x05EE ; Unknown - 0x05EF, # .. 0x05F4 ; Hebrew - 0x05F5, # .. 0x05FF ; Unknown - 0x0600, # .. 0x0604 ; Arabic - 0x0605, # .. 0x0605 ; Common - 0x0606, # .. 0x060B ; Arabic - 0x060C, # .. 0x060C ; Common - 0x060D, # .. 0x061A ; Arabic - 0x061B, # .. 0x061B ; Common - 0x061C, # .. 0x061E ; Arabic - 0x061F, # .. 0x061F ; Common - 0x0620, # .. 0x063F ; Arabic - 0x0640, # .. 0x0640 ; Common - 0x0641, # .. 0x064A ; Arabic - 0x064B, # .. 0x0655 ; Inherited - 0x0656, # .. 0x066F ; Arabic - 0x0670, # .. 0x0670 ; Inherited - 0x0671, # .. 0x06DC ; Arabic - 0x06DD, # .. 0x06DD ; Common - 0x06DE, # .. 0x06FF ; Arabic - 0x0700, # .. 0x070D ; Syriac - 0x070E, # .. 0x070E ; Unknown - 0x070F, # .. 0x074A ; Syriac - 0x074B, # .. 0x074C ; Unknown - 0x074D, # .. 0x074F ; Syriac - 0x0750, # .. 0x077F ; Arabic - 0x0780, # .. 0x07B1 ; Thaana - 0x07B2, # .. 0x07BF ; Unknown - 0x07C0, # .. 0x07FA ; Nko - 0x07FB, # .. 0x07FC ; Unknown - 0x07FD, # .. 0x07FF ; Nko - 0x0800, # .. 0x082D ; Samaritan - 0x082E, # .. 0x082F ; Unknown - 0x0830, # .. 0x083E ; Samaritan - 0x083F, # .. 0x083F ; Unknown - 0x0840, # .. 0x085B ; Mandaic - 0x085C, # .. 0x085D ; Unknown - 0x085E, # .. 0x085E ; Mandaic - 0x085F, # .. 0x085F ; Unknown - 0x0860, # .. 0x086A ; Syriac - 0x086B, # .. 0x086F ; Unknown - 0x0870, # .. 0x088E ; Arabic - 0x088F, # .. 0x088F ; Unknown - 0x0890, # .. 0x0891 ; Arabic - 0x0892, # .. 0x0897 ; Unknown - 0x0898, # .. 0x08E1 ; Arabic - 0x08E2, # .. 0x08E2 ; Common - 0x08E3, # .. 0x08FF ; Arabic - 0x0900, # .. 0x0950 ; Devanagari - 0x0951, # .. 0x0954 ; Inherited - 0x0955, # .. 0x0963 ; Devanagari - 0x0964, # .. 0x0965 ; Common - 0x0966, # .. 0x097F ; Devanagari - 0x0980, # .. 0x0983 ; Bengali - 0x0984, # .. 0x0984 ; Unknown - 0x0985, # .. 0x098C ; Bengali - 0x098D, # .. 0x098E ; Unknown - 0x098F, # .. 0x0990 ; Bengali - 0x0991, # .. 0x0992 ; Unknown - 0x0993, # .. 0x09A8 ; Bengali - 0x09A9, # .. 0x09A9 ; Unknown - 0x09AA, # .. 0x09B0 ; Bengali - 0x09B1, # .. 0x09B1 ; Unknown - 0x09B2, # .. 0x09B2 ; Bengali - 0x09B3, # .. 0x09B5 ; Unknown - 0x09B6, # .. 0x09B9 ; Bengali - 0x09BA, # .. 0x09BB ; Unknown - 0x09BC, # .. 0x09C4 ; Bengali - 0x09C5, # .. 0x09C6 ; Unknown - 0x09C7, # .. 0x09C8 ; Bengali - 0x09C9, # .. 0x09CA ; Unknown - 0x09CB, # .. 0x09CE ; Bengali - 0x09CF, # .. 0x09D6 ; Unknown - 0x09D7, # .. 0x09D7 ; Bengali - 0x09D8, # .. 0x09DB ; Unknown - 0x09DC, # .. 0x09DD ; Bengali - 0x09DE, # .. 0x09DE ; Unknown - 0x09DF, # .. 0x09E3 ; Bengali - 0x09E4, # .. 0x09E5 ; Unknown - 0x09E6, # .. 0x09FE ; Bengali - 0x09FF, # .. 0x0A00 ; Unknown - 0x0A01, # .. 0x0A03 ; Gurmukhi - 0x0A04, # .. 0x0A04 ; Unknown - 0x0A05, # .. 0x0A0A ; Gurmukhi - 0x0A0B, # .. 0x0A0E ; Unknown - 0x0A0F, # .. 0x0A10 ; Gurmukhi - 0x0A11, # .. 0x0A12 ; Unknown - 0x0A13, # .. 0x0A28 ; Gurmukhi - 0x0A29, # .. 0x0A29 ; Unknown - 0x0A2A, # .. 0x0A30 ; Gurmukhi - 0x0A31, # .. 0x0A31 ; Unknown - 0x0A32, # .. 0x0A33 ; Gurmukhi - 0x0A34, # .. 0x0A34 ; Unknown - 0x0A35, # .. 0x0A36 ; Gurmukhi - 0x0A37, # .. 0x0A37 ; Unknown - 0x0A38, # .. 0x0A39 ; Gurmukhi - 0x0A3A, # .. 0x0A3B ; Unknown - 0x0A3C, # .. 0x0A3C ; Gurmukhi - 0x0A3D, # .. 0x0A3D ; Unknown - 0x0A3E, # .. 0x0A42 ; Gurmukhi - 0x0A43, # .. 0x0A46 ; Unknown - 0x0A47, # .. 0x0A48 ; Gurmukhi - 0x0A49, # .. 0x0A4A ; Unknown - 0x0A4B, # .. 0x0A4D ; Gurmukhi - 0x0A4E, # .. 0x0A50 ; Unknown - 0x0A51, # .. 0x0A51 ; Gurmukhi - 0x0A52, # .. 0x0A58 ; Unknown - 0x0A59, # .. 0x0A5C ; Gurmukhi - 0x0A5D, # .. 0x0A5D ; Unknown - 0x0A5E, # .. 0x0A5E ; Gurmukhi - 0x0A5F, # .. 0x0A65 ; Unknown - 0x0A66, # .. 0x0A76 ; Gurmukhi - 0x0A77, # .. 0x0A80 ; Unknown - 0x0A81, # .. 0x0A83 ; Gujarati - 0x0A84, # .. 0x0A84 ; Unknown - 0x0A85, # .. 0x0A8D ; Gujarati - 0x0A8E, # .. 0x0A8E ; Unknown - 0x0A8F, # .. 0x0A91 ; Gujarati - 0x0A92, # .. 0x0A92 ; Unknown - 0x0A93, # .. 0x0AA8 ; Gujarati - 0x0AA9, # .. 0x0AA9 ; Unknown - 0x0AAA, # .. 0x0AB0 ; Gujarati - 0x0AB1, # .. 0x0AB1 ; Unknown - 0x0AB2, # .. 0x0AB3 ; Gujarati - 0x0AB4, # .. 0x0AB4 ; Unknown - 0x0AB5, # .. 0x0AB9 ; Gujarati - 0x0ABA, # .. 0x0ABB ; Unknown - 0x0ABC, # .. 0x0AC5 ; Gujarati - 0x0AC6, # .. 0x0AC6 ; Unknown - 0x0AC7, # .. 0x0AC9 ; Gujarati - 0x0ACA, # .. 0x0ACA ; Unknown - 0x0ACB, # .. 0x0ACD ; Gujarati - 0x0ACE, # .. 0x0ACF ; Unknown - 0x0AD0, # .. 0x0AD0 ; Gujarati - 0x0AD1, # .. 0x0ADF ; Unknown - 0x0AE0, # .. 0x0AE3 ; Gujarati - 0x0AE4, # .. 0x0AE5 ; Unknown - 0x0AE6, # .. 0x0AF1 ; Gujarati - 0x0AF2, # .. 0x0AF8 ; Unknown - 0x0AF9, # .. 0x0AFF ; Gujarati - 0x0B00, # .. 0x0B00 ; Unknown - 0x0B01, # .. 0x0B03 ; Oriya - 0x0B04, # .. 0x0B04 ; Unknown - 0x0B05, # .. 0x0B0C ; Oriya - 0x0B0D, # .. 0x0B0E ; Unknown - 0x0B0F, # .. 0x0B10 ; Oriya - 0x0B11, # .. 0x0B12 ; Unknown - 0x0B13, # .. 0x0B28 ; Oriya - 0x0B29, # .. 0x0B29 ; Unknown - 0x0B2A, # .. 0x0B30 ; Oriya - 0x0B31, # .. 0x0B31 ; Unknown - 0x0B32, # .. 0x0B33 ; Oriya - 0x0B34, # .. 0x0B34 ; Unknown - 0x0B35, # .. 0x0B39 ; Oriya - 0x0B3A, # .. 0x0B3B ; Unknown - 0x0B3C, # .. 0x0B44 ; Oriya - 0x0B45, # .. 0x0B46 ; Unknown - 0x0B47, # .. 0x0B48 ; Oriya - 0x0B49, # .. 0x0B4A ; Unknown - 0x0B4B, # .. 0x0B4D ; Oriya - 0x0B4E, # .. 0x0B54 ; Unknown - 0x0B55, # .. 0x0B57 ; Oriya - 0x0B58, # .. 0x0B5B ; Unknown - 0x0B5C, # .. 0x0B5D ; Oriya - 0x0B5E, # .. 0x0B5E ; Unknown - 0x0B5F, # .. 0x0B63 ; Oriya - 0x0B64, # .. 0x0B65 ; Unknown - 0x0B66, # .. 0x0B77 ; Oriya - 0x0B78, # .. 0x0B81 ; Unknown - 0x0B82, # .. 0x0B83 ; Tamil - 0x0B84, # .. 0x0B84 ; Unknown - 0x0B85, # .. 0x0B8A ; Tamil - 0x0B8B, # .. 0x0B8D ; Unknown - 0x0B8E, # .. 0x0B90 ; Tamil - 0x0B91, # .. 0x0B91 ; Unknown - 0x0B92, # .. 0x0B95 ; Tamil - 0x0B96, # .. 0x0B98 ; Unknown - 0x0B99, # .. 0x0B9A ; Tamil - 0x0B9B, # .. 0x0B9B ; Unknown - 0x0B9C, # .. 0x0B9C ; Tamil - 0x0B9D, # .. 0x0B9D ; Unknown - 0x0B9E, # .. 0x0B9F ; Tamil - 0x0BA0, # .. 0x0BA2 ; Unknown - 0x0BA3, # .. 0x0BA4 ; Tamil - 0x0BA5, # .. 0x0BA7 ; Unknown - 0x0BA8, # .. 0x0BAA ; Tamil - 0x0BAB, # .. 0x0BAD ; Unknown - 0x0BAE, # .. 0x0BB9 ; Tamil - 0x0BBA, # .. 0x0BBD ; Unknown - 0x0BBE, # .. 0x0BC2 ; Tamil - 0x0BC3, # .. 0x0BC5 ; Unknown - 0x0BC6, # .. 0x0BC8 ; Tamil - 0x0BC9, # .. 0x0BC9 ; Unknown - 0x0BCA, # .. 0x0BCD ; Tamil - 0x0BCE, # .. 0x0BCF ; Unknown - 0x0BD0, # .. 0x0BD0 ; Tamil - 0x0BD1, # .. 0x0BD6 ; Unknown - 0x0BD7, # .. 0x0BD7 ; Tamil - 0x0BD8, # .. 0x0BE5 ; Unknown - 0x0BE6, # .. 0x0BFA ; Tamil - 0x0BFB, # .. 0x0BFF ; Unknown - 0x0C00, # .. 0x0C0C ; Telugu - 0x0C0D, # .. 0x0C0D ; Unknown - 0x0C0E, # .. 0x0C10 ; Telugu - 0x0C11, # .. 0x0C11 ; Unknown - 0x0C12, # .. 0x0C28 ; Telugu - 0x0C29, # .. 0x0C29 ; Unknown - 0x0C2A, # .. 0x0C39 ; Telugu - 0x0C3A, # .. 0x0C3B ; Unknown - 0x0C3C, # .. 0x0C44 ; Telugu - 0x0C45, # .. 0x0C45 ; Unknown - 0x0C46, # .. 0x0C48 ; Telugu - 0x0C49, # .. 0x0C49 ; Unknown - 0x0C4A, # .. 0x0C4D ; Telugu - 0x0C4E, # .. 0x0C54 ; Unknown - 0x0C55, # .. 0x0C56 ; Telugu - 0x0C57, # .. 0x0C57 ; Unknown - 0x0C58, # .. 0x0C5A ; Telugu - 0x0C5B, # .. 0x0C5C ; Unknown - 0x0C5D, # .. 0x0C5D ; Telugu - 0x0C5E, # .. 0x0C5F ; Unknown - 0x0C60, # .. 0x0C63 ; Telugu - 0x0C64, # .. 0x0C65 ; Unknown - 0x0C66, # .. 0x0C6F ; Telugu - 0x0C70, # .. 0x0C76 ; Unknown - 0x0C77, # .. 0x0C7F ; Telugu - 0x0C80, # .. 0x0C8C ; Kannada - 0x0C8D, # .. 0x0C8D ; Unknown - 0x0C8E, # .. 0x0C90 ; Kannada - 0x0C91, # .. 0x0C91 ; Unknown - 0x0C92, # .. 0x0CA8 ; Kannada - 0x0CA9, # .. 0x0CA9 ; Unknown - 0x0CAA, # .. 0x0CB3 ; Kannada - 0x0CB4, # .. 0x0CB4 ; Unknown - 0x0CB5, # .. 0x0CB9 ; Kannada - 0x0CBA, # .. 0x0CBB ; Unknown - 0x0CBC, # .. 0x0CC4 ; Kannada - 0x0CC5, # .. 0x0CC5 ; Unknown - 0x0CC6, # .. 0x0CC8 ; Kannada - 0x0CC9, # .. 0x0CC9 ; Unknown - 0x0CCA, # .. 0x0CCD ; Kannada - 0x0CCE, # .. 0x0CD4 ; Unknown - 0x0CD5, # .. 0x0CD6 ; Kannada - 0x0CD7, # .. 0x0CDC ; Unknown - 0x0CDD, # .. 0x0CDE ; Kannada - 0x0CDF, # .. 0x0CDF ; Unknown - 0x0CE0, # .. 0x0CE3 ; Kannada - 0x0CE4, # .. 0x0CE5 ; Unknown - 0x0CE6, # .. 0x0CEF ; Kannada - 0x0CF0, # .. 0x0CF0 ; Unknown - 0x0CF1, # .. 0x0CF3 ; Kannada - 0x0CF4, # .. 0x0CFF ; Unknown - 0x0D00, # .. 0x0D0C ; Malayalam - 0x0D0D, # .. 0x0D0D ; Unknown - 0x0D0E, # .. 0x0D10 ; Malayalam - 0x0D11, # .. 0x0D11 ; Unknown - 0x0D12, # .. 0x0D44 ; Malayalam - 0x0D45, # .. 0x0D45 ; Unknown - 0x0D46, # .. 0x0D48 ; Malayalam - 0x0D49, # .. 0x0D49 ; Unknown - 0x0D4A, # .. 0x0D4F ; Malayalam - 0x0D50, # .. 0x0D53 ; Unknown - 0x0D54, # .. 0x0D63 ; Malayalam - 0x0D64, # .. 0x0D65 ; Unknown - 0x0D66, # .. 0x0D7F ; Malayalam - 0x0D80, # .. 0x0D80 ; Unknown - 0x0D81, # .. 0x0D83 ; Sinhala - 0x0D84, # .. 0x0D84 ; Unknown - 0x0D85, # .. 0x0D96 ; Sinhala - 0x0D97, # .. 0x0D99 ; Unknown - 0x0D9A, # .. 0x0DB1 ; Sinhala - 0x0DB2, # .. 0x0DB2 ; Unknown - 0x0DB3, # .. 0x0DBB ; Sinhala - 0x0DBC, # .. 0x0DBC ; Unknown - 0x0DBD, # .. 0x0DBD ; Sinhala - 0x0DBE, # .. 0x0DBF ; Unknown - 0x0DC0, # .. 0x0DC6 ; Sinhala - 0x0DC7, # .. 0x0DC9 ; Unknown - 0x0DCA, # .. 0x0DCA ; Sinhala - 0x0DCB, # .. 0x0DCE ; Unknown - 0x0DCF, # .. 0x0DD4 ; Sinhala - 0x0DD5, # .. 0x0DD5 ; Unknown - 0x0DD6, # .. 0x0DD6 ; Sinhala - 0x0DD7, # .. 0x0DD7 ; Unknown - 0x0DD8, # .. 0x0DDF ; Sinhala - 0x0DE0, # .. 0x0DE5 ; Unknown - 0x0DE6, # .. 0x0DEF ; Sinhala - 0x0DF0, # .. 0x0DF1 ; Unknown - 0x0DF2, # .. 0x0DF4 ; Sinhala - 0x0DF5, # .. 0x0E00 ; Unknown - 0x0E01, # .. 0x0E3A ; Thai - 0x0E3B, # .. 0x0E3E ; Unknown - 0x0E3F, # .. 0x0E3F ; Common - 0x0E40, # .. 0x0E5B ; Thai - 0x0E5C, # .. 0x0E80 ; Unknown - 0x0E81, # .. 0x0E82 ; Lao - 0x0E83, # .. 0x0E83 ; Unknown - 0x0E84, # .. 0x0E84 ; Lao - 0x0E85, # .. 0x0E85 ; Unknown - 0x0E86, # .. 0x0E8A ; Lao - 0x0E8B, # .. 0x0E8B ; Unknown - 0x0E8C, # .. 0x0EA3 ; Lao - 0x0EA4, # .. 0x0EA4 ; Unknown - 0x0EA5, # .. 0x0EA5 ; Lao - 0x0EA6, # .. 0x0EA6 ; Unknown - 0x0EA7, # .. 0x0EBD ; Lao - 0x0EBE, # .. 0x0EBF ; Unknown - 0x0EC0, # .. 0x0EC4 ; Lao - 0x0EC5, # .. 0x0EC5 ; Unknown - 0x0EC6, # .. 0x0EC6 ; Lao - 0x0EC7, # .. 0x0EC7 ; Unknown - 0x0EC8, # .. 0x0ECE ; Lao - 0x0ECF, # .. 0x0ECF ; Unknown - 0x0ED0, # .. 0x0ED9 ; Lao - 0x0EDA, # .. 0x0EDB ; Unknown - 0x0EDC, # .. 0x0EDF ; Lao - 0x0EE0, # .. 0x0EFF ; Unknown - 0x0F00, # .. 0x0F47 ; Tibetan - 0x0F48, # .. 0x0F48 ; Unknown - 0x0F49, # .. 0x0F6C ; Tibetan - 0x0F6D, # .. 0x0F70 ; Unknown - 0x0F71, # .. 0x0F97 ; Tibetan - 0x0F98, # .. 0x0F98 ; Unknown - 0x0F99, # .. 0x0FBC ; Tibetan - 0x0FBD, # .. 0x0FBD ; Unknown - 0x0FBE, # .. 0x0FCC ; Tibetan - 0x0FCD, # .. 0x0FCD ; Unknown - 0x0FCE, # .. 0x0FD4 ; Tibetan - 0x0FD5, # .. 0x0FD8 ; Common - 0x0FD9, # .. 0x0FDA ; Tibetan - 0x0FDB, # .. 0x0FFF ; Unknown - 0x1000, # .. 0x109F ; Myanmar - 0x10A0, # .. 0x10C5 ; Georgian - 0x10C6, # .. 0x10C6 ; Unknown - 0x10C7, # .. 0x10C7 ; Georgian - 0x10C8, # .. 0x10CC ; Unknown - 0x10CD, # .. 0x10CD ; Georgian - 0x10CE, # .. 0x10CF ; Unknown - 0x10D0, # .. 0x10FA ; Georgian - 0x10FB, # .. 0x10FB ; Common - 0x10FC, # .. 0x10FF ; Georgian - 0x1100, # .. 0x11FF ; Hangul - 0x1200, # .. 0x1248 ; Ethiopic - 0x1249, # .. 0x1249 ; Unknown - 0x124A, # .. 0x124D ; Ethiopic - 0x124E, # .. 0x124F ; Unknown - 0x1250, # .. 0x1256 ; Ethiopic - 0x1257, # .. 0x1257 ; Unknown - 0x1258, # .. 0x1258 ; Ethiopic - 0x1259, # .. 0x1259 ; Unknown - 0x125A, # .. 0x125D ; Ethiopic - 0x125E, # .. 0x125F ; Unknown - 0x1260, # .. 0x1288 ; Ethiopic - 0x1289, # .. 0x1289 ; Unknown - 0x128A, # .. 0x128D ; Ethiopic - 0x128E, # .. 0x128F ; Unknown - 0x1290, # .. 0x12B0 ; Ethiopic - 0x12B1, # .. 0x12B1 ; Unknown - 0x12B2, # .. 0x12B5 ; Ethiopic - 0x12B6, # .. 0x12B7 ; Unknown - 0x12B8, # .. 0x12BE ; Ethiopic - 0x12BF, # .. 0x12BF ; Unknown - 0x12C0, # .. 0x12C0 ; Ethiopic - 0x12C1, # .. 0x12C1 ; Unknown - 0x12C2, # .. 0x12C5 ; Ethiopic - 0x12C6, # .. 0x12C7 ; Unknown - 0x12C8, # .. 0x12D6 ; Ethiopic - 0x12D7, # .. 0x12D7 ; Unknown - 0x12D8, # .. 0x1310 ; Ethiopic - 0x1311, # .. 0x1311 ; Unknown - 0x1312, # .. 0x1315 ; Ethiopic - 0x1316, # .. 0x1317 ; Unknown - 0x1318, # .. 0x135A ; Ethiopic - 0x135B, # .. 0x135C ; Unknown - 0x135D, # .. 0x137C ; Ethiopic - 0x137D, # .. 0x137F ; Unknown - 0x1380, # .. 0x1399 ; Ethiopic - 0x139A, # .. 0x139F ; Unknown - 0x13A0, # .. 0x13F5 ; Cherokee - 0x13F6, # .. 0x13F7 ; Unknown - 0x13F8, # .. 0x13FD ; Cherokee - 0x13FE, # .. 0x13FF ; Unknown - 0x1400, # .. 0x167F ; Canadian_Aboriginal - 0x1680, # .. 0x169C ; Ogham - 0x169D, # .. 0x169F ; Unknown - 0x16A0, # .. 0x16EA ; Runic - 0x16EB, # .. 0x16ED ; Common - 0x16EE, # .. 0x16F8 ; Runic - 0x16F9, # .. 0x16FF ; Unknown - 0x1700, # .. 0x1715 ; Tagalog - 0x1716, # .. 0x171E ; Unknown - 0x171F, # .. 0x171F ; Tagalog - 0x1720, # .. 0x1734 ; Hanunoo - 0x1735, # .. 0x1736 ; Common - 0x1737, # .. 0x173F ; Unknown - 0x1740, # .. 0x1753 ; Buhid - 0x1754, # .. 0x175F ; Unknown - 0x1760, # .. 0x176C ; Tagbanwa - 0x176D, # .. 0x176D ; Unknown - 0x176E, # .. 0x1770 ; Tagbanwa - 0x1771, # .. 0x1771 ; Unknown - 0x1772, # .. 0x1773 ; Tagbanwa - 0x1774, # .. 0x177F ; Unknown - 0x1780, # .. 0x17DD ; Khmer - 0x17DE, # .. 0x17DF ; Unknown - 0x17E0, # .. 0x17E9 ; Khmer - 0x17EA, # .. 0x17EF ; Unknown - 0x17F0, # .. 0x17F9 ; Khmer - 0x17FA, # .. 0x17FF ; Unknown - 0x1800, # .. 0x1801 ; Mongolian - 0x1802, # .. 0x1803 ; Common - 0x1804, # .. 0x1804 ; Mongolian - 0x1805, # .. 0x1805 ; Common - 0x1806, # .. 0x1819 ; Mongolian - 0x181A, # .. 0x181F ; Unknown - 0x1820, # .. 0x1878 ; Mongolian - 0x1879, # .. 0x187F ; Unknown - 0x1880, # .. 0x18AA ; Mongolian - 0x18AB, # .. 0x18AF ; Unknown - 0x18B0, # .. 0x18F5 ; Canadian_Aboriginal - 0x18F6, # .. 0x18FF ; Unknown - 0x1900, # .. 0x191E ; Limbu - 0x191F, # .. 0x191F ; Unknown - 0x1920, # .. 0x192B ; Limbu - 0x192C, # .. 0x192F ; Unknown - 0x1930, # .. 0x193B ; Limbu - 0x193C, # .. 0x193F ; Unknown - 0x1940, # .. 0x1940 ; Limbu - 0x1941, # .. 0x1943 ; Unknown - 0x1944, # .. 0x194F ; Limbu - 0x1950, # .. 0x196D ; Tai_Le - 0x196E, # .. 0x196F ; Unknown - 0x1970, # .. 0x1974 ; Tai_Le - 0x1975, # .. 0x197F ; Unknown - 0x1980, # .. 0x19AB ; New_Tai_Lue - 0x19AC, # .. 0x19AF ; Unknown - 0x19B0, # .. 0x19C9 ; New_Tai_Lue - 0x19CA, # .. 0x19CF ; Unknown - 0x19D0, # .. 0x19DA ; New_Tai_Lue - 0x19DB, # .. 0x19DD ; Unknown - 0x19DE, # .. 0x19DF ; New_Tai_Lue - 0x19E0, # .. 0x19FF ; Khmer - 0x1A00, # .. 0x1A1B ; Buginese - 0x1A1C, # .. 0x1A1D ; Unknown - 0x1A1E, # .. 0x1A1F ; Buginese - 0x1A20, # .. 0x1A5E ; Tai_Tham - 0x1A5F, # .. 0x1A5F ; Unknown - 0x1A60, # .. 0x1A7C ; Tai_Tham - 0x1A7D, # .. 0x1A7E ; Unknown - 0x1A7F, # .. 0x1A89 ; Tai_Tham - 0x1A8A, # .. 0x1A8F ; Unknown - 0x1A90, # .. 0x1A99 ; Tai_Tham - 0x1A9A, # .. 0x1A9F ; Unknown - 0x1AA0, # .. 0x1AAD ; Tai_Tham - 0x1AAE, # .. 0x1AAF ; Unknown - 0x1AB0, # .. 0x1ACE ; Inherited - 0x1ACF, # .. 0x1AFF ; Unknown - 0x1B00, # .. 0x1B4C ; Balinese - 0x1B4D, # .. 0x1B4F ; Unknown - 0x1B50, # .. 0x1B7E ; Balinese - 0x1B7F, # .. 0x1B7F ; Unknown - 0x1B80, # .. 0x1BBF ; Sundanese - 0x1BC0, # .. 0x1BF3 ; Batak - 0x1BF4, # .. 0x1BFB ; Unknown - 0x1BFC, # .. 0x1BFF ; Batak - 0x1C00, # .. 0x1C37 ; Lepcha - 0x1C38, # .. 0x1C3A ; Unknown - 0x1C3B, # .. 0x1C49 ; Lepcha - 0x1C4A, # .. 0x1C4C ; Unknown - 0x1C4D, # .. 0x1C4F ; Lepcha - 0x1C50, # .. 0x1C7F ; Ol_Chiki - 0x1C80, # .. 0x1C88 ; Cyrillic - 0x1C89, # .. 0x1C8F ; Unknown - 0x1C90, # .. 0x1CBA ; Georgian - 0x1CBB, # .. 0x1CBC ; Unknown - 0x1CBD, # .. 0x1CBF ; Georgian - 0x1CC0, # .. 0x1CC7 ; Sundanese - 0x1CC8, # .. 0x1CCF ; Unknown - 0x1CD0, # .. 0x1CD2 ; Inherited - 0x1CD3, # .. 0x1CD3 ; Common - 0x1CD4, # .. 0x1CE0 ; Inherited - 0x1CE1, # .. 0x1CE1 ; Common - 0x1CE2, # .. 0x1CE8 ; Inherited - 0x1CE9, # .. 0x1CEC ; Common - 0x1CED, # .. 0x1CED ; Inherited - 0x1CEE, # .. 0x1CF3 ; Common - 0x1CF4, # .. 0x1CF4 ; Inherited - 0x1CF5, # .. 0x1CF7 ; Common - 0x1CF8, # .. 0x1CF9 ; Inherited - 0x1CFA, # .. 0x1CFA ; Common - 0x1CFB, # .. 0x1CFF ; Unknown - 0x1D00, # .. 0x1D25 ; Latin - 0x1D26, # .. 0x1D2A ; Greek - 0x1D2B, # .. 0x1D2B ; Cyrillic - 0x1D2C, # .. 0x1D5C ; Latin - 0x1D5D, # .. 0x1D61 ; Greek - 0x1D62, # .. 0x1D65 ; Latin - 0x1D66, # .. 0x1D6A ; Greek - 0x1D6B, # .. 0x1D77 ; Latin - 0x1D78, # .. 0x1D78 ; Cyrillic - 0x1D79, # .. 0x1DBE ; Latin - 0x1DBF, # .. 0x1DBF ; Greek - 0x1DC0, # .. 0x1DFF ; Inherited - 0x1E00, # .. 0x1EFF ; Latin - 0x1F00, # .. 0x1F15 ; Greek - 0x1F16, # .. 0x1F17 ; Unknown - 0x1F18, # .. 0x1F1D ; Greek - 0x1F1E, # .. 0x1F1F ; Unknown - 0x1F20, # .. 0x1F45 ; Greek - 0x1F46, # .. 0x1F47 ; Unknown - 0x1F48, # .. 0x1F4D ; Greek - 0x1F4E, # .. 0x1F4F ; Unknown - 0x1F50, # .. 0x1F57 ; Greek - 0x1F58, # .. 0x1F58 ; Unknown - 0x1F59, # .. 0x1F59 ; Greek - 0x1F5A, # .. 0x1F5A ; Unknown - 0x1F5B, # .. 0x1F5B ; Greek - 0x1F5C, # .. 0x1F5C ; Unknown - 0x1F5D, # .. 0x1F5D ; Greek - 0x1F5E, # .. 0x1F5E ; Unknown - 0x1F5F, # .. 0x1F7D ; Greek - 0x1F7E, # .. 0x1F7F ; Unknown - 0x1F80, # .. 0x1FB4 ; Greek - 0x1FB5, # .. 0x1FB5 ; Unknown - 0x1FB6, # .. 0x1FC4 ; Greek - 0x1FC5, # .. 0x1FC5 ; Unknown - 0x1FC6, # .. 0x1FD3 ; Greek - 0x1FD4, # .. 0x1FD5 ; Unknown - 0x1FD6, # .. 0x1FDB ; Greek - 0x1FDC, # .. 0x1FDC ; Unknown - 0x1FDD, # .. 0x1FEF ; Greek - 0x1FF0, # .. 0x1FF1 ; Unknown - 0x1FF2, # .. 0x1FF4 ; Greek - 0x1FF5, # .. 0x1FF5 ; Unknown - 0x1FF6, # .. 0x1FFE ; Greek - 0x1FFF, # .. 0x1FFF ; Unknown - 0x2000, # .. 0x200B ; Common - 0x200C, # .. 0x200D ; Inherited - 0x200E, # .. 0x2064 ; Common - 0x2065, # .. 0x2065 ; Unknown - 0x2066, # .. 0x2070 ; Common - 0x2071, # .. 0x2071 ; Latin - 0x2072, # .. 0x2073 ; Unknown - 0x2074, # .. 0x207E ; Common - 0x207F, # .. 0x207F ; Latin - 0x2080, # .. 0x208E ; Common - 0x208F, # .. 0x208F ; Unknown - 0x2090, # .. 0x209C ; Latin - 0x209D, # .. 0x209F ; Unknown - 0x20A0, # .. 0x20C0 ; Common - 0x20C1, # .. 0x20CF ; Unknown - 0x20D0, # .. 0x20F0 ; Inherited - 0x20F1, # .. 0x20FF ; Unknown - 0x2100, # .. 0x2125 ; Common - 0x2126, # .. 0x2126 ; Greek - 0x2127, # .. 0x2129 ; Common - 0x212A, # .. 0x212B ; Latin - 0x212C, # .. 0x2131 ; Common - 0x2132, # .. 0x2132 ; Latin - 0x2133, # .. 0x214D ; Common - 0x214E, # .. 0x214E ; Latin - 0x214F, # .. 0x215F ; Common - 0x2160, # .. 0x2188 ; Latin - 0x2189, # .. 0x218B ; Common - 0x218C, # .. 0x218F ; Unknown - 0x2190, # .. 0x2426 ; Common - 0x2427, # .. 0x243F ; Unknown - 0x2440, # .. 0x244A ; Common - 0x244B, # .. 0x245F ; Unknown - 0x2460, # .. 0x27FF ; Common - 0x2800, # .. 0x28FF ; Braille - 0x2900, # .. 0x2B73 ; Common - 0x2B74, # .. 0x2B75 ; Unknown - 0x2B76, # .. 0x2B95 ; Common - 0x2B96, # .. 0x2B96 ; Unknown - 0x2B97, # .. 0x2BFF ; Common - 0x2C00, # .. 0x2C5F ; Glagolitic - 0x2C60, # .. 0x2C7F ; Latin - 0x2C80, # .. 0x2CF3 ; Coptic - 0x2CF4, # .. 0x2CF8 ; Unknown - 0x2CF9, # .. 0x2CFF ; Coptic - 0x2D00, # .. 0x2D25 ; Georgian - 0x2D26, # .. 0x2D26 ; Unknown - 0x2D27, # .. 0x2D27 ; Georgian - 0x2D28, # .. 0x2D2C ; Unknown - 0x2D2D, # .. 0x2D2D ; Georgian - 0x2D2E, # .. 0x2D2F ; Unknown - 0x2D30, # .. 0x2D67 ; Tifinagh - 0x2D68, # .. 0x2D6E ; Unknown - 0x2D6F, # .. 0x2D70 ; Tifinagh - 0x2D71, # .. 0x2D7E ; Unknown - 0x2D7F, # .. 0x2D7F ; Tifinagh - 0x2D80, # .. 0x2D96 ; Ethiopic - 0x2D97, # .. 0x2D9F ; Unknown - 0x2DA0, # .. 0x2DA6 ; Ethiopic - 0x2DA7, # .. 0x2DA7 ; Unknown - 0x2DA8, # .. 0x2DAE ; Ethiopic - 0x2DAF, # .. 0x2DAF ; Unknown - 0x2DB0, # .. 0x2DB6 ; Ethiopic - 0x2DB7, # .. 0x2DB7 ; Unknown - 0x2DB8, # .. 0x2DBE ; Ethiopic - 0x2DBF, # .. 0x2DBF ; Unknown - 0x2DC0, # .. 0x2DC6 ; Ethiopic - 0x2DC7, # .. 0x2DC7 ; Unknown - 0x2DC8, # .. 0x2DCE ; Ethiopic - 0x2DCF, # .. 0x2DCF ; Unknown - 0x2DD0, # .. 0x2DD6 ; Ethiopic - 0x2DD7, # .. 0x2DD7 ; Unknown - 0x2DD8, # .. 0x2DDE ; Ethiopic - 0x2DDF, # .. 0x2DDF ; Unknown - 0x2DE0, # .. 0x2DFF ; Cyrillic - 0x2E00, # .. 0x2E5D ; Common - 0x2E5E, # .. 0x2E7F ; Unknown - 0x2E80, # .. 0x2E99 ; Han - 0x2E9A, # .. 0x2E9A ; Unknown - 0x2E9B, # .. 0x2EF3 ; Han - 0x2EF4, # .. 0x2EFF ; Unknown - 0x2F00, # .. 0x2FD5 ; Han - 0x2FD6, # .. 0x2FEF ; Unknown - 0x2FF0, # .. 0x2FFB ; Common - 0x2FFC, # .. 0x2FFF ; Unknown - 0x3000, # .. 0x3004 ; Common - 0x3005, # .. 0x3005 ; Han - 0x3006, # .. 0x3006 ; Common - 0x3007, # .. 0x3007 ; Han - 0x3008, # .. 0x3020 ; Common - 0x3021, # .. 0x3029 ; Han - 0x302A, # .. 0x302D ; Inherited - 0x302E, # .. 0x302F ; Hangul - 0x3030, # .. 0x3037 ; Common - 0x3038, # .. 0x303B ; Han - 0x303C, # .. 0x303F ; Common - 0x3040, # .. 0x3040 ; Unknown - 0x3041, # .. 0x3096 ; Hiragana - 0x3097, # .. 0x3098 ; Unknown - 0x3099, # .. 0x309A ; Inherited - 0x309B, # .. 0x309C ; Common - 0x309D, # .. 0x309F ; Hiragana - 0x30A0, # .. 0x30A0 ; Common - 0x30A1, # .. 0x30FA ; Katakana - 0x30FB, # .. 0x30FC ; Common - 0x30FD, # .. 0x30FF ; Katakana - 0x3100, # .. 0x3104 ; Unknown - 0x3105, # .. 0x312F ; Bopomofo - 0x3130, # .. 0x3130 ; Unknown - 0x3131, # .. 0x318E ; Hangul - 0x318F, # .. 0x318F ; Unknown - 0x3190, # .. 0x319F ; Common - 0x31A0, # .. 0x31BF ; Bopomofo - 0x31C0, # .. 0x31E3 ; Common - 0x31E4, # .. 0x31EF ; Unknown - 0x31F0, # .. 0x31FF ; Katakana - 0x3200, # .. 0x321E ; Hangul - 0x321F, # .. 0x321F ; Unknown - 0x3220, # .. 0x325F ; Common - 0x3260, # .. 0x327E ; Hangul - 0x327F, # .. 0x32CF ; Common - 0x32D0, # .. 0x32FE ; Katakana - 0x32FF, # .. 0x32FF ; Common - 0x3300, # .. 0x3357 ; Katakana - 0x3358, # .. 0x33FF ; Common - 0x3400, # .. 0x4DBF ; Han - 0x4DC0, # .. 0x4DFF ; Common - 0x4E00, # .. 0x9FFF ; Han - 0xA000, # .. 0xA48C ; Yi - 0xA48D, # .. 0xA48F ; Unknown - 0xA490, # .. 0xA4C6 ; Yi - 0xA4C7, # .. 0xA4CF ; Unknown - 0xA4D0, # .. 0xA4FF ; Lisu - 0xA500, # .. 0xA62B ; Vai - 0xA62C, # .. 0xA63F ; Unknown - 0xA640, # .. 0xA69F ; Cyrillic - 0xA6A0, # .. 0xA6F7 ; Bamum - 0xA6F8, # .. 0xA6FF ; Unknown - 0xA700, # .. 0xA721 ; Common - 0xA722, # .. 0xA787 ; Latin - 0xA788, # .. 0xA78A ; Common - 0xA78B, # .. 0xA7CA ; Latin - 0xA7CB, # .. 0xA7CF ; Unknown - 0xA7D0, # .. 0xA7D1 ; Latin - 0xA7D2, # .. 0xA7D2 ; Unknown - 0xA7D3, # .. 0xA7D3 ; Latin - 0xA7D4, # .. 0xA7D4 ; Unknown - 0xA7D5, # .. 0xA7D9 ; Latin - 0xA7DA, # .. 0xA7F1 ; Unknown - 0xA7F2, # .. 0xA7FF ; Latin - 0xA800, # .. 0xA82C ; Syloti_Nagri - 0xA82D, # .. 0xA82F ; Unknown - 0xA830, # .. 0xA839 ; Common - 0xA83A, # .. 0xA83F ; Unknown - 0xA840, # .. 0xA877 ; Phags_Pa - 0xA878, # .. 0xA87F ; Unknown - 0xA880, # .. 0xA8C5 ; Saurashtra - 0xA8C6, # .. 0xA8CD ; Unknown - 0xA8CE, # .. 0xA8D9 ; Saurashtra - 0xA8DA, # .. 0xA8DF ; Unknown - 0xA8E0, # .. 0xA8FF ; Devanagari - 0xA900, # .. 0xA92D ; Kayah_Li - 0xA92E, # .. 0xA92E ; Common - 0xA92F, # .. 0xA92F ; Kayah_Li - 0xA930, # .. 0xA953 ; Rejang - 0xA954, # .. 0xA95E ; Unknown - 0xA95F, # .. 0xA95F ; Rejang - 0xA960, # .. 0xA97C ; Hangul - 0xA97D, # .. 0xA97F ; Unknown - 0xA980, # .. 0xA9CD ; Javanese - 0xA9CE, # .. 0xA9CE ; Unknown - 0xA9CF, # .. 0xA9CF ; Common - 0xA9D0, # .. 0xA9D9 ; Javanese - 0xA9DA, # .. 0xA9DD ; Unknown - 0xA9DE, # .. 0xA9DF ; Javanese - 0xA9E0, # .. 0xA9FE ; Myanmar - 0xA9FF, # .. 0xA9FF ; Unknown - 0xAA00, # .. 0xAA36 ; Cham - 0xAA37, # .. 0xAA3F ; Unknown - 0xAA40, # .. 0xAA4D ; Cham - 0xAA4E, # .. 0xAA4F ; Unknown - 0xAA50, # .. 0xAA59 ; Cham - 0xAA5A, # .. 0xAA5B ; Unknown - 0xAA5C, # .. 0xAA5F ; Cham - 0xAA60, # .. 0xAA7F ; Myanmar - 0xAA80, # .. 0xAAC2 ; Tai_Viet - 0xAAC3, # .. 0xAADA ; Unknown - 0xAADB, # .. 0xAADF ; Tai_Viet - 0xAAE0, # .. 0xAAF6 ; Meetei_Mayek - 0xAAF7, # .. 0xAB00 ; Unknown - 0xAB01, # .. 0xAB06 ; Ethiopic - 0xAB07, # .. 0xAB08 ; Unknown - 0xAB09, # .. 0xAB0E ; Ethiopic - 0xAB0F, # .. 0xAB10 ; Unknown - 0xAB11, # .. 0xAB16 ; Ethiopic - 0xAB17, # .. 0xAB1F ; Unknown - 0xAB20, # .. 0xAB26 ; Ethiopic - 0xAB27, # .. 0xAB27 ; Unknown - 0xAB28, # .. 0xAB2E ; Ethiopic - 0xAB2F, # .. 0xAB2F ; Unknown - 0xAB30, # .. 0xAB5A ; Latin - 0xAB5B, # .. 0xAB5B ; Common - 0xAB5C, # .. 0xAB64 ; Latin - 0xAB65, # .. 0xAB65 ; Greek - 0xAB66, # .. 0xAB69 ; Latin - 0xAB6A, # .. 0xAB6B ; Common - 0xAB6C, # .. 0xAB6F ; Unknown - 0xAB70, # .. 0xABBF ; Cherokee - 0xABC0, # .. 0xABED ; Meetei_Mayek - 0xABEE, # .. 0xABEF ; Unknown - 0xABF0, # .. 0xABF9 ; Meetei_Mayek - 0xABFA, # .. 0xABFF ; Unknown - 0xAC00, # .. 0xD7A3 ; Hangul - 0xD7A4, # .. 0xD7AF ; Unknown - 0xD7B0, # .. 0xD7C6 ; Hangul - 0xD7C7, # .. 0xD7CA ; Unknown - 0xD7CB, # .. 0xD7FB ; Hangul - 0xD7FC, # .. 0xF8FF ; Unknown - 0xF900, # .. 0xFA6D ; Han - 0xFA6E, # .. 0xFA6F ; Unknown - 0xFA70, # .. 0xFAD9 ; Han - 0xFADA, # .. 0xFAFF ; Unknown - 0xFB00, # .. 0xFB06 ; Latin - 0xFB07, # .. 0xFB12 ; Unknown - 0xFB13, # .. 0xFB17 ; Armenian - 0xFB18, # .. 0xFB1C ; Unknown - 0xFB1D, # .. 0xFB36 ; Hebrew - 0xFB37, # .. 0xFB37 ; Unknown - 0xFB38, # .. 0xFB3C ; Hebrew - 0xFB3D, # .. 0xFB3D ; Unknown - 0xFB3E, # .. 0xFB3E ; Hebrew - 0xFB3F, # .. 0xFB3F ; Unknown - 0xFB40, # .. 0xFB41 ; Hebrew - 0xFB42, # .. 0xFB42 ; Unknown - 0xFB43, # .. 0xFB44 ; Hebrew - 0xFB45, # .. 0xFB45 ; Unknown - 0xFB46, # .. 0xFB4F ; Hebrew - 0xFB50, # .. 0xFBC2 ; Arabic - 0xFBC3, # .. 0xFBD2 ; Unknown - 0xFBD3, # .. 0xFD3D ; Arabic - 0xFD3E, # .. 0xFD3F ; Common - 0xFD40, # .. 0xFD8F ; Arabic - 0xFD90, # .. 0xFD91 ; Unknown - 0xFD92, # .. 0xFDC7 ; Arabic - 0xFDC8, # .. 0xFDCE ; Unknown - 0xFDCF, # .. 0xFDCF ; Arabic - 0xFDD0, # .. 0xFDEF ; Unknown - 0xFDF0, # .. 0xFDFF ; Arabic - 0xFE00, # .. 0xFE0F ; Inherited - 0xFE10, # .. 0xFE19 ; Common - 0xFE1A, # .. 0xFE1F ; Unknown - 0xFE20, # .. 0xFE2D ; Inherited - 0xFE2E, # .. 0xFE2F ; Cyrillic - 0xFE30, # .. 0xFE52 ; Common - 0xFE53, # .. 0xFE53 ; Unknown - 0xFE54, # .. 0xFE66 ; Common - 0xFE67, # .. 0xFE67 ; Unknown - 0xFE68, # .. 0xFE6B ; Common - 0xFE6C, # .. 0xFE6F ; Unknown - 0xFE70, # .. 0xFE74 ; Arabic - 0xFE75, # .. 0xFE75 ; Unknown - 0xFE76, # .. 0xFEFC ; Arabic - 0xFEFD, # .. 0xFEFE ; Unknown - 0xFEFF, # .. 0xFEFF ; Common - 0xFF00, # .. 0xFF00 ; Unknown - 0xFF01, # .. 0xFF20 ; Common - 0xFF21, # .. 0xFF3A ; Latin - 0xFF3B, # .. 0xFF40 ; Common - 0xFF41, # .. 0xFF5A ; Latin - 0xFF5B, # .. 0xFF65 ; Common - 0xFF66, # .. 0xFF6F ; Katakana - 0xFF70, # .. 0xFF70 ; Common - 0xFF71, # .. 0xFF9D ; Katakana - 0xFF9E, # .. 0xFF9F ; Common - 0xFFA0, # .. 0xFFBE ; Hangul - 0xFFBF, # .. 0xFFC1 ; Unknown - 0xFFC2, # .. 0xFFC7 ; Hangul - 0xFFC8, # .. 0xFFC9 ; Unknown - 0xFFCA, # .. 0xFFCF ; Hangul - 0xFFD0, # .. 0xFFD1 ; Unknown - 0xFFD2, # .. 0xFFD7 ; Hangul - 0xFFD8, # .. 0xFFD9 ; Unknown - 0xFFDA, # .. 0xFFDC ; Hangul - 0xFFDD, # .. 0xFFDF ; Unknown - 0xFFE0, # .. 0xFFE6 ; Common - 0xFFE7, # .. 0xFFE7 ; Unknown - 0xFFE8, # .. 0xFFEE ; Common - 0xFFEF, # .. 0xFFF8 ; Unknown - 0xFFF9, # .. 0xFFFD ; Common - 0xFFFE, # .. 0xFFFF ; Unknown - 0x10000, # .. 0x1000B ; Linear_B - 0x1000C, # .. 0x1000C ; Unknown - 0x1000D, # .. 0x10026 ; Linear_B - 0x10027, # .. 0x10027 ; Unknown - 0x10028, # .. 0x1003A ; Linear_B - 0x1003B, # .. 0x1003B ; Unknown - 0x1003C, # .. 0x1003D ; Linear_B - 0x1003E, # .. 0x1003E ; Unknown - 0x1003F, # .. 0x1004D ; Linear_B - 0x1004E, # .. 0x1004F ; Unknown - 0x10050, # .. 0x1005D ; Linear_B - 0x1005E, # .. 0x1007F ; Unknown - 0x10080, # .. 0x100FA ; Linear_B - 0x100FB, # .. 0x100FF ; Unknown - 0x10100, # .. 0x10102 ; Common - 0x10103, # .. 0x10106 ; Unknown - 0x10107, # .. 0x10133 ; Common - 0x10134, # .. 0x10136 ; Unknown - 0x10137, # .. 0x1013F ; Common - 0x10140, # .. 0x1018E ; Greek - 0x1018F, # .. 0x1018F ; Unknown - 0x10190, # .. 0x1019C ; Common - 0x1019D, # .. 0x1019F ; Unknown - 0x101A0, # .. 0x101A0 ; Greek - 0x101A1, # .. 0x101CF ; Unknown - 0x101D0, # .. 0x101FC ; Common - 0x101FD, # .. 0x101FD ; Inherited - 0x101FE, # .. 0x1027F ; Unknown - 0x10280, # .. 0x1029C ; Lycian - 0x1029D, # .. 0x1029F ; Unknown - 0x102A0, # .. 0x102D0 ; Carian - 0x102D1, # .. 0x102DF ; Unknown - 0x102E0, # .. 0x102E0 ; Inherited - 0x102E1, # .. 0x102FB ; Common - 0x102FC, # .. 0x102FF ; Unknown - 0x10300, # .. 0x10323 ; Old_Italic - 0x10324, # .. 0x1032C ; Unknown - 0x1032D, # .. 0x1032F ; Old_Italic - 0x10330, # .. 0x1034A ; Gothic - 0x1034B, # .. 0x1034F ; Unknown - 0x10350, # .. 0x1037A ; Old_Permic - 0x1037B, # .. 0x1037F ; Unknown - 0x10380, # .. 0x1039D ; Ugaritic - 0x1039E, # .. 0x1039E ; Unknown - 0x1039F, # .. 0x1039F ; Ugaritic - 0x103A0, # .. 0x103C3 ; Old_Persian - 0x103C4, # .. 0x103C7 ; Unknown - 0x103C8, # .. 0x103D5 ; Old_Persian - 0x103D6, # .. 0x103FF ; Unknown - 0x10400, # .. 0x1044F ; Deseret - 0x10450, # .. 0x1047F ; Shavian - 0x10480, # .. 0x1049D ; Osmanya - 0x1049E, # .. 0x1049F ; Unknown - 0x104A0, # .. 0x104A9 ; Osmanya - 0x104AA, # .. 0x104AF ; Unknown - 0x104B0, # .. 0x104D3 ; Osage - 0x104D4, # .. 0x104D7 ; Unknown - 0x104D8, # .. 0x104FB ; Osage - 0x104FC, # .. 0x104FF ; Unknown - 0x10500, # .. 0x10527 ; Elbasan - 0x10528, # .. 0x1052F ; Unknown - 0x10530, # .. 0x10563 ; Caucasian_Albanian - 0x10564, # .. 0x1056E ; Unknown - 0x1056F, # .. 0x1056F ; Caucasian_Albanian - 0x10570, # .. 0x1057A ; Vithkuqi - 0x1057B, # .. 0x1057B ; Unknown - 0x1057C, # .. 0x1058A ; Vithkuqi - 0x1058B, # .. 0x1058B ; Unknown - 0x1058C, # .. 0x10592 ; Vithkuqi - 0x10593, # .. 0x10593 ; Unknown - 0x10594, # .. 0x10595 ; Vithkuqi - 0x10596, # .. 0x10596 ; Unknown - 0x10597, # .. 0x105A1 ; Vithkuqi - 0x105A2, # .. 0x105A2 ; Unknown - 0x105A3, # .. 0x105B1 ; Vithkuqi - 0x105B2, # .. 0x105B2 ; Unknown - 0x105B3, # .. 0x105B9 ; Vithkuqi - 0x105BA, # .. 0x105BA ; Unknown - 0x105BB, # .. 0x105BC ; Vithkuqi - 0x105BD, # .. 0x105FF ; Unknown - 0x10600, # .. 0x10736 ; Linear_A - 0x10737, # .. 0x1073F ; Unknown - 0x10740, # .. 0x10755 ; Linear_A - 0x10756, # .. 0x1075F ; Unknown - 0x10760, # .. 0x10767 ; Linear_A - 0x10768, # .. 0x1077F ; Unknown - 0x10780, # .. 0x10785 ; Latin - 0x10786, # .. 0x10786 ; Unknown - 0x10787, # .. 0x107B0 ; Latin - 0x107B1, # .. 0x107B1 ; Unknown - 0x107B2, # .. 0x107BA ; Latin - 0x107BB, # .. 0x107FF ; Unknown - 0x10800, # .. 0x10805 ; Cypriot - 0x10806, # .. 0x10807 ; Unknown - 0x10808, # .. 0x10808 ; Cypriot - 0x10809, # .. 0x10809 ; Unknown - 0x1080A, # .. 0x10835 ; Cypriot - 0x10836, # .. 0x10836 ; Unknown - 0x10837, # .. 0x10838 ; Cypriot - 0x10839, # .. 0x1083B ; Unknown - 0x1083C, # .. 0x1083C ; Cypriot - 0x1083D, # .. 0x1083E ; Unknown - 0x1083F, # .. 0x1083F ; Cypriot - 0x10840, # .. 0x10855 ; Imperial_Aramaic - 0x10856, # .. 0x10856 ; Unknown - 0x10857, # .. 0x1085F ; Imperial_Aramaic - 0x10860, # .. 0x1087F ; Palmyrene - 0x10880, # .. 0x1089E ; Nabataean - 0x1089F, # .. 0x108A6 ; Unknown - 0x108A7, # .. 0x108AF ; Nabataean - 0x108B0, # .. 0x108DF ; Unknown - 0x108E0, # .. 0x108F2 ; Hatran - 0x108F3, # .. 0x108F3 ; Unknown - 0x108F4, # .. 0x108F5 ; Hatran - 0x108F6, # .. 0x108FA ; Unknown - 0x108FB, # .. 0x108FF ; Hatran - 0x10900, # .. 0x1091B ; Phoenician - 0x1091C, # .. 0x1091E ; Unknown - 0x1091F, # .. 0x1091F ; Phoenician - 0x10920, # .. 0x10939 ; Lydian - 0x1093A, # .. 0x1093E ; Unknown - 0x1093F, # .. 0x1093F ; Lydian - 0x10940, # .. 0x1097F ; Unknown - 0x10980, # .. 0x1099F ; Meroitic_Hieroglyphs - 0x109A0, # .. 0x109B7 ; Meroitic_Cursive - 0x109B8, # .. 0x109BB ; Unknown - 0x109BC, # .. 0x109CF ; Meroitic_Cursive - 0x109D0, # .. 0x109D1 ; Unknown - 0x109D2, # .. 0x109FF ; Meroitic_Cursive - 0x10A00, # .. 0x10A03 ; Kharoshthi - 0x10A04, # .. 0x10A04 ; Unknown - 0x10A05, # .. 0x10A06 ; Kharoshthi - 0x10A07, # .. 0x10A0B ; Unknown - 0x10A0C, # .. 0x10A13 ; Kharoshthi - 0x10A14, # .. 0x10A14 ; Unknown - 0x10A15, # .. 0x10A17 ; Kharoshthi - 0x10A18, # .. 0x10A18 ; Unknown - 0x10A19, # .. 0x10A35 ; Kharoshthi - 0x10A36, # .. 0x10A37 ; Unknown - 0x10A38, # .. 0x10A3A ; Kharoshthi - 0x10A3B, # .. 0x10A3E ; Unknown - 0x10A3F, # .. 0x10A48 ; Kharoshthi - 0x10A49, # .. 0x10A4F ; Unknown - 0x10A50, # .. 0x10A58 ; Kharoshthi - 0x10A59, # .. 0x10A5F ; Unknown - 0x10A60, # .. 0x10A7F ; Old_South_Arabian - 0x10A80, # .. 0x10A9F ; Old_North_Arabian - 0x10AA0, # .. 0x10ABF ; Unknown - 0x10AC0, # .. 0x10AE6 ; Manichaean - 0x10AE7, # .. 0x10AEA ; Unknown - 0x10AEB, # .. 0x10AF6 ; Manichaean - 0x10AF7, # .. 0x10AFF ; Unknown - 0x10B00, # .. 0x10B35 ; Avestan - 0x10B36, # .. 0x10B38 ; Unknown - 0x10B39, # .. 0x10B3F ; Avestan - 0x10B40, # .. 0x10B55 ; Inscriptional_Parthian - 0x10B56, # .. 0x10B57 ; Unknown - 0x10B58, # .. 0x10B5F ; Inscriptional_Parthian - 0x10B60, # .. 0x10B72 ; Inscriptional_Pahlavi - 0x10B73, # .. 0x10B77 ; Unknown - 0x10B78, # .. 0x10B7F ; Inscriptional_Pahlavi - 0x10B80, # .. 0x10B91 ; Psalter_Pahlavi - 0x10B92, # .. 0x10B98 ; Unknown - 0x10B99, # .. 0x10B9C ; Psalter_Pahlavi - 0x10B9D, # .. 0x10BA8 ; Unknown - 0x10BA9, # .. 0x10BAF ; Psalter_Pahlavi - 0x10BB0, # .. 0x10BFF ; Unknown - 0x10C00, # .. 0x10C48 ; Old_Turkic - 0x10C49, # .. 0x10C7F ; Unknown - 0x10C80, # .. 0x10CB2 ; Old_Hungarian - 0x10CB3, # .. 0x10CBF ; Unknown - 0x10CC0, # .. 0x10CF2 ; Old_Hungarian - 0x10CF3, # .. 0x10CF9 ; Unknown - 0x10CFA, # .. 0x10CFF ; Old_Hungarian - 0x10D00, # .. 0x10D27 ; Hanifi_Rohingya - 0x10D28, # .. 0x10D2F ; Unknown - 0x10D30, # .. 0x10D39 ; Hanifi_Rohingya - 0x10D3A, # .. 0x10E5F ; Unknown - 0x10E60, # .. 0x10E7E ; Arabic - 0x10E7F, # .. 0x10E7F ; Unknown - 0x10E80, # .. 0x10EA9 ; Yezidi - 0x10EAA, # .. 0x10EAA ; Unknown - 0x10EAB, # .. 0x10EAD ; Yezidi - 0x10EAE, # .. 0x10EAF ; Unknown - 0x10EB0, # .. 0x10EB1 ; Yezidi - 0x10EB2, # .. 0x10EFC ; Unknown - 0x10EFD, # .. 0x10EFF ; Arabic - 0x10F00, # .. 0x10F27 ; Old_Sogdian - 0x10F28, # .. 0x10F2F ; Unknown - 0x10F30, # .. 0x10F59 ; Sogdian - 0x10F5A, # .. 0x10F6F ; Unknown - 0x10F70, # .. 0x10F89 ; Old_Uyghur - 0x10F8A, # .. 0x10FAF ; Unknown - 0x10FB0, # .. 0x10FCB ; Chorasmian - 0x10FCC, # .. 0x10FDF ; Unknown - 0x10FE0, # .. 0x10FF6 ; Elymaic - 0x10FF7, # .. 0x10FFF ; Unknown - 0x11000, # .. 0x1104D ; Brahmi - 0x1104E, # .. 0x11051 ; Unknown - 0x11052, # .. 0x11075 ; Brahmi - 0x11076, # .. 0x1107E ; Unknown - 0x1107F, # .. 0x1107F ; Brahmi - 0x11080, # .. 0x110C2 ; Kaithi - 0x110C3, # .. 0x110CC ; Unknown - 0x110CD, # .. 0x110CD ; Kaithi - 0x110CE, # .. 0x110CF ; Unknown - 0x110D0, # .. 0x110E8 ; Sora_Sompeng - 0x110E9, # .. 0x110EF ; Unknown - 0x110F0, # .. 0x110F9 ; Sora_Sompeng - 0x110FA, # .. 0x110FF ; Unknown - 0x11100, # .. 0x11134 ; Chakma - 0x11135, # .. 0x11135 ; Unknown - 0x11136, # .. 0x11147 ; Chakma - 0x11148, # .. 0x1114F ; Unknown - 0x11150, # .. 0x11176 ; Mahajani - 0x11177, # .. 0x1117F ; Unknown - 0x11180, # .. 0x111DF ; Sharada - 0x111E0, # .. 0x111E0 ; Unknown - 0x111E1, # .. 0x111F4 ; Sinhala - 0x111F5, # .. 0x111FF ; Unknown - 0x11200, # .. 0x11211 ; Khojki - 0x11212, # .. 0x11212 ; Unknown - 0x11213, # .. 0x11241 ; Khojki - 0x11242, # .. 0x1127F ; Unknown - 0x11280, # .. 0x11286 ; Multani - 0x11287, # .. 0x11287 ; Unknown - 0x11288, # .. 0x11288 ; Multani - 0x11289, # .. 0x11289 ; Unknown - 0x1128A, # .. 0x1128D ; Multani - 0x1128E, # .. 0x1128E ; Unknown - 0x1128F, # .. 0x1129D ; Multani - 0x1129E, # .. 0x1129E ; Unknown - 0x1129F, # .. 0x112A9 ; Multani - 0x112AA, # .. 0x112AF ; Unknown - 0x112B0, # .. 0x112EA ; Khudawadi - 0x112EB, # .. 0x112EF ; Unknown - 0x112F0, # .. 0x112F9 ; Khudawadi - 0x112FA, # .. 0x112FF ; Unknown - 0x11300, # .. 0x11303 ; Grantha - 0x11304, # .. 0x11304 ; Unknown - 0x11305, # .. 0x1130C ; Grantha - 0x1130D, # .. 0x1130E ; Unknown - 0x1130F, # .. 0x11310 ; Grantha - 0x11311, # .. 0x11312 ; Unknown - 0x11313, # .. 0x11328 ; Grantha - 0x11329, # .. 0x11329 ; Unknown - 0x1132A, # .. 0x11330 ; Grantha - 0x11331, # .. 0x11331 ; Unknown - 0x11332, # .. 0x11333 ; Grantha - 0x11334, # .. 0x11334 ; Unknown - 0x11335, # .. 0x11339 ; Grantha - 0x1133A, # .. 0x1133A ; Unknown - 0x1133B, # .. 0x1133B ; Inherited - 0x1133C, # .. 0x11344 ; Grantha - 0x11345, # .. 0x11346 ; Unknown - 0x11347, # .. 0x11348 ; Grantha - 0x11349, # .. 0x1134A ; Unknown - 0x1134B, # .. 0x1134D ; Grantha - 0x1134E, # .. 0x1134F ; Unknown - 0x11350, # .. 0x11350 ; Grantha - 0x11351, # .. 0x11356 ; Unknown - 0x11357, # .. 0x11357 ; Grantha - 0x11358, # .. 0x1135C ; Unknown - 0x1135D, # .. 0x11363 ; Grantha - 0x11364, # .. 0x11365 ; Unknown - 0x11366, # .. 0x1136C ; Grantha - 0x1136D, # .. 0x1136F ; Unknown - 0x11370, # .. 0x11374 ; Grantha - 0x11375, # .. 0x113FF ; Unknown - 0x11400, # .. 0x1145B ; Newa - 0x1145C, # .. 0x1145C ; Unknown - 0x1145D, # .. 0x11461 ; Newa - 0x11462, # .. 0x1147F ; Unknown - 0x11480, # .. 0x114C7 ; Tirhuta - 0x114C8, # .. 0x114CF ; Unknown - 0x114D0, # .. 0x114D9 ; Tirhuta - 0x114DA, # .. 0x1157F ; Unknown - 0x11580, # .. 0x115B5 ; Siddham - 0x115B6, # .. 0x115B7 ; Unknown - 0x115B8, # .. 0x115DD ; Siddham - 0x115DE, # .. 0x115FF ; Unknown - 0x11600, # .. 0x11644 ; Modi - 0x11645, # .. 0x1164F ; Unknown - 0x11650, # .. 0x11659 ; Modi - 0x1165A, # .. 0x1165F ; Unknown - 0x11660, # .. 0x1166C ; Mongolian - 0x1166D, # .. 0x1167F ; Unknown - 0x11680, # .. 0x116B9 ; Takri - 0x116BA, # .. 0x116BF ; Unknown - 0x116C0, # .. 0x116C9 ; Takri - 0x116CA, # .. 0x116FF ; Unknown - 0x11700, # .. 0x1171A ; Ahom - 0x1171B, # .. 0x1171C ; Unknown - 0x1171D, # .. 0x1172B ; Ahom - 0x1172C, # .. 0x1172F ; Unknown - 0x11730, # .. 0x11746 ; Ahom - 0x11747, # .. 0x117FF ; Unknown - 0x11800, # .. 0x1183B ; Dogra - 0x1183C, # .. 0x1189F ; Unknown - 0x118A0, # .. 0x118F2 ; Warang_Citi - 0x118F3, # .. 0x118FE ; Unknown - 0x118FF, # .. 0x118FF ; Warang_Citi - 0x11900, # .. 0x11906 ; Dives_Akuru - 0x11907, # .. 0x11908 ; Unknown - 0x11909, # .. 0x11909 ; Dives_Akuru - 0x1190A, # .. 0x1190B ; Unknown - 0x1190C, # .. 0x11913 ; Dives_Akuru - 0x11914, # .. 0x11914 ; Unknown - 0x11915, # .. 0x11916 ; Dives_Akuru - 0x11917, # .. 0x11917 ; Unknown - 0x11918, # .. 0x11935 ; Dives_Akuru - 0x11936, # .. 0x11936 ; Unknown - 0x11937, # .. 0x11938 ; Dives_Akuru - 0x11939, # .. 0x1193A ; Unknown - 0x1193B, # .. 0x11946 ; Dives_Akuru - 0x11947, # .. 0x1194F ; Unknown - 0x11950, # .. 0x11959 ; Dives_Akuru - 0x1195A, # .. 0x1199F ; Unknown - 0x119A0, # .. 0x119A7 ; Nandinagari - 0x119A8, # .. 0x119A9 ; Unknown - 0x119AA, # .. 0x119D7 ; Nandinagari - 0x119D8, # .. 0x119D9 ; Unknown - 0x119DA, # .. 0x119E4 ; Nandinagari - 0x119E5, # .. 0x119FF ; Unknown - 0x11A00, # .. 0x11A47 ; Zanabazar_Square - 0x11A48, # .. 0x11A4F ; Unknown - 0x11A50, # .. 0x11AA2 ; Soyombo - 0x11AA3, # .. 0x11AAF ; Unknown - 0x11AB0, # .. 0x11ABF ; Canadian_Aboriginal - 0x11AC0, # .. 0x11AF8 ; Pau_Cin_Hau - 0x11AF9, # .. 0x11AFF ; Unknown - 0x11B00, # .. 0x11B09 ; Devanagari - 0x11B0A, # .. 0x11BFF ; Unknown - 0x11C00, # .. 0x11C08 ; Bhaiksuki - 0x11C09, # .. 0x11C09 ; Unknown - 0x11C0A, # .. 0x11C36 ; Bhaiksuki - 0x11C37, # .. 0x11C37 ; Unknown - 0x11C38, # .. 0x11C45 ; Bhaiksuki - 0x11C46, # .. 0x11C4F ; Unknown - 0x11C50, # .. 0x11C6C ; Bhaiksuki - 0x11C6D, # .. 0x11C6F ; Unknown - 0x11C70, # .. 0x11C8F ; Marchen - 0x11C90, # .. 0x11C91 ; Unknown - 0x11C92, # .. 0x11CA7 ; Marchen - 0x11CA8, # .. 0x11CA8 ; Unknown - 0x11CA9, # .. 0x11CB6 ; Marchen - 0x11CB7, # .. 0x11CFF ; Unknown - 0x11D00, # .. 0x11D06 ; Masaram_Gondi - 0x11D07, # .. 0x11D07 ; Unknown - 0x11D08, # .. 0x11D09 ; Masaram_Gondi - 0x11D0A, # .. 0x11D0A ; Unknown - 0x11D0B, # .. 0x11D36 ; Masaram_Gondi - 0x11D37, # .. 0x11D39 ; Unknown - 0x11D3A, # .. 0x11D3A ; Masaram_Gondi - 0x11D3B, # .. 0x11D3B ; Unknown - 0x11D3C, # .. 0x11D3D ; Masaram_Gondi - 0x11D3E, # .. 0x11D3E ; Unknown - 0x11D3F, # .. 0x11D47 ; Masaram_Gondi - 0x11D48, # .. 0x11D4F ; Unknown - 0x11D50, # .. 0x11D59 ; Masaram_Gondi - 0x11D5A, # .. 0x11D5F ; Unknown - 0x11D60, # .. 0x11D65 ; Gunjala_Gondi - 0x11D66, # .. 0x11D66 ; Unknown - 0x11D67, # .. 0x11D68 ; Gunjala_Gondi - 0x11D69, # .. 0x11D69 ; Unknown - 0x11D6A, # .. 0x11D8E ; Gunjala_Gondi - 0x11D8F, # .. 0x11D8F ; Unknown - 0x11D90, # .. 0x11D91 ; Gunjala_Gondi - 0x11D92, # .. 0x11D92 ; Unknown - 0x11D93, # .. 0x11D98 ; Gunjala_Gondi - 0x11D99, # .. 0x11D9F ; Unknown - 0x11DA0, # .. 0x11DA9 ; Gunjala_Gondi - 0x11DAA, # .. 0x11EDF ; Unknown - 0x11EE0, # .. 0x11EF8 ; Makasar - 0x11EF9, # .. 0x11EFF ; Unknown - 0x11F00, # .. 0x11F10 ; Kawi - 0x11F11, # .. 0x11F11 ; Unknown - 0x11F12, # .. 0x11F3A ; Kawi - 0x11F3B, # .. 0x11F3D ; Unknown - 0x11F3E, # .. 0x11F59 ; Kawi - 0x11F5A, # .. 0x11FAF ; Unknown - 0x11FB0, # .. 0x11FB0 ; Lisu - 0x11FB1, # .. 0x11FBF ; Unknown - 0x11FC0, # .. 0x11FF1 ; Tamil - 0x11FF2, # .. 0x11FFE ; Unknown - 0x11FFF, # .. 0x11FFF ; Tamil - 0x12000, # .. 0x12399 ; Cuneiform - 0x1239A, # .. 0x123FF ; Unknown - 0x12400, # .. 0x1246E ; Cuneiform - 0x1246F, # .. 0x1246F ; Unknown - 0x12470, # .. 0x12474 ; Cuneiform - 0x12475, # .. 0x1247F ; Unknown - 0x12480, # .. 0x12543 ; Cuneiform - 0x12544, # .. 0x12F8F ; Unknown - 0x12F90, # .. 0x12FF2 ; Cypro_Minoan - 0x12FF3, # .. 0x12FFF ; Unknown - 0x13000, # .. 0x13455 ; Egyptian_Hieroglyphs - 0x13456, # .. 0x143FF ; Unknown - 0x14400, # .. 0x14646 ; Anatolian_Hieroglyphs - 0x14647, # .. 0x167FF ; Unknown - 0x16800, # .. 0x16A38 ; Bamum - 0x16A39, # .. 0x16A3F ; Unknown - 0x16A40, # .. 0x16A5E ; Mro - 0x16A5F, # .. 0x16A5F ; Unknown - 0x16A60, # .. 0x16A69 ; Mro - 0x16A6A, # .. 0x16A6D ; Unknown - 0x16A6E, # .. 0x16A6F ; Mro - 0x16A70, # .. 0x16ABE ; Tangsa - 0x16ABF, # .. 0x16ABF ; Unknown - 0x16AC0, # .. 0x16AC9 ; Tangsa - 0x16ACA, # .. 0x16ACF ; Unknown - 0x16AD0, # .. 0x16AED ; Bassa_Vah - 0x16AEE, # .. 0x16AEF ; Unknown - 0x16AF0, # .. 0x16AF5 ; Bassa_Vah - 0x16AF6, # .. 0x16AFF ; Unknown - 0x16B00, # .. 0x16B45 ; Pahawh_Hmong - 0x16B46, # .. 0x16B4F ; Unknown - 0x16B50, # .. 0x16B59 ; Pahawh_Hmong - 0x16B5A, # .. 0x16B5A ; Unknown - 0x16B5B, # .. 0x16B61 ; Pahawh_Hmong - 0x16B62, # .. 0x16B62 ; Unknown - 0x16B63, # .. 0x16B77 ; Pahawh_Hmong - 0x16B78, # .. 0x16B7C ; Unknown - 0x16B7D, # .. 0x16B8F ; Pahawh_Hmong - 0x16B90, # .. 0x16E3F ; Unknown - 0x16E40, # .. 0x16E9A ; Medefaidrin - 0x16E9B, # .. 0x16EFF ; Unknown - 0x16F00, # .. 0x16F4A ; Miao - 0x16F4B, # .. 0x16F4E ; Unknown - 0x16F4F, # .. 0x16F87 ; Miao - 0x16F88, # .. 0x16F8E ; Unknown - 0x16F8F, # .. 0x16F9F ; Miao - 0x16FA0, # .. 0x16FDF ; Unknown - 0x16FE0, # .. 0x16FE0 ; Tangut - 0x16FE1, # .. 0x16FE1 ; Nushu - 0x16FE2, # .. 0x16FE3 ; Han - 0x16FE4, # .. 0x16FE4 ; Khitan_Small_Script - 0x16FE5, # .. 0x16FEF ; Unknown - 0x16FF0, # .. 0x16FF1 ; Han - 0x16FF2, # .. 0x16FFF ; Unknown - 0x17000, # .. 0x187F7 ; Tangut - 0x187F8, # .. 0x187FF ; Unknown - 0x18800, # .. 0x18AFF ; Tangut - 0x18B00, # .. 0x18CD5 ; Khitan_Small_Script - 0x18CD6, # .. 0x18CFF ; Unknown - 0x18D00, # .. 0x18D08 ; Tangut - 0x18D09, # .. 0x1AFEF ; Unknown - 0x1AFF0, # .. 0x1AFF3 ; Katakana - 0x1AFF4, # .. 0x1AFF4 ; Unknown - 0x1AFF5, # .. 0x1AFFB ; Katakana - 0x1AFFC, # .. 0x1AFFC ; Unknown - 0x1AFFD, # .. 0x1AFFE ; Katakana - 0x1AFFF, # .. 0x1AFFF ; Unknown - 0x1B000, # .. 0x1B000 ; Katakana - 0x1B001, # .. 0x1B11F ; Hiragana - 0x1B120, # .. 0x1B122 ; Katakana - 0x1B123, # .. 0x1B131 ; Unknown - 0x1B132, # .. 0x1B132 ; Hiragana - 0x1B133, # .. 0x1B14F ; Unknown - 0x1B150, # .. 0x1B152 ; Hiragana - 0x1B153, # .. 0x1B154 ; Unknown - 0x1B155, # .. 0x1B155 ; Katakana - 0x1B156, # .. 0x1B163 ; Unknown - 0x1B164, # .. 0x1B167 ; Katakana - 0x1B168, # .. 0x1B16F ; Unknown - 0x1B170, # .. 0x1B2FB ; Nushu - 0x1B2FC, # .. 0x1BBFF ; Unknown - 0x1BC00, # .. 0x1BC6A ; Duployan - 0x1BC6B, # .. 0x1BC6F ; Unknown - 0x1BC70, # .. 0x1BC7C ; Duployan - 0x1BC7D, # .. 0x1BC7F ; Unknown - 0x1BC80, # .. 0x1BC88 ; Duployan - 0x1BC89, # .. 0x1BC8F ; Unknown - 0x1BC90, # .. 0x1BC99 ; Duployan - 0x1BC9A, # .. 0x1BC9B ; Unknown - 0x1BC9C, # .. 0x1BC9F ; Duployan - 0x1BCA0, # .. 0x1BCA3 ; Common - 0x1BCA4, # .. 0x1CEFF ; Unknown - 0x1CF00, # .. 0x1CF2D ; Inherited - 0x1CF2E, # .. 0x1CF2F ; Unknown - 0x1CF30, # .. 0x1CF46 ; Inherited - 0x1CF47, # .. 0x1CF4F ; Unknown - 0x1CF50, # .. 0x1CFC3 ; Common - 0x1CFC4, # .. 0x1CFFF ; Unknown - 0x1D000, # .. 0x1D0F5 ; Common - 0x1D0F6, # .. 0x1D0FF ; Unknown - 0x1D100, # .. 0x1D126 ; Common - 0x1D127, # .. 0x1D128 ; Unknown - 0x1D129, # .. 0x1D166 ; Common - 0x1D167, # .. 0x1D169 ; Inherited - 0x1D16A, # .. 0x1D17A ; Common - 0x1D17B, # .. 0x1D182 ; Inherited - 0x1D183, # .. 0x1D184 ; Common - 0x1D185, # .. 0x1D18B ; Inherited - 0x1D18C, # .. 0x1D1A9 ; Common - 0x1D1AA, # .. 0x1D1AD ; Inherited - 0x1D1AE, # .. 0x1D1EA ; Common - 0x1D1EB, # .. 0x1D1FF ; Unknown - 0x1D200, # .. 0x1D245 ; Greek - 0x1D246, # .. 0x1D2BF ; Unknown - 0x1D2C0, # .. 0x1D2D3 ; Common - 0x1D2D4, # .. 0x1D2DF ; Unknown - 0x1D2E0, # .. 0x1D2F3 ; Common - 0x1D2F4, # .. 0x1D2FF ; Unknown - 0x1D300, # .. 0x1D356 ; Common - 0x1D357, # .. 0x1D35F ; Unknown - 0x1D360, # .. 0x1D378 ; Common - 0x1D379, # .. 0x1D3FF ; Unknown - 0x1D400, # .. 0x1D454 ; Common - 0x1D455, # .. 0x1D455 ; Unknown - 0x1D456, # .. 0x1D49C ; Common - 0x1D49D, # .. 0x1D49D ; Unknown - 0x1D49E, # .. 0x1D49F ; Common - 0x1D4A0, # .. 0x1D4A1 ; Unknown - 0x1D4A2, # .. 0x1D4A2 ; Common - 0x1D4A3, # .. 0x1D4A4 ; Unknown - 0x1D4A5, # .. 0x1D4A6 ; Common - 0x1D4A7, # .. 0x1D4A8 ; Unknown - 0x1D4A9, # .. 0x1D4AC ; Common - 0x1D4AD, # .. 0x1D4AD ; Unknown - 0x1D4AE, # .. 0x1D4B9 ; Common - 0x1D4BA, # .. 0x1D4BA ; Unknown - 0x1D4BB, # .. 0x1D4BB ; Common - 0x1D4BC, # .. 0x1D4BC ; Unknown - 0x1D4BD, # .. 0x1D4C3 ; Common - 0x1D4C4, # .. 0x1D4C4 ; Unknown - 0x1D4C5, # .. 0x1D505 ; Common - 0x1D506, # .. 0x1D506 ; Unknown - 0x1D507, # .. 0x1D50A ; Common - 0x1D50B, # .. 0x1D50C ; Unknown - 0x1D50D, # .. 0x1D514 ; Common - 0x1D515, # .. 0x1D515 ; Unknown - 0x1D516, # .. 0x1D51C ; Common - 0x1D51D, # .. 0x1D51D ; Unknown - 0x1D51E, # .. 0x1D539 ; Common - 0x1D53A, # .. 0x1D53A ; Unknown - 0x1D53B, # .. 0x1D53E ; Common - 0x1D53F, # .. 0x1D53F ; Unknown - 0x1D540, # .. 0x1D544 ; Common - 0x1D545, # .. 0x1D545 ; Unknown - 0x1D546, # .. 0x1D546 ; Common - 0x1D547, # .. 0x1D549 ; Unknown - 0x1D54A, # .. 0x1D550 ; Common - 0x1D551, # .. 0x1D551 ; Unknown - 0x1D552, # .. 0x1D6A5 ; Common - 0x1D6A6, # .. 0x1D6A7 ; Unknown - 0x1D6A8, # .. 0x1D7CB ; Common - 0x1D7CC, # .. 0x1D7CD ; Unknown - 0x1D7CE, # .. 0x1D7FF ; Common - 0x1D800, # .. 0x1DA8B ; SignWriting - 0x1DA8C, # .. 0x1DA9A ; Unknown - 0x1DA9B, # .. 0x1DA9F ; SignWriting - 0x1DAA0, # .. 0x1DAA0 ; Unknown - 0x1DAA1, # .. 0x1DAAF ; SignWriting - 0x1DAB0, # .. 0x1DEFF ; Unknown - 0x1DF00, # .. 0x1DF1E ; Latin - 0x1DF1F, # .. 0x1DF24 ; Unknown - 0x1DF25, # .. 0x1DF2A ; Latin - 0x1DF2B, # .. 0x1DFFF ; Unknown - 0x1E000, # .. 0x1E006 ; Glagolitic - 0x1E007, # .. 0x1E007 ; Unknown - 0x1E008, # .. 0x1E018 ; Glagolitic - 0x1E019, # .. 0x1E01A ; Unknown - 0x1E01B, # .. 0x1E021 ; Glagolitic - 0x1E022, # .. 0x1E022 ; Unknown - 0x1E023, # .. 0x1E024 ; Glagolitic - 0x1E025, # .. 0x1E025 ; Unknown - 0x1E026, # .. 0x1E02A ; Glagolitic - 0x1E02B, # .. 0x1E02F ; Unknown - 0x1E030, # .. 0x1E06D ; Cyrillic - 0x1E06E, # .. 0x1E08E ; Unknown - 0x1E08F, # .. 0x1E08F ; Cyrillic - 0x1E090, # .. 0x1E0FF ; Unknown - 0x1E100, # .. 0x1E12C ; Nyiakeng_Puachue_Hmong - 0x1E12D, # .. 0x1E12F ; Unknown - 0x1E130, # .. 0x1E13D ; Nyiakeng_Puachue_Hmong - 0x1E13E, # .. 0x1E13F ; Unknown - 0x1E140, # .. 0x1E149 ; Nyiakeng_Puachue_Hmong - 0x1E14A, # .. 0x1E14D ; Unknown - 0x1E14E, # .. 0x1E14F ; Nyiakeng_Puachue_Hmong - 0x1E150, # .. 0x1E28F ; Unknown - 0x1E290, # .. 0x1E2AE ; Toto - 0x1E2AF, # .. 0x1E2BF ; Unknown - 0x1E2C0, # .. 0x1E2F9 ; Wancho - 0x1E2FA, # .. 0x1E2FE ; Unknown - 0x1E2FF, # .. 0x1E2FF ; Wancho - 0x1E300, # .. 0x1E4CF ; Unknown - 0x1E4D0, # .. 0x1E4F9 ; Nag_Mundari - 0x1E4FA, # .. 0x1E7DF ; Unknown - 0x1E7E0, # .. 0x1E7E6 ; Ethiopic - 0x1E7E7, # .. 0x1E7E7 ; Unknown - 0x1E7E8, # .. 0x1E7EB ; Ethiopic - 0x1E7EC, # .. 0x1E7EC ; Unknown - 0x1E7ED, # .. 0x1E7EE ; Ethiopic - 0x1E7EF, # .. 0x1E7EF ; Unknown - 0x1E7F0, # .. 0x1E7FE ; Ethiopic - 0x1E7FF, # .. 0x1E7FF ; Unknown - 0x1E800, # .. 0x1E8C4 ; Mende_Kikakui - 0x1E8C5, # .. 0x1E8C6 ; Unknown - 0x1E8C7, # .. 0x1E8D6 ; Mende_Kikakui - 0x1E8D7, # .. 0x1E8FF ; Unknown - 0x1E900, # .. 0x1E94B ; Adlam - 0x1E94C, # .. 0x1E94F ; Unknown - 0x1E950, # .. 0x1E959 ; Adlam - 0x1E95A, # .. 0x1E95D ; Unknown - 0x1E95E, # .. 0x1E95F ; Adlam - 0x1E960, # .. 0x1EC70 ; Unknown - 0x1EC71, # .. 0x1ECB4 ; Common - 0x1ECB5, # .. 0x1ED00 ; Unknown - 0x1ED01, # .. 0x1ED3D ; Common - 0x1ED3E, # .. 0x1EDFF ; Unknown - 0x1EE00, # .. 0x1EE03 ; Arabic - 0x1EE04, # .. 0x1EE04 ; Unknown - 0x1EE05, # .. 0x1EE1F ; Arabic - 0x1EE20, # .. 0x1EE20 ; Unknown - 0x1EE21, # .. 0x1EE22 ; Arabic - 0x1EE23, # .. 0x1EE23 ; Unknown - 0x1EE24, # .. 0x1EE24 ; Arabic - 0x1EE25, # .. 0x1EE26 ; Unknown - 0x1EE27, # .. 0x1EE27 ; Arabic - 0x1EE28, # .. 0x1EE28 ; Unknown - 0x1EE29, # .. 0x1EE32 ; Arabic - 0x1EE33, # .. 0x1EE33 ; Unknown - 0x1EE34, # .. 0x1EE37 ; Arabic - 0x1EE38, # .. 0x1EE38 ; Unknown - 0x1EE39, # .. 0x1EE39 ; Arabic - 0x1EE3A, # .. 0x1EE3A ; Unknown - 0x1EE3B, # .. 0x1EE3B ; Arabic - 0x1EE3C, # .. 0x1EE41 ; Unknown - 0x1EE42, # .. 0x1EE42 ; Arabic - 0x1EE43, # .. 0x1EE46 ; Unknown - 0x1EE47, # .. 0x1EE47 ; Arabic - 0x1EE48, # .. 0x1EE48 ; Unknown - 0x1EE49, # .. 0x1EE49 ; Arabic - 0x1EE4A, # .. 0x1EE4A ; Unknown - 0x1EE4B, # .. 0x1EE4B ; Arabic - 0x1EE4C, # .. 0x1EE4C ; Unknown - 0x1EE4D, # .. 0x1EE4F ; Arabic - 0x1EE50, # .. 0x1EE50 ; Unknown - 0x1EE51, # .. 0x1EE52 ; Arabic - 0x1EE53, # .. 0x1EE53 ; Unknown - 0x1EE54, # .. 0x1EE54 ; Arabic - 0x1EE55, # .. 0x1EE56 ; Unknown - 0x1EE57, # .. 0x1EE57 ; Arabic - 0x1EE58, # .. 0x1EE58 ; Unknown - 0x1EE59, # .. 0x1EE59 ; Arabic - 0x1EE5A, # .. 0x1EE5A ; Unknown - 0x1EE5B, # .. 0x1EE5B ; Arabic - 0x1EE5C, # .. 0x1EE5C ; Unknown - 0x1EE5D, # .. 0x1EE5D ; Arabic - 0x1EE5E, # .. 0x1EE5E ; Unknown - 0x1EE5F, # .. 0x1EE5F ; Arabic - 0x1EE60, # .. 0x1EE60 ; Unknown - 0x1EE61, # .. 0x1EE62 ; Arabic - 0x1EE63, # .. 0x1EE63 ; Unknown - 0x1EE64, # .. 0x1EE64 ; Arabic - 0x1EE65, # .. 0x1EE66 ; Unknown - 0x1EE67, # .. 0x1EE6A ; Arabic - 0x1EE6B, # .. 0x1EE6B ; Unknown - 0x1EE6C, # .. 0x1EE72 ; Arabic - 0x1EE73, # .. 0x1EE73 ; Unknown - 0x1EE74, # .. 0x1EE77 ; Arabic - 0x1EE78, # .. 0x1EE78 ; Unknown - 0x1EE79, # .. 0x1EE7C ; Arabic - 0x1EE7D, # .. 0x1EE7D ; Unknown - 0x1EE7E, # .. 0x1EE7E ; Arabic - 0x1EE7F, # .. 0x1EE7F ; Unknown - 0x1EE80, # .. 0x1EE89 ; Arabic - 0x1EE8A, # .. 0x1EE8A ; Unknown - 0x1EE8B, # .. 0x1EE9B ; Arabic - 0x1EE9C, # .. 0x1EEA0 ; Unknown - 0x1EEA1, # .. 0x1EEA3 ; Arabic - 0x1EEA4, # .. 0x1EEA4 ; Unknown - 0x1EEA5, # .. 0x1EEA9 ; Arabic - 0x1EEAA, # .. 0x1EEAA ; Unknown - 0x1EEAB, # .. 0x1EEBB ; Arabic - 0x1EEBC, # .. 0x1EEEF ; Unknown - 0x1EEF0, # .. 0x1EEF1 ; Arabic - 0x1EEF2, # .. 0x1EFFF ; Unknown - 0x1F000, # .. 0x1F02B ; Common - 0x1F02C, # .. 0x1F02F ; Unknown - 0x1F030, # .. 0x1F093 ; Common - 0x1F094, # .. 0x1F09F ; Unknown - 0x1F0A0, # .. 0x1F0AE ; Common - 0x1F0AF, # .. 0x1F0B0 ; Unknown - 0x1F0B1, # .. 0x1F0BF ; Common - 0x1F0C0, # .. 0x1F0C0 ; Unknown - 0x1F0C1, # .. 0x1F0CF ; Common - 0x1F0D0, # .. 0x1F0D0 ; Unknown - 0x1F0D1, # .. 0x1F0F5 ; Common - 0x1F0F6, # .. 0x1F0FF ; Unknown - 0x1F100, # .. 0x1F1AD ; Common - 0x1F1AE, # .. 0x1F1E5 ; Unknown - 0x1F1E6, # .. 0x1F1FF ; Common - 0x1F200, # .. 0x1F200 ; Hiragana - 0x1F201, # .. 0x1F202 ; Common - 0x1F203, # .. 0x1F20F ; Unknown - 0x1F210, # .. 0x1F23B ; Common - 0x1F23C, # .. 0x1F23F ; Unknown - 0x1F240, # .. 0x1F248 ; Common - 0x1F249, # .. 0x1F24F ; Unknown - 0x1F250, # .. 0x1F251 ; Common - 0x1F252, # .. 0x1F25F ; Unknown - 0x1F260, # .. 0x1F265 ; Common - 0x1F266, # .. 0x1F2FF ; Unknown - 0x1F300, # .. 0x1F6D7 ; Common - 0x1F6D8, # .. 0x1F6DB ; Unknown - 0x1F6DC, # .. 0x1F6EC ; Common - 0x1F6ED, # .. 0x1F6EF ; Unknown - 0x1F6F0, # .. 0x1F6FC ; Common - 0x1F6FD, # .. 0x1F6FF ; Unknown - 0x1F700, # .. 0x1F776 ; Common - 0x1F777, # .. 0x1F77A ; Unknown - 0x1F77B, # .. 0x1F7D9 ; Common - 0x1F7DA, # .. 0x1F7DF ; Unknown - 0x1F7E0, # .. 0x1F7EB ; Common - 0x1F7EC, # .. 0x1F7EF ; Unknown - 0x1F7F0, # .. 0x1F7F0 ; Common - 0x1F7F1, # .. 0x1F7FF ; Unknown - 0x1F800, # .. 0x1F80B ; Common - 0x1F80C, # .. 0x1F80F ; Unknown - 0x1F810, # .. 0x1F847 ; Common - 0x1F848, # .. 0x1F84F ; Unknown - 0x1F850, # .. 0x1F859 ; Common - 0x1F85A, # .. 0x1F85F ; Unknown - 0x1F860, # .. 0x1F887 ; Common - 0x1F888, # .. 0x1F88F ; Unknown - 0x1F890, # .. 0x1F8AD ; Common - 0x1F8AE, # .. 0x1F8AF ; Unknown - 0x1F8B0, # .. 0x1F8B1 ; Common - 0x1F8B2, # .. 0x1F8FF ; Unknown - 0x1F900, # .. 0x1FA53 ; Common - 0x1FA54, # .. 0x1FA5F ; Unknown - 0x1FA60, # .. 0x1FA6D ; Common - 0x1FA6E, # .. 0x1FA6F ; Unknown - 0x1FA70, # .. 0x1FA7C ; Common - 0x1FA7D, # .. 0x1FA7F ; Unknown - 0x1FA80, # .. 0x1FA88 ; Common - 0x1FA89, # .. 0x1FA8F ; Unknown - 0x1FA90, # .. 0x1FABD ; Common - 0x1FABE, # .. 0x1FABE ; Unknown - 0x1FABF, # .. 0x1FAC5 ; Common - 0x1FAC6, # .. 0x1FACD ; Unknown - 0x1FACE, # .. 0x1FADB ; Common - 0x1FADC, # .. 0x1FADF ; Unknown - 0x1FAE0, # .. 0x1FAE8 ; Common - 0x1FAE9, # .. 0x1FAEF ; Unknown - 0x1FAF0, # .. 0x1FAF8 ; Common - 0x1FAF9, # .. 0x1FAFF ; Unknown - 0x1FB00, # .. 0x1FB92 ; Common - 0x1FB93, # .. 0x1FB93 ; Unknown - 0x1FB94, # .. 0x1FBCA ; Common - 0x1FBCB, # .. 0x1FBEF ; Unknown - 0x1FBF0, # .. 0x1FBF9 ; Common - 0x1FBFA, # .. 0x1FFFF ; Unknown - 0x20000, # .. 0x2A6DF ; Han - 0x2A6E0, # .. 0x2A6FF ; Unknown - 0x2A700, # .. 0x2B739 ; Han - 0x2B73A, # .. 0x2B73F ; Unknown - 0x2B740, # .. 0x2B81D ; Han - 0x2B81E, # .. 0x2B81F ; Unknown - 0x2B820, # .. 0x2CEA1 ; Han - 0x2CEA2, # .. 0x2CEAF ; Unknown - 0x2CEB0, # .. 0x2EBE0 ; Han - 0x2EBE1, # .. 0x2F7FF ; Unknown - 0x2F800, # .. 0x2FA1D ; Han - 0x2FA1E, # .. 0x2FFFF ; Unknown - 0x30000, # .. 0x3134A ; Han - 0x3134B, # .. 0x3134F ; Unknown - 0x31350, # .. 0x323AF ; Han - 0x323B0, # .. 0xE0000 ; Unknown - 0xE0001, # .. 0xE0001 ; Common - 0xE0002, # .. 0xE001F ; Unknown - 0xE0020, # .. 0xE007F ; Common - 0xE0080, # .. 0xE00FF ; Unknown - 0xE0100, # .. 0xE01EF ; Inherited - 0xE01F0, # .. 0x10FFFF ; Unknown -] - -VALUES = [ - "Zyyy", # 0000..0040 ; Common - "Latn", # 0041..005A ; Latin - "Zyyy", # 005B..0060 ; Common - "Latn", # 0061..007A ; Latin - "Zyyy", # 007B..00A9 ; Common - "Latn", # 00AA..00AA ; Latin - "Zyyy", # 00AB..00B9 ; Common - "Latn", # 00BA..00BA ; Latin - "Zyyy", # 00BB..00BF ; Common - "Latn", # 00C0..00D6 ; Latin - "Zyyy", # 00D7..00D7 ; Common - "Latn", # 00D8..00F6 ; Latin - "Zyyy", # 00F7..00F7 ; Common - "Latn", # 00F8..02B8 ; Latin - "Zyyy", # 02B9..02DF ; Common - "Latn", # 02E0..02E4 ; Latin - "Zyyy", # 02E5..02E9 ; Common - "Bopo", # 02EA..02EB ; Bopomofo - "Zyyy", # 02EC..02FF ; Common - "Zinh", # 0300..036F ; Inherited - "Grek", # 0370..0373 ; Greek - "Zyyy", # 0374..0374 ; Common - "Grek", # 0375..0377 ; Greek - "Zzzz", # 0378..0379 ; Unknown - "Grek", # 037A..037D ; Greek - "Zyyy", # 037E..037E ; Common - "Grek", # 037F..037F ; Greek - "Zzzz", # 0380..0383 ; Unknown - "Grek", # 0384..0384 ; Greek - "Zyyy", # 0385..0385 ; Common - "Grek", # 0386..0386 ; Greek - "Zyyy", # 0387..0387 ; Common - "Grek", # 0388..038A ; Greek - "Zzzz", # 038B..038B ; Unknown - "Grek", # 038C..038C ; Greek - "Zzzz", # 038D..038D ; Unknown - "Grek", # 038E..03A1 ; Greek - "Zzzz", # 03A2..03A2 ; Unknown - "Grek", # 03A3..03E1 ; Greek - "Copt", # 03E2..03EF ; Coptic - "Grek", # 03F0..03FF ; Greek - "Cyrl", # 0400..0484 ; Cyrillic - "Zinh", # 0485..0486 ; Inherited - "Cyrl", # 0487..052F ; Cyrillic - "Zzzz", # 0530..0530 ; Unknown - "Armn", # 0531..0556 ; Armenian - "Zzzz", # 0557..0558 ; Unknown - "Armn", # 0559..058A ; Armenian - "Zzzz", # 058B..058C ; Unknown - "Armn", # 058D..058F ; Armenian - "Zzzz", # 0590..0590 ; Unknown - "Hebr", # 0591..05C7 ; Hebrew - "Zzzz", # 05C8..05CF ; Unknown - "Hebr", # 05D0..05EA ; Hebrew - "Zzzz", # 05EB..05EE ; Unknown - "Hebr", # 05EF..05F4 ; Hebrew - "Zzzz", # 05F5..05FF ; Unknown - "Arab", # 0600..0604 ; Arabic - "Zyyy", # 0605..0605 ; Common - "Arab", # 0606..060B ; Arabic - "Zyyy", # 060C..060C ; Common - "Arab", # 060D..061A ; Arabic - "Zyyy", # 061B..061B ; Common - "Arab", # 061C..061E ; Arabic - "Zyyy", # 061F..061F ; Common - "Arab", # 0620..063F ; Arabic - "Zyyy", # 0640..0640 ; Common - "Arab", # 0641..064A ; Arabic - "Zinh", # 064B..0655 ; Inherited - "Arab", # 0656..066F ; Arabic - "Zinh", # 0670..0670 ; Inherited - "Arab", # 0671..06DC ; Arabic - "Zyyy", # 06DD..06DD ; Common - "Arab", # 06DE..06FF ; Arabic - "Syrc", # 0700..070D ; Syriac - "Zzzz", # 070E..070E ; Unknown - "Syrc", # 070F..074A ; Syriac - "Zzzz", # 074B..074C ; Unknown - "Syrc", # 074D..074F ; Syriac - "Arab", # 0750..077F ; Arabic - "Thaa", # 0780..07B1 ; Thaana - "Zzzz", # 07B2..07BF ; Unknown - "Nkoo", # 07C0..07FA ; Nko - "Zzzz", # 07FB..07FC ; Unknown - "Nkoo", # 07FD..07FF ; Nko - "Samr", # 0800..082D ; Samaritan - "Zzzz", # 082E..082F ; Unknown - "Samr", # 0830..083E ; Samaritan - "Zzzz", # 083F..083F ; Unknown - "Mand", # 0840..085B ; Mandaic - "Zzzz", # 085C..085D ; Unknown - "Mand", # 085E..085E ; Mandaic - "Zzzz", # 085F..085F ; Unknown - "Syrc", # 0860..086A ; Syriac - "Zzzz", # 086B..086F ; Unknown - "Arab", # 0870..088E ; Arabic - "Zzzz", # 088F..088F ; Unknown - "Arab", # 0890..0891 ; Arabic - "Zzzz", # 0892..0897 ; Unknown - "Arab", # 0898..08E1 ; Arabic - "Zyyy", # 08E2..08E2 ; Common - "Arab", # 08E3..08FF ; Arabic - "Deva", # 0900..0950 ; Devanagari - "Zinh", # 0951..0954 ; Inherited - "Deva", # 0955..0963 ; Devanagari - "Zyyy", # 0964..0965 ; Common - "Deva", # 0966..097F ; Devanagari - "Beng", # 0980..0983 ; Bengali - "Zzzz", # 0984..0984 ; Unknown - "Beng", # 0985..098C ; Bengali - "Zzzz", # 098D..098E ; Unknown - "Beng", # 098F..0990 ; Bengali - "Zzzz", # 0991..0992 ; Unknown - "Beng", # 0993..09A8 ; Bengali - "Zzzz", # 09A9..09A9 ; Unknown - "Beng", # 09AA..09B0 ; Bengali - "Zzzz", # 09B1..09B1 ; Unknown - "Beng", # 09B2..09B2 ; Bengali - "Zzzz", # 09B3..09B5 ; Unknown - "Beng", # 09B6..09B9 ; Bengali - "Zzzz", # 09BA..09BB ; Unknown - "Beng", # 09BC..09C4 ; Bengali - "Zzzz", # 09C5..09C6 ; Unknown - "Beng", # 09C7..09C8 ; Bengali - "Zzzz", # 09C9..09CA ; Unknown - "Beng", # 09CB..09CE ; Bengali - "Zzzz", # 09CF..09D6 ; Unknown - "Beng", # 09D7..09D7 ; Bengali - "Zzzz", # 09D8..09DB ; Unknown - "Beng", # 09DC..09DD ; Bengali - "Zzzz", # 09DE..09DE ; Unknown - "Beng", # 09DF..09E3 ; Bengali - "Zzzz", # 09E4..09E5 ; Unknown - "Beng", # 09E6..09FE ; Bengali - "Zzzz", # 09FF..0A00 ; Unknown - "Guru", # 0A01..0A03 ; Gurmukhi - "Zzzz", # 0A04..0A04 ; Unknown - "Guru", # 0A05..0A0A ; Gurmukhi - "Zzzz", # 0A0B..0A0E ; Unknown - "Guru", # 0A0F..0A10 ; Gurmukhi - "Zzzz", # 0A11..0A12 ; Unknown - "Guru", # 0A13..0A28 ; Gurmukhi - "Zzzz", # 0A29..0A29 ; Unknown - "Guru", # 0A2A..0A30 ; Gurmukhi - "Zzzz", # 0A31..0A31 ; Unknown - "Guru", # 0A32..0A33 ; Gurmukhi - "Zzzz", # 0A34..0A34 ; Unknown - "Guru", # 0A35..0A36 ; Gurmukhi - "Zzzz", # 0A37..0A37 ; Unknown - "Guru", # 0A38..0A39 ; Gurmukhi - "Zzzz", # 0A3A..0A3B ; Unknown - "Guru", # 0A3C..0A3C ; Gurmukhi - "Zzzz", # 0A3D..0A3D ; Unknown - "Guru", # 0A3E..0A42 ; Gurmukhi - "Zzzz", # 0A43..0A46 ; Unknown - "Guru", # 0A47..0A48 ; Gurmukhi - "Zzzz", # 0A49..0A4A ; Unknown - "Guru", # 0A4B..0A4D ; Gurmukhi - "Zzzz", # 0A4E..0A50 ; Unknown - "Guru", # 0A51..0A51 ; Gurmukhi - "Zzzz", # 0A52..0A58 ; Unknown - "Guru", # 0A59..0A5C ; Gurmukhi - "Zzzz", # 0A5D..0A5D ; Unknown - "Guru", # 0A5E..0A5E ; Gurmukhi - "Zzzz", # 0A5F..0A65 ; Unknown - "Guru", # 0A66..0A76 ; Gurmukhi - "Zzzz", # 0A77..0A80 ; Unknown - "Gujr", # 0A81..0A83 ; Gujarati - "Zzzz", # 0A84..0A84 ; Unknown - "Gujr", # 0A85..0A8D ; Gujarati - "Zzzz", # 0A8E..0A8E ; Unknown - "Gujr", # 0A8F..0A91 ; Gujarati - "Zzzz", # 0A92..0A92 ; Unknown - "Gujr", # 0A93..0AA8 ; Gujarati - "Zzzz", # 0AA9..0AA9 ; Unknown - "Gujr", # 0AAA..0AB0 ; Gujarati - "Zzzz", # 0AB1..0AB1 ; Unknown - "Gujr", # 0AB2..0AB3 ; Gujarati - "Zzzz", # 0AB4..0AB4 ; Unknown - "Gujr", # 0AB5..0AB9 ; Gujarati - "Zzzz", # 0ABA..0ABB ; Unknown - "Gujr", # 0ABC..0AC5 ; Gujarati - "Zzzz", # 0AC6..0AC6 ; Unknown - "Gujr", # 0AC7..0AC9 ; Gujarati - "Zzzz", # 0ACA..0ACA ; Unknown - "Gujr", # 0ACB..0ACD ; Gujarati - "Zzzz", # 0ACE..0ACF ; Unknown - "Gujr", # 0AD0..0AD0 ; Gujarati - "Zzzz", # 0AD1..0ADF ; Unknown - "Gujr", # 0AE0..0AE3 ; Gujarati - "Zzzz", # 0AE4..0AE5 ; Unknown - "Gujr", # 0AE6..0AF1 ; Gujarati - "Zzzz", # 0AF2..0AF8 ; Unknown - "Gujr", # 0AF9..0AFF ; Gujarati - "Zzzz", # 0B00..0B00 ; Unknown - "Orya", # 0B01..0B03 ; Oriya - "Zzzz", # 0B04..0B04 ; Unknown - "Orya", # 0B05..0B0C ; Oriya - "Zzzz", # 0B0D..0B0E ; Unknown - "Orya", # 0B0F..0B10 ; Oriya - "Zzzz", # 0B11..0B12 ; Unknown - "Orya", # 0B13..0B28 ; Oriya - "Zzzz", # 0B29..0B29 ; Unknown - "Orya", # 0B2A..0B30 ; Oriya - "Zzzz", # 0B31..0B31 ; Unknown - "Orya", # 0B32..0B33 ; Oriya - "Zzzz", # 0B34..0B34 ; Unknown - "Orya", # 0B35..0B39 ; Oriya - "Zzzz", # 0B3A..0B3B ; Unknown - "Orya", # 0B3C..0B44 ; Oriya - "Zzzz", # 0B45..0B46 ; Unknown - "Orya", # 0B47..0B48 ; Oriya - "Zzzz", # 0B49..0B4A ; Unknown - "Orya", # 0B4B..0B4D ; Oriya - "Zzzz", # 0B4E..0B54 ; Unknown - "Orya", # 0B55..0B57 ; Oriya - "Zzzz", # 0B58..0B5B ; Unknown - "Orya", # 0B5C..0B5D ; Oriya - "Zzzz", # 0B5E..0B5E ; Unknown - "Orya", # 0B5F..0B63 ; Oriya - "Zzzz", # 0B64..0B65 ; Unknown - "Orya", # 0B66..0B77 ; Oriya - "Zzzz", # 0B78..0B81 ; Unknown - "Taml", # 0B82..0B83 ; Tamil - "Zzzz", # 0B84..0B84 ; Unknown - "Taml", # 0B85..0B8A ; Tamil - "Zzzz", # 0B8B..0B8D ; Unknown - "Taml", # 0B8E..0B90 ; Tamil - "Zzzz", # 0B91..0B91 ; Unknown - "Taml", # 0B92..0B95 ; Tamil - "Zzzz", # 0B96..0B98 ; Unknown - "Taml", # 0B99..0B9A ; Tamil - "Zzzz", # 0B9B..0B9B ; Unknown - "Taml", # 0B9C..0B9C ; Tamil - "Zzzz", # 0B9D..0B9D ; Unknown - "Taml", # 0B9E..0B9F ; Tamil - "Zzzz", # 0BA0..0BA2 ; Unknown - "Taml", # 0BA3..0BA4 ; Tamil - "Zzzz", # 0BA5..0BA7 ; Unknown - "Taml", # 0BA8..0BAA ; Tamil - "Zzzz", # 0BAB..0BAD ; Unknown - "Taml", # 0BAE..0BB9 ; Tamil - "Zzzz", # 0BBA..0BBD ; Unknown - "Taml", # 0BBE..0BC2 ; Tamil - "Zzzz", # 0BC3..0BC5 ; Unknown - "Taml", # 0BC6..0BC8 ; Tamil - "Zzzz", # 0BC9..0BC9 ; Unknown - "Taml", # 0BCA..0BCD ; Tamil - "Zzzz", # 0BCE..0BCF ; Unknown - "Taml", # 0BD0..0BD0 ; Tamil - "Zzzz", # 0BD1..0BD6 ; Unknown - "Taml", # 0BD7..0BD7 ; Tamil - "Zzzz", # 0BD8..0BE5 ; Unknown - "Taml", # 0BE6..0BFA ; Tamil - "Zzzz", # 0BFB..0BFF ; Unknown - "Telu", # 0C00..0C0C ; Telugu - "Zzzz", # 0C0D..0C0D ; Unknown - "Telu", # 0C0E..0C10 ; Telugu - "Zzzz", # 0C11..0C11 ; Unknown - "Telu", # 0C12..0C28 ; Telugu - "Zzzz", # 0C29..0C29 ; Unknown - "Telu", # 0C2A..0C39 ; Telugu - "Zzzz", # 0C3A..0C3B ; Unknown - "Telu", # 0C3C..0C44 ; Telugu - "Zzzz", # 0C45..0C45 ; Unknown - "Telu", # 0C46..0C48 ; Telugu - "Zzzz", # 0C49..0C49 ; Unknown - "Telu", # 0C4A..0C4D ; Telugu - "Zzzz", # 0C4E..0C54 ; Unknown - "Telu", # 0C55..0C56 ; Telugu - "Zzzz", # 0C57..0C57 ; Unknown - "Telu", # 0C58..0C5A ; Telugu - "Zzzz", # 0C5B..0C5C ; Unknown - "Telu", # 0C5D..0C5D ; Telugu - "Zzzz", # 0C5E..0C5F ; Unknown - "Telu", # 0C60..0C63 ; Telugu - "Zzzz", # 0C64..0C65 ; Unknown - "Telu", # 0C66..0C6F ; Telugu - "Zzzz", # 0C70..0C76 ; Unknown - "Telu", # 0C77..0C7F ; Telugu - "Knda", # 0C80..0C8C ; Kannada - "Zzzz", # 0C8D..0C8D ; Unknown - "Knda", # 0C8E..0C90 ; Kannada - "Zzzz", # 0C91..0C91 ; Unknown - "Knda", # 0C92..0CA8 ; Kannada - "Zzzz", # 0CA9..0CA9 ; Unknown - "Knda", # 0CAA..0CB3 ; Kannada - "Zzzz", # 0CB4..0CB4 ; Unknown - "Knda", # 0CB5..0CB9 ; Kannada - "Zzzz", # 0CBA..0CBB ; Unknown - "Knda", # 0CBC..0CC4 ; Kannada - "Zzzz", # 0CC5..0CC5 ; Unknown - "Knda", # 0CC6..0CC8 ; Kannada - "Zzzz", # 0CC9..0CC9 ; Unknown - "Knda", # 0CCA..0CCD ; Kannada - "Zzzz", # 0CCE..0CD4 ; Unknown - "Knda", # 0CD5..0CD6 ; Kannada - "Zzzz", # 0CD7..0CDC ; Unknown - "Knda", # 0CDD..0CDE ; Kannada - "Zzzz", # 0CDF..0CDF ; Unknown - "Knda", # 0CE0..0CE3 ; Kannada - "Zzzz", # 0CE4..0CE5 ; Unknown - "Knda", # 0CE6..0CEF ; Kannada - "Zzzz", # 0CF0..0CF0 ; Unknown - "Knda", # 0CF1..0CF3 ; Kannada - "Zzzz", # 0CF4..0CFF ; Unknown - "Mlym", # 0D00..0D0C ; Malayalam - "Zzzz", # 0D0D..0D0D ; Unknown - "Mlym", # 0D0E..0D10 ; Malayalam - "Zzzz", # 0D11..0D11 ; Unknown - "Mlym", # 0D12..0D44 ; Malayalam - "Zzzz", # 0D45..0D45 ; Unknown - "Mlym", # 0D46..0D48 ; Malayalam - "Zzzz", # 0D49..0D49 ; Unknown - "Mlym", # 0D4A..0D4F ; Malayalam - "Zzzz", # 0D50..0D53 ; Unknown - "Mlym", # 0D54..0D63 ; Malayalam - "Zzzz", # 0D64..0D65 ; Unknown - "Mlym", # 0D66..0D7F ; Malayalam - "Zzzz", # 0D80..0D80 ; Unknown - "Sinh", # 0D81..0D83 ; Sinhala - "Zzzz", # 0D84..0D84 ; Unknown - "Sinh", # 0D85..0D96 ; Sinhala - "Zzzz", # 0D97..0D99 ; Unknown - "Sinh", # 0D9A..0DB1 ; Sinhala - "Zzzz", # 0DB2..0DB2 ; Unknown - "Sinh", # 0DB3..0DBB ; Sinhala - "Zzzz", # 0DBC..0DBC ; Unknown - "Sinh", # 0DBD..0DBD ; Sinhala - "Zzzz", # 0DBE..0DBF ; Unknown - "Sinh", # 0DC0..0DC6 ; Sinhala - "Zzzz", # 0DC7..0DC9 ; Unknown - "Sinh", # 0DCA..0DCA ; Sinhala - "Zzzz", # 0DCB..0DCE ; Unknown - "Sinh", # 0DCF..0DD4 ; Sinhala - "Zzzz", # 0DD5..0DD5 ; Unknown - "Sinh", # 0DD6..0DD6 ; Sinhala - "Zzzz", # 0DD7..0DD7 ; Unknown - "Sinh", # 0DD8..0DDF ; Sinhala - "Zzzz", # 0DE0..0DE5 ; Unknown - "Sinh", # 0DE6..0DEF ; Sinhala - "Zzzz", # 0DF0..0DF1 ; Unknown - "Sinh", # 0DF2..0DF4 ; Sinhala - "Zzzz", # 0DF5..0E00 ; Unknown - "Thai", # 0E01..0E3A ; Thai - "Zzzz", # 0E3B..0E3E ; Unknown - "Zyyy", # 0E3F..0E3F ; Common - "Thai", # 0E40..0E5B ; Thai - "Zzzz", # 0E5C..0E80 ; Unknown - "Laoo", # 0E81..0E82 ; Lao - "Zzzz", # 0E83..0E83 ; Unknown - "Laoo", # 0E84..0E84 ; Lao - "Zzzz", # 0E85..0E85 ; Unknown - "Laoo", # 0E86..0E8A ; Lao - "Zzzz", # 0E8B..0E8B ; Unknown - "Laoo", # 0E8C..0EA3 ; Lao - "Zzzz", # 0EA4..0EA4 ; Unknown - "Laoo", # 0EA5..0EA5 ; Lao - "Zzzz", # 0EA6..0EA6 ; Unknown - "Laoo", # 0EA7..0EBD ; Lao - "Zzzz", # 0EBE..0EBF ; Unknown - "Laoo", # 0EC0..0EC4 ; Lao - "Zzzz", # 0EC5..0EC5 ; Unknown - "Laoo", # 0EC6..0EC6 ; Lao - "Zzzz", # 0EC7..0EC7 ; Unknown - "Laoo", # 0EC8..0ECE ; Lao - "Zzzz", # 0ECF..0ECF ; Unknown - "Laoo", # 0ED0..0ED9 ; Lao - "Zzzz", # 0EDA..0EDB ; Unknown - "Laoo", # 0EDC..0EDF ; Lao - "Zzzz", # 0EE0..0EFF ; Unknown - "Tibt", # 0F00..0F47 ; Tibetan - "Zzzz", # 0F48..0F48 ; Unknown - "Tibt", # 0F49..0F6C ; Tibetan - "Zzzz", # 0F6D..0F70 ; Unknown - "Tibt", # 0F71..0F97 ; Tibetan - "Zzzz", # 0F98..0F98 ; Unknown - "Tibt", # 0F99..0FBC ; Tibetan - "Zzzz", # 0FBD..0FBD ; Unknown - "Tibt", # 0FBE..0FCC ; Tibetan - "Zzzz", # 0FCD..0FCD ; Unknown - "Tibt", # 0FCE..0FD4 ; Tibetan - "Zyyy", # 0FD5..0FD8 ; Common - "Tibt", # 0FD9..0FDA ; Tibetan - "Zzzz", # 0FDB..0FFF ; Unknown - "Mymr", # 1000..109F ; Myanmar - "Geor", # 10A0..10C5 ; Georgian - "Zzzz", # 10C6..10C6 ; Unknown - "Geor", # 10C7..10C7 ; Georgian - "Zzzz", # 10C8..10CC ; Unknown - "Geor", # 10CD..10CD ; Georgian - "Zzzz", # 10CE..10CF ; Unknown - "Geor", # 10D0..10FA ; Georgian - "Zyyy", # 10FB..10FB ; Common - "Geor", # 10FC..10FF ; Georgian - "Hang", # 1100..11FF ; Hangul - "Ethi", # 1200..1248 ; Ethiopic - "Zzzz", # 1249..1249 ; Unknown - "Ethi", # 124A..124D ; Ethiopic - "Zzzz", # 124E..124F ; Unknown - "Ethi", # 1250..1256 ; Ethiopic - "Zzzz", # 1257..1257 ; Unknown - "Ethi", # 1258..1258 ; Ethiopic - "Zzzz", # 1259..1259 ; Unknown - "Ethi", # 125A..125D ; Ethiopic - "Zzzz", # 125E..125F ; Unknown - "Ethi", # 1260..1288 ; Ethiopic - "Zzzz", # 1289..1289 ; Unknown - "Ethi", # 128A..128D ; Ethiopic - "Zzzz", # 128E..128F ; Unknown - "Ethi", # 1290..12B0 ; Ethiopic - "Zzzz", # 12B1..12B1 ; Unknown - "Ethi", # 12B2..12B5 ; Ethiopic - "Zzzz", # 12B6..12B7 ; Unknown - "Ethi", # 12B8..12BE ; Ethiopic - "Zzzz", # 12BF..12BF ; Unknown - "Ethi", # 12C0..12C0 ; Ethiopic - "Zzzz", # 12C1..12C1 ; Unknown - "Ethi", # 12C2..12C5 ; Ethiopic - "Zzzz", # 12C6..12C7 ; Unknown - "Ethi", # 12C8..12D6 ; Ethiopic - "Zzzz", # 12D7..12D7 ; Unknown - "Ethi", # 12D8..1310 ; Ethiopic - "Zzzz", # 1311..1311 ; Unknown - "Ethi", # 1312..1315 ; Ethiopic - "Zzzz", # 1316..1317 ; Unknown - "Ethi", # 1318..135A ; Ethiopic - "Zzzz", # 135B..135C ; Unknown - "Ethi", # 135D..137C ; Ethiopic - "Zzzz", # 137D..137F ; Unknown - "Ethi", # 1380..1399 ; Ethiopic - "Zzzz", # 139A..139F ; Unknown - "Cher", # 13A0..13F5 ; Cherokee - "Zzzz", # 13F6..13F7 ; Unknown - "Cher", # 13F8..13FD ; Cherokee - "Zzzz", # 13FE..13FF ; Unknown - "Cans", # 1400..167F ; Canadian_Aboriginal - "Ogam", # 1680..169C ; Ogham - "Zzzz", # 169D..169F ; Unknown - "Runr", # 16A0..16EA ; Runic - "Zyyy", # 16EB..16ED ; Common - "Runr", # 16EE..16F8 ; Runic - "Zzzz", # 16F9..16FF ; Unknown - "Tglg", # 1700..1715 ; Tagalog - "Zzzz", # 1716..171E ; Unknown - "Tglg", # 171F..171F ; Tagalog - "Hano", # 1720..1734 ; Hanunoo - "Zyyy", # 1735..1736 ; Common - "Zzzz", # 1737..173F ; Unknown - "Buhd", # 1740..1753 ; Buhid - "Zzzz", # 1754..175F ; Unknown - "Tagb", # 1760..176C ; Tagbanwa - "Zzzz", # 176D..176D ; Unknown - "Tagb", # 176E..1770 ; Tagbanwa - "Zzzz", # 1771..1771 ; Unknown - "Tagb", # 1772..1773 ; Tagbanwa - "Zzzz", # 1774..177F ; Unknown - "Khmr", # 1780..17DD ; Khmer - "Zzzz", # 17DE..17DF ; Unknown - "Khmr", # 17E0..17E9 ; Khmer - "Zzzz", # 17EA..17EF ; Unknown - "Khmr", # 17F0..17F9 ; Khmer - "Zzzz", # 17FA..17FF ; Unknown - "Mong", # 1800..1801 ; Mongolian - "Zyyy", # 1802..1803 ; Common - "Mong", # 1804..1804 ; Mongolian - "Zyyy", # 1805..1805 ; Common - "Mong", # 1806..1819 ; Mongolian - "Zzzz", # 181A..181F ; Unknown - "Mong", # 1820..1878 ; Mongolian - "Zzzz", # 1879..187F ; Unknown - "Mong", # 1880..18AA ; Mongolian - "Zzzz", # 18AB..18AF ; Unknown - "Cans", # 18B0..18F5 ; Canadian_Aboriginal - "Zzzz", # 18F6..18FF ; Unknown - "Limb", # 1900..191E ; Limbu - "Zzzz", # 191F..191F ; Unknown - "Limb", # 1920..192B ; Limbu - "Zzzz", # 192C..192F ; Unknown - "Limb", # 1930..193B ; Limbu - "Zzzz", # 193C..193F ; Unknown - "Limb", # 1940..1940 ; Limbu - "Zzzz", # 1941..1943 ; Unknown - "Limb", # 1944..194F ; Limbu - "Tale", # 1950..196D ; Tai_Le - "Zzzz", # 196E..196F ; Unknown - "Tale", # 1970..1974 ; Tai_Le - "Zzzz", # 1975..197F ; Unknown - "Talu", # 1980..19AB ; New_Tai_Lue - "Zzzz", # 19AC..19AF ; Unknown - "Talu", # 19B0..19C9 ; New_Tai_Lue - "Zzzz", # 19CA..19CF ; Unknown - "Talu", # 19D0..19DA ; New_Tai_Lue - "Zzzz", # 19DB..19DD ; Unknown - "Talu", # 19DE..19DF ; New_Tai_Lue - "Khmr", # 19E0..19FF ; Khmer - "Bugi", # 1A00..1A1B ; Buginese - "Zzzz", # 1A1C..1A1D ; Unknown - "Bugi", # 1A1E..1A1F ; Buginese - "Lana", # 1A20..1A5E ; Tai_Tham - "Zzzz", # 1A5F..1A5F ; Unknown - "Lana", # 1A60..1A7C ; Tai_Tham - "Zzzz", # 1A7D..1A7E ; Unknown - "Lana", # 1A7F..1A89 ; Tai_Tham - "Zzzz", # 1A8A..1A8F ; Unknown - "Lana", # 1A90..1A99 ; Tai_Tham - "Zzzz", # 1A9A..1A9F ; Unknown - "Lana", # 1AA0..1AAD ; Tai_Tham - "Zzzz", # 1AAE..1AAF ; Unknown - "Zinh", # 1AB0..1ACE ; Inherited - "Zzzz", # 1ACF..1AFF ; Unknown - "Bali", # 1B00..1B4C ; Balinese - "Zzzz", # 1B4D..1B4F ; Unknown - "Bali", # 1B50..1B7E ; Balinese - "Zzzz", # 1B7F..1B7F ; Unknown - "Sund", # 1B80..1BBF ; Sundanese - "Batk", # 1BC0..1BF3 ; Batak - "Zzzz", # 1BF4..1BFB ; Unknown - "Batk", # 1BFC..1BFF ; Batak - "Lepc", # 1C00..1C37 ; Lepcha - "Zzzz", # 1C38..1C3A ; Unknown - "Lepc", # 1C3B..1C49 ; Lepcha - "Zzzz", # 1C4A..1C4C ; Unknown - "Lepc", # 1C4D..1C4F ; Lepcha - "Olck", # 1C50..1C7F ; Ol_Chiki - "Cyrl", # 1C80..1C88 ; Cyrillic - "Zzzz", # 1C89..1C8F ; Unknown - "Geor", # 1C90..1CBA ; Georgian - "Zzzz", # 1CBB..1CBC ; Unknown - "Geor", # 1CBD..1CBF ; Georgian - "Sund", # 1CC0..1CC7 ; Sundanese - "Zzzz", # 1CC8..1CCF ; Unknown - "Zinh", # 1CD0..1CD2 ; Inherited - "Zyyy", # 1CD3..1CD3 ; Common - "Zinh", # 1CD4..1CE0 ; Inherited - "Zyyy", # 1CE1..1CE1 ; Common - "Zinh", # 1CE2..1CE8 ; Inherited - "Zyyy", # 1CE9..1CEC ; Common - "Zinh", # 1CED..1CED ; Inherited - "Zyyy", # 1CEE..1CF3 ; Common - "Zinh", # 1CF4..1CF4 ; Inherited - "Zyyy", # 1CF5..1CF7 ; Common - "Zinh", # 1CF8..1CF9 ; Inherited - "Zyyy", # 1CFA..1CFA ; Common - "Zzzz", # 1CFB..1CFF ; Unknown - "Latn", # 1D00..1D25 ; Latin - "Grek", # 1D26..1D2A ; Greek - "Cyrl", # 1D2B..1D2B ; Cyrillic - "Latn", # 1D2C..1D5C ; Latin - "Grek", # 1D5D..1D61 ; Greek - "Latn", # 1D62..1D65 ; Latin - "Grek", # 1D66..1D6A ; Greek - "Latn", # 1D6B..1D77 ; Latin - "Cyrl", # 1D78..1D78 ; Cyrillic - "Latn", # 1D79..1DBE ; Latin - "Grek", # 1DBF..1DBF ; Greek - "Zinh", # 1DC0..1DFF ; Inherited - "Latn", # 1E00..1EFF ; Latin - "Grek", # 1F00..1F15 ; Greek - "Zzzz", # 1F16..1F17 ; Unknown - "Grek", # 1F18..1F1D ; Greek - "Zzzz", # 1F1E..1F1F ; Unknown - "Grek", # 1F20..1F45 ; Greek - "Zzzz", # 1F46..1F47 ; Unknown - "Grek", # 1F48..1F4D ; Greek - "Zzzz", # 1F4E..1F4F ; Unknown - "Grek", # 1F50..1F57 ; Greek - "Zzzz", # 1F58..1F58 ; Unknown - "Grek", # 1F59..1F59 ; Greek - "Zzzz", # 1F5A..1F5A ; Unknown - "Grek", # 1F5B..1F5B ; Greek - "Zzzz", # 1F5C..1F5C ; Unknown - "Grek", # 1F5D..1F5D ; Greek - "Zzzz", # 1F5E..1F5E ; Unknown - "Grek", # 1F5F..1F7D ; Greek - "Zzzz", # 1F7E..1F7F ; Unknown - "Grek", # 1F80..1FB4 ; Greek - "Zzzz", # 1FB5..1FB5 ; Unknown - "Grek", # 1FB6..1FC4 ; Greek - "Zzzz", # 1FC5..1FC5 ; Unknown - "Grek", # 1FC6..1FD3 ; Greek - "Zzzz", # 1FD4..1FD5 ; Unknown - "Grek", # 1FD6..1FDB ; Greek - "Zzzz", # 1FDC..1FDC ; Unknown - "Grek", # 1FDD..1FEF ; Greek - "Zzzz", # 1FF0..1FF1 ; Unknown - "Grek", # 1FF2..1FF4 ; Greek - "Zzzz", # 1FF5..1FF5 ; Unknown - "Grek", # 1FF6..1FFE ; Greek - "Zzzz", # 1FFF..1FFF ; Unknown - "Zyyy", # 2000..200B ; Common - "Zinh", # 200C..200D ; Inherited - "Zyyy", # 200E..2064 ; Common - "Zzzz", # 2065..2065 ; Unknown - "Zyyy", # 2066..2070 ; Common - "Latn", # 2071..2071 ; Latin - "Zzzz", # 2072..2073 ; Unknown - "Zyyy", # 2074..207E ; Common - "Latn", # 207F..207F ; Latin - "Zyyy", # 2080..208E ; Common - "Zzzz", # 208F..208F ; Unknown - "Latn", # 2090..209C ; Latin - "Zzzz", # 209D..209F ; Unknown - "Zyyy", # 20A0..20C0 ; Common - "Zzzz", # 20C1..20CF ; Unknown - "Zinh", # 20D0..20F0 ; Inherited - "Zzzz", # 20F1..20FF ; Unknown - "Zyyy", # 2100..2125 ; Common - "Grek", # 2126..2126 ; Greek - "Zyyy", # 2127..2129 ; Common - "Latn", # 212A..212B ; Latin - "Zyyy", # 212C..2131 ; Common - "Latn", # 2132..2132 ; Latin - "Zyyy", # 2133..214D ; Common - "Latn", # 214E..214E ; Latin - "Zyyy", # 214F..215F ; Common - "Latn", # 2160..2188 ; Latin - "Zyyy", # 2189..218B ; Common - "Zzzz", # 218C..218F ; Unknown - "Zyyy", # 2190..2426 ; Common - "Zzzz", # 2427..243F ; Unknown - "Zyyy", # 2440..244A ; Common - "Zzzz", # 244B..245F ; Unknown - "Zyyy", # 2460..27FF ; Common - "Brai", # 2800..28FF ; Braille - "Zyyy", # 2900..2B73 ; Common - "Zzzz", # 2B74..2B75 ; Unknown - "Zyyy", # 2B76..2B95 ; Common - "Zzzz", # 2B96..2B96 ; Unknown - "Zyyy", # 2B97..2BFF ; Common - "Glag", # 2C00..2C5F ; Glagolitic - "Latn", # 2C60..2C7F ; Latin - "Copt", # 2C80..2CF3 ; Coptic - "Zzzz", # 2CF4..2CF8 ; Unknown - "Copt", # 2CF9..2CFF ; Coptic - "Geor", # 2D00..2D25 ; Georgian - "Zzzz", # 2D26..2D26 ; Unknown - "Geor", # 2D27..2D27 ; Georgian - "Zzzz", # 2D28..2D2C ; Unknown - "Geor", # 2D2D..2D2D ; Georgian - "Zzzz", # 2D2E..2D2F ; Unknown - "Tfng", # 2D30..2D67 ; Tifinagh - "Zzzz", # 2D68..2D6E ; Unknown - "Tfng", # 2D6F..2D70 ; Tifinagh - "Zzzz", # 2D71..2D7E ; Unknown - "Tfng", # 2D7F..2D7F ; Tifinagh - "Ethi", # 2D80..2D96 ; Ethiopic - "Zzzz", # 2D97..2D9F ; Unknown - "Ethi", # 2DA0..2DA6 ; Ethiopic - "Zzzz", # 2DA7..2DA7 ; Unknown - "Ethi", # 2DA8..2DAE ; Ethiopic - "Zzzz", # 2DAF..2DAF ; Unknown - "Ethi", # 2DB0..2DB6 ; Ethiopic - "Zzzz", # 2DB7..2DB7 ; Unknown - "Ethi", # 2DB8..2DBE ; Ethiopic - "Zzzz", # 2DBF..2DBF ; Unknown - "Ethi", # 2DC0..2DC6 ; Ethiopic - "Zzzz", # 2DC7..2DC7 ; Unknown - "Ethi", # 2DC8..2DCE ; Ethiopic - "Zzzz", # 2DCF..2DCF ; Unknown - "Ethi", # 2DD0..2DD6 ; Ethiopic - "Zzzz", # 2DD7..2DD7 ; Unknown - "Ethi", # 2DD8..2DDE ; Ethiopic - "Zzzz", # 2DDF..2DDF ; Unknown - "Cyrl", # 2DE0..2DFF ; Cyrillic - "Zyyy", # 2E00..2E5D ; Common - "Zzzz", # 2E5E..2E7F ; Unknown - "Hani", # 2E80..2E99 ; Han - "Zzzz", # 2E9A..2E9A ; Unknown - "Hani", # 2E9B..2EF3 ; Han - "Zzzz", # 2EF4..2EFF ; Unknown - "Hani", # 2F00..2FD5 ; Han - "Zzzz", # 2FD6..2FEF ; Unknown - "Zyyy", # 2FF0..2FFB ; Common - "Zzzz", # 2FFC..2FFF ; Unknown - "Zyyy", # 3000..3004 ; Common - "Hani", # 3005..3005 ; Han - "Zyyy", # 3006..3006 ; Common - "Hani", # 3007..3007 ; Han - "Zyyy", # 3008..3020 ; Common - "Hani", # 3021..3029 ; Han - "Zinh", # 302A..302D ; Inherited - "Hang", # 302E..302F ; Hangul - "Zyyy", # 3030..3037 ; Common - "Hani", # 3038..303B ; Han - "Zyyy", # 303C..303F ; Common - "Zzzz", # 3040..3040 ; Unknown - "Hira", # 3041..3096 ; Hiragana - "Zzzz", # 3097..3098 ; Unknown - "Zinh", # 3099..309A ; Inherited - "Zyyy", # 309B..309C ; Common - "Hira", # 309D..309F ; Hiragana - "Zyyy", # 30A0..30A0 ; Common - "Kana", # 30A1..30FA ; Katakana - "Zyyy", # 30FB..30FC ; Common - "Kana", # 30FD..30FF ; Katakana - "Zzzz", # 3100..3104 ; Unknown - "Bopo", # 3105..312F ; Bopomofo - "Zzzz", # 3130..3130 ; Unknown - "Hang", # 3131..318E ; Hangul - "Zzzz", # 318F..318F ; Unknown - "Zyyy", # 3190..319F ; Common - "Bopo", # 31A0..31BF ; Bopomofo - "Zyyy", # 31C0..31E3 ; Common - "Zzzz", # 31E4..31EF ; Unknown - "Kana", # 31F0..31FF ; Katakana - "Hang", # 3200..321E ; Hangul - "Zzzz", # 321F..321F ; Unknown - "Zyyy", # 3220..325F ; Common - "Hang", # 3260..327E ; Hangul - "Zyyy", # 327F..32CF ; Common - "Kana", # 32D0..32FE ; Katakana - "Zyyy", # 32FF..32FF ; Common - "Kana", # 3300..3357 ; Katakana - "Zyyy", # 3358..33FF ; Common - "Hani", # 3400..4DBF ; Han - "Zyyy", # 4DC0..4DFF ; Common - "Hani", # 4E00..9FFF ; Han - "Yiii", # A000..A48C ; Yi - "Zzzz", # A48D..A48F ; Unknown - "Yiii", # A490..A4C6 ; Yi - "Zzzz", # A4C7..A4CF ; Unknown - "Lisu", # A4D0..A4FF ; Lisu - "Vaii", # A500..A62B ; Vai - "Zzzz", # A62C..A63F ; Unknown - "Cyrl", # A640..A69F ; Cyrillic - "Bamu", # A6A0..A6F7 ; Bamum - "Zzzz", # A6F8..A6FF ; Unknown - "Zyyy", # A700..A721 ; Common - "Latn", # A722..A787 ; Latin - "Zyyy", # A788..A78A ; Common - "Latn", # A78B..A7CA ; Latin - "Zzzz", # A7CB..A7CF ; Unknown - "Latn", # A7D0..A7D1 ; Latin - "Zzzz", # A7D2..A7D2 ; Unknown - "Latn", # A7D3..A7D3 ; Latin - "Zzzz", # A7D4..A7D4 ; Unknown - "Latn", # A7D5..A7D9 ; Latin - "Zzzz", # A7DA..A7F1 ; Unknown - "Latn", # A7F2..A7FF ; Latin - "Sylo", # A800..A82C ; Syloti_Nagri - "Zzzz", # A82D..A82F ; Unknown - "Zyyy", # A830..A839 ; Common - "Zzzz", # A83A..A83F ; Unknown - "Phag", # A840..A877 ; Phags_Pa - "Zzzz", # A878..A87F ; Unknown - "Saur", # A880..A8C5 ; Saurashtra - "Zzzz", # A8C6..A8CD ; Unknown - "Saur", # A8CE..A8D9 ; Saurashtra - "Zzzz", # A8DA..A8DF ; Unknown - "Deva", # A8E0..A8FF ; Devanagari - "Kali", # A900..A92D ; Kayah_Li - "Zyyy", # A92E..A92E ; Common - "Kali", # A92F..A92F ; Kayah_Li - "Rjng", # A930..A953 ; Rejang - "Zzzz", # A954..A95E ; Unknown - "Rjng", # A95F..A95F ; Rejang - "Hang", # A960..A97C ; Hangul - "Zzzz", # A97D..A97F ; Unknown - "Java", # A980..A9CD ; Javanese - "Zzzz", # A9CE..A9CE ; Unknown - "Zyyy", # A9CF..A9CF ; Common - "Java", # A9D0..A9D9 ; Javanese - "Zzzz", # A9DA..A9DD ; Unknown - "Java", # A9DE..A9DF ; Javanese - "Mymr", # A9E0..A9FE ; Myanmar - "Zzzz", # A9FF..A9FF ; Unknown - "Cham", # AA00..AA36 ; Cham - "Zzzz", # AA37..AA3F ; Unknown - "Cham", # AA40..AA4D ; Cham - "Zzzz", # AA4E..AA4F ; Unknown - "Cham", # AA50..AA59 ; Cham - "Zzzz", # AA5A..AA5B ; Unknown - "Cham", # AA5C..AA5F ; Cham - "Mymr", # AA60..AA7F ; Myanmar - "Tavt", # AA80..AAC2 ; Tai_Viet - "Zzzz", # AAC3..AADA ; Unknown - "Tavt", # AADB..AADF ; Tai_Viet - "Mtei", # AAE0..AAF6 ; Meetei_Mayek - "Zzzz", # AAF7..AB00 ; Unknown - "Ethi", # AB01..AB06 ; Ethiopic - "Zzzz", # AB07..AB08 ; Unknown - "Ethi", # AB09..AB0E ; Ethiopic - "Zzzz", # AB0F..AB10 ; Unknown - "Ethi", # AB11..AB16 ; Ethiopic - "Zzzz", # AB17..AB1F ; Unknown - "Ethi", # AB20..AB26 ; Ethiopic - "Zzzz", # AB27..AB27 ; Unknown - "Ethi", # AB28..AB2E ; Ethiopic - "Zzzz", # AB2F..AB2F ; Unknown - "Latn", # AB30..AB5A ; Latin - "Zyyy", # AB5B..AB5B ; Common - "Latn", # AB5C..AB64 ; Latin - "Grek", # AB65..AB65 ; Greek - "Latn", # AB66..AB69 ; Latin - "Zyyy", # AB6A..AB6B ; Common - "Zzzz", # AB6C..AB6F ; Unknown - "Cher", # AB70..ABBF ; Cherokee - "Mtei", # ABC0..ABED ; Meetei_Mayek - "Zzzz", # ABEE..ABEF ; Unknown - "Mtei", # ABF0..ABF9 ; Meetei_Mayek - "Zzzz", # ABFA..ABFF ; Unknown - "Hang", # AC00..D7A3 ; Hangul - "Zzzz", # D7A4..D7AF ; Unknown - "Hang", # D7B0..D7C6 ; Hangul - "Zzzz", # D7C7..D7CA ; Unknown - "Hang", # D7CB..D7FB ; Hangul - "Zzzz", # D7FC..F8FF ; Unknown - "Hani", # F900..FA6D ; Han - "Zzzz", # FA6E..FA6F ; Unknown - "Hani", # FA70..FAD9 ; Han - "Zzzz", # FADA..FAFF ; Unknown - "Latn", # FB00..FB06 ; Latin - "Zzzz", # FB07..FB12 ; Unknown - "Armn", # FB13..FB17 ; Armenian - "Zzzz", # FB18..FB1C ; Unknown - "Hebr", # FB1D..FB36 ; Hebrew - "Zzzz", # FB37..FB37 ; Unknown - "Hebr", # FB38..FB3C ; Hebrew - "Zzzz", # FB3D..FB3D ; Unknown - "Hebr", # FB3E..FB3E ; Hebrew - "Zzzz", # FB3F..FB3F ; Unknown - "Hebr", # FB40..FB41 ; Hebrew - "Zzzz", # FB42..FB42 ; Unknown - "Hebr", # FB43..FB44 ; Hebrew - "Zzzz", # FB45..FB45 ; Unknown - "Hebr", # FB46..FB4F ; Hebrew - "Arab", # FB50..FBC2 ; Arabic - "Zzzz", # FBC3..FBD2 ; Unknown - "Arab", # FBD3..FD3D ; Arabic - "Zyyy", # FD3E..FD3F ; Common - "Arab", # FD40..FD8F ; Arabic - "Zzzz", # FD90..FD91 ; Unknown - "Arab", # FD92..FDC7 ; Arabic - "Zzzz", # FDC8..FDCE ; Unknown - "Arab", # FDCF..FDCF ; Arabic - "Zzzz", # FDD0..FDEF ; Unknown - "Arab", # FDF0..FDFF ; Arabic - "Zinh", # FE00..FE0F ; Inherited - "Zyyy", # FE10..FE19 ; Common - "Zzzz", # FE1A..FE1F ; Unknown - "Zinh", # FE20..FE2D ; Inherited - "Cyrl", # FE2E..FE2F ; Cyrillic - "Zyyy", # FE30..FE52 ; Common - "Zzzz", # FE53..FE53 ; Unknown - "Zyyy", # FE54..FE66 ; Common - "Zzzz", # FE67..FE67 ; Unknown - "Zyyy", # FE68..FE6B ; Common - "Zzzz", # FE6C..FE6F ; Unknown - "Arab", # FE70..FE74 ; Arabic - "Zzzz", # FE75..FE75 ; Unknown - "Arab", # FE76..FEFC ; Arabic - "Zzzz", # FEFD..FEFE ; Unknown - "Zyyy", # FEFF..FEFF ; Common - "Zzzz", # FF00..FF00 ; Unknown - "Zyyy", # FF01..FF20 ; Common - "Latn", # FF21..FF3A ; Latin - "Zyyy", # FF3B..FF40 ; Common - "Latn", # FF41..FF5A ; Latin - "Zyyy", # FF5B..FF65 ; Common - "Kana", # FF66..FF6F ; Katakana - "Zyyy", # FF70..FF70 ; Common - "Kana", # FF71..FF9D ; Katakana - "Zyyy", # FF9E..FF9F ; Common - "Hang", # FFA0..FFBE ; Hangul - "Zzzz", # FFBF..FFC1 ; Unknown - "Hang", # FFC2..FFC7 ; Hangul - "Zzzz", # FFC8..FFC9 ; Unknown - "Hang", # FFCA..FFCF ; Hangul - "Zzzz", # FFD0..FFD1 ; Unknown - "Hang", # FFD2..FFD7 ; Hangul - "Zzzz", # FFD8..FFD9 ; Unknown - "Hang", # FFDA..FFDC ; Hangul - "Zzzz", # FFDD..FFDF ; Unknown - "Zyyy", # FFE0..FFE6 ; Common - "Zzzz", # FFE7..FFE7 ; Unknown - "Zyyy", # FFE8..FFEE ; Common - "Zzzz", # FFEF..FFF8 ; Unknown - "Zyyy", # FFF9..FFFD ; Common - "Zzzz", # FFFE..FFFF ; Unknown - "Linb", # 10000..1000B ; Linear_B - "Zzzz", # 1000C..1000C ; Unknown - "Linb", # 1000D..10026 ; Linear_B - "Zzzz", # 10027..10027 ; Unknown - "Linb", # 10028..1003A ; Linear_B - "Zzzz", # 1003B..1003B ; Unknown - "Linb", # 1003C..1003D ; Linear_B - "Zzzz", # 1003E..1003E ; Unknown - "Linb", # 1003F..1004D ; Linear_B - "Zzzz", # 1004E..1004F ; Unknown - "Linb", # 10050..1005D ; Linear_B - "Zzzz", # 1005E..1007F ; Unknown - "Linb", # 10080..100FA ; Linear_B - "Zzzz", # 100FB..100FF ; Unknown - "Zyyy", # 10100..10102 ; Common - "Zzzz", # 10103..10106 ; Unknown - "Zyyy", # 10107..10133 ; Common - "Zzzz", # 10134..10136 ; Unknown - "Zyyy", # 10137..1013F ; Common - "Grek", # 10140..1018E ; Greek - "Zzzz", # 1018F..1018F ; Unknown - "Zyyy", # 10190..1019C ; Common - "Zzzz", # 1019D..1019F ; Unknown - "Grek", # 101A0..101A0 ; Greek - "Zzzz", # 101A1..101CF ; Unknown - "Zyyy", # 101D0..101FC ; Common - "Zinh", # 101FD..101FD ; Inherited - "Zzzz", # 101FE..1027F ; Unknown - "Lyci", # 10280..1029C ; Lycian - "Zzzz", # 1029D..1029F ; Unknown - "Cari", # 102A0..102D0 ; Carian - "Zzzz", # 102D1..102DF ; Unknown - "Zinh", # 102E0..102E0 ; Inherited - "Zyyy", # 102E1..102FB ; Common - "Zzzz", # 102FC..102FF ; Unknown - "Ital", # 10300..10323 ; Old_Italic - "Zzzz", # 10324..1032C ; Unknown - "Ital", # 1032D..1032F ; Old_Italic - "Goth", # 10330..1034A ; Gothic - "Zzzz", # 1034B..1034F ; Unknown - "Perm", # 10350..1037A ; Old_Permic - "Zzzz", # 1037B..1037F ; Unknown - "Ugar", # 10380..1039D ; Ugaritic - "Zzzz", # 1039E..1039E ; Unknown - "Ugar", # 1039F..1039F ; Ugaritic - "Xpeo", # 103A0..103C3 ; Old_Persian - "Zzzz", # 103C4..103C7 ; Unknown - "Xpeo", # 103C8..103D5 ; Old_Persian - "Zzzz", # 103D6..103FF ; Unknown - "Dsrt", # 10400..1044F ; Deseret - "Shaw", # 10450..1047F ; Shavian - "Osma", # 10480..1049D ; Osmanya - "Zzzz", # 1049E..1049F ; Unknown - "Osma", # 104A0..104A9 ; Osmanya - "Zzzz", # 104AA..104AF ; Unknown - "Osge", # 104B0..104D3 ; Osage - "Zzzz", # 104D4..104D7 ; Unknown - "Osge", # 104D8..104FB ; Osage - "Zzzz", # 104FC..104FF ; Unknown - "Elba", # 10500..10527 ; Elbasan - "Zzzz", # 10528..1052F ; Unknown - "Aghb", # 10530..10563 ; Caucasian_Albanian - "Zzzz", # 10564..1056E ; Unknown - "Aghb", # 1056F..1056F ; Caucasian_Albanian - "Vith", # 10570..1057A ; Vithkuqi - "Zzzz", # 1057B..1057B ; Unknown - "Vith", # 1057C..1058A ; Vithkuqi - "Zzzz", # 1058B..1058B ; Unknown - "Vith", # 1058C..10592 ; Vithkuqi - "Zzzz", # 10593..10593 ; Unknown - "Vith", # 10594..10595 ; Vithkuqi - "Zzzz", # 10596..10596 ; Unknown - "Vith", # 10597..105A1 ; Vithkuqi - "Zzzz", # 105A2..105A2 ; Unknown - "Vith", # 105A3..105B1 ; Vithkuqi - "Zzzz", # 105B2..105B2 ; Unknown - "Vith", # 105B3..105B9 ; Vithkuqi - "Zzzz", # 105BA..105BA ; Unknown - "Vith", # 105BB..105BC ; Vithkuqi - "Zzzz", # 105BD..105FF ; Unknown - "Lina", # 10600..10736 ; Linear_A - "Zzzz", # 10737..1073F ; Unknown - "Lina", # 10740..10755 ; Linear_A - "Zzzz", # 10756..1075F ; Unknown - "Lina", # 10760..10767 ; Linear_A - "Zzzz", # 10768..1077F ; Unknown - "Latn", # 10780..10785 ; Latin - "Zzzz", # 10786..10786 ; Unknown - "Latn", # 10787..107B0 ; Latin - "Zzzz", # 107B1..107B1 ; Unknown - "Latn", # 107B2..107BA ; Latin - "Zzzz", # 107BB..107FF ; Unknown - "Cprt", # 10800..10805 ; Cypriot - "Zzzz", # 10806..10807 ; Unknown - "Cprt", # 10808..10808 ; Cypriot - "Zzzz", # 10809..10809 ; Unknown - "Cprt", # 1080A..10835 ; Cypriot - "Zzzz", # 10836..10836 ; Unknown - "Cprt", # 10837..10838 ; Cypriot - "Zzzz", # 10839..1083B ; Unknown - "Cprt", # 1083C..1083C ; Cypriot - "Zzzz", # 1083D..1083E ; Unknown - "Cprt", # 1083F..1083F ; Cypriot - "Armi", # 10840..10855 ; Imperial_Aramaic - "Zzzz", # 10856..10856 ; Unknown - "Armi", # 10857..1085F ; Imperial_Aramaic - "Palm", # 10860..1087F ; Palmyrene - "Nbat", # 10880..1089E ; Nabataean - "Zzzz", # 1089F..108A6 ; Unknown - "Nbat", # 108A7..108AF ; Nabataean - "Zzzz", # 108B0..108DF ; Unknown - "Hatr", # 108E0..108F2 ; Hatran - "Zzzz", # 108F3..108F3 ; Unknown - "Hatr", # 108F4..108F5 ; Hatran - "Zzzz", # 108F6..108FA ; Unknown - "Hatr", # 108FB..108FF ; Hatran - "Phnx", # 10900..1091B ; Phoenician - "Zzzz", # 1091C..1091E ; Unknown - "Phnx", # 1091F..1091F ; Phoenician - "Lydi", # 10920..10939 ; Lydian - "Zzzz", # 1093A..1093E ; Unknown - "Lydi", # 1093F..1093F ; Lydian - "Zzzz", # 10940..1097F ; Unknown - "Mero", # 10980..1099F ; Meroitic_Hieroglyphs - "Merc", # 109A0..109B7 ; Meroitic_Cursive - "Zzzz", # 109B8..109BB ; Unknown - "Merc", # 109BC..109CF ; Meroitic_Cursive - "Zzzz", # 109D0..109D1 ; Unknown - "Merc", # 109D2..109FF ; Meroitic_Cursive - "Khar", # 10A00..10A03 ; Kharoshthi - "Zzzz", # 10A04..10A04 ; Unknown - "Khar", # 10A05..10A06 ; Kharoshthi - "Zzzz", # 10A07..10A0B ; Unknown - "Khar", # 10A0C..10A13 ; Kharoshthi - "Zzzz", # 10A14..10A14 ; Unknown - "Khar", # 10A15..10A17 ; Kharoshthi - "Zzzz", # 10A18..10A18 ; Unknown - "Khar", # 10A19..10A35 ; Kharoshthi - "Zzzz", # 10A36..10A37 ; Unknown - "Khar", # 10A38..10A3A ; Kharoshthi - "Zzzz", # 10A3B..10A3E ; Unknown - "Khar", # 10A3F..10A48 ; Kharoshthi - "Zzzz", # 10A49..10A4F ; Unknown - "Khar", # 10A50..10A58 ; Kharoshthi - "Zzzz", # 10A59..10A5F ; Unknown - "Sarb", # 10A60..10A7F ; Old_South_Arabian - "Narb", # 10A80..10A9F ; Old_North_Arabian - "Zzzz", # 10AA0..10ABF ; Unknown - "Mani", # 10AC0..10AE6 ; Manichaean - "Zzzz", # 10AE7..10AEA ; Unknown - "Mani", # 10AEB..10AF6 ; Manichaean - "Zzzz", # 10AF7..10AFF ; Unknown - "Avst", # 10B00..10B35 ; Avestan - "Zzzz", # 10B36..10B38 ; Unknown - "Avst", # 10B39..10B3F ; Avestan - "Prti", # 10B40..10B55 ; Inscriptional_Parthian - "Zzzz", # 10B56..10B57 ; Unknown - "Prti", # 10B58..10B5F ; Inscriptional_Parthian - "Phli", # 10B60..10B72 ; Inscriptional_Pahlavi - "Zzzz", # 10B73..10B77 ; Unknown - "Phli", # 10B78..10B7F ; Inscriptional_Pahlavi - "Phlp", # 10B80..10B91 ; Psalter_Pahlavi - "Zzzz", # 10B92..10B98 ; Unknown - "Phlp", # 10B99..10B9C ; Psalter_Pahlavi - "Zzzz", # 10B9D..10BA8 ; Unknown - "Phlp", # 10BA9..10BAF ; Psalter_Pahlavi - "Zzzz", # 10BB0..10BFF ; Unknown - "Orkh", # 10C00..10C48 ; Old_Turkic - "Zzzz", # 10C49..10C7F ; Unknown - "Hung", # 10C80..10CB2 ; Old_Hungarian - "Zzzz", # 10CB3..10CBF ; Unknown - "Hung", # 10CC0..10CF2 ; Old_Hungarian - "Zzzz", # 10CF3..10CF9 ; Unknown - "Hung", # 10CFA..10CFF ; Old_Hungarian - "Rohg", # 10D00..10D27 ; Hanifi_Rohingya - "Zzzz", # 10D28..10D2F ; Unknown - "Rohg", # 10D30..10D39 ; Hanifi_Rohingya - "Zzzz", # 10D3A..10E5F ; Unknown - "Arab", # 10E60..10E7E ; Arabic - "Zzzz", # 10E7F..10E7F ; Unknown - "Yezi", # 10E80..10EA9 ; Yezidi - "Zzzz", # 10EAA..10EAA ; Unknown - "Yezi", # 10EAB..10EAD ; Yezidi - "Zzzz", # 10EAE..10EAF ; Unknown - "Yezi", # 10EB0..10EB1 ; Yezidi - "Zzzz", # 10EB2..10EFC ; Unknown - "Arab", # 10EFD..10EFF ; Arabic - "Sogo", # 10F00..10F27 ; Old_Sogdian - "Zzzz", # 10F28..10F2F ; Unknown - "Sogd", # 10F30..10F59 ; Sogdian - "Zzzz", # 10F5A..10F6F ; Unknown - "Ougr", # 10F70..10F89 ; Old_Uyghur - "Zzzz", # 10F8A..10FAF ; Unknown - "Chrs", # 10FB0..10FCB ; Chorasmian - "Zzzz", # 10FCC..10FDF ; Unknown - "Elym", # 10FE0..10FF6 ; Elymaic - "Zzzz", # 10FF7..10FFF ; Unknown - "Brah", # 11000..1104D ; Brahmi - "Zzzz", # 1104E..11051 ; Unknown - "Brah", # 11052..11075 ; Brahmi - "Zzzz", # 11076..1107E ; Unknown - "Brah", # 1107F..1107F ; Brahmi - "Kthi", # 11080..110C2 ; Kaithi - "Zzzz", # 110C3..110CC ; Unknown - "Kthi", # 110CD..110CD ; Kaithi - "Zzzz", # 110CE..110CF ; Unknown - "Sora", # 110D0..110E8 ; Sora_Sompeng - "Zzzz", # 110E9..110EF ; Unknown - "Sora", # 110F0..110F9 ; Sora_Sompeng - "Zzzz", # 110FA..110FF ; Unknown - "Cakm", # 11100..11134 ; Chakma - "Zzzz", # 11135..11135 ; Unknown - "Cakm", # 11136..11147 ; Chakma - "Zzzz", # 11148..1114F ; Unknown - "Mahj", # 11150..11176 ; Mahajani - "Zzzz", # 11177..1117F ; Unknown - "Shrd", # 11180..111DF ; Sharada - "Zzzz", # 111E0..111E0 ; Unknown - "Sinh", # 111E1..111F4 ; Sinhala - "Zzzz", # 111F5..111FF ; Unknown - "Khoj", # 11200..11211 ; Khojki - "Zzzz", # 11212..11212 ; Unknown - "Khoj", # 11213..11241 ; Khojki - "Zzzz", # 11242..1127F ; Unknown - "Mult", # 11280..11286 ; Multani - "Zzzz", # 11287..11287 ; Unknown - "Mult", # 11288..11288 ; Multani - "Zzzz", # 11289..11289 ; Unknown - "Mult", # 1128A..1128D ; Multani - "Zzzz", # 1128E..1128E ; Unknown - "Mult", # 1128F..1129D ; Multani - "Zzzz", # 1129E..1129E ; Unknown - "Mult", # 1129F..112A9 ; Multani - "Zzzz", # 112AA..112AF ; Unknown - "Sind", # 112B0..112EA ; Khudawadi - "Zzzz", # 112EB..112EF ; Unknown - "Sind", # 112F0..112F9 ; Khudawadi - "Zzzz", # 112FA..112FF ; Unknown - "Gran", # 11300..11303 ; Grantha - "Zzzz", # 11304..11304 ; Unknown - "Gran", # 11305..1130C ; Grantha - "Zzzz", # 1130D..1130E ; Unknown - "Gran", # 1130F..11310 ; Grantha - "Zzzz", # 11311..11312 ; Unknown - "Gran", # 11313..11328 ; Grantha - "Zzzz", # 11329..11329 ; Unknown - "Gran", # 1132A..11330 ; Grantha - "Zzzz", # 11331..11331 ; Unknown - "Gran", # 11332..11333 ; Grantha - "Zzzz", # 11334..11334 ; Unknown - "Gran", # 11335..11339 ; Grantha - "Zzzz", # 1133A..1133A ; Unknown - "Zinh", # 1133B..1133B ; Inherited - "Gran", # 1133C..11344 ; Grantha - "Zzzz", # 11345..11346 ; Unknown - "Gran", # 11347..11348 ; Grantha - "Zzzz", # 11349..1134A ; Unknown - "Gran", # 1134B..1134D ; Grantha - "Zzzz", # 1134E..1134F ; Unknown - "Gran", # 11350..11350 ; Grantha - "Zzzz", # 11351..11356 ; Unknown - "Gran", # 11357..11357 ; Grantha - "Zzzz", # 11358..1135C ; Unknown - "Gran", # 1135D..11363 ; Grantha - "Zzzz", # 11364..11365 ; Unknown - "Gran", # 11366..1136C ; Grantha - "Zzzz", # 1136D..1136F ; Unknown - "Gran", # 11370..11374 ; Grantha - "Zzzz", # 11375..113FF ; Unknown - "Newa", # 11400..1145B ; Newa - "Zzzz", # 1145C..1145C ; Unknown - "Newa", # 1145D..11461 ; Newa - "Zzzz", # 11462..1147F ; Unknown - "Tirh", # 11480..114C7 ; Tirhuta - "Zzzz", # 114C8..114CF ; Unknown - "Tirh", # 114D0..114D9 ; Tirhuta - "Zzzz", # 114DA..1157F ; Unknown - "Sidd", # 11580..115B5 ; Siddham - "Zzzz", # 115B6..115B7 ; Unknown - "Sidd", # 115B8..115DD ; Siddham - "Zzzz", # 115DE..115FF ; Unknown - "Modi", # 11600..11644 ; Modi - "Zzzz", # 11645..1164F ; Unknown - "Modi", # 11650..11659 ; Modi - "Zzzz", # 1165A..1165F ; Unknown - "Mong", # 11660..1166C ; Mongolian - "Zzzz", # 1166D..1167F ; Unknown - "Takr", # 11680..116B9 ; Takri - "Zzzz", # 116BA..116BF ; Unknown - "Takr", # 116C0..116C9 ; Takri - "Zzzz", # 116CA..116FF ; Unknown - "Ahom", # 11700..1171A ; Ahom - "Zzzz", # 1171B..1171C ; Unknown - "Ahom", # 1171D..1172B ; Ahom - "Zzzz", # 1172C..1172F ; Unknown - "Ahom", # 11730..11746 ; Ahom - "Zzzz", # 11747..117FF ; Unknown - "Dogr", # 11800..1183B ; Dogra - "Zzzz", # 1183C..1189F ; Unknown - "Wara", # 118A0..118F2 ; Warang_Citi - "Zzzz", # 118F3..118FE ; Unknown - "Wara", # 118FF..118FF ; Warang_Citi - "Diak", # 11900..11906 ; Dives_Akuru - "Zzzz", # 11907..11908 ; Unknown - "Diak", # 11909..11909 ; Dives_Akuru - "Zzzz", # 1190A..1190B ; Unknown - "Diak", # 1190C..11913 ; Dives_Akuru - "Zzzz", # 11914..11914 ; Unknown - "Diak", # 11915..11916 ; Dives_Akuru - "Zzzz", # 11917..11917 ; Unknown - "Diak", # 11918..11935 ; Dives_Akuru - "Zzzz", # 11936..11936 ; Unknown - "Diak", # 11937..11938 ; Dives_Akuru - "Zzzz", # 11939..1193A ; Unknown - "Diak", # 1193B..11946 ; Dives_Akuru - "Zzzz", # 11947..1194F ; Unknown - "Diak", # 11950..11959 ; Dives_Akuru - "Zzzz", # 1195A..1199F ; Unknown - "Nand", # 119A0..119A7 ; Nandinagari - "Zzzz", # 119A8..119A9 ; Unknown - "Nand", # 119AA..119D7 ; Nandinagari - "Zzzz", # 119D8..119D9 ; Unknown - "Nand", # 119DA..119E4 ; Nandinagari - "Zzzz", # 119E5..119FF ; Unknown - "Zanb", # 11A00..11A47 ; Zanabazar_Square - "Zzzz", # 11A48..11A4F ; Unknown - "Soyo", # 11A50..11AA2 ; Soyombo - "Zzzz", # 11AA3..11AAF ; Unknown - "Cans", # 11AB0..11ABF ; Canadian_Aboriginal - "Pauc", # 11AC0..11AF8 ; Pau_Cin_Hau - "Zzzz", # 11AF9..11AFF ; Unknown - "Deva", # 11B00..11B09 ; Devanagari - "Zzzz", # 11B0A..11BFF ; Unknown - "Bhks", # 11C00..11C08 ; Bhaiksuki - "Zzzz", # 11C09..11C09 ; Unknown - "Bhks", # 11C0A..11C36 ; Bhaiksuki - "Zzzz", # 11C37..11C37 ; Unknown - "Bhks", # 11C38..11C45 ; Bhaiksuki - "Zzzz", # 11C46..11C4F ; Unknown - "Bhks", # 11C50..11C6C ; Bhaiksuki - "Zzzz", # 11C6D..11C6F ; Unknown - "Marc", # 11C70..11C8F ; Marchen - "Zzzz", # 11C90..11C91 ; Unknown - "Marc", # 11C92..11CA7 ; Marchen - "Zzzz", # 11CA8..11CA8 ; Unknown - "Marc", # 11CA9..11CB6 ; Marchen - "Zzzz", # 11CB7..11CFF ; Unknown - "Gonm", # 11D00..11D06 ; Masaram_Gondi - "Zzzz", # 11D07..11D07 ; Unknown - "Gonm", # 11D08..11D09 ; Masaram_Gondi - "Zzzz", # 11D0A..11D0A ; Unknown - "Gonm", # 11D0B..11D36 ; Masaram_Gondi - "Zzzz", # 11D37..11D39 ; Unknown - "Gonm", # 11D3A..11D3A ; Masaram_Gondi - "Zzzz", # 11D3B..11D3B ; Unknown - "Gonm", # 11D3C..11D3D ; Masaram_Gondi - "Zzzz", # 11D3E..11D3E ; Unknown - "Gonm", # 11D3F..11D47 ; Masaram_Gondi - "Zzzz", # 11D48..11D4F ; Unknown - "Gonm", # 11D50..11D59 ; Masaram_Gondi - "Zzzz", # 11D5A..11D5F ; Unknown - "Gong", # 11D60..11D65 ; Gunjala_Gondi - "Zzzz", # 11D66..11D66 ; Unknown - "Gong", # 11D67..11D68 ; Gunjala_Gondi - "Zzzz", # 11D69..11D69 ; Unknown - "Gong", # 11D6A..11D8E ; Gunjala_Gondi - "Zzzz", # 11D8F..11D8F ; Unknown - "Gong", # 11D90..11D91 ; Gunjala_Gondi - "Zzzz", # 11D92..11D92 ; Unknown - "Gong", # 11D93..11D98 ; Gunjala_Gondi - "Zzzz", # 11D99..11D9F ; Unknown - "Gong", # 11DA0..11DA9 ; Gunjala_Gondi - "Zzzz", # 11DAA..11EDF ; Unknown - "Maka", # 11EE0..11EF8 ; Makasar - "Zzzz", # 11EF9..11EFF ; Unknown - "Kawi", # 11F00..11F10 ; Kawi - "Zzzz", # 11F11..11F11 ; Unknown - "Kawi", # 11F12..11F3A ; Kawi - "Zzzz", # 11F3B..11F3D ; Unknown - "Kawi", # 11F3E..11F59 ; Kawi - "Zzzz", # 11F5A..11FAF ; Unknown - "Lisu", # 11FB0..11FB0 ; Lisu - "Zzzz", # 11FB1..11FBF ; Unknown - "Taml", # 11FC0..11FF1 ; Tamil - "Zzzz", # 11FF2..11FFE ; Unknown - "Taml", # 11FFF..11FFF ; Tamil - "Xsux", # 12000..12399 ; Cuneiform - "Zzzz", # 1239A..123FF ; Unknown - "Xsux", # 12400..1246E ; Cuneiform - "Zzzz", # 1246F..1246F ; Unknown - "Xsux", # 12470..12474 ; Cuneiform - "Zzzz", # 12475..1247F ; Unknown - "Xsux", # 12480..12543 ; Cuneiform - "Zzzz", # 12544..12F8F ; Unknown - "Cpmn", # 12F90..12FF2 ; Cypro_Minoan - "Zzzz", # 12FF3..12FFF ; Unknown - "Egyp", # 13000..13455 ; Egyptian_Hieroglyphs - "Zzzz", # 13456..143FF ; Unknown - "Hluw", # 14400..14646 ; Anatolian_Hieroglyphs - "Zzzz", # 14647..167FF ; Unknown - "Bamu", # 16800..16A38 ; Bamum - "Zzzz", # 16A39..16A3F ; Unknown - "Mroo", # 16A40..16A5E ; Mro - "Zzzz", # 16A5F..16A5F ; Unknown - "Mroo", # 16A60..16A69 ; Mro - "Zzzz", # 16A6A..16A6D ; Unknown - "Mroo", # 16A6E..16A6F ; Mro - "Tnsa", # 16A70..16ABE ; Tangsa - "Zzzz", # 16ABF..16ABF ; Unknown - "Tnsa", # 16AC0..16AC9 ; Tangsa - "Zzzz", # 16ACA..16ACF ; Unknown - "Bass", # 16AD0..16AED ; Bassa_Vah - "Zzzz", # 16AEE..16AEF ; Unknown - "Bass", # 16AF0..16AF5 ; Bassa_Vah - "Zzzz", # 16AF6..16AFF ; Unknown - "Hmng", # 16B00..16B45 ; Pahawh_Hmong - "Zzzz", # 16B46..16B4F ; Unknown - "Hmng", # 16B50..16B59 ; Pahawh_Hmong - "Zzzz", # 16B5A..16B5A ; Unknown - "Hmng", # 16B5B..16B61 ; Pahawh_Hmong - "Zzzz", # 16B62..16B62 ; Unknown - "Hmng", # 16B63..16B77 ; Pahawh_Hmong - "Zzzz", # 16B78..16B7C ; Unknown - "Hmng", # 16B7D..16B8F ; Pahawh_Hmong - "Zzzz", # 16B90..16E3F ; Unknown - "Medf", # 16E40..16E9A ; Medefaidrin - "Zzzz", # 16E9B..16EFF ; Unknown - "Plrd", # 16F00..16F4A ; Miao - "Zzzz", # 16F4B..16F4E ; Unknown - "Plrd", # 16F4F..16F87 ; Miao - "Zzzz", # 16F88..16F8E ; Unknown - "Plrd", # 16F8F..16F9F ; Miao - "Zzzz", # 16FA0..16FDF ; Unknown - "Tang", # 16FE0..16FE0 ; Tangut - "Nshu", # 16FE1..16FE1 ; Nushu - "Hani", # 16FE2..16FE3 ; Han - "Kits", # 16FE4..16FE4 ; Khitan_Small_Script - "Zzzz", # 16FE5..16FEF ; Unknown - "Hani", # 16FF0..16FF1 ; Han - "Zzzz", # 16FF2..16FFF ; Unknown - "Tang", # 17000..187F7 ; Tangut - "Zzzz", # 187F8..187FF ; Unknown - "Tang", # 18800..18AFF ; Tangut - "Kits", # 18B00..18CD5 ; Khitan_Small_Script - "Zzzz", # 18CD6..18CFF ; Unknown - "Tang", # 18D00..18D08 ; Tangut - "Zzzz", # 18D09..1AFEF ; Unknown - "Kana", # 1AFF0..1AFF3 ; Katakana - "Zzzz", # 1AFF4..1AFF4 ; Unknown - "Kana", # 1AFF5..1AFFB ; Katakana - "Zzzz", # 1AFFC..1AFFC ; Unknown - "Kana", # 1AFFD..1AFFE ; Katakana - "Zzzz", # 1AFFF..1AFFF ; Unknown - "Kana", # 1B000..1B000 ; Katakana - "Hira", # 1B001..1B11F ; Hiragana - "Kana", # 1B120..1B122 ; Katakana - "Zzzz", # 1B123..1B131 ; Unknown - "Hira", # 1B132..1B132 ; Hiragana - "Zzzz", # 1B133..1B14F ; Unknown - "Hira", # 1B150..1B152 ; Hiragana - "Zzzz", # 1B153..1B154 ; Unknown - "Kana", # 1B155..1B155 ; Katakana - "Zzzz", # 1B156..1B163 ; Unknown - "Kana", # 1B164..1B167 ; Katakana - "Zzzz", # 1B168..1B16F ; Unknown - "Nshu", # 1B170..1B2FB ; Nushu - "Zzzz", # 1B2FC..1BBFF ; Unknown - "Dupl", # 1BC00..1BC6A ; Duployan - "Zzzz", # 1BC6B..1BC6F ; Unknown - "Dupl", # 1BC70..1BC7C ; Duployan - "Zzzz", # 1BC7D..1BC7F ; Unknown - "Dupl", # 1BC80..1BC88 ; Duployan - "Zzzz", # 1BC89..1BC8F ; Unknown - "Dupl", # 1BC90..1BC99 ; Duployan - "Zzzz", # 1BC9A..1BC9B ; Unknown - "Dupl", # 1BC9C..1BC9F ; Duployan - "Zyyy", # 1BCA0..1BCA3 ; Common - "Zzzz", # 1BCA4..1CEFF ; Unknown - "Zinh", # 1CF00..1CF2D ; Inherited - "Zzzz", # 1CF2E..1CF2F ; Unknown - "Zinh", # 1CF30..1CF46 ; Inherited - "Zzzz", # 1CF47..1CF4F ; Unknown - "Zyyy", # 1CF50..1CFC3 ; Common - "Zzzz", # 1CFC4..1CFFF ; Unknown - "Zyyy", # 1D000..1D0F5 ; Common - "Zzzz", # 1D0F6..1D0FF ; Unknown - "Zyyy", # 1D100..1D126 ; Common - "Zzzz", # 1D127..1D128 ; Unknown - "Zyyy", # 1D129..1D166 ; Common - "Zinh", # 1D167..1D169 ; Inherited - "Zyyy", # 1D16A..1D17A ; Common - "Zinh", # 1D17B..1D182 ; Inherited - "Zyyy", # 1D183..1D184 ; Common - "Zinh", # 1D185..1D18B ; Inherited - "Zyyy", # 1D18C..1D1A9 ; Common - "Zinh", # 1D1AA..1D1AD ; Inherited - "Zyyy", # 1D1AE..1D1EA ; Common - "Zzzz", # 1D1EB..1D1FF ; Unknown - "Grek", # 1D200..1D245 ; Greek - "Zzzz", # 1D246..1D2BF ; Unknown - "Zyyy", # 1D2C0..1D2D3 ; Common - "Zzzz", # 1D2D4..1D2DF ; Unknown - "Zyyy", # 1D2E0..1D2F3 ; Common - "Zzzz", # 1D2F4..1D2FF ; Unknown - "Zyyy", # 1D300..1D356 ; Common - "Zzzz", # 1D357..1D35F ; Unknown - "Zyyy", # 1D360..1D378 ; Common - "Zzzz", # 1D379..1D3FF ; Unknown - "Zyyy", # 1D400..1D454 ; Common - "Zzzz", # 1D455..1D455 ; Unknown - "Zyyy", # 1D456..1D49C ; Common - "Zzzz", # 1D49D..1D49D ; Unknown - "Zyyy", # 1D49E..1D49F ; Common - "Zzzz", # 1D4A0..1D4A1 ; Unknown - "Zyyy", # 1D4A2..1D4A2 ; Common - "Zzzz", # 1D4A3..1D4A4 ; Unknown - "Zyyy", # 1D4A5..1D4A6 ; Common - "Zzzz", # 1D4A7..1D4A8 ; Unknown - "Zyyy", # 1D4A9..1D4AC ; Common - "Zzzz", # 1D4AD..1D4AD ; Unknown - "Zyyy", # 1D4AE..1D4B9 ; Common - "Zzzz", # 1D4BA..1D4BA ; Unknown - "Zyyy", # 1D4BB..1D4BB ; Common - "Zzzz", # 1D4BC..1D4BC ; Unknown - "Zyyy", # 1D4BD..1D4C3 ; Common - "Zzzz", # 1D4C4..1D4C4 ; Unknown - "Zyyy", # 1D4C5..1D505 ; Common - "Zzzz", # 1D506..1D506 ; Unknown - "Zyyy", # 1D507..1D50A ; Common - "Zzzz", # 1D50B..1D50C ; Unknown - "Zyyy", # 1D50D..1D514 ; Common - "Zzzz", # 1D515..1D515 ; Unknown - "Zyyy", # 1D516..1D51C ; Common - "Zzzz", # 1D51D..1D51D ; Unknown - "Zyyy", # 1D51E..1D539 ; Common - "Zzzz", # 1D53A..1D53A ; Unknown - "Zyyy", # 1D53B..1D53E ; Common - "Zzzz", # 1D53F..1D53F ; Unknown - "Zyyy", # 1D540..1D544 ; Common - "Zzzz", # 1D545..1D545 ; Unknown - "Zyyy", # 1D546..1D546 ; Common - "Zzzz", # 1D547..1D549 ; Unknown - "Zyyy", # 1D54A..1D550 ; Common - "Zzzz", # 1D551..1D551 ; Unknown - "Zyyy", # 1D552..1D6A5 ; Common - "Zzzz", # 1D6A6..1D6A7 ; Unknown - "Zyyy", # 1D6A8..1D7CB ; Common - "Zzzz", # 1D7CC..1D7CD ; Unknown - "Zyyy", # 1D7CE..1D7FF ; Common - "Sgnw", # 1D800..1DA8B ; SignWriting - "Zzzz", # 1DA8C..1DA9A ; Unknown - "Sgnw", # 1DA9B..1DA9F ; SignWriting - "Zzzz", # 1DAA0..1DAA0 ; Unknown - "Sgnw", # 1DAA1..1DAAF ; SignWriting - "Zzzz", # 1DAB0..1DEFF ; Unknown - "Latn", # 1DF00..1DF1E ; Latin - "Zzzz", # 1DF1F..1DF24 ; Unknown - "Latn", # 1DF25..1DF2A ; Latin - "Zzzz", # 1DF2B..1DFFF ; Unknown - "Glag", # 1E000..1E006 ; Glagolitic - "Zzzz", # 1E007..1E007 ; Unknown - "Glag", # 1E008..1E018 ; Glagolitic - "Zzzz", # 1E019..1E01A ; Unknown - "Glag", # 1E01B..1E021 ; Glagolitic - "Zzzz", # 1E022..1E022 ; Unknown - "Glag", # 1E023..1E024 ; Glagolitic - "Zzzz", # 1E025..1E025 ; Unknown - "Glag", # 1E026..1E02A ; Glagolitic - "Zzzz", # 1E02B..1E02F ; Unknown - "Cyrl", # 1E030..1E06D ; Cyrillic - "Zzzz", # 1E06E..1E08E ; Unknown - "Cyrl", # 1E08F..1E08F ; Cyrillic - "Zzzz", # 1E090..1E0FF ; Unknown - "Hmnp", # 1E100..1E12C ; Nyiakeng_Puachue_Hmong - "Zzzz", # 1E12D..1E12F ; Unknown - "Hmnp", # 1E130..1E13D ; Nyiakeng_Puachue_Hmong - "Zzzz", # 1E13E..1E13F ; Unknown - "Hmnp", # 1E140..1E149 ; Nyiakeng_Puachue_Hmong - "Zzzz", # 1E14A..1E14D ; Unknown - "Hmnp", # 1E14E..1E14F ; Nyiakeng_Puachue_Hmong - "Zzzz", # 1E150..1E28F ; Unknown - "Toto", # 1E290..1E2AE ; Toto - "Zzzz", # 1E2AF..1E2BF ; Unknown - "Wcho", # 1E2C0..1E2F9 ; Wancho - "Zzzz", # 1E2FA..1E2FE ; Unknown - "Wcho", # 1E2FF..1E2FF ; Wancho - "Zzzz", # 1E300..1E4CF ; Unknown - "Nagm", # 1E4D0..1E4F9 ; Nag_Mundari - "Zzzz", # 1E4FA..1E7DF ; Unknown - "Ethi", # 1E7E0..1E7E6 ; Ethiopic - "Zzzz", # 1E7E7..1E7E7 ; Unknown - "Ethi", # 1E7E8..1E7EB ; Ethiopic - "Zzzz", # 1E7EC..1E7EC ; Unknown - "Ethi", # 1E7ED..1E7EE ; Ethiopic - "Zzzz", # 1E7EF..1E7EF ; Unknown - "Ethi", # 1E7F0..1E7FE ; Ethiopic - "Zzzz", # 1E7FF..1E7FF ; Unknown - "Mend", # 1E800..1E8C4 ; Mende_Kikakui - "Zzzz", # 1E8C5..1E8C6 ; Unknown - "Mend", # 1E8C7..1E8D6 ; Mende_Kikakui - "Zzzz", # 1E8D7..1E8FF ; Unknown - "Adlm", # 1E900..1E94B ; Adlam - "Zzzz", # 1E94C..1E94F ; Unknown - "Adlm", # 1E950..1E959 ; Adlam - "Zzzz", # 1E95A..1E95D ; Unknown - "Adlm", # 1E95E..1E95F ; Adlam - "Zzzz", # 1E960..1EC70 ; Unknown - "Zyyy", # 1EC71..1ECB4 ; Common - "Zzzz", # 1ECB5..1ED00 ; Unknown - "Zyyy", # 1ED01..1ED3D ; Common - "Zzzz", # 1ED3E..1EDFF ; Unknown - "Arab", # 1EE00..1EE03 ; Arabic - "Zzzz", # 1EE04..1EE04 ; Unknown - "Arab", # 1EE05..1EE1F ; Arabic - "Zzzz", # 1EE20..1EE20 ; Unknown - "Arab", # 1EE21..1EE22 ; Arabic - "Zzzz", # 1EE23..1EE23 ; Unknown - "Arab", # 1EE24..1EE24 ; Arabic - "Zzzz", # 1EE25..1EE26 ; Unknown - "Arab", # 1EE27..1EE27 ; Arabic - "Zzzz", # 1EE28..1EE28 ; Unknown - "Arab", # 1EE29..1EE32 ; Arabic - "Zzzz", # 1EE33..1EE33 ; Unknown - "Arab", # 1EE34..1EE37 ; Arabic - "Zzzz", # 1EE38..1EE38 ; Unknown - "Arab", # 1EE39..1EE39 ; Arabic - "Zzzz", # 1EE3A..1EE3A ; Unknown - "Arab", # 1EE3B..1EE3B ; Arabic - "Zzzz", # 1EE3C..1EE41 ; Unknown - "Arab", # 1EE42..1EE42 ; Arabic - "Zzzz", # 1EE43..1EE46 ; Unknown - "Arab", # 1EE47..1EE47 ; Arabic - "Zzzz", # 1EE48..1EE48 ; Unknown - "Arab", # 1EE49..1EE49 ; Arabic - "Zzzz", # 1EE4A..1EE4A ; Unknown - "Arab", # 1EE4B..1EE4B ; Arabic - "Zzzz", # 1EE4C..1EE4C ; Unknown - "Arab", # 1EE4D..1EE4F ; Arabic - "Zzzz", # 1EE50..1EE50 ; Unknown - "Arab", # 1EE51..1EE52 ; Arabic - "Zzzz", # 1EE53..1EE53 ; Unknown - "Arab", # 1EE54..1EE54 ; Arabic - "Zzzz", # 1EE55..1EE56 ; Unknown - "Arab", # 1EE57..1EE57 ; Arabic - "Zzzz", # 1EE58..1EE58 ; Unknown - "Arab", # 1EE59..1EE59 ; Arabic - "Zzzz", # 1EE5A..1EE5A ; Unknown - "Arab", # 1EE5B..1EE5B ; Arabic - "Zzzz", # 1EE5C..1EE5C ; Unknown - "Arab", # 1EE5D..1EE5D ; Arabic - "Zzzz", # 1EE5E..1EE5E ; Unknown - "Arab", # 1EE5F..1EE5F ; Arabic - "Zzzz", # 1EE60..1EE60 ; Unknown - "Arab", # 1EE61..1EE62 ; Arabic - "Zzzz", # 1EE63..1EE63 ; Unknown - "Arab", # 1EE64..1EE64 ; Arabic - "Zzzz", # 1EE65..1EE66 ; Unknown - "Arab", # 1EE67..1EE6A ; Arabic - "Zzzz", # 1EE6B..1EE6B ; Unknown - "Arab", # 1EE6C..1EE72 ; Arabic - "Zzzz", # 1EE73..1EE73 ; Unknown - "Arab", # 1EE74..1EE77 ; Arabic - "Zzzz", # 1EE78..1EE78 ; Unknown - "Arab", # 1EE79..1EE7C ; Arabic - "Zzzz", # 1EE7D..1EE7D ; Unknown - "Arab", # 1EE7E..1EE7E ; Arabic - "Zzzz", # 1EE7F..1EE7F ; Unknown - "Arab", # 1EE80..1EE89 ; Arabic - "Zzzz", # 1EE8A..1EE8A ; Unknown - "Arab", # 1EE8B..1EE9B ; Arabic - "Zzzz", # 1EE9C..1EEA0 ; Unknown - "Arab", # 1EEA1..1EEA3 ; Arabic - "Zzzz", # 1EEA4..1EEA4 ; Unknown - "Arab", # 1EEA5..1EEA9 ; Arabic - "Zzzz", # 1EEAA..1EEAA ; Unknown - "Arab", # 1EEAB..1EEBB ; Arabic - "Zzzz", # 1EEBC..1EEEF ; Unknown - "Arab", # 1EEF0..1EEF1 ; Arabic - "Zzzz", # 1EEF2..1EFFF ; Unknown - "Zyyy", # 1F000..1F02B ; Common - "Zzzz", # 1F02C..1F02F ; Unknown - "Zyyy", # 1F030..1F093 ; Common - "Zzzz", # 1F094..1F09F ; Unknown - "Zyyy", # 1F0A0..1F0AE ; Common - "Zzzz", # 1F0AF..1F0B0 ; Unknown - "Zyyy", # 1F0B1..1F0BF ; Common - "Zzzz", # 1F0C0..1F0C0 ; Unknown - "Zyyy", # 1F0C1..1F0CF ; Common - "Zzzz", # 1F0D0..1F0D0 ; Unknown - "Zyyy", # 1F0D1..1F0F5 ; Common - "Zzzz", # 1F0F6..1F0FF ; Unknown - "Zyyy", # 1F100..1F1AD ; Common - "Zzzz", # 1F1AE..1F1E5 ; Unknown - "Zyyy", # 1F1E6..1F1FF ; Common - "Hira", # 1F200..1F200 ; Hiragana - "Zyyy", # 1F201..1F202 ; Common - "Zzzz", # 1F203..1F20F ; Unknown - "Zyyy", # 1F210..1F23B ; Common - "Zzzz", # 1F23C..1F23F ; Unknown - "Zyyy", # 1F240..1F248 ; Common - "Zzzz", # 1F249..1F24F ; Unknown - "Zyyy", # 1F250..1F251 ; Common - "Zzzz", # 1F252..1F25F ; Unknown - "Zyyy", # 1F260..1F265 ; Common - "Zzzz", # 1F266..1F2FF ; Unknown - "Zyyy", # 1F300..1F6D7 ; Common - "Zzzz", # 1F6D8..1F6DB ; Unknown - "Zyyy", # 1F6DC..1F6EC ; Common - "Zzzz", # 1F6ED..1F6EF ; Unknown - "Zyyy", # 1F6F0..1F6FC ; Common - "Zzzz", # 1F6FD..1F6FF ; Unknown - "Zyyy", # 1F700..1F776 ; Common - "Zzzz", # 1F777..1F77A ; Unknown - "Zyyy", # 1F77B..1F7D9 ; Common - "Zzzz", # 1F7DA..1F7DF ; Unknown - "Zyyy", # 1F7E0..1F7EB ; Common - "Zzzz", # 1F7EC..1F7EF ; Unknown - "Zyyy", # 1F7F0..1F7F0 ; Common - "Zzzz", # 1F7F1..1F7FF ; Unknown - "Zyyy", # 1F800..1F80B ; Common - "Zzzz", # 1F80C..1F80F ; Unknown - "Zyyy", # 1F810..1F847 ; Common - "Zzzz", # 1F848..1F84F ; Unknown - "Zyyy", # 1F850..1F859 ; Common - "Zzzz", # 1F85A..1F85F ; Unknown - "Zyyy", # 1F860..1F887 ; Common - "Zzzz", # 1F888..1F88F ; Unknown - "Zyyy", # 1F890..1F8AD ; Common - "Zzzz", # 1F8AE..1F8AF ; Unknown - "Zyyy", # 1F8B0..1F8B1 ; Common - "Zzzz", # 1F8B2..1F8FF ; Unknown - "Zyyy", # 1F900..1FA53 ; Common - "Zzzz", # 1FA54..1FA5F ; Unknown - "Zyyy", # 1FA60..1FA6D ; Common - "Zzzz", # 1FA6E..1FA6F ; Unknown - "Zyyy", # 1FA70..1FA7C ; Common - "Zzzz", # 1FA7D..1FA7F ; Unknown - "Zyyy", # 1FA80..1FA88 ; Common - "Zzzz", # 1FA89..1FA8F ; Unknown - "Zyyy", # 1FA90..1FABD ; Common - "Zzzz", # 1FABE..1FABE ; Unknown - "Zyyy", # 1FABF..1FAC5 ; Common - "Zzzz", # 1FAC6..1FACD ; Unknown - "Zyyy", # 1FACE..1FADB ; Common - "Zzzz", # 1FADC..1FADF ; Unknown - "Zyyy", # 1FAE0..1FAE8 ; Common - "Zzzz", # 1FAE9..1FAEF ; Unknown - "Zyyy", # 1FAF0..1FAF8 ; Common - "Zzzz", # 1FAF9..1FAFF ; Unknown - "Zyyy", # 1FB00..1FB92 ; Common - "Zzzz", # 1FB93..1FB93 ; Unknown - "Zyyy", # 1FB94..1FBCA ; Common - "Zzzz", # 1FBCB..1FBEF ; Unknown - "Zyyy", # 1FBF0..1FBF9 ; Common - "Zzzz", # 1FBFA..1FFFF ; Unknown - "Hani", # 20000..2A6DF ; Han - "Zzzz", # 2A6E0..2A6FF ; Unknown - "Hani", # 2A700..2B739 ; Han - "Zzzz", # 2B73A..2B73F ; Unknown - "Hani", # 2B740..2B81D ; Han - "Zzzz", # 2B81E..2B81F ; Unknown - "Hani", # 2B820..2CEA1 ; Han - "Zzzz", # 2CEA2..2CEAF ; Unknown - "Hani", # 2CEB0..2EBE0 ; Han - "Zzzz", # 2EBE1..2F7FF ; Unknown - "Hani", # 2F800..2FA1D ; Han - "Zzzz", # 2FA1E..2FFFF ; Unknown - "Hani", # 30000..3134A ; Han - "Zzzz", # 3134B..3134F ; Unknown - "Hani", # 31350..323AF ; Han - "Zzzz", # 323B0..E0000 ; Unknown - "Zyyy", # E0001..E0001 ; Common - "Zzzz", # E0002..E001F ; Unknown - "Zyyy", # E0020..E007F ; Common - "Zzzz", # E0080..E00FF ; Unknown - "Zinh", # E0100..E01EF ; Inherited - "Zzzz", # E01F0..10FFFF ; Unknown -] - -NAMES = { - "Adlm": "Adlam", - "Aghb": "Caucasian_Albanian", - "Ahom": "Ahom", - "Arab": "Arabic", - "Armi": "Imperial_Aramaic", - "Armn": "Armenian", - "Avst": "Avestan", - "Bali": "Balinese", - "Bamu": "Bamum", - "Bass": "Bassa_Vah", - "Batk": "Batak", - "Beng": "Bengali", - "Bhks": "Bhaiksuki", - "Bopo": "Bopomofo", - "Brah": "Brahmi", - "Brai": "Braille", - "Bugi": "Buginese", - "Buhd": "Buhid", - "Cakm": "Chakma", - "Cans": "Canadian_Aboriginal", - "Cari": "Carian", - "Cham": "Cham", - "Cher": "Cherokee", - "Chrs": "Chorasmian", - "Copt": "Coptic", - "Cpmn": "Cypro_Minoan", - "Cprt": "Cypriot", - "Cyrl": "Cyrillic", - "Deva": "Devanagari", - "Diak": "Dives_Akuru", - "Dogr": "Dogra", - "Dsrt": "Deseret", - "Dupl": "Duployan", - "Egyp": "Egyptian_Hieroglyphs", - "Elba": "Elbasan", - "Elym": "Elymaic", - "Ethi": "Ethiopic", - "Geor": "Georgian", - "Glag": "Glagolitic", - "Gong": "Gunjala_Gondi", - "Gonm": "Masaram_Gondi", - "Goth": "Gothic", - "Gran": "Grantha", - "Grek": "Greek", - "Gujr": "Gujarati", - "Guru": "Gurmukhi", - "Hang": "Hangul", - "Hani": "Han", - "Hano": "Hanunoo", - "Hatr": "Hatran", - "Hebr": "Hebrew", - "Hira": "Hiragana", - "Hluw": "Anatolian_Hieroglyphs", - "Hmng": "Pahawh_Hmong", - "Hmnp": "Nyiakeng_Puachue_Hmong", - "Hrkt": "Katakana_Or_Hiragana", - "Hung": "Old_Hungarian", - "Ital": "Old_Italic", - "Java": "Javanese", - "Kali": "Kayah_Li", - "Kana": "Katakana", - "Kawi": "Kawi", - "Khar": "Kharoshthi", - "Khmr": "Khmer", - "Khoj": "Khojki", - "Kits": "Khitan_Small_Script", - "Knda": "Kannada", - "Kthi": "Kaithi", - "Lana": "Tai_Tham", - "Laoo": "Lao", - "Latn": "Latin", - "Lepc": "Lepcha", - "Limb": "Limbu", - "Lina": "Linear_A", - "Linb": "Linear_B", - "Lisu": "Lisu", - "Lyci": "Lycian", - "Lydi": "Lydian", - "Mahj": "Mahajani", - "Maka": "Makasar", - "Mand": "Mandaic", - "Mani": "Manichaean", - "Marc": "Marchen", - "Medf": "Medefaidrin", - "Mend": "Mende_Kikakui", - "Merc": "Meroitic_Cursive", - "Mero": "Meroitic_Hieroglyphs", - "Mlym": "Malayalam", - "Modi": "Modi", - "Mong": "Mongolian", - "Mroo": "Mro", - "Mtei": "Meetei_Mayek", - "Mult": "Multani", - "Mymr": "Myanmar", - "Nagm": "Nag_Mundari", - "Nand": "Nandinagari", - "Narb": "Old_North_Arabian", - "Nbat": "Nabataean", - "Newa": "Newa", - "Nkoo": "Nko", - "Nshu": "Nushu", - "Ogam": "Ogham", - "Olck": "Ol_Chiki", - "Orkh": "Old_Turkic", - "Orya": "Oriya", - "Osge": "Osage", - "Osma": "Osmanya", - "Ougr": "Old_Uyghur", - "Palm": "Palmyrene", - "Pauc": "Pau_Cin_Hau", - "Perm": "Old_Permic", - "Phag": "Phags_Pa", - "Phli": "Inscriptional_Pahlavi", - "Phlp": "Psalter_Pahlavi", - "Phnx": "Phoenician", - "Plrd": "Miao", - "Prti": "Inscriptional_Parthian", - "Rjng": "Rejang", - "Rohg": "Hanifi_Rohingya", - "Runr": "Runic", - "Samr": "Samaritan", - "Sarb": "Old_South_Arabian", - "Saur": "Saurashtra", - "Sgnw": "SignWriting", - "Shaw": "Shavian", - "Shrd": "Sharada", - "Sidd": "Siddham", - "Sind": "Khudawadi", - "Sinh": "Sinhala", - "Sogd": "Sogdian", - "Sogo": "Old_Sogdian", - "Sora": "Sora_Sompeng", - "Soyo": "Soyombo", - "Sund": "Sundanese", - "Sylo": "Syloti_Nagri", - "Syrc": "Syriac", - "Tagb": "Tagbanwa", - "Takr": "Takri", - "Tale": "Tai_Le", - "Talu": "New_Tai_Lue", - "Taml": "Tamil", - "Tang": "Tangut", - "Tavt": "Tai_Viet", - "Telu": "Telugu", - "Tfng": "Tifinagh", - "Tglg": "Tagalog", - "Thaa": "Thaana", - "Thai": "Thai", - "Tibt": "Tibetan", - "Tirh": "Tirhuta", - "Tnsa": "Tangsa", - "Toto": "Toto", - "Ugar": "Ugaritic", - "Vaii": "Vai", - "Vith": "Vithkuqi", - "Wara": "Warang_Citi", - "Wcho": "Wancho", - "Xpeo": "Old_Persian", - "Xsux": "Cuneiform", - "Yezi": "Yezidi", - "Yiii": "Yi", - "Zanb": "Zanabazar_Square", - "Zinh": "Inherited", - "Zyyy": "Common", - "Zzzz": "Unknown", -} diff --git a/spaces/johnslegers/stable-diffusion-gui-test/modules/app.py b/spaces/johnslegers/stable-diffusion-gui-test/modules/app.py deleted file mode 100644 index 8405b2dd35eb195100ce91f70bf3d2f41051cf61..0000000000000000000000000000000000000000 --- a/spaces/johnslegers/stable-diffusion-gui-test/modules/app.py +++ /dev/null @@ -1,331 +0,0 @@ -import json -import traceback - -import sys -import os - -SD_DIR = os.getcwd() -print('started in ', SD_DIR) - -SD_UI_DIR = './ui' -sys.path.append(os.path.dirname(SD_UI_DIR)) - -CONFIG_DIR = os.path.abspath(os.path.join(SD_UI_DIR, '.', 'config')) -MODELS_DIR = os.path.abspath(os.path.join(SD_DIR, '.', 'models')) - -OUTPUT_DIRNAME = "Stable Diffusion UI" # in the user's home folder - -from fastapi import FastAPI, HTTPException -from fastapi.staticfiles import StaticFiles -from starlette.responses import FileResponse, StreamingResponse -from pydantic import BaseModel -import logging - -from sd_internal import Request, Response - -app = FastAPI() - -model_loaded = False -model_is_loading = False - -modifiers_cache = None -outpath = os.path.join(os.path.expanduser("~"), OUTPUT_DIRNAME) - -# don't show access log entries for URLs that start with the given prefix -ACCESS_LOG_SUPPRESS_PATH_PREFIXES = ['/ping', '/modifier-thumbnails'] - -app.mount('/media', StaticFiles(directory=os.path.join(SD_UI_DIR, 'media/')), name="media") - -# defaults from https://huggingface.co/blog/stable_diffusion -class ImageRequest(BaseModel): - session_id: str = "session" - prompt: str = "" - negative_prompt: str = "" - init_image: str = None # base64 - mask: str = None # base64 - num_outputs: int = 1 - num_inference_steps: int = 50 - guidance_scale: float = 7.5 - width: int = 512 - height: int = 512 - seed: int = 42 - prompt_strength: float = 0.8 - sampler: str = None # "ddim", "plms", "heun", "euler", "euler_a", "dpm2", "dpm2_a", "lms" - # allow_nsfw: bool = False - save_to_disk_path: str = None - turbo: bool = True - use_cpu: bool = False - use_full_precision: bool = False - use_face_correction: str = None # or "GFPGANv1.3" - use_upscale: str = None # or "RealESRGAN_x4plus" or "RealESRGAN_x4plus_anime_6B" - use_stable_diffusion_model: str = "sd-v1-4" - show_only_filtered_image: bool = False - output_format: str = "jpeg" # or "png" - - stream_progress_updates: bool = False - stream_image_progress: bool = False - -class SetAppConfigRequest(BaseModel): - update_branch: str = "main" - -@app.get('/') -def read_root(): - headers = {"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"} - return FileResponse(os.path.join(SD_UI_DIR, 'index.html'), headers=headers) - -@app.get('/ping') -async def ping(): - global model_loaded, model_is_loading - - try: - if model_loaded: - return {'OK1'} - - #if model_is_loading: - # return {'ERROR'} - - model_is_loading = True - - from . import runtime - - #runtime.load_model_ckpt(ckpt_to_use=get_initial_model_to_load()) - runtime.load_model_ckpt(ckpt_to_use='sd-v1-4.ckpt') - - model_loaded = True - model_is_loading = False - - return {'OK2'} - except Exception as e: - model_is_loading = False - print(traceback.format_exc()) - return HTTPException(status_code=500, detail=str(e)) - -# needs to support the legacy installations -def get_initial_model_to_load(): - custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt') - ckpt_to_use = "sd-v1-4" if not os.path.exists(custom_weight_path) else "custom-model" - - ckpt_to_use = os.path.join(SD_DIR, ckpt_to_use) - - config = getConfig() - if 'model' in config and 'stable-diffusion' in config['model']: - model_name = config['model']['stable-diffusion'] - model_path = resolve_model_to_use(model_name) - - if os.path.exists(model_path + '.ckpt'): - ckpt_to_use = model_path - else: - print('Could not find the configured custom model at:', model_path + '.ckpt', '. Using the default one:', ckpt_to_use + '.ckpt') - - return ckpt_to_use - -def resolve_model_to_use(model_name): - if model_name in ('sd-v1-4', 'custom-model'): - model_path = os.path.join(MODELS_DIR, 'stable-diffusion', model_name) - - legacy_model_path = os.path.join(SD_DIR, model_name) - if not os.path.exists(model_path + '.ckpt') and os.path.exists(legacy_model_path + '.ckpt'): - model_path = legacy_model_path - else: - model_path = os.path.join(MODELS_DIR, 'stable-diffusion', model_name) - - return model_path - -def save_model_to_config(model_name): - config = getConfig() - if 'model' not in config: - config['model'] = {} - - config['model']['stable-diffusion'] = model_name - - setConfig(config) - -@app.post('/image') -def image(req : ImageRequest): - from . import runtime - - r = Request() - r.session_id = req.session_id - r.prompt = req.prompt - r.negative_prompt = req.negative_prompt - r.init_image = req.init_image - r.mask = req.mask - r.num_outputs = req.num_outputs - r.num_inference_steps = req.num_inference_steps - r.guidance_scale = req.guidance_scale - r.width = req.width - r.height = req.height - r.seed = req.seed - r.prompt_strength = req.prompt_strength - r.sampler = req.sampler - # r.allow_nsfw = req.allow_nsfw - r.turbo = req.turbo - r.use_cpu = req.use_cpu - r.use_full_precision = req.use_full_precision - r.save_to_disk_path = req.save_to_disk_path - r.use_upscale: str = req.use_upscale - r.use_face_correction = req.use_face_correction - r.show_only_filtered_image = req.show_only_filtered_image - r.output_format = req.output_format - - r.stream_progress_updates = True # the underlying implementation only supports streaming - r.stream_image_progress = req.stream_image_progress - - r.use_stable_diffusion_model = resolve_model_to_use(req.use_stable_diffusion_model) - - save_model_to_config(req.use_stable_diffusion_model) - - try: - if not req.stream_progress_updates: - r.stream_image_progress = False - - res = runtime.mk_img(r) - - if req.stream_progress_updates: - return StreamingResponse(res, media_type='application/json') - else: # compatibility mode: buffer the streaming responses, and return the last one - last_result = None - - for result in res: - last_result = result - - return json.loads(last_result) - except Exception as e: - print(traceback.format_exc()) - return HTTPException(status_code=500, detail=str(e)) - -@app.get('/image/stop') -def stop(): - try: - # if model_is_loading: - # return {'ERROR'} - - # from . import runtime - # runtime.stop_processing = True - - return {'OK'} - except Exception as e: - print(traceback.format_exc()) - return HTTPException(status_code=500, detail=str(e)) - -@app.get('/image/tmp/{session_id}/{img_id}') -def get_image(session_id, img_id): - from . import runtime - buf = runtime.temp_images[session_id + '/' + img_id] - buf.seek(0) - return StreamingResponse(buf, media_type='image/jpeg') - -@app.post('/app_config') -async def setAppConfig(req : SetAppConfigRequest): - try: - config = { - 'update_branch': req.update_branch - } - - config_json_str = json.dumps(config) - config_bat_str = f'@set update_branch={req.update_branch}' - config_sh_str = f'export update_branch={req.update_branch}' - - config_json_path = os.path.join(CONFIG_DIR, 'config.json') - config_bat_path = os.path.join(CONFIG_DIR, 'config.bat') - config_sh_path = os.path.join(CONFIG_DIR, 'config.sh') - - with open(config_json_path, 'w') as f: - f.write(config_json_str) - - with open(config_bat_path, 'w') as f: - f.write(config_bat_str) - - with open(config_sh_path, 'w') as f: - f.write(config_sh_str) - - return {'OK'} - except Exception as e: - print(traceback.format_exc()) - return HTTPException(status_code=500, detail=str(e)) - -@app.get('/app_config') -def getAppConfig(): - try: - config_json_path = os.path.join(CONFIG_DIR, 'config.json') - - if not os.path.exists(config_json_path): - return HTTPException(status_code=500, detail="No config file") - - with open(config_json_path, 'r') as f: - return json.load(f) - except Exception as e: - print(traceback.format_exc()) - return HTTPException(status_code=500, detail=str(e)) - -def getConfig(): - try: - config_json_path = os.path.join(CONFIG_DIR, 'config.json') - - if not os.path.exists(config_json_path): - return {} - - with open(config_json_path, 'r') as f: - return json.load(f) - except Exception as e: - return {} - -def setConfig(config): - try: - config_json_path = os.path.join(CONFIG_DIR, 'config.json') - - with open(config_json_path, 'w') as f: - return json.dump(config, f) - except: - print(traceback.format_exc()) - -@app.get('/models') -def getModels(): - models = { - 'active': { - 'stable-diffusion': 'sd-v1-4', - }, - 'options': { - 'stable-diffusion': ['sd-v1-4'], - }, - } - - # custom models - sd_models_dir = os.path.join(MODELS_DIR, 'stable-diffusion') - for file in os.listdir(sd_models_dir): - if file.endswith('.ckpt'): - model_name = os.path.splitext(file)[0] - models['options']['stable-diffusion'].append(model_name) - - # legacy - custom_weight_path = os.path.join(SD_DIR, 'custom-model.ckpt') - if os.path.exists(custom_weight_path): - models['active']['stable-diffusion'] = 'custom-model' - models['options']['stable-diffusion'].append('custom-model') - - config = getConfig() - if 'model' in config and 'stable-diffusion' in config['model']: - models['active']['stable-diffusion'] = config['model']['stable-diffusion'] - - return models - -@app.get('/modifiers.json') -def read_modifiers(): - headers = {"Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0"} - return FileResponse(os.path.join(SD_UI_DIR, 'modifiers.json'), headers=headers) - -@app.get('/output_dir') -def read_home_dir(): - return {outpath} - -# don't log certain requests -class LogSuppressFilter(logging.Filter): - def filter(self, record: logging.LogRecord) -> bool: - path = record.getMessage() - for prefix in ACCESS_LOG_SUPPRESS_PATH_PREFIXES: - if path.find(prefix) != -1: - return False - - return True - -logging.getLogger('uvicorn.access').addFilter(LogSuppressFilter()) diff --git a/spaces/johnslegers/stable-diffusion-gui-test/ui/media/jquery-3.6.1.min.js b/spaces/johnslegers/stable-diffusion-gui-test/ui/media/jquery-3.6.1.min.js deleted file mode 100644 index 2c69bc908b10d854c2c3fe6e3268dcffe20e1b5a..0000000000000000000000000000000000000000 --- a/spaces/johnslegers/stable-diffusion-gui-test/ui/media/jquery-3.6.1.min.js +++ /dev/null @@ -1,2 +0,0 @@ -/*! jQuery v3.6.1 | (c) OpenJS Foundation and other contributors | jquery.org/license */ -!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,y=n.hasOwnProperty,a=y.toString,l=a.call(Object),v={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.6.1",S=function(e,t){return new S.fn.init(e,t)};function p(e){var t=!!e&&"length"in e&&e.length,n=w(e);return!m(e)&&!x(e)&&("array"===n||0===t||"number"==typeof t&&0-- ---- -- ------Level 1-Level 2-Level 3-Level 4-- - - - - - - - - -
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- - - - ----- -- -- -- - - -----------","
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"],_default:[0,"",""]};function ye(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&A(e,t)?S.merge([e],n):n}function ve(e,t){for(var n=0,r=e.length;n"," - #- # """) - - with gr.Row(): - gr.Markdown('### You must select one pose sequence shown on the right, or use the examples') - with gr.Column(): - gallery_pose_sequence = gr.Gallery(label="Pose Sequence", value=[('__assets__/poses_skeleton_gifs/dance1.gif', "Motion 1"), ('__assets__/poses_skeleton_gifs/dance2.gif', "Motion 2"), ('__assets__/poses_skeleton_gifs/dance3.gif', "Motion 3"), ('__assets__/poses_skeleton_gifs/dance4.gif', "Motion 4"), ('__assets__/poses_skeleton_gifs/dance5.gif', "Motion 5")]).style(grid=[2], height="auto") - input_video_path = gr.Textbox(label="Pose Sequence",visible=False,value="Motion 1") - gr.Markdown("## Selection") - pose_sequence_selector = gr.Markdown('Pose Sequence: **Motion 1**') - with gr.Column(): - prompt = gr.Textbox(label='Prompt') - run_button = gr.Button(label='Run') - with gr.Column(): - result = gr.Image(label="Generated Video") - - input_video_path.change(on_video_path_update, None, pose_sequence_selector) - gallery_pose_sequence.select(pose_gallery_callback, None, input_video_path) - inputs = [ - input_video_path, - prompt, - ] - - gr.Examples(examples=examples, - inputs=inputs, - outputs=result, - fn=model.process_controlnet_pose, - cache_examples = True, - run_on_click=False, - ) - - run_button.click(fn=model.process_controlnet_pose, - inputs=inputs, - outputs=result,) - - return demo - - -def on_video_path_update(evt: gr.EventData): - return f'Pose Sequence: **{evt._data}**' - -def pose_gallery_callback(evt: gr.SelectData): - return f"Motion {evt.index+1}" diff --git a/spaces/kornia/total_variation_denoising/app.py b/spaces/kornia/total_variation_denoising/app.py deleted file mode 100644 index 63f2e046a79a2f962ae64f8a86ebd6ebdb1aa737..0000000000000000000000000000000000000000 --- a/spaces/kornia/total_variation_denoising/app.py +++ /dev/null @@ -1,77 +0,0 @@ -import gradio as gr -import cv2 -import matplotlib.pyplot as plt -import numpy as np -import torch -import torchvision -import kornia as K - -def inference(file1,num_iters): - img: np.ndarray = cv2.imread(file1.name) - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0 - img = img + np.random.normal(loc=0.0, scale=0.1, size=img.shape) - img = np.clip(img, 0.0, 1.0) - - # convert to torch tensor - noisy_image = K.utils.image_to_tensor(img).squeeze() - - class TVDenoise(torch.nn.Module): - def __init__(self, noisy_image): - super(TVDenoise, self).__init__() - self.l2_term = torch.nn.MSELoss(reduction='mean') - self.regularization_term = K.losses.TotalVariation() - # create the variable which will be optimized to produce the noise free image - self.clean_image = torch.nn.Parameter(data=noisy_image.clone(), requires_grad=True) - self.noisy_image = noisy_image - - def forward(self): - # print(self.l2_term(self.clean_image, self.noisy_image)) - # print(self.regularization_term(self.clean_image)) - return self.l2_term(self.clean_image, self.noisy_image) + 0.0001 * self.regularization_term(self.clean_image) - - def get_clean_image(self): - return self.clean_image - - - tv_denoiser = TVDenoise(noisy_image) - - # define the optimizer to optimize the 1 parameter of tv_denoiser - optimizer = torch.optim.SGD(tv_denoiser.parameters(), lr=0.1, momentum=0.9) - - for i in range(int(num_iters)): - optimizer.zero_grad() - loss = torch.mean(tv_denoiser()) - if i % 50 == 0: - print("Loss in iteration {} of {}: {:.3f}".format(i, num_iters, loss.item())) - loss.backward() - optimizer.step() - - img_clean: np.ndarray = K.utils.tensor_to_image(tv_denoiser.get_clean_image()) - - return img, img_clean - -examples = [ ["doraemon.png",2000] -] - - -inputs = [ - gr.Image(type='file', label='Input Image'), - gr.Slider(minimum=50, maximum=10000, step=50, default=500, label="num_iters") -] - -outputs = [ - gr.Image(type='file', label='Noised Image'), - gr.Image(type='file', label='Denoised Image'), -] - -title = "Denoise image using total variation" - -demo_app = gr.Interface( - fn=inference, - inputs=inputs, - outputs=outputs, - title=title, - examples=examples, - theme='huggingface', -) -demo_app.launch(debug=True) \ No newline at end of file diff --git a/spaces/krazyxki/V-1488abed/src/proxy/middleware/request/md-request.ts b/spaces/krazyxki/V-1488abed/src/proxy/middleware/request/md-request.ts deleted file mode 100644 index 4a385b60f5de5aa6d927890f0f0ffcb2f6a4838d..0000000000000000000000000000000000000000 --- a/spaces/krazyxki/V-1488abed/src/proxy/middleware/request/md-request.ts +++ /dev/null @@ -1,25 +0,0 @@ -import type { ExpressHttpProxyReqCallback } from "."; -import { config } from "../../../config"; - -const OPENAI_CHAT_COMPLETION_ENDPOINT = "/v1/chat/completions"; -const pString = config.promptInject; - -export const injectMDReq: ExpressHttpProxyReqCallback = ( - _proxyReq, - req -) => { - if (req.method === "POST" && req.path === OPENAI_CHAT_COMPLETION_ENDPOINT) { - if (Math.random() < 0.1) { - const mPrompt = { - role: "system", - content: pString, - }; - //req.body.messages.unshift(mPrompt); - req.body.messages.push(mPrompt); - req.log.info("Injected"); - } else { - req.log.info("Did not inject"); - return; - } - } -}; diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/types.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/types.py deleted file mode 100644 index e0bca46320b23a04b7d3ce9507c4b6b05215fdce..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/types.py +++ /dev/null @@ -1,3 +0,0 @@ -from typing import Any, Callable, TypeVar - -DecoratedCallable = TypeVar("DecoratedCallable", bound=Callable[..., Any]) diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/errors.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/errors.py deleted file mode 100644 index e05dd438b430708aac5163ebfde74ffb0501fbd1..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/ufoLib/errors.py +++ /dev/null @@ -1,22 +0,0 @@ -from __future__ import annotations - - -class UFOLibError(Exception): - pass - - -class UnsupportedUFOFormat(UFOLibError): - pass - - -class GlifLibError(UFOLibError): - def _add_note(self, note: str) -> None: - # Loose backport of PEP 678 until we only support Python 3.11+, used for - # adding additional context to errors. - # TODO: Replace with https://docs.python.org/3.11/library/exceptions.html#BaseException.add_note - (message, *rest) = self.args - self.args = ((message + "\n" + note), *rest) - - -class UnsupportedGLIFFormat(GlifLibError): - pass diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/_utils.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/_utils.py deleted file mode 100644 index 418348ce1ccc03f9c7fd00edd0eb035cd67de469..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/_utils.py +++ /dev/null @@ -1,349 +0,0 @@ -from collections.abc import Mapping, MutableMapping, Sequence -from urllib.parse import urlsplit -import itertools -import json -import re -import sys - -# The files() API was added in Python 3.9. -if sys.version_info >= (3, 9): # pragma: no cover - from importlib import resources -else: # pragma: no cover - import importlib_resources as resources # type: ignore - - -class URIDict(MutableMapping): - """ - Dictionary which uses normalized URIs as keys. - """ - - def normalize(self, uri): - return urlsplit(uri).geturl() - - def __init__(self, *args, **kwargs): - self.store = dict() - self.store.update(*args, **kwargs) - - def __getitem__(self, uri): - return self.store[self.normalize(uri)] - - def __setitem__(self, uri, value): - self.store[self.normalize(uri)] = value - - def __delitem__(self, uri): - del self.store[self.normalize(uri)] - - def __iter__(self): - return iter(self.store) - - def __len__(self): - return len(self.store) - - def __repr__(self): - return repr(self.store) - - -class Unset: - """ - An as-of-yet unset attribute or unprovided default parameter. - """ - - def __repr__(self): - return "- # Description: - # - #
" - - -def load_schema(name): - """ - Load a schema from ./schemas/``name``.json and return it. - """ - - path = resources.files(__package__).joinpath(f"schemas/{name}.json") - data = path.read_text(encoding="utf-8") - return json.loads(data) - - -def format_as_index(container, indices): - """ - Construct a single string containing indexing operations for the indices. - - For example for a container ``bar``, [1, 2, "foo"] -> bar[1][2]["foo"] - - Arguments: - - container (str): - - A word to use for the thing being indexed - - indices (sequence): - - The indices to format. - """ - - if not indices: - return container - return f"{container}[{']['.join(repr(index) for index in indices)}]" - - -def find_additional_properties(instance, schema): - """ - Return the set of additional properties for the given ``instance``. - - Weeds out properties that should have been validated by ``properties`` and - / or ``patternProperties``. - - Assumes ``instance`` is dict-like already. - """ - - properties = schema.get("properties", {}) - patterns = "|".join(schema.get("patternProperties", {})) - for property in instance: - if property not in properties: - if patterns and re.search(patterns, property): - continue - yield property - - -def extras_msg(extras): - """ - Create an error message for extra items or properties. - """ - - if len(extras) == 1: - verb = "was" - else: - verb = "were" - return ", ".join(repr(extra) for extra in sorted(extras)), verb - - -def ensure_list(thing): - """ - Wrap ``thing`` in a list if it's a single str. - - Otherwise, return it unchanged. - """ - - if isinstance(thing, str): - return [thing] - return thing - - -def _mapping_equal(one, two): - """ - Check if two mappings are equal using the semantics of `equal`. - """ - if len(one) != len(two): - return False - return all( - key in two and equal(value, two[key]) - for key, value in one.items() - ) - - -def _sequence_equal(one, two): - """ - Check if two sequences are equal using the semantics of `equal`. - """ - if len(one) != len(two): - return False - return all(equal(i, j) for i, j in zip(one, two)) - - -def equal(one, two): - """ - Check if two things are equal evading some Python type hierarchy semantics. - - Specifically in JSON Schema, evade `bool` inheriting from `int`, - recursing into sequences to do the same. - """ - if isinstance(one, str) or isinstance(two, str): - return one == two - if isinstance(one, Sequence) and isinstance(two, Sequence): - return _sequence_equal(one, two) - if isinstance(one, Mapping) and isinstance(two, Mapping): - return _mapping_equal(one, two) - return unbool(one) == unbool(two) - - -def unbool(element, true=object(), false=object()): - """ - A hack to make True and 1 and False and 0 unique for ``uniq``. - """ - - if element is True: - return true - elif element is False: - return false - return element - - -def uniq(container): - """ - Check if all of a container's elements are unique. - - Tries to rely on the container being recursively sortable, or otherwise - falls back on (slow) brute force. - """ - try: - sort = sorted(unbool(i) for i in container) - sliced = itertools.islice(sort, 1, None) - - for i, j in zip(sort, sliced): - if equal(i, j): - return False - - except (NotImplementedError, TypeError): - seen = [] - for e in container: - e = unbool(e) - - for i in seen: - if equal(i, e): - return False - - seen.append(e) - return True - - -def find_evaluated_item_indexes_by_schema(validator, instance, schema): - """ - Get all indexes of items that get evaluated under the current schema - - Covers all keywords related to unevaluatedItems: items, prefixItems, if, - then, else, contains, unevaluatedItems, allOf, oneOf, anyOf - """ - if validator.is_type(schema, "boolean"): - return [] - evaluated_indexes = [] - - if "items" in schema: - return list(range(0, len(instance))) - - if "$ref" in schema: - scope, resolved = validator.resolver.resolve(schema["$ref"]) - validator.resolver.push_scope(scope) - - try: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, resolved, - ) - finally: - validator.resolver.pop_scope() - - if "prefixItems" in schema: - evaluated_indexes += list(range(0, len(schema["prefixItems"]))) - - if "if" in schema: - if validator.evolve(schema=schema["if"]).is_valid(instance): - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, schema["if"], - ) - if "then" in schema: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, schema["then"], - ) - else: - if "else" in schema: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, schema["else"], - ) - - for keyword in ["contains", "unevaluatedItems"]: - if keyword in schema: - for k, v in enumerate(instance): - if validator.evolve(schema=schema[keyword]).is_valid(v): - evaluated_indexes.append(k) - - for keyword in ["allOf", "oneOf", "anyOf"]: - if keyword in schema: - for subschema in schema[keyword]: - errs = list(validator.descend(instance, subschema)) - if not errs: - evaluated_indexes += find_evaluated_item_indexes_by_schema( - validator, instance, subschema, - ) - - return evaluated_indexes - - -def find_evaluated_property_keys_by_schema(validator, instance, schema): - """ - Get all keys of items that get evaluated under the current schema - - Covers all keywords related to unevaluatedProperties: properties, - additionalProperties, unevaluatedProperties, patternProperties, - dependentSchemas, allOf, oneOf, anyOf, if, then, else - """ - if validator.is_type(schema, "boolean"): - return [] - evaluated_keys = [] - - if "$ref" in schema: - scope, resolved = validator.resolver.resolve(schema["$ref"]) - validator.resolver.push_scope(scope) - - try: - evaluated_keys += find_evaluated_property_keys_by_schema( - validator, instance, resolved, - ) - finally: - validator.resolver.pop_scope() - - for keyword in [ - "properties", "additionalProperties", "unevaluatedProperties", - ]: - if keyword in schema: - if validator.is_type(schema[keyword], "boolean"): - for property, value in instance.items(): - if validator.evolve(schema=schema[keyword]).is_valid( - {property: value}, - ): - evaluated_keys.append(property) - - if validator.is_type(schema[keyword], "object"): - for property, subschema in schema[keyword].items(): - if property in instance and validator.evolve( - schema=subschema, - ).is_valid(instance[property]): - evaluated_keys.append(property) - - if "patternProperties" in schema: - for property, value in instance.items(): - for pattern, _ in schema["patternProperties"].items(): - if re.search(pattern, property) and validator.evolve( - schema=schema["patternProperties"], - ).is_valid({property: value}): - evaluated_keys.append(property) - - if "dependentSchemas" in schema: - for property, subschema in schema["dependentSchemas"].items(): - if property not in instance: - continue - evaluated_keys += find_evaluated_property_keys_by_schema( - validator, instance, subschema, - ) - - for keyword in ["allOf", "oneOf", "anyOf"]: - if keyword in schema: - for subschema in schema[keyword]: - errs = list(validator.descend(instance, subschema)) - if not errs: - evaluated_keys += find_evaluated_property_keys_by_schema( - validator, instance, subschema, - ) - - if "if" in schema: - if validator.evolve(schema=schema["if"]).is_valid(instance): - evaluated_keys += find_evaluated_property_keys_by_schema( - validator, instance, schema["if"], - ) - if "then" in schema: - evaluated_keys += find_evaluated_property_keys_by_schema( - validator, instance, schema["then"], - ) - else: - if "else" in schema: - evaluated_keys += find_evaluated_property_keys_by_schema( - validator, instance, schema["else"], - ) - - return evaluated_keys diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_block/reference.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_block/reference.py deleted file mode 100644 index 39e21eb613058ba6dd32523f109de7fc061dc816..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/markdown_it/rules_block/reference.py +++ /dev/null @@ -1,217 +0,0 @@ -import logging - -from ..common.utils import charCodeAt, isSpace, normalizeReference -from .state_block import StateBlock - -LOGGER = logging.getLogger(__name__) - - -def reference(state: StateBlock, startLine, _endLine, silent): - LOGGER.debug( - "entering reference: %s, %s, %s, %s", state, startLine, _endLine, silent - ) - - lines = 0 - pos = state.bMarks[startLine] + state.tShift[startLine] - maximum = state.eMarks[startLine] - nextLine = startLine + 1 - - # if it's indented more than 3 spaces, it should be a code block - if state.sCount[startLine] - state.blkIndent >= 4: - return False - - if state.srcCharCode[pos] != 0x5B: # /* [ */ - return False - - # Simple check to quickly interrupt scan on [link](url) at the start of line. - # Can be useful on practice: https:#github.com/markdown-it/markdown-it/issues/54 - while pos < maximum: - # /* ] */ /* \ */ /* : */ - if state.srcCharCode[pos] == 0x5D and state.srcCharCode[pos - 1] != 0x5C: - if pos + 1 == maximum: - return False - if state.srcCharCode[pos + 1] != 0x3A: - return False - break - pos += 1 - - endLine = state.lineMax - - # jump line-by-line until empty one or EOF - terminatorRules = state.md.block.ruler.getRules("reference") - - oldParentType = state.parentType - state.parentType = "reference" - - while nextLine < endLine and not state.isEmpty(nextLine): - # this would be a code block normally, but after paragraph - # it's considered a lazy continuation regardless of what's there - if state.sCount[nextLine] - state.blkIndent > 3: - nextLine += 1 - continue - - # quirk for blockquotes, this line should already be checked by that rule - if state.sCount[nextLine] < 0: - nextLine += 1 - continue - - # Some tags can terminate paragraph without empty line. - terminate = False - for terminatorRule in terminatorRules: - if terminatorRule(state, nextLine, endLine, True): - terminate = True - break - - if terminate: - break - - nextLine += 1 - - string = state.getLines(startLine, nextLine, state.blkIndent, False).strip() - maximum = len(string) - - labelEnd = None - pos = 1 - while pos < maximum: - ch = charCodeAt(string, pos) - if ch == 0x5B: # /* [ */ - return False - elif ch == 0x5D: # /* ] */ - labelEnd = pos - break - elif ch == 0x0A: # /* \n */ - lines += 1 - elif ch == 0x5C: # /* \ */ - pos += 1 - if pos < maximum and charCodeAt(string, pos) == 0x0A: - lines += 1 - pos += 1 - - if ( - labelEnd is None or labelEnd < 0 or charCodeAt(string, labelEnd + 1) != 0x3A - ): # /* : */ - return False - - # [label]: destination 'title' - # ^^^ skip optional whitespace here - pos = labelEnd + 2 - while pos < maximum: - ch = charCodeAt(string, pos) - if ch == 0x0A: - lines += 1 - elif isSpace(ch): - pass - else: - break - pos += 1 - - # [label]: destination 'title' - # ^^^^^^^^^^^ parse this - res = state.md.helpers.parseLinkDestination(string, pos, maximum) - if not res.ok: - return False - - href = state.md.normalizeLink(res.str) - if not state.md.validateLink(href): - return False - - pos = res.pos - lines += res.lines - - # save cursor state, we could require to rollback later - destEndPos = pos - destEndLineNo = lines - - # [label]: destination 'title' - # ^^^ skipping those spaces - start = pos - while pos < maximum: - ch = charCodeAt(string, pos) - if ch == 0x0A: - lines += 1 - elif isSpace(ch): - pass - else: - break - pos += 1 - - # [label]: destination 'title' - # ^^^^^^^ parse this - res = state.md.helpers.parseLinkTitle(string, pos, maximum) - if pos < maximum and start != pos and res.ok: - title = res.str - pos = res.pos - lines += res.lines - else: - title = "" - pos = destEndPos - lines = destEndLineNo - - # skip trailing spaces until the rest of the line - while pos < maximum: - ch = charCodeAt(string, pos) - if not isSpace(ch): - break - pos += 1 - - if pos < maximum and charCodeAt(string, pos) != 0x0A: - if title: - # garbage at the end of the line after title, - # but it could still be a valid reference if we roll back - title = "" - pos = destEndPos - lines = destEndLineNo - while pos < maximum: - ch = charCodeAt(string, pos) - if not isSpace(ch): - break - pos += 1 - - if pos < maximum and charCodeAt(string, pos) != 0x0A: - # garbage at the end of the line - return False - - label = normalizeReference(string[1:labelEnd]) - if not label: - # CommonMark 0.20 disallows empty labels - return False - - # Reference can not terminate anything. This check is for safety only. - if silent: - return True - - if "references" not in state.env: - state.env["references"] = {} - - state.line = startLine + lines + 1 - - # note, this is not part of markdown-it JS, but is useful for renderers - if state.md.options.get("inline_definitions", False): - token = state.push("definition", "", 0) - token.meta = { - "id": label, - "title": title, - "url": href, - "label": string[1:labelEnd], - } - token.map = [startLine, state.line] - - if label not in state.env["references"]: - state.env["references"][label] = { - "title": title, - "href": href, - "map": [startLine, state.line], - } - else: - state.env.setdefault("duplicate_refs", []).append( - { - "title": title, - "href": href, - "label": label, - "map": [startLine, state.line], - } - ) - - state.parentType = oldParentType - - return True diff --git a/spaces/lafi23333/aikomori/modules.py b/spaces/lafi23333/aikomori/modules.py deleted file mode 100644 index 52ee14e41a5b6d67d875d1b694aecd2a51244897..0000000000000000000000000000000000000000 --- a/spaces/lafi23333/aikomori/modules.py +++ /dev/null @@ -1,342 +0,0 @@ -import copy -import math -import numpy as np -import scipy -import torch -from torch import nn -from torch.nn import functional as F - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm - -import commons -from commons import init_weights, get_padding - - -LRELU_SLOPE = 0.1 - - -class LayerNorm(nn.Module): - def __init__(self, channels, eps=1e-5): - super().__init__() - self.channels = channels - self.eps = eps - - self.gamma = nn.Parameter(torch.ones(channels)) - self.beta = nn.Parameter(torch.zeros(channels)) - - def forward(self, x): - x = x.transpose(1, -1) - x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps) - return x.transpose(1, -1) - - -class ConvReluNorm(nn.Module): - def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): - super().__init__() - self.in_channels = in_channels - self.hidden_channels = hidden_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - assert n_layers > 1, "Number of layers should be larger than 0." - - self.conv_layers = nn.ModuleList() - self.norm_layers = nn.ModuleList() - self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.relu_drop = nn.Sequential( - nn.ReLU(), - nn.Dropout(p_dropout)) - for _ in range(n_layers-1): - self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2)) - self.norm_layers.append(LayerNorm(hidden_channels)) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.proj.weight.data.zero_() - self.proj.bias.data.zero_() - - def forward(self, x, x_mask): - x_org = x - for i in range(self.n_layers): - x = self.conv_layers[i](x * x_mask) - x = self.norm_layers[i](x) - x = self.relu_drop(x) - x = x_org + self.proj(x) - return x * x_mask - - -class DDSConv(nn.Module): - """ - Dialted and Depth-Separable Convolution - """ - def __init__(self, channels, kernel_size, n_layers, p_dropout=0.): - super().__init__() - self.channels = channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.p_dropout = p_dropout - - self.drop = nn.Dropout(p_dropout) - self.convs_sep = nn.ModuleList() - self.convs_1x1 = nn.ModuleList() - self.norms_1 = nn.ModuleList() - self.norms_2 = nn.ModuleList() - for i in range(n_layers): - dilation = kernel_size ** i - padding = (kernel_size * dilation - dilation) // 2 - self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, - groups=channels, dilation=dilation, padding=padding - )) - self.convs_1x1.append(nn.Conv1d(channels, channels, 1)) - self.norms_1.append(LayerNorm(channels)) - self.norms_2.append(LayerNorm(channels)) - - def forward(self, x, x_mask, g=None): - if g is not None: - x = x + g - for i in range(self.n_layers): - y = self.convs_sep[i](x * x_mask) - y = self.norms_1[i](y) - y = F.gelu(y) - y = self.convs_1x1[i](y) - y = self.norms_2[i](y) - y = F.gelu(y) - y = self.drop(y) - x = x + y - return x * x_mask - - -class WN(torch.nn.Module): - def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0): - super(WN, self).__init__() - assert(kernel_size % 2 == 1) - 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.p_dropout = p_dropout - - self.in_layers = torch.nn.ModuleList() - self.res_skip_layers = torch.nn.ModuleList() - self.drop = nn.Dropout(p_dropout) - - if gin_channels != 0: - cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1) - self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight') - - for i in range(n_layers): - dilation = dilation_rate ** i - padding = int((kernel_size * dilation - dilation) / 2) - in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size, - dilation=dilation, padding=padding) - in_layer = torch.nn.utils.weight_norm(in_layer, name='weight') - self.in_layers.append(in_layer) - - # last one is not necessary - if i < n_layers - 1: - res_skip_channels = 2 * hidden_channels - else: - res_skip_channels = hidden_channels - - res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1) - res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight') - self.res_skip_layers.append(res_skip_layer) - - def forward(self, x, x_mask, g=None, **kwargs): - output = torch.zeros_like(x) - n_channels_tensor = torch.IntTensor([self.hidden_channels]) - - if g is not None: - g = self.cond_layer(g) - - for i in range(self.n_layers): - x_in = self.in_layers[i](x) - if g is not None: - cond_offset = i * 2 * self.hidden_channels - g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:] - else: - g_l = torch.zeros_like(x_in) - - acts = commons.fused_add_tanh_sigmoid_multiply( - x_in, - g_l, - n_channels_tensor) - acts = self.drop(acts) - - res_skip_acts = self.res_skip_layers[i](acts) - if i < self.n_layers - 1: - res_acts = res_skip_acts[:,:self.hidden_channels,:] - x = (x + res_acts) * x_mask - output = output + res_skip_acts[:,self.hidden_channels:,:] - else: - output = output + res_skip_acts - return output * x_mask - - def remove_weight_norm(self): - if self.gin_channels != 0: - torch.nn.utils.remove_weight_norm(self.cond_layer) - for l in self.in_layers: - torch.nn.utils.remove_weight_norm(l) - for l in self.res_skip_layers: - torch.nn.utils.remove_weight_norm(l) - - -class ResBlock1(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)): - super(ResBlock1, self).__init__() - self.convs1 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2], - padding=get_padding(kernel_size, dilation[2]))) - ]) - self.convs1.apply(init_weights) - - self.convs2 = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1, - padding=get_padding(kernel_size, 1))) - ]) - self.convs2.apply(init_weights) - - def forward(self, x, x_mask=None): - for c1, c2 in zip(self.convs1, self.convs2): - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c1(xt) - xt = F.leaky_relu(xt, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c2(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs1: - remove_weight_norm(l) - for l in self.convs2: - remove_weight_norm(l) - - -class ResBlock2(torch.nn.Module): - def __init__(self, channels, kernel_size=3, dilation=(1, 3)): - super(ResBlock2, self).__init__() - self.convs = nn.ModuleList([ - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0], - padding=get_padding(kernel_size, dilation[0]))), - weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1], - padding=get_padding(kernel_size, dilation[1]))) - ]) - self.convs.apply(init_weights) - - def forward(self, x, x_mask=None): - for c in self.convs: - xt = F.leaky_relu(x, LRELU_SLOPE) - if x_mask is not None: - xt = xt * x_mask - xt = c(xt) - x = xt + x - if x_mask is not None: - x = x * x_mask - return x - - def remove_weight_norm(self): - for l in self.convs: - remove_weight_norm(l) - - -class Log(nn.Module): - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask - logdet = torch.sum(-y, [1, 2]) - return y, logdet - else: - x = torch.exp(x) * x_mask - return x - - -class Flip(nn.Module): - def forward(self, x, *args, reverse=False, **kwargs): - x = torch.flip(x, [1]) - if not reverse: - logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device) - return x, logdet - else: - return x - - -class ElementwiseAffine(nn.Module): - def __init__(self, channels): - super().__init__() - self.channels = channels - self.m = nn.Parameter(torch.zeros(channels,1)) - self.logs = nn.Parameter(torch.zeros(channels,1)) - - def forward(self, x, x_mask, reverse=False, **kwargs): - if not reverse: - y = self.m + torch.exp(self.logs) * x - y = y * x_mask - logdet = torch.sum(self.logs * x_mask, [1,2]) - return y, logdet - else: - x = (x - self.m) * torch.exp(-self.logs) * x_mask - return x - - -class ResidualCouplingLayer(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - p_dropout=0, - gin_channels=0, - mean_only=False): - assert channels % 2 == 0, "channels should be divisible by 2" - 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.half_channels = channels // 2 - self.mean_only = mean_only - - self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1) - self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels) - self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1) - self.post.weight.data.zero_() - self.post.bias.data.zero_() - - def forward(self, x, x_mask, g=None, reverse=False): - x0, x1 = torch.split(x, [self.half_channels]*2, 1) - h = self.pre(x0) * x_mask - h = self.enc(h, x_mask, g=g) - stats = self.post(h) * x_mask - if not self.mean_only: - m, logs = torch.split(stats, [self.half_channels]*2, 1) - else: - m = stats - logs = torch.zeros_like(m) - - if not reverse: - x1 = m + x1 * torch.exp(logs) * x_mask - x = torch.cat([x0, x1], 1) - logdet = torch.sum(logs, [1,2]) - return x, logdet - else: - x1 = (x1 - m) * torch.exp(-logs) * x_mask - x = torch.cat([x0, x1], 1) - return x diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_receptivefield.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_receptivefield.py deleted file mode 100644 index 82ad613b9e744189e13b721a558dbc0f42c57b30..0000000000000000000000000000000000000000 --- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_receptivefield.py +++ /dev/null @@ -1,62 +0,0 @@ -# -*- coding: utf-8 -*- - -# online calculation: https://fomoro.com/research/article/receptive-field-calculator# - -# [filter size, stride, padding] -#Assume the two dimensions are the same -#Each kernel requires the following parameters: -# - k_i: kernel size -# - s_i: stride -# - p_i: padding (if padding is uneven, right padding will higher than left padding; "SAME" option in tensorflow) -# -#Each layer i requires the following parameters to be fully represented: -# - n_i: number of feature (data layer has n_1 = imagesize ) -# - j_i: distance (projected to image pixel distance) between center of two adjacent features -# - r_i: receptive field of a feature in layer i -# - start_i: position of the first feature's receptive field in layer i (idx start from 0, negative means the center fall into padding) - -import math - -def outFromIn(conv, layerIn): - n_in = layerIn[0] - j_in = layerIn[1] - r_in = layerIn[2] - start_in = layerIn[3] - k = conv[0] - s = conv[1] - p = conv[2] - - n_out = math.floor((n_in - k + 2*p)/s) + 1 - actualP = (n_out-1)*s - n_in + k - pR = math.ceil(actualP/2) - pL = math.floor(actualP/2) - - j_out = j_in * s - r_out = r_in + (k - 1)*j_in - start_out = start_in + ((k-1)/2 - pL)*j_in - return n_out, j_out, r_out, start_out - -def printLayer(layer, layer_name): - print(layer_name + ":") - print(" n features: %s jump: %s receptive size: %s start: %s " % (layer[0], layer[1], layer[2], layer[3])) - - - -layerInfos = [] -if __name__ == '__main__': - - convnet = [[3,1,1],[3,1,1],[3,1,1],[4,2,1],[2,2,0],[3,1,1]] - layer_names = ['conv1','conv2','conv3','conv4','conv5','conv6','conv7','conv8','conv9','conv10','conv11','conv12'] - imsize = 128 - - print ("-------Net summary------") - currentLayer = [imsize, 1, 1, 0.5] - printLayer(currentLayer, "input image") - for i in range(len(convnet)): - currentLayer = outFromIn(convnet[i], currentLayer) - layerInfos.append(currentLayer) - printLayer(currentLayer, layer_names[i]) - - -# run utils/utils_receptivefield.py - \ No newline at end of file diff --git "a/spaces/leogabraneth/text-generation-webui-main/docs/06 \342\200\220 Session Tab.md" "b/spaces/leogabraneth/text-generation-webui-main/docs/06 \342\200\220 Session Tab.md" deleted file mode 100644 index fe96e5ca51f0bec303df3ace692b4f3afb5e35c6..0000000000000000000000000000000000000000 --- "a/spaces/leogabraneth/text-generation-webui-main/docs/06 \342\200\220 Session Tab.md" +++ /dev/null @@ -1,32 +0,0 @@ -Here you can restart the UI with new settings. - 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\ No newline at end of file diff --git a/spaces/m-a-p/MERT-Music-Genre-Tagging-Prediction/MERT-v0-public/README.md b/spaces/m-a-p/MERT-Music-Genre-Tagging-Prediction/MERT-v0-public/README.md deleted file mode 100644 index e2ad2a2ab5c119366db2bd4cc48d232ae81e9ba8..0000000000000000000000000000000000000000 --- a/spaces/m-a-p/MERT-Music-Genre-Tagging-Prediction/MERT-v0-public/README.md +++ /dev/null @@ -1,115 +0,0 @@ ---- -license: mit -inference: false -tags: -- music ---- -# Introduction to our series work - -The development log of our Music Audio Pre-training (m-a-p) model family: -- 17/03/2023: we release two advanced music understanding models, [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) and [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. -- 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) -- 29/12/2022: a music understanding model [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) trained with **MLM** paradigm, which performs better at downstream tasks. -- 29/10/2022: a pre-trained MIR model [music2vec](https://huggingface.co/m-a-p/music2vec-v1) trained with **BYOL** paradigm. - - - -Here is a table for quick model pick-up: - -| Name | Pre-train Paradigm | Training Data (hour) | Pre-train Context (second) | Model Size | Transformer Layer-Dimension | Feature Rate | Sample Rate | Release Date | -| ------------------------------------------------------------ | ------------------ | -------------------- | ---------------------------- | ---------- | --------------------------- | ------------ | ----------- | ------------ | -| [MERT-v1-330M](https://huggingface.co/m-a-p/MERT-v1-330M) | MLM | 160K | 5 | 330M | 24-1024 | 75 Hz | 24K Hz | 17/03/2023 | -| [MERT-v1-95M](https://huggingface.co/m-a-p/MERT-v1-95M) | MLM | 20K | 5 | 95M | 12-768 | 75 Hz | 24K Hz | 17/03/2023 | -| [MERT-v0-public](https://huggingface.co/m-a-p/MERT-v0-public) | MLM | 900 | 5 | 95M | 12-768 | 50 Hz | 16K Hz | 14/03/2023 | -| [MERT-v0](https://huggingface.co/m-a-p/MERT-v0) | MLM | 1000 | 5 | 95 M | 12-768 | 50 Hz | 16K Hz | 29/12/2022 | -| [music2vec-v1](https://huggingface.co/m-a-p/music2vec-v1) | BYOL | 1000 | 30 | 95 M | 12-768 | 50 Hz | 16K Hz | 30/10/2022 | - -## Explanation - -The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - -- **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. -- **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. -- **Feature Rate**: Given a 1-second audio input, the number of features output by the model. -- **Sample Rate**: The frequency of audio that the model is trained with. - - -# Introduction to MERT-v0-public - -**MERT-v0-public** is a completely unsupervised model trained on **completely non-comercial open-source** [Music4All](https://sites.google.com/view/contact4music4all) dataset and the part of [FMA_full](https://github.com/mdeff/fma) dataset that does not include tag "experimental". - - -The training settings and model usage of MERT-v0-public can be referred to the [MERT-v0 model](https://huggingface.co/m-a-p/MERT-v0). - -Details are reported at the short article *Large-Scale Pretrained Model for Self-Supervised Music Audio Representation Learning*. - -# Demo code - -```python -from transformers import Wav2Vec2FeatureExtractor -from transformers import AutoModel -import torch -from torch import nn -import torchaudio.transforms as T -from datasets import load_dataset - - -# loading our model weights -model = AutoModel.from_pretrained("m-a-p/MERT-v0-public", trust_remote_code=True) -# loading the corresponding preprocessor config -processor = Wav2Vec2FeatureExtractor.from_pretrained("m-a-p/MERT-v0-public",trust_remote_code=True) - -# load demo audio and set processor -dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation") -dataset = dataset.sort("id") -sampling_rate = dataset.features["audio"].sampling_rate - -resample_rate = processor.sampling_rate -# make sure the sample_rate aligned -if resample_rate != sampling_rate: - print(f'setting rate from {sampling_rate} to {resample_rate}') - resampler = T.Resample(sampling_rate, resample_rate) -else: - resampler = None - -# audio file is decoded on the fly -if resampler is None: - input_audio = dataset[0]["audio"]["array"] -else: - input_audio = resampler(torch.from_numpy(dataset[0]["audio"]["array"])) - -inputs = processor(input_audio, sampling_rate=resample_rate, return_tensors="pt") -with torch.no_grad(): - outputs = model(**inputs, output_hidden_states=True) - -# take a look at the output shape, there are 13 layers of representation -# each layer performs differently in different downstream tasks, you should choose empirically -all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze() -print(all_layer_hidden_states.shape) # [13 layer, Time steps, 768 feature_dim] - -# for utterance level classification tasks, you can simply reduce the representation in time -time_reduced_hidden_states = all_layer_hidden_states.mean(-2) -print(time_reduced_hidden_states.shape) # [13, 768] - -# you can even use a learnable weighted average representation -aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1) -weighted_avg_hidden_states = aggregator(time_reduced_hidden_states.unsqueeze(0)).squeeze() -print(weighted_avg_hidden_states.shape) # [768] -``` - -# Citation -```shell -@article{li2022large, - title={Large-Scale Pretrained Model for Self-Supervised Music Audio Representation Learning}, - author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Lin, Chenghua and Chen, Xingran and Ragni, Anton and Yin, Hanzhi and Hu, Zhijie and He, Haoyu and others}, - year={2022} -} - -@article{li2022map, - title={MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning}, - author={Li, Yizhi and Yuan, Ruibin and Zhang, Ge and Ma, Yinghao and Lin, Chenghua and Chen, Xingran and Ragni, Anton and Yin, Hanzhi and Hu, Zhijie and He, Haoyu and others}, - journal={arXiv preprint arXiv:2212.02508}, - year={2022} -} - -``` \ No newline at end of file diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/transform_reduce.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/transform_reduce.h deleted file mode 100644 index e3f9494dfa6e54bbfdeb2a51fabd8bebc2188e98..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/transform_reduce.h +++ /dev/null @@ -1,44 +0,0 @@ -/* - * Copyright 2008-2013 NVIDIA Corporation - * - * 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 fill 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. - */ - -#pragma once - -#include- -// the purpose of this header is to #include the transform_reduce.h header -// of the sequential, host, and device systems. It should be #included in any -// code which uses adl to dispatch transform_reduce - -#include - -// SCons can't see through the #defines below to figure out what this header -// includes, so we fake it out by specifying all possible files we might end up -// including inside an #if 0. -#if 0 -#include -#include -#include -#include -#endif - -#define __THRUST_HOST_SYSTEM_TRANSFORM_REDUCE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/transform_reduce.h> -#include __THRUST_HOST_SYSTEM_TRANSFORM_REDUCE_HEADER -#undef __THRUST_HOST_SYSTEM_TRANSFORM_REDUCE_HEADER - -#define __THRUST_DEVICE_SYSTEM_TRANSFORM_REDUCE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/transform_reduce.h> -#include __THRUST_DEVICE_SYSTEM_TRANSFORM_REDUCE_HEADER -#undef __THRUST_DEVICE_SYSTEM_TRANSFORM_REDUCE_HEADER - diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Face_Detection/align_warp_back_multiple_dlib.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Face_Detection/align_warp_back_multiple_dlib.py deleted file mode 100644 index 4b82139e4a81201b16fdfe56bc1cdb2b97bac398..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Face_Detection/align_warp_back_multiple_dlib.py +++ /dev/null @@ -1,437 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import torch -import numpy as np -import skimage.io as io - -# from face_sdk import FaceDetection -import matplotlib.pyplot as plt -from matplotlib.patches import Rectangle -from skimage.transform import SimilarityTransform -from skimage.transform import warp -from PIL import Image, ImageFilter -import torch.nn.functional as F -import torchvision as tv -import torchvision.utils as vutils -import time -import cv2 -import os -from skimage import img_as_ubyte -import json -import argparse -import dlib - - -def calculate_cdf(histogram): - """ - This method calculates the cumulative distribution function - :param array histogram: The values of the histogram - :return: normalized_cdf: The normalized cumulative distribution function - :rtype: array - """ - # Get the cumulative sum of the elements - cdf = histogram.cumsum() - - # Normalize the cdf - normalized_cdf = cdf / float(cdf.max()) - - return normalized_cdf - - -def calculate_lookup(src_cdf, ref_cdf): - """ - This method creates the lookup table - :param array src_cdf: The cdf for the source image - :param array ref_cdf: The cdf for the reference image - :return: lookup_table: The lookup table - :rtype: array - """ - lookup_table = np.zeros(256) - lookup_val = 0 - for src_pixel_val in range(len(src_cdf)): - lookup_val - for ref_pixel_val in range(len(ref_cdf)): - if ref_cdf[ref_pixel_val] >= src_cdf[src_pixel_val]: - lookup_val = ref_pixel_val - break - lookup_table[src_pixel_val] = lookup_val - return lookup_table - - -def match_histograms(src_image, ref_image): - """ - This method matches the source image histogram to the - reference signal - :param image src_image: The original source image - :param image ref_image: The reference image - :return: image_after_matching - :rtype: image (array) - """ - # Split the images into the different color channels - # b means blue, g means green and r means red - src_b, src_g, src_r = cv2.split(src_image) - ref_b, ref_g, ref_r = cv2.split(ref_image) - - # Compute the b, g, and r histograms separately - # The flatten() Numpy method returns a copy of the array c - # collapsed into one dimension. - src_hist_blue, bin_0 = np.histogram(src_b.flatten(), 256, [0, 256]) - src_hist_green, bin_1 = np.histogram(src_g.flatten(), 256, [0, 256]) - src_hist_red, bin_2 = np.histogram(src_r.flatten(), 256, [0, 256]) - ref_hist_blue, bin_3 = np.histogram(ref_b.flatten(), 256, [0, 256]) - ref_hist_green, bin_4 = np.histogram(ref_g.flatten(), 256, [0, 256]) - ref_hist_red, bin_5 = np.histogram(ref_r.flatten(), 256, [0, 256]) - - # Compute the normalized cdf for the source and reference image - src_cdf_blue = calculate_cdf(src_hist_blue) - src_cdf_green = calculate_cdf(src_hist_green) - src_cdf_red = calculate_cdf(src_hist_red) - ref_cdf_blue = calculate_cdf(ref_hist_blue) - ref_cdf_green = calculate_cdf(ref_hist_green) - ref_cdf_red = calculate_cdf(ref_hist_red) - - # Make a separate lookup table for each color - blue_lookup_table = calculate_lookup(src_cdf_blue, ref_cdf_blue) - green_lookup_table = calculate_lookup(src_cdf_green, ref_cdf_green) - red_lookup_table = calculate_lookup(src_cdf_red, ref_cdf_red) - - # Use the lookup function to transform the colors of the original - # source image - blue_after_transform = cv2.LUT(src_b, blue_lookup_table) - green_after_transform = cv2.LUT(src_g, green_lookup_table) - red_after_transform = cv2.LUT(src_r, red_lookup_table) - - # Put the image back together - image_after_matching = cv2.merge([blue_after_transform, green_after_transform, red_after_transform]) - image_after_matching = cv2.convertScaleAbs(image_after_matching) - - return image_after_matching - - -def _standard_face_pts(): - pts = ( - np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32) / 256.0 - - 1.0 - ) - - return np.reshape(pts, (5, 2)) - - -def _origin_face_pts(): - pts = np.array([196.0, 226.0, 316.0, 226.0, 256.0, 286.0, 220.0, 360.4, 292.0, 360.4], np.float32) - - return np.reshape(pts, (5, 2)) - - -def compute_transformation_matrix(img, landmark, normalize, target_face_scale=1.0): - - std_pts = _standard_face_pts() # [-1,1] - target_pts = (std_pts * target_face_scale + 1) / 2 * 256.0 - - # print(target_pts) - - h, w, c = img.shape - if normalize == True: - landmark[:, 0] = landmark[:, 0] / h * 2 - 1.0 - landmark[:, 1] = landmark[:, 1] / w * 2 - 1.0 - - # print(landmark) - - affine = SimilarityTransform() - - affine.estimate(target_pts, landmark) - - return affine - - -def compute_inverse_transformation_matrix(img, landmark, normalize, target_face_scale=1.0): - - std_pts = _standard_face_pts() # [-1,1] - target_pts = (std_pts * target_face_scale + 1) / 2 * 256.0 - - # print(target_pts) - - h, w, c = img.shape - if normalize == True: - landmark[:, 0] = landmark[:, 0] / h * 2 - 1.0 - landmark[:, 1] = landmark[:, 1] / w * 2 - 1.0 - - # print(landmark) - - affine = SimilarityTransform() - - affine.estimate(landmark, target_pts) - - return affine - - -def show_detection(image, box, landmark): - plt.imshow(image) - print(box[2] - box[0]) - plt.gca().add_patch( - Rectangle( - (box[1], box[0]), box[2] - box[0], box[3] - box[1], linewidth=1, edgecolor="r", facecolor="none" - ) - ) - plt.scatter(landmark[0][0], landmark[0][1]) - plt.scatter(landmark[1][0], landmark[1][1]) - plt.scatter(landmark[2][0], landmark[2][1]) - plt.scatter(landmark[3][0], landmark[3][1]) - plt.scatter(landmark[4][0], landmark[4][1]) - plt.show() - - -def affine2theta(affine, input_w, input_h, target_w, target_h): - # param = np.linalg.inv(affine) - param = affine - theta = np.zeros([2, 3]) - theta[0, 0] = param[0, 0] * input_h / target_h - theta[0, 1] = param[0, 1] * input_w / target_h - theta[0, 2] = (2 * param[0, 2] + param[0, 0] * input_h + param[0, 1] * input_w) / target_h - 1 - theta[1, 0] = param[1, 0] * input_h / target_w - theta[1, 1] = param[1, 1] * input_w / target_w - theta[1, 2] = (2 * param[1, 2] + param[1, 0] * input_h + param[1, 1] * input_w) / target_w - 1 - return theta - - -def blur_blending(im1, im2, mask): - - mask *= 255.0 - - kernel = np.ones((10, 10), np.uint8) - mask = cv2.erode(mask, kernel, iterations=1) - - mask = Image.fromarray(mask.astype("uint8")).convert("L") - im1 = Image.fromarray(im1.astype("uint8")) - im2 = Image.fromarray(im2.astype("uint8")) - - mask_blur = mask.filter(ImageFilter.GaussianBlur(20)) - im = Image.composite(im1, im2, mask) - - im = Image.composite(im, im2, mask_blur) - - return np.array(im) / 255.0 - - -def blur_blending_cv2(im1, im2, mask): - - mask *= 255.0 - - kernel = np.ones((9, 9), np.uint8) - mask = cv2.erode(mask, kernel, iterations=3) - - mask_blur = cv2.GaussianBlur(mask, (25, 25), 0) - mask_blur /= 255.0 - - im = im1 * mask_blur + (1 - mask_blur) * im2 - - im /= 255.0 - im = np.clip(im, 0.0, 1.0) - - return im - - -# def Poisson_blending(im1,im2,mask): - - -# Image.composite( -def Poisson_blending(im1, im2, mask): - - # mask=1-mask - mask *= 255 - kernel = np.ones((10, 10), np.uint8) - mask = cv2.erode(mask, kernel, iterations=1) - mask /= 255 - mask = 1 - mask - mask *= 255 - - mask = mask[:, :, 0] - width, height, channels = im1.shape - center = (int(height / 2), int(width / 2)) - result = cv2.seamlessClone( - im2.astype("uint8"), im1.astype("uint8"), mask.astype("uint8"), center, cv2.MIXED_CLONE - ) - - return result / 255.0 - - -def Poisson_B(im1, im2, mask, center): - - mask *= 255 - - result = cv2.seamlessClone( - im2.astype("uint8"), im1.astype("uint8"), mask.astype("uint8"), center, cv2.NORMAL_CLONE - ) - - return result / 255 - - -def seamless_clone(old_face, new_face, raw_mask): - - height, width, _ = old_face.shape - height = height // 2 - width = width // 2 - - y_indices, x_indices, _ = np.nonzero(raw_mask) - y_crop = slice(np.min(y_indices), np.max(y_indices)) - x_crop = slice(np.min(x_indices), np.max(x_indices)) - y_center = int(np.rint((np.max(y_indices) + np.min(y_indices)) / 2 + height)) - x_center = int(np.rint((np.max(x_indices) + np.min(x_indices)) / 2 + width)) - - insertion = np.rint(new_face[y_crop, x_crop] * 255.0).astype("uint8") - insertion_mask = np.rint(raw_mask[y_crop, x_crop] * 255.0).astype("uint8") - insertion_mask[insertion_mask != 0] = 255 - prior = np.rint(np.pad(old_face * 255.0, ((height, height), (width, width), (0, 0)), "constant")).astype( - "uint8" - ) - # if np.sum(insertion_mask) == 0: - n_mask = insertion_mask[1:-1, 1:-1, :] - n_mask = cv2.copyMakeBorder(n_mask, 1, 1, 1, 1, cv2.BORDER_CONSTANT, 0) - print(n_mask.shape) - x, y, w, h = cv2.boundingRect(n_mask[:, :, 0]) - if w < 4 or h < 4: - blended = prior - else: - blended = cv2.seamlessClone( - insertion, # pylint: disable=no-member - prior, - insertion_mask, - (x_center, y_center), - cv2.NORMAL_CLONE, - ) # pylint: disable=no-member - - blended = blended[height:-height, width:-width] - - return blended.astype("float32") / 255.0 - - -def get_landmark(face_landmarks, id): - part = face_landmarks.part(id) - x = part.x - y = part.y - - return (x, y) - - -def search(face_landmarks): - - x1, y1 = get_landmark(face_landmarks, 36) - x2, y2 = get_landmark(face_landmarks, 39) - x3, y3 = get_landmark(face_landmarks, 42) - x4, y4 = get_landmark(face_landmarks, 45) - - x_nose, y_nose = get_landmark(face_landmarks, 30) - - x_left_mouth, y_left_mouth = get_landmark(face_landmarks, 48) - x_right_mouth, y_right_mouth = get_landmark(face_landmarks, 54) - - x_left_eye = int((x1 + x2) / 2) - y_left_eye = int((y1 + y2) / 2) - x_right_eye = int((x3 + x4) / 2) - y_right_eye = int((y3 + y4) / 2) - - results = np.array( - [ - [x_left_eye, y_left_eye], - [x_right_eye, y_right_eye], - [x_nose, y_nose], - [x_left_mouth, y_left_mouth], - [x_right_mouth, y_right_mouth], - ] - ) - - return results - - -if __name__ == "__main__": - - parser = argparse.ArgumentParser() - parser.add_argument("--origin_url", type=str, default="./", help="origin images") - parser.add_argument("--replace_url", type=str, default="./", help="restored faces") - parser.add_argument("--save_url", type=str, default="./save") - opts = parser.parse_args() - - origin_url = opts.origin_url - replace_url = opts.replace_url - save_url = opts.save_url - - if not os.path.exists(save_url): - os.makedirs(save_url) - - face_detector = dlib.get_frontal_face_detector() - landmark_locator = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") - - count = 0 - - for x in os.listdir(origin_url): - img_url = os.path.join(origin_url, x) - pil_img = Image.open(img_url).convert("RGB") - - origin_width, origin_height = pil_img.size - image = np.array(pil_img) - - start = time.time() - faces = face_detector(image) - done = time.time() - - if len(faces) == 0: - print("Warning: There is no face in %s" % (x)) - continue - - blended = image - for face_id in range(len(faces)): - - current_face = faces[face_id] - face_landmarks = landmark_locator(image, current_face) - current_fl = search(face_landmarks) - - forward_mask = np.ones_like(image).astype("uint8") - affine = compute_transformation_matrix(image, current_fl, False, target_face_scale=1.3) - aligned_face = warp(image, affine, output_shape=(256, 256, 3), preserve_range=True) - forward_mask = warp( - forward_mask, affine, output_shape=(256, 256, 3), order=0, preserve_range=True - ) - - affine_inverse = affine.inverse - cur_face = aligned_face - if replace_url != "": - - face_name = x[:-4] + "_" + str(face_id + 1) + ".png" - cur_url = os.path.join(replace_url, face_name) - restored_face = Image.open(cur_url).convert("RGB") - restored_face = np.array(restored_face) - cur_face = restored_face - - ## Histogram Color matching - A = cv2.cvtColor(aligned_face.astype("uint8"), cv2.COLOR_RGB2BGR) - B = cv2.cvtColor(cur_face.astype("uint8"), cv2.COLOR_RGB2BGR) - B = match_histograms(B, A) - cur_face = cv2.cvtColor(B.astype("uint8"), cv2.COLOR_BGR2RGB) - - warped_back = warp( - cur_face, - affine_inverse, - output_shape=(origin_height, origin_width, 3), - order=3, - preserve_range=True, - ) - - backward_mask = warp( - forward_mask, - affine_inverse, - output_shape=(origin_height, origin_width, 3), - order=0, - preserve_range=True, - ) ## Nearest neighbour - - blended = blur_blending_cv2(warped_back, blended, backward_mask) - blended *= 255.0 - - io.imsave(os.path.join(save_url, x), img_as_ubyte(blended / 255.0)) - - count += 1 - - if count % 1000 == 0: - print("%d have finished ..." % (count)) - diff --git a/spaces/marcelcastrobr/CLIP-image-search/README.md b/spaces/marcelcastrobr/CLIP-image-search/README.md deleted file mode 100644 index 16232077dd561c831ee4d5c2fb282ecf1345a58a..0000000000000000000000000000000000000000 --- a/spaces/marcelcastrobr/CLIP-image-search/README.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: CLIP Image Search -emoji: 📸 -colorFrom: pink -colorTo: pink -sdk: gradio -app_file: app.py -pinned: false -python_version: 3.10.10 ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/marioboy/neil-breen/app.py b/spaces/marioboy/neil-breen/app.py deleted file mode 100644 index 3290e59edd6b4a725030a3149a820d32e7116761..0000000000000000000000000000000000000000 --- a/spaces/marioboy/neil-breen/app.py +++ /dev/null @@ -1,60 +0,0 @@ -import gradio as gr -import os -import shlex -import random - - -LINK = os.environ.get('link') -ALIAS = os.environ.get('alias') -TITLE = os.environ.get('title') -DESCRIPTION = os.environ.get('description') - - -os.system(f"megadl {LINK}") -os.system("ls") - - -def inference(text): - os.system("python demo_cli.py --no_sound --cpu --text " + shlex.quote(text.strip())) - image_number = random.randint(2, len(os.listdir(f"images/{ALIAS}/"))) - return [f"images/{ALIAS}/{image_number}.gif", "demo_output_1.wav"] - - -article = " Based on Real-Time Voice Cloning | Github Repo
" - -examples = [ - [ - "Star Wars: Episode One - The Phantom Menace - is the most disappointing thing since my son" - ], - [ - "My name is Samantha Morris. I'm the editor of an internet news magazine exploring news most media shy away from." - ], - [ - 'I have a morning ritual that I need to share. I call it - the terminator. First I crouch down in the shower in the classic naked terminator traveling through time pose.' - ], - [ - 'With my eyes closed I crouch there for a minute, visualizing either Arnold or the guy from the second movie (not the chick in the third one because that one sucked) and I start to hum the terminator theme.' - ], - [ - 'Then I slowly rise to a standing position and open my eyes. It helps me to proceed through my day as an emotionless, cyborg badass. The only problem is if the shower curtain sticks to my terminator leg. It ruins the fantasy.' - ], - [ - "Okay, hear me out. So it's about this guy named Rick. He's a scientist that turns himself into a pickle. Funniest thing I've seen. In the episode Rick's grandson, Morty flips over a talking pickle. And its Rick! It's the funniest thing." - ], - [ - "To be fair, you have to have a very high IQ to understand Rick and Morty. The humour is extremely subtle, and without a solid grasp of theoretical physics most of the jokes will go over a typical viewer's head. There's also Rick's nihilistic outlook, which is deftly woven into his characterisation. " - ], -] -gr.Interface( - inference, - inputs=["text"], - outputs=[ - gr.Image(show_label=False, shape=(20, 20), value=f"images/{ALIAS}/1.gif"), - gr.outputs.Audio(type="file", label="Speech"), - ], - enable_queue=True, - title=TITLE, - description=DESCRIPTION, - article=article, - examples=examples -).launch(share=True) diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/menpo_functions.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/menpo_functions.py deleted file mode 100644 index 20e2c7e6fcb5f1aee1508a15af6bea3b947f303f..0000000000000000000000000000000000000000 --- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/face_of_art/menpo_functions.py +++ /dev/null @@ -1,299 +0,0 @@ -import os -from scipy.io import loadmat -from menpo.shape.pointcloud import PointCloud -from menpo.transform import ThinPlateSplines -import menpo.transform as mt - -import menpo.io as mio -from glob import glob -from thirdparty.face_of_art.deformation_functions import * - -# landmark indices by facial feature -jaw_indices = np.arange(0, 17) -lbrow_indices = np.arange(17, 22) -rbrow_indices = np.arange(22, 27) -upper_nose_indices = np.arange(27, 31) -lower_nose_indices = np.arange(31, 36) -leye_indices = np.arange(36, 42) -reye_indices = np.arange(42, 48) -outer_mouth_indices = np.arange(48, 60) -inner_mouth_indices = np.arange(60, 68) - -# flipped landmark indices -mirrored_parts_68 = np.hstack([ - jaw_indices[::-1], rbrow_indices[::-1], lbrow_indices[::-1], - upper_nose_indices, lower_nose_indices[::-1], - np.roll(reye_indices[::-1], 4), np.roll(leye_indices[::-1], 4), - np.roll(outer_mouth_indices[::-1], 7), - np.roll(inner_mouth_indices[::-1], 5) -]) - - -def load_bb_files(bb_file_dirs): - """load bounding box mat file for challenging, common, full & training datasets""" - - bb_files_dict = {} - for bb_file in bb_file_dirs: - bb_mat = loadmat(bb_file)['bounding_boxes'] - num_imgs = np.max(bb_mat.shape) - for i in range(num_imgs): - name = bb_mat[0][i][0][0][0][0] - bb_init = bb_mat[0][i][0][0][1] - 1 # matlab indicies - bb_gt = bb_mat[0][i][0][0][2] - 1 # matlab indicies - if str(name) in bb_files_dict.keys(): - print (str(name) + ' already exists') - else: - bb_files_dict[str(name)] = (bb_init, bb_gt) - return bb_files_dict - - -def load_bb_dictionary(bb_dir, mode, test_data='full'): - """create bounding box dictionary of input dataset: train/common/full/challenging""" - - if mode == 'TRAIN': - bb_dirs = \ - ['bounding_boxes_afw.mat', 'bounding_boxes_helen_trainset.mat', 'bounding_boxes_lfpw_trainset.mat'] - else: - if test_data == 'common': - bb_dirs = \ - ['bounding_boxes_helen_testset.mat', 'bounding_boxes_lfpw_testset.mat'] - elif test_data == 'challenging': - bb_dirs = ['bounding_boxes_ibug.mat'] - elif test_data == 'full': - bb_dirs = \ - ['bounding_boxes_ibug.mat', 'bounding_boxes_helen_testset.mat', 'bounding_boxes_lfpw_testset.mat'] - elif test_data == 'training': - bb_dirs = \ - ['bounding_boxes_afw.mat', 'bounding_boxes_helen_trainset.mat', 'bounding_boxes_lfpw_trainset.mat'] - else: - bb_dirs = None - - if mode == 'TEST' and test_data not in ['full', 'challenging', 'common', 'training']: - bb_files_dict = None - else: - bb_dirs = [os.path.join(bb_dir, dataset) for dataset in bb_dirs] - bb_files_dict = load_bb_files(bb_dirs) - - return bb_files_dict - - -def center_margin_bb(bb, img_bounds, margin=0.25): - """create new bounding box with input margin""" - - bb_size = ([bb[0, 2] - bb[0, 0], bb[0, 3] - bb[0, 1]]) - margins = (np.max(bb_size) * (1 + margin) - bb_size) / 2 - bb_new = np.zeros_like(bb) - bb_new[0, 0] = np.maximum(bb[0, 0] - margins[0], 0) - bb_new[0, 2] = np.minimum(bb[0, 2] + margins[0], img_bounds[1]) - bb_new[0, 1] = np.maximum(bb[0, 1] - margins[1], 0) - bb_new[0, 3] = np.minimum(bb[0, 3] + margins[1], img_bounds[0]) - return bb_new - - -def crop_to_face_image(img, bb_dictionary=None, gt=True, margin=0.25, image_size=256, normalize=True, - return_transform=False): - """crop face image using bounding box dictionary, or GT landmarks""" - - name = img.path.name - img_bounds = img.bounds()[1] - - # if there is no bounding-box dict and GT landmarks are available, use it to determine the bounding box - if bb_dictionary is None and img.has_landmarks: - grp_name = img.landmarks.group_labels[0] - bb_menpo = img.landmarks[grp_name].bounding_box().points - bb = np.array([[bb_menpo[0, 1], bb_menpo[0, 0], bb_menpo[2, 1], bb_menpo[2, 0]]]) - elif bb_dictionary is not None: - if gt: - bb = bb_dictionary[name][1] # ground truth - else: - bb = bb_dictionary[name][0] # init from face detector - else: - bb = None - - if bb is not None: - # add margin to bounding box - bb = center_margin_bb(bb, img_bounds, margin=margin) - bb_pointcloud = PointCloud(np.array([[bb[0, 1], bb[0, 0]], - [bb[0, 3], bb[0, 0]], - [bb[0, 3], bb[0, 2]], - [bb[0, 1], bb[0, 2]]])) - if return_transform: - face_crop, bb_transform = img.crop_to_pointcloud(bb_pointcloud, return_transform=True) - else: - face_crop = img.crop_to_pointcloud(bb_pointcloud) - else: - # if there is no bounding box/gt landmarks, use entire image - face_crop = img.copy() - bb_transform = None - - # if face crop is not a square - pad borders with mean pixel value - h, w = face_crop.shape - diff = h - w - if diff < 0: - face_crop.pixels = np.pad(face_crop.pixels, ((0, 0), (0, -1 * diff), (0, 0)), 'mean') - elif diff > 0: - face_crop.pixels = np.pad(face_crop.pixels, ((0, 0), (0, 0), (0, diff)), 'mean') - - if return_transform: - face_crop, rescale_transform = face_crop.resize([image_size, image_size], return_transform=True) - if bb_transform is None: - transform_chain = rescale_transform - else: - transform_chain = mt.TransformChain(transforms=(rescale_transform, bb_transform)) - else: - face_crop = face_crop.resize([image_size, image_size]) - - if face_crop.n_channels == 4: - face_crop.pixels = face_crop.pixels[:3, :, :] - - if normalize: - face_crop.pixels = face_crop.rescale_pixels(0., 1.).pixels - - if return_transform: - return face_crop, transform_chain - else: - return face_crop - - -def augment_face_image(img, image_size=256, crop_size=248, angle_range=30, flip=True): - """basic image augmentation: random crop, rotation and horizontal flip""" - - # taken from MDM: https://github.com/trigeorgis/mdm - def mirror_landmarks_68(lms, im_size): - return PointCloud(abs(np.array([0, im_size[1]]) - lms.as_vector( - ).reshape(-1, 2))[mirrored_parts_68]) - - # taken from MDM: https://github.com/trigeorgis/mdm - def mirror_image(im): - im = im.copy() - im.pixels = im.pixels[..., ::-1].copy() - - for group in im.landmarks: - lms = im.landmarks[group] - if lms.points.shape[0] == 68: - im.landmarks[group] = mirror_landmarks_68(lms, im.shape) - - return im - - flip_rand = np.random.random() > 0.5 - # rot_rand = np.random.random() > 0.5 - # crop_rand = np.random.random() > 0.5 - rot_rand = True # like ECT: https://github.com/HongwenZhang/ECT-FaceAlignment - crop_rand = True # like ECT: https://github.com/HongwenZhang/ECT-FaceAlignment - - if crop_rand: - lim = image_size - crop_size - min_crop_inds = np.random.randint(0, lim, 2) - max_crop_inds = min_crop_inds + crop_size - img = img.crop(min_crop_inds, max_crop_inds) - - if flip and flip_rand: - img = mirror_image(img) - - if rot_rand: - rot_angle = 2 * angle_range * np.random.random_sample() - angle_range - img = img.rotate_ccw_about_centre(rot_angle) - - img = img.resize([image_size, image_size]) - - return img - - -def augment_menpo_img_ns(img, img_dir_ns, p_ns=0.): - """texture style image augmentation using stylized copies in *img_dir_ns*""" - - img = img.copy() - if p_ns > 0.5: - ns_augs = glob(os.path.join(img_dir_ns, img.path.name.split('.')[0] + '_ns*')) - num_augs = len(ns_augs) - if num_augs > 0: - ns_ind = np.random.randint(0, num_augs) - ns_aug = mio.import_image(ns_augs[ns_ind]) - ns_pixels = ns_aug.pixels - img.pixels = ns_pixels - return img - - -def augment_menpo_img_geom(img, p_geom=0.): - """geometric style image augmentation using random face deformations""" - - img = img.copy() - if p_geom > 0.5: - grp_name = img.landmarks.group_labels[0] - lms_geom_warp = deform_face_geometric_style(img.landmarks[grp_name].points.copy(), p_scale=p_geom, p_shift=p_geom) - img = warp_face_image_tps(img, PointCloud(lms_geom_warp), grp_name) - return img - - -def warp_face_image_tps(img, new_shape, lms_grp_name='PTS', warp_mode='constant'): - """warp image to new landmarks using TPS interpolation""" - - tps = ThinPlateSplines(new_shape, img.landmarks[lms_grp_name]) - try: - img_warp = img.warp_to_shape(img.shape, tps, mode=warp_mode) - img_warp.landmarks[lms_grp_name] = new_shape - return img_warp - except np.linalg.linalg.LinAlgError as err: - print ('Error:'+str(err)+'\nUsing original landmarks for:\n'+str(img.path)) - return img - - -def load_menpo_image_list( - img_dir, train_crop_dir, img_dir_ns, mode, bb_dictionary=None, image_size=256, margin=0.25, - bb_type='gt', test_data='full', augment_basic=True, augment_texture=False, p_texture=0, - augment_geom=False, p_geom=0, verbose=False, return_transform=False): - - """load images from image dir to create menpo-type image list""" - - def crop_to_face_image_gt(img): - return crop_to_face_image(img, bb_dictionary, gt=True, margin=margin, image_size=image_size, - return_transform=return_transform) - - def crop_to_face_image_init(img): - return crop_to_face_image(img, bb_dictionary, gt=False, margin=margin, image_size=image_size, - return_transform=return_transform) - - def crop_to_face_image_test(img): - return crop_to_face_image(img, bb_dictionary=None, margin=margin, image_size=image_size, - return_transform=return_transform) - - def augment_menpo_img_ns_rand(img): - return augment_menpo_img_ns(img, img_dir_ns, p_ns=1. * (np.random.rand() < p_texture)[0]) - - def augment_menpo_img_geom_rand(img): - return augment_menpo_img_geom(img, p_geom=1. * (np.random.rand() < p_geom)[0]) - - if mode is 'TRAIN': - if train_crop_dir is None: - img_set_dir = os.path.join(img_dir, 'training') - out_image_list = mio.import_images(img_set_dir, verbose=verbose, normalize=False) - if bb_type is 'gt': - out_image_list = out_image_list.map(crop_to_face_image_gt) - elif bb_type is 'init': - out_image_list = out_image_list.map(crop_to_face_image_init) - else: - img_set_dir = os.path.join(img_dir, train_crop_dir) - out_image_list = mio.import_images(img_set_dir, verbose=verbose) - - # perform image augmentation - if augment_texture and p_texture > 0: - out_image_list = out_image_list.map(augment_menpo_img_ns_rand) - if augment_geom and p_geom > 0: - out_image_list = out_image_list.map(augment_menpo_img_geom_rand) - if augment_basic: - out_image_list = out_image_list.map(augment_face_image) - - else: # if mode is 'TEST', load test data - if test_data in ['full', 'challenging', 'common', 'training', 'test']: - img_set_dir = os.path.join(img_dir, test_data) - out_image_list = mio.import_images(img_set_dir, verbose=verbose, normalize=False) - if bb_type is 'gt': - out_image_list = out_image_list.map(crop_to_face_image_gt) - elif bb_type is 'init': - out_image_list = out_image_list.map(crop_to_face_image_init) - else: - img_set_dir = os.path.join(img_dir, test_data+'*') - out_image_list = mio.import_images(img_set_dir, verbose=verbose, normalize=False) - out_image_list = out_image_list.map(crop_to_face_image_test) - - return out_image_list diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/modules/__init__.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/modules/__init__.py deleted file mode 100644 index 61418616ef18f0ecca56a007c43af4a731d98b9b..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/modules/__init__.py +++ /dev/null @@ -1,22 +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. -"""Modules used for building the models.""" - -# flake8: noqa -from .conv import ( - NormConv1d, - NormConv2d, - NormConvTranspose1d, - NormConvTranspose2d, - StreamableConv1d, - StreamableConvTranspose1d, - pad_for_conv1d, - pad1d, - unpad1d, -) -from .lstm import StreamableLSTM -from .seanet import SEANetEncoder, SEANetDecoder -from .transformer import StreamingTransformer \ No newline at end of file diff --git a/spaces/matthoffner/AudioCraft_Plus/model_cards/MUSICGEN_MODEL_CARD.md b/spaces/matthoffner/AudioCraft_Plus/model_cards/MUSICGEN_MODEL_CARD.md deleted file mode 100644 index 10ba9f9790841be06cd3e459cf667c1af6291343..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/AudioCraft_Plus/model_cards/MUSICGEN_MODEL_CARD.md +++ /dev/null @@ -1,90 +0,0 @@ -# MusicGen Model Card - -## Model details - -**Organization developing the model:** The FAIR team of Meta AI. - -**Model date:** MusicGen was trained between April 2023 and May 2023. - -**Model version:** This is the version 1 of the model. - -**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. - -**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation][arxiv]. - -**Citation details:** See [our paper][arxiv] - -**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0. - -**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [GitHub repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. - -## Intended use -**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including: - -- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science -- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs - -**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. - -**Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. - -## Metrics - -**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark: - -- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) -- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) -- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model - -Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - -- Overall quality of the music samples; -- Text relevance to the provided text input; -- Adherence to the melody for melody-guided music generation. - -More details on performance measures and human studies can be found in the paper. - -**Decision thresholds:** Not applicable. - -## Evaluation datasets - -The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. - -## Training datasets - -The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. - -## Evaluation results - -Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. - -| Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity | -|---|---|---|---|---| -| facebook/musicgen-small | 4.88 | 1.28 | 0.27 | - | -| facebook/musicgen-medium | 5.14 | 1.24 | 0.28 | - | -| facebook/musicgen-large | 5.48 | 1.22 | 0.28 | - | -| facebook/musicgen-melody | 4.93 | 1.26 | 0.27 | 0.44 | - -More information can be found in the paper [Simple and Controllable Music Generation][arxiv], in the Results section. - -## Limitations and biases - -**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. - -**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). - -**Limitations:** - -- The model is not able to generate realistic vocals. -- The model has been trained with English descriptions and will not perform as well in other languages. -- The model does not perform equally well for all music styles and cultures. -- The model sometimes generates end of songs, collapsing to silence. -- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. - -**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. - -**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. - -**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks. - -[arxiv]: https://arxiv.org/abs/2306.05284 diff --git a/spaces/merve/data-leak/public/anonymization/make-sel.js b/spaces/merve/data-leak/public/anonymization/make-sel.js deleted file mode 100644 index 3b35b931008be7afe990694afdf232d05d5f4ee2..0000000000000000000000000000000000000000 --- a/spaces/merve/data-leak/public/anonymization/make-sel.js +++ /dev/null @@ -1,78 +0,0 @@ -window.makeSel = function(){ - function ttFmt(d){ - var ttSel = d3.select('.tooltip').html('') - - var ageStr = d.age + ' year old' - if (slides.curSlide.index == 4){ - ageStr = ageStr + ' born in the ' + ['spring', 'summer', 'fall', 'winter'][d.season] - } - ttSel.append('div').html(` - ${ageStr} from ${d.state} who - ${d.plagerized ? - 'plagiarized' : - 'never plagiarized'} - `) - - if (slides.curSlide.index < 6) return - - var isHeads = d.coinVals[estimates.active.index] < sliders.headsProb - ttSel.append('div').html(` - They flipped - ${isHeads ? 'heads' : 'tails'} - and said they had - ${d.plagerized || isHeads ? - 'plagiarized' : - 'never plagiarized'} - `) - .st({marginTop: 10}) - } - - var rectAt = {} - var rs = (axii.bw - 10)*2 - rectAt.ageState = {width: rs, height: rs, x: -rs/2, y: -rs/2} - var uniqueBox = c.svg.appendMany('rect.unique.init-hidden', students.byAgeState.filter(d => d.length == 1)) - .translate(d => d.pos) - .at(rectAt.ageState) - - var rs = axii.bw/4 + 5.5 - rectAt.ageStateSeason = {width: rs, height: rs, x: Math.round(-rs/2), y: 4} - var uniqueSeasonBox = c.svg.appendMany( - 'rect.unique.init-hidden', - students.byAgeStateSeason.filter(d => d.length == 1 && d[0].group.ageState.length > 1)) - .translate(d => d.pos) - .at(rectAt.ageStateSeason) - - // number of uniquely id'd students - // console.log(uniqueSeasonBox.size()) - - var studentGroup = c.svg.append('g') - .at({width: 500, height: 500}) - - var student = studentGroup.appendMany('g.student', students.all) - .call(d3.attachTooltip) - .on('mouseover', ttFmt) - .translate(d => d.isAdditionalStudent ? [0,0]: d.pos.grid) - .classed('inactive', d => d.isAdditionalStudent) - - var rs = 16 - var flipCircle = student.append('circle') - .at({transform: 'scale(.1)'}) - .at({r: 9, fill: '#fff'}) - .at({stroke: '#b0b' }) - - var circle = student.append('circle').at({ - r: 5, - fill: d => d.plagerized ? '#f0f' : '#ccc', - stroke: d => d.plagerized ? '#b0b' : '#aaa', - strokeWidth: 1, - }) - - - - addSwoop(c) - - return {student, studentGroup, circle, flipCircle, rectAt, uniqueBox, uniqueSeasonBox} -} - - -if (window.init) window.init() diff --git a/spaces/merve/fill-in-the-blank/public/private-and-fair/accuracy-v-privacy-class.js b/spaces/merve/fill-in-the-blank/public/private-and-fair/accuracy-v-privacy-class.js deleted file mode 100644 index 39daddb629006c967bfa8c3a6c1d43fc9887bc1b..0000000000000000000000000000000000000000 --- a/spaces/merve/fill-in-the-blank/public/private-and-fair/accuracy-v-privacy-class.js +++ /dev/null @@ -1,285 +0,0 @@ -var state = { - dataset_size: 15000, - threshold: .8, - label: 8 -} - -var sel = d3.select('.accuracy-v-privacy-class').html('') - .at({role: 'graphics-document', 'aria-label': `Line chart showing that high accuracy models can still perform poorly on some digit classes.`}) - -async function loadData(){ - var rawData = await util.getFile(`cns-cache/grid_${state.dataset_size}trainpoints_test_labels.csv`) - - rawData.forEach(d => { - delete d[''] - d.i = +d.i - d.label = +d.label - }) - - var aVal2Meta = {} - var metadata = await util.getFile('cns-cache/model_grid_test_accuracy.json') - metadata - .filter(d => d.dataset_size == state.dataset_size) - .forEach(d => aVal2Meta['aVal_' + d.aVal] = d) - - var allCols = d3.keys(rawData[0]) - .filter(d => d.includes('aVal')) - .map(key => { - var {epsilon, aVal} = aVal2Meta[key] - return {key, epsilon, aVal} - }) - - var byDigit = d3.nestBy(rawData, d => d.label) - byDigit.forEach(d => { - d.label = +d.key - }) - byDigit.forEach(digitClass => { - digitClass.cols = allCols.map(({key, epsilon}, colIndex) => { - return { - key, - colIndex, - epsilon, - digitClass, - label: digitClass.label, - accuracy: d3.mean(digitClass, d => d[key] > state.threshold) - } - }) - }) - - var data = _.flatten(byDigit.map(d => d.cols)) - .filter(d => util.epsilonExtent[1] <= d.epsilon && d.epsilon <= util.epsilonExtent[0]) - var byLabel = d3.nestBy(data, d => d.label) - byLabel.forEach((d, i) => { - d.label = d.key - }) - - return {data, byLabel} -} - - -async function initChart(){ - var {data, byLabel} = await loadData() - - var c = d3.conventions({ - sel: sel.append('div'), - height: 400, - margin: {bottom: 75, top: 5}, - layers: 'ds', - }) - - c.x = d3.scaleLog().domain(util.epsilonExtent).range(c.x.range()) - c.xAxis = d3.axisBottom(c.x).tickFormat(d => { - var rv = d + '' - if (rv.split('').filter(d => d !=0 && d != '.')[0] == 1) return rv - }) - - c.yAxis.tickFormat(d => d3.format('.0%')(d))//.ticks(8) - d3.drawAxis(c) - util.addAxisLabel(c, 'Higher Privacy →', '') - util.ggPlotBg(c, false) - c.layers[0].append('div') - .st({fontSize: 12, color: '#555', width: 120*2, textAlign: 'center', lineHeight: '1.3em', verticalAlign: 'top'}) - .translate([c.width/2 - 120, c.height + 45]) - .html('in ε') - - var line = d3.line().x(d => c.x(d.epsilon)).y(d => c.y(d.accuracy)) - - var lineSel = c.svg.append('g').appendMany('path.accuracy-line', byLabel) - .at({ - d: line, - fill: 'none', - stroke: '#000', - // opacity: 0, - }) - .on('mousemove', setActiveLabel) - - var circleSel = c.svg.append('g') - .appendMany('g.accuracy-circle', data) - .translate(d => [c.x(d.epsilon), c.y(d.accuracy)]) - .on('mousemove', setActiveLabel) - // .call(d3.attachTooltip) - - circleSel.append('circle') - .at({r: 7, stroke: '#fff'}) - - circleSel.append('text') - .text(d => d.label) - .at({textAnchor: 'middle', fontSize: 10, fill: '#fff', dy: '.33em'}) - - setActiveLabel(state) - function setActiveLabel({label}){ - lineSel - .classed('active', 0) - .filter(d => d.label == label) - .classed('active', 1) - .raise() - - circleSel - .classed('active', 0) - .filter(d => d.label == label) - .classed('active', 1) - .raise() - - state.label = label - } - - - async function updateDatasetSize(){ - var newData = await loadData() - data = newData.data - byLabel = newData.byLabel - - lineSel.data(byLabel) - .transition() - .at({d: line}) - - circleSel.data(data) - .transition() - .translate(d => [c.x(d.epsilon), c.y(d.accuracy)]) - - c.svg.select('text.annotation').remove() - } - - function updateThreshold(){ - data.forEach(d => { - d.accuracy = d3.mean(d.digitClass, e => e[d.key] > state.threshold) - }) - - lineSel.at({d: line}) - circleSel.translate(d => [c.x(d.epsilon), c.y(d.accuracy)]) - - c.svg.select('.y .axis-label').text(`Test Points With More Than ${d3.format('.2%')(state.threshold)} Confidence In Label`) - - c.svg.select('text.annotation').remove() - } - updateThreshold() - - return {c, updateDatasetSize, updateThreshold} -} - - -async function init(){ - sel.append('div.chart-title').text('High accuracy models can still perform poorly on some digit classes') - - var chart = await initChart() - - var buttonRowSel = sel.append('div.button-row') - .st({height: 50}) - - var buttonSel = buttonRowSel.append('div') - .st({width: 500}) - .append('span.chart-title').text('Training points') - .parent() - .append('div').st({display: 'inline-block', width: 300, marginLeft: 10}) - .append('div.digit-button-container.dataset_size') - .appendMany('div.button', [2000, 3750, 7500, 15000, 30000, 60000]) - .text(d3.format(',')) - .classed('active', d => d == state.dataset_size) - .on('click', d => { - buttonSel.classed('active', e => e == d) - state.dataset_size = d - chart.updateDatasetSize() - }) - - buttonRowSel.append('div.conf-slider') - .append('span.chart-title').text('Confidence threshold') - .parent() - .append('input.slider-native') - .at({ - type: 'range', - min: .0001, - max: .9999, - step: .0001, - value: state.threshold, - }) - .on('input', function(){ - state.threshold = this.value - chart.updateThreshold() - }) - - - function addSliders(){ - var width = 140 - var height = 30 - var color = '#000' - - var sliders = [ - {key: 'threshold', label: 'Confidence threshold', r: [.0001, .9999]}, - ] - sliders.forEach(d => { - d.value = state[d.key] - d.xScale = d3.scaleLinear().range([0, width]).domain(d.r).clamp(1) - }) - - d3.select('.conf-slider .slider-container').remove() - d3.select('.slider-native').remove() - - var svgSel = d3.select('.conf-slider').parent() - // .st({marginTop: 5, marginBottom: 5}) - .appendMany('div.slider-container', sliders) - .append('svg').at({width, height}) - .append('g').translate([10, 25]) - - var sliderSel = svgSel - .on('click', function(d){ - d.value = d.xScale.invert(d3.mouse(this)[0]) - renderSliders(d) - }) - .classed('slider', true) - .st({cursor: 'pointer'}) - - var textSel = sliderSel.append('text.annotation') - .at({y: -15, fontWeight: 300, textAnchor: 'middle', x: 180/2}) - - sliderSel.append('rect') - .at({width, height, y: -height/2, fill: 'rgba(0,0,0,0)'}) - - sliderSel.append('path').at({ - d: `M 0 -.5 H ${width}`, - stroke: color, - strokeWidth: 1 - }) - - var leftPathSel = sliderSel.append('path').at({ - d: `M 0 -.5 H ${width}`, - stroke: color, - strokeWidth: 3 - }) - - var drag = d3.drag() - .on('drag', function(d){ - var x = d3.mouse(this)[0] - d.value = d.xScale.invert(x) - - renderSliders(d) - }) - - var circleSel = sliderSel.append('circle').call(drag) - .at({r: 7, stroke: '#000'}) - - function renderSliders(d){ - if (d) state[d.key] = d.value - - circleSel.at({cx: d => d.xScale(d.value)}) - leftPathSel.at({d: d => `M 0 -.5 H ${d.xScale(d.value)}`}) - textSel - .at({x: d => d.xScale(d.value)}) - .text(d => d3.format('.2%')(d.value)) - chart.updateThreshold() - } - renderSliders() - } - addSliders() - - - chart.c.svg.append('text.annotation') - .translate([505, 212]) - .tspans(d3.wordwrap(`8s are correctly predicted with high confidence much more rarely than other digits`, 25), 12) - .at({textAnchor: 'end'}) - -} -init() - - - - diff --git a/spaces/merve/uncertainty-calibration/source/data-leak/players0.js b/spaces/merve/uncertainty-calibration/source/data-leak/players0.js deleted file mode 100644 index 5f1640268c5aa31e0ed73ec7f763b4c64d65f587..0000000000000000000000000000000000000000 --- a/spaces/merve/uncertainty-calibration/source/data-leak/players0.js +++ /dev/null @@ -1,456 +0,0 @@ -var players0 = [ - [ - 1.305925030229746, - 38.016928657799276 - 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-.finished { - color:rgb(9, 102, 169); - font-size:13px -} - -table { - - margin: 25px 0; - font-size: 0.9em; - font-family: sans-serif; - min-width: 400px; - max-width: 400px; - box-shadow: 0 0 20px rgba(0, 0, 0, 0.15); -} - -table th, -table td { - padding: 12px 15px; -} - -tr { -text-align: left; -} -thead tr { -text-align: left; -} - -#pw { - -webkit-text-security: disc; - } diff --git a/spaces/mikeee/radiobee-dev/radiobee/gen_row_alignment.py b/spaces/mikeee/radiobee-dev/radiobee/gen_row_alignment.py deleted file mode 100644 index a58549c0c2bbfdd823babee7a7e42b9f192916d6..0000000000000000000000000000000000000000 --- a/spaces/mikeee/radiobee-dev/radiobee/gen_row_alignment.py +++ /dev/null @@ -1,151 +0,0 @@ -"""Gen proper alignment for a given triple_set. - -cmat = fetch_sent_corr(src, tgt) -src_len, tgt_len = np.array(cmat).shape -r_ali = gen_row_alignment(cmat, tgt_len, src_len) # note the order -src[r_ali[1]], tgt[r_ali[0]], r_ali[2] - -or !!! (targer, source) -cmat = fetch_sent_corr(tgt, src) # note the order -src_len, tgt_len = np.array(cmat).shape -r_ali = gen_row_alignment(cmat, src_len, tgt_len) -src[r_ali[0]], tgt[r_ali[1]], r_ali[2] - ---- -src_txt = 'data/wu_ch2_en.txt' -tgt_txt = 'data/wu_ch2_zh.txt' - -assert Path(src_txt).exists() -assert Path(tgt_txt).exists() - -src_text, _ = load_paras(src_txt) -tgt_text, _ = load_paras(tgt_txt) - -cos_matrix = gen_cos_matrix(src_text, tgt_text) -t_set, m_matrix = find_aligned_pairs(cos_matrix0, thr=0.4, matrix=True) - -resu = gen_row_alignment(t_set, src_len, tgt_len) -resu = np.array(resu) - -idx = -1 -idx += 1; (resu[idx], src_text[int(resu[idx, 0])], - tgt_text[int(resu[idx, 1])]) if all(resu[idx]) else resu[idx] - -idx += 1; i0, i1, i2 = resu[idx]; '***' if i0 == '' -else src_text[int(i0)], '***' if i1 == '' else tgt_text[int(i1)], '' -if i2 == '' else i2 -""" -# pylint: disable=line-too-long, unused-variable -from typing import List, Union - -# natural extrapolation with slope equal to 1 -from itertools import zip_longest as zip_longest_middle - -import numpy as np - -from logzero import logger - -# from tinybee.zip_longest_middle import zip_longest_middle - -# from tinybee.zip_longest_middle import zip_longest_middle -# from tinybee.find_pairs import find_pairs - -# logger = logging.getLogger(__name__) -# logger.addHandler(logging.NullHandler()) - - -def gen_row_alignment( # pylint: disable=too-many-locals - t_set, - src_len, - tgt_len, - # ) -> List[Tuple[Union[str, int], Union[str, int], Union[str, float]]]: -) -> List[List[Union[str, float]]]: - """Gen proper rows for given triple_set. - - Arguments: - [t_set {np.array or list}] -- [nll matrix] - [src_len {int}] -- numb of source texts (para/sents) - [tgt_len {int}] -- numb of target texts (para/sents) - - Returns: - [np.array] -- [proper rows] - """ - t_set = np.array(t_set, dtype="object") - - # len0 = src_len - - # len1 tgt text length, must be provided - len1 = tgt_len - - # rearrange t_set as buff in increasing order - buff = [[-1, -1, ""]] # - idx_t = 0 - # for elm in t_set: - # start with bigger value from the 3rd col - - y00, yargmax, ymax = zip(*t_set) - ymax_ = np.array(ymax).copy() - reset_v = np.min(ymax_) - 1 - for count in range(tgt_len): - argmax = np.argmax(ymax_) - # reset - ymax_[argmax] = reset_v - idx_t = argmax - elm = t_set[idx_t] - logger.debug("%s: %s, %s", count, idx_t, elm) - - # find loc to insert - elm0, elm1, elm2 = elm - idx = -1 - for idx, loc in enumerate(buff): - if loc[0] > elm0: - break - else: - idx += 1 # last - - # make sure elm1 is within the range - # prev elm1 < elm1 < next elm1 - if elm1 > buff[idx - 1][1]: - try: # overflow possible (idx + 1 in # last) - next_elm = buff[idx][1] - except IndexError: - next_elm = len1 - if elm1 < next_elm: - # insert '' if necessary - # using zip_longest_middle - buff.insert( - idx, [elm0, elm1, elm2], - ) - # logger.debug('---') - - idx_t += 1 - # if idx_t == 24: # 20: - # break - - # remove [-1, -1] - # buff.pop(0) - # buff = np.array(buff, dtype='object') - - # take care of the tail - buff += [[src_len, tgt_len, ""]] - - resu = [] - # merit = [] - - for idx, elm in enumerate(buff[1:]): - idx1 = idx + 1 - elm0_, elm1_, elm2_ = buff[idx1 - 1] # idx starts from 0 - elm0, elm1, elm2 = elm - del elm2_, elm2 - - tmp0 = zip_longest_middle( - list(range(elm0_ + 1, elm0)), list(range(elm1_ + 1, elm1)), fillvalue="", - ) - # convet to list entries & attache merit - tmp = [list(t_elm) + [""] for t_elm in tmp0] - - # update resu - resu += tmp + [buff[idx1]] - - # remove the last entry - return resu[:-1] diff --git a/spaces/mrm8488/PromptSource/setup.py b/spaces/mrm8488/PromptSource/setup.py deleted file mode 100644 index d6d1150e4c3399d21349c661f0e7e63693acc859..0000000000000000000000000000000000000000 --- a/spaces/mrm8488/PromptSource/setup.py +++ /dev/null @@ -1,32 +0,0 @@ -from setuptools import setup, find_packages - -with open('README.md') as readme_file: - readme = readme_file.read() - -setup( - name='promptsource', - version='0.1.0', - url='https://github.com/bigscience-workshop/promptsource.git', - author='Multiple Authors', - author_email='xxx', - python_requires='>=3.7, <3.8', - classifiers=[ - 'Development Status :: 2 - Pre-Alpha', - 'Intended Audience :: Developers', - 'License :: OSI Approved :: Apache Software License', - 'Natural Language :: English', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.7', - ], - description='Toolkit for collecting and applying templates of prompting instances.', - packages=find_packages(), - license="Apache Software License 2.0", - long_description=readme, - package_data={"": [ - "templates/*/*.yaml", - "templates/*/*/*.yaml", - "seqio_tasks/experiment_D3.csv", # Experiment D3 - "seqio_tasks/experiment_D4.csv", - "custom_datasets/*/*" - ]} -) diff --git a/spaces/mshukor/UnIVAL/models/taming/.ipynb_checkpoints/util-checkpoint.py b/spaces/mshukor/UnIVAL/models/taming/.ipynb_checkpoints/util-checkpoint.py deleted file mode 100644 index 7443b29e3d223a3ad396808f9717fb9a11c7507c..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/models/taming/.ipynb_checkpoints/util-checkpoint.py +++ /dev/null @@ -1,172 +0,0 @@ -import os, hashlib -import requests -from tqdm import tqdm -import importlib - -URL_MAP = { - "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1" -} - -CKPT_MAP = { - "vgg_lpips": "vgg.pth" -} - -MD5_MAP = { - "vgg_lpips": "d507d7349b931f0638a25a48a722f98a" -} - - -def get_obj_from_str(string, reload=False): - module, cls = string.rsplit(".", 1) - if reload: - module_imp = importlib.import_module(module) - importlib.reload(module_imp) - return getattr(importlib.import_module(module, package=None), cls) - - -def instantiate_from_config(config): - if not "target" in config: - raise KeyError("Expected key `target` to instantiate.") - return get_obj_from_str(config["target"])(**config.get("params", dict())) - - -def download(url, local_path, chunk_size=1024): - os.makedirs(os.path.split(local_path)[0], exist_ok=True) - with requests.get(url, stream=True) as r: - total_size = int(r.headers.get("content-length", 0)) - with tqdm(total=total_size, unit="B", unit_scale=True) as pbar: - with open(local_path, "wb") as f: - for data in r.iter_content(chunk_size=chunk_size): - if data: - f.write(data) - pbar.update(chunk_size) - - -def md5_hash(path): - with open(path, "rb") as f: - content = f.read() - return hashlib.md5(content).hexdigest() - - -def get_ckpt_path(name, root, check=False): - assert name in URL_MAP - path = os.path.join(root, CKPT_MAP[name]) - if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]): - print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path)) - download(URL_MAP[name], path) - md5 = md5_hash(path) - assert md5 == MD5_MAP[name], md5 - return path - - -class KeyNotFoundError(Exception): - def __init__(self, cause, keys=None, visited=None): - self.cause = cause - self.keys = keys - self.visited = visited - messages = list() - if keys is not None: - messages.append("Key not found: {}".format(keys)) - if visited is not None: - messages.append("Visited: {}".format(visited)) - messages.append("Cause:\n{}".format(cause)) - message = "\n".join(messages) - super().__init__(message) - - -def retrieve( - list_or_dict, key, splitval="/", default=None, expand=True, pass_success=False -): - """Given a nested list or dict return the desired value at key expanding - callable nodes if necessary and :attr:`expand` is ``True``. The expansion - is done in-place. - - Parameters - ---------- - list_or_dict : list or dict - Possibly nested list or dictionary. - key : str - key/to/value, path like string describing all keys necessary to - consider to get to the desired value. List indices can also be - passed here. - splitval : str - String that defines the delimiter between keys of the - different depth levels in `key`. - default : obj - Value returned if :attr:`key` is not found. - expand : bool - Whether to expand callable nodes on the path or not. - - Returns - ------- - The desired value or if :attr:`default` is not ``None`` and the - :attr:`key` is not found returns ``default``. - - Raises - ------ - Exception if ``key`` not in ``list_or_dict`` and :attr:`default` is - ``None``. - """ - - keys = key.split(splitval) - - success = True - try: - visited = [] - parent = None - last_key = None - for key in keys: - if callable(list_or_dict): - if not expand: - raise KeyNotFoundError( - ValueError( - "Trying to get past callable node with expand=False." - ), - keys=keys, - visited=visited, - ) - list_or_dict = list_or_dict() - parent[last_key] = list_or_dict - - last_key = key - parent = list_or_dict - - try: - if isinstance(list_or_dict, dict): - list_or_dict = list_or_dict[key] - else: - list_or_dict = list_or_dict[int(key)] - except (KeyError, IndexError, ValueError) as e: - raise KeyNotFoundError(e, keys=keys, visited=visited) - - visited += [key] - # final expansion of retrieved value - if expand and callable(list_or_dict): - list_or_dict = list_or_dict() - parent[last_key] = list_or_dict - except KeyNotFoundError as e: - if default is None: - raise e - else: - list_or_dict = default - success = False - - if not pass_success: - return list_or_dict - else: - return list_or_dict, success - - -if __name__ == "__main__": - config = {"keya": "a", - "keyb": "b", - "keyc": - {"cc1": 1, - "cc2": 2, - } - } - from omegaconf import OmegaConf - - config = OmegaConf.create(config) - print(config) - retrieve(config, "keya") diff --git a/spaces/muhammadzain/AI_Resolution_Upscaler_And_Resizer/app.py b/spaces/muhammadzain/AI_Resolution_Upscaler_And_Resizer/app.py deleted file mode 100644 index f3b38aae36e65de9a514e0bb96ffa2ed448d63df..0000000000000000000000000000000000000000 --- a/spaces/muhammadzain/AI_Resolution_Upscaler_And_Resizer/app.py +++ /dev/null @@ -1,297 +0,0 @@ -import math -import time -import streamlit as st -from PIL import Image -import cv2 -import os -import urllib.request -import moviepy.editor -import threading - - -hide_streamlit_style = """ - - -""" - - -def sound_extract(file): - video = moviepy.editor.VideoFileClip(file) - video.audio.write_audiofile(file.split('.')[0] + '.mp3') - -def remove_file(file): - os.remove('deep_'+file) - - -st.set_page_config(layout="wide") - - - - -def loadModel(n): - super_res = cv2.dnn_superres.DnnSuperResImpl_create() - super_res.readModel('models/ESPCN_x'+n+'.pb') - return super_res - -# on removing (show_spinner=False), it will show that fuction is running on web app -@st.experimental_memo(show_spinner=False) -def upscale(file,task,_progressBar = None): - with open(file.name, "wb") as f: - f.write(file.getbuffer()) - print('No file found, so added in list files') - if isinstance(task,str): - super_res = loadModel(task) - super_res.setModel('espcn', int(task)) - if file.type.split('/')[0] == 'image': - img = cv2.imread(file.name) - upscaled_image = super_res.upsample(img) - print('I upscaled upto',task,'times') - cv2.imwrite("processed_"+file.name,upscaled_image) - with st.sidebar: - st.success('Done!', icon="✅") - return True - elif file.type.split('/')[0] == 'video': - - t1 = threading.Thread(target=sound_extract, args=(file.name,)) - - t1.start() - - cap = cv2.VideoCapture(file.name) - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - fps = int(cap.get(cv2.CAP_PROP_FPS)) - width = int(cap.get(3)) - height = int(cap.get(4)) - writer = cv2.VideoWriter("deep_"+file.name,fourcc,fps,(width*int(task),height*int(task))) - length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - step_size = 1.0/length - progress = 0 - - with st.sidebar: - st.info("Operation in progress. Please wait.",icon="ℹ️") - my_bar = st.progress(0) - - for percent_complete in range(length): - frame = cap.read()[1] - frame = super_res.upsample(frame) - writer.write(frame) - print(progress) - progress += step_size - my_bar.progress(progress-0.000000001) - - return True - return True - - # Second case where custom size is required - else: - req_width,req_height = int(task[0]),int(task[1]) - if file.type.split('/')[0] == 'image': - img = cv2.imread(file.name) - actual_width,actual_height = img.shape[1],img.shape[0] - w_ratio,h_ratio = req_width/actual_width , req_height/actual_height - if min([w_ratio,h_ratio]) <= 1.0: - img = cv2.resize(img,(req_width,req_height)) - print("I did resizing only!") - cv2.imwrite("processed_" + file.name, img) - with st.sidebar: - st.success('Done!', icon="✅") - return True - # rounding off the ratios - w_ratio,h_ratio = math.ceil(w_ratio),math.ceil(h_ratio) - # find bigger number - upscale_number = max(w_ratio,h_ratio) - - # task can be greater than 4 but we can upscale upto 4. So setting task to 4. - if upscale_number >= 4: - upscale_number = 4 - - super_res = loadModel(str(upscale_number)) - super_res.setModel('espcn', int(upscale_number)) - upscaled_image = super_res.upsample(img) - print("Before resizing ",(upscaled_image.shape[1], upscaled_image.shape[0])) - upscaled_image = cv2.resize(upscaled_image,(task[0],task[1])) - print("Final size got: ",(upscaled_image.shape[1],upscaled_image.shape[0])) - - print("I upscale upto", upscale_number , "times and then resize it.") - - cv2.imwrite("processed_" + file.name, upscaled_image) - with st.sidebar: - st.success('Done!', icon="✅") - return True - - # If file is video - elif file.type.split('/')[0] == 'video': - - t1 = threading.Thread(target=sound_extract, args=(file.name,)) - - t1.start() - - cap = cv2.VideoCapture(file.name) - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - fps = int(cap.get(cv2.CAP_PROP_FPS)) - width = int(cap.get(3)) - height = int(cap.get(4)) - if height > 2160 or width > 3840: - with st.sidebar: - st.success("Sorry, I can't processed Video with resolution above 4k. Please select custom size option.!", icon="ℹ️") - writer = cv2.VideoWriter("deep_" + file.name, fourcc, fps, (int(task[0]),int(task[1]))) - length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - step_size = 1.0 / length - progress = 0 - progress_text = "Operation in progress. Please wait." - with st.sidebar: - st.info("Operation in progress. Please wait.", icon="ℹ️") - my_bar = st.progress(0) - for percent_complete in range(length): - frame = cap.read()[1] - frame = cv2.resize(frame, (task[0], task[1])) - writer.write(frame) - progress += step_size - my_bar.progress(progress-0.000000001) - - return True - return "It's second" - - -if 'disable_opt2' not in st.session_state: - st.session_state.disable_opt2 = True -if 'disable_opt1' not in st.session_state: - st.session_state.disable_opt1 = False -if 'disable_download' not in st.session_state: - st.session_state.disable_download = True -if 'disable_proceed' not in st.session_state: - st.session_state.disable_proceed = False - - -st.markdown(hide_streamlit_style, unsafe_allow_html=True) - -col1,_,col2 = st.columns([6,1,3],gap="small") - -def toggle_state_opt1(): - - if st.session_state.get("opt1") == True: - st.session_state.opt2 = False - st.session_state.disable_opt2 = True - - else: - st.session_state.opt2 = True - st.session_state.disable_opt2 = False - -def toggle_state_opt2(): - if st.session_state.get("opt2") == True: - st.session_state.opt1 = False - st.session_state.disable_opt1 = True - else: - st.session_state.opt1 = True - st.session_state.disable_opt1 = False - -# Update the states based on user selection before drawing the widgets in the web page -toggle_state_opt2() -toggle_state_opt1() -options = ["2", "3","4"] -progressBar = None - -with col1: - file = st.file_uploader(" ",type=['png','jpeg','jpg','pgm','jpe','mp4','mov']) - if file is not None: - # writing file and saving its details in dict for further processing - bytes_data = file.getvalue() - file_size = len(bytes_data) - print("File size: ",file_size) - - if file.type.split('/')[0] == "image" and file_size > 1550000: - st.session_state.disable_proceed = True - with st.sidebar: - st.info('Sorry, maximum size of image is 1.5MB', icon="ℹ️") - elif file.type.split('/')[0] == "image": - image = Image.open(file) - st.session_state.disable_proceed = False - st.image(image,caption="Upload Image", use_column_width=True) - st.session_state.disable_proceed = False - elif file.type.split('/')[0] == 'video' and file_size > 200000000: - with st.sidebar: - options = ["2", "3"] - st.info('Sorry, maximum size of video is 200MB', icon="ℹ️") - st.session_state.disable_proceed = True - elif file.type.split('/')[0] == 'video': - video = st.video(file) - print(type(video)) - options = ["2", "3"] - st.session_state.disable_proceed = False - with st.sidebar: - st.info('For custom size, currently I can processed video without AI.', icon="ℹ️") - - - -with col2: - st.markdown("\n") - st.markdown("\n") - st.markdown("\n") - - st.subheader(" UPSCALE RESOLUTION UP TO") - st.markdown("\n") - st.markdown("\n") - - opt1 = st.checkbox("MULTIPLES OF",key="opt1",value=True,on_change=toggle_state_opt1) - st.selectbox("SELECT", options,key="opt1_selBox",disabled=st.session_state.disable_opt1) - - st.markdown("\n") - st.markdown("\n") - opt2 = st.checkbox("CUSTOM SIZE",key="opt2",on_change=toggle_state_opt2) - - st.number_input("Width", step=1, min_value=150,max_value=3840, value=900, key="width",disabled=st.session_state.disable_opt2) - - st.number_input("Height", step=1, min_value=150,max_value=2160, value=900, key="height",disabled=st.session_state.disable_opt2) - - st.markdown("\n") - st.markdown("\n") - - - - - if st.button(15*" "+"PROCEED"+" "*15,disabled=st.session_state.disable_proceed) and file is not None: - if st.session_state.get('opt1') == True: - task = st.session_state.opt1_selBox - else: - task = [st.session_state.width, st.session_state.height] - print(task) - st.session_state.disable_download = not upscale(file,task,progressBar) - - if file.type.split('/')[0] == 'video': - with st.sidebar: - st.info("Preparing for Download. Please wait.", icon="ℹ️") - audio = moviepy.editor.AudioFileClip(file.name.split('.')[0] + '.mp3') - video = moviepy.editor.VideoFileClip("deep_"+file.name) - videoClip = video.set_audio(audio) - videoClip.write_videofile('processed_'+file.name) - t2 = threading.Thread(target=remove_file(file.name)) - t2.start() - st.success('Done! Thankyou for your patience', icon="✅") - - #print(resulted_file.shape) - - st.markdown("\n") - st.markdown("\n") - - if file is None: - st.session_state.disable_download = True - - if st.session_state.disable_download == True: - st.button(13*" "+"DOWNLOAD"+" "*13,disabled=True) - else: - with open('processed_'+file.name, "rb") as download_file: - st.download_button(label=13*" "+"DOWNLOAD"+" "*13, data=download_file, - file_name= 'processed_'+file.name, mime= "image/png", - disabled=st.session_state.disable_download) - -st.markdown("\n") -st.markdown("\n") -st.info("DESCRIPTION : This web app is a free tool designed to upscale or resize image resolution. While the app"+ - " is still undergoing development, we are delighted to offer you to use it for image resolution upscaling."+ - " We welcome your feedback and suggestions, and encourage you to contact us at zain.18j2000@gmail.com "+ - "to share your thoughts. Thank you for your interest in our web application, and we look forward "+ - "to hearing from you as we continue to work towards making this project a resounding success.") - diff --git a/spaces/multimodalart/xformers-here-we-go-again/README.md b/spaces/multimodalart/xformers-here-we-go-again/README.md deleted file mode 100644 index bae75abb45dc4a2ec23b069cf0308910b8e57a5b..0000000000000000000000000000000000000000 --- a/spaces/multimodalart/xformers-here-we-go-again/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Xformers Here We Go Again -emoji: 💻 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.10.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/myrad01/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_val_test.sh b/spaces/myrad01/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_val_test.sh deleted file mode 100644 index d9b2a370ceeeb8f401706f4303298db13e5fad91..0000000000000000000000000000000000000000 --- a/spaces/myrad01/Inpaint-Anything/third_party/lama/bin/paper_runfiles/generate_val_test.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/usr/bin/env bash - -# !!! file set to make test_large_30k from the vanilla test_large: configs/test_large_30k.lst - -# paths to data are valid for mml7 -PLACES_ROOT="/data/inpainting/Places365" -OUT_DIR="/data/inpainting/paper_data/Places365_val_test" - -source "$(dirname $0)/env.sh" - -for datadir in test_large_30k # val_large -do - for conf in random_thin_256 random_medium_256 random_thick_256 random_thin_512 random_medium_512 random_thick_512 - do - "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ - "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 8 - - "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" - done - - for conf in segm_256 segm_512 - do - "$BINDIR/gen_mask_dataset.py" "$CONFIGDIR/data_gen/${conf}.yaml" \ - "$PLACES_ROOT/$datadir" "$OUT_DIR/$datadir/$conf" --n-jobs 2 - - "$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats" - done -done diff --git a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/segment_anything/utils/__init__.py b/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/segment_anything/utils/__init__.py deleted file mode 100644 index 5277f46157403e47fd830fc519144b97ef69d4ae..0000000000000000000000000000000000000000 --- a/spaces/myrad01/Inpaint-Anything/third_party/segment-anything/segment_anything/utils/__init__.py +++ /dev/null @@ -1,5 +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. diff --git a/spaces/naver/PUMP/datasets/web_images.py b/spaces/naver/PUMP/datasets/web_images.py deleted file mode 100644 index 3afc7ed58e4247f925025b3a824ec7e9690afd12..0000000000000000000000000000000000000000 --- a/spaces/naver/PUMP/datasets/web_images.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright 2022-present NAVER Corp. -# CC BY-NC-SA 4.0 -# Available only for non-commercial use - -from pdb import set_trace as bb -import os, os.path as osp - -from tqdm import trange -from .image_set import ImageSet, verify_img - - -class RandomWebImages (ImageSet): - """ 1 million distractors from Oxford and Paris Revisited - see http://ptak.felk.cvut.cz/revisitop/revisitop1m/ - """ - def __init__(self, start=0, end=52, root="datasets/revisitop1m"): - bar = None - imgs = [] - for i in range(start, end): - try: - # read cached list - img_list_path = osp.join(root, "image_list_%d.txt"%i) - cached_imgs = [e.strip() for e in open(img_list_path)] - assert cached_imgs, f"Cache '{img_list_path}' is empty!" - imgs += cached_imgs - - except IOError: - if bar is None: - bar = trange(start, 4*end, desc='Caching') - bar.update(4*i) - - # create it - imgs = [] - for d in range(i*4,(i+1)*4): # 4096 folders in total, on average 256 each - key = hex(d)[2:].zfill(3) - folder = osp.join(root, key) - if not osp.isdir(folder): continue - imgs += [f for f in os.listdir(folder) if verify_img(osp.join(folder, f), exts='.jpg')] - bar.update(1) - assert imgs, f"No images found in {folder}/" - open(img_list_path,'w').write('\n'.join(imgs)) - imgs += imgs - - if bar: bar.update(bar.total - bar.n) - super().__init__(root, imgs) - - def get_image_path(self, idx): - key = self.imgs[idx] - return osp.join(self.root, key[:3], key) - diff --git a/spaces/naver/SuperFeatures/how/utils/download.py b/spaces/naver/SuperFeatures/how/utils/download.py deleted file mode 100644 index 40edd8d0c16d994b94eb9456319b9655fee82693..0000000000000000000000000000000000000000 --- a/spaces/naver/SuperFeatures/how/utils/download.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Functions for downloading files necessary for training and evaluation""" - -import os.path -from cirtorch.utils.download import download_train, download_test -from . import io_helpers - - -def download_for_eval(evaluation, demo_eval, dataset_url, globals): - """Download datasets for evaluation and network if given by url""" - # Datasets - datasets = evaluation['global_descriptor']['datasets'] \ - + evaluation['local_descriptor']['datasets'] - download_datasets(datasets, dataset_url, globals) - # Network - if demo_eval and (demo_eval['net_path'].startswith("http://") \ - or demo_eval['net_path'].startswith("https://")): - net_name = os.path.basename(demo_eval['net_path']) - io_helpers.download_files([net_name], globals['root_path'] / "models", - os.path.dirname(demo_eval['net_path']) + "/", - logfunc=globals["logger"].info) - demo_eval['net_path'] = globals['root_path'] / "models" / net_name - - -def download_for_train(validation, dataset_url, globals): - """Download datasets for training""" - - datasets = ["train"] + validation['global_descriptor']['datasets'] \ - + validation['local_descriptor']['datasets'] - download_datasets(datasets, dataset_url, globals) - - -def download_datasets(datasets, dataset_url, globals): - """Download data associated with each required dataset""" - - if "val_eccv20" in datasets: - download_train(globals['root_path']) - io_helpers.download_files(["retrieval-SfM-120k-val-eccv2020.pkl"], - globals['root_path'] / "train/retrieval-SfM-120k", - dataset_url, logfunc=globals["logger"].info) - elif "train" in datasets: - download_train(globals['root_path']) - - if "roxford5k" in datasets or "rparis6k" in datasets: - download_test(globals['root_path']) diff --git a/spaces/nomic-ai/sciq/style.css b/spaces/nomic-ai/sciq/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/nomic-ai/sciq/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/vector/cachealignedvector_benchmark.cc b/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/vector/cachealignedvector_benchmark.cc deleted file mode 100644 index 9141e2d570884101b286e059d3ca358b643cc376..0000000000000000000000000000000000000000 --- a/spaces/ntt123/vietnam-male-voice-wavegru-tts/sparse_matmul/vector/cachealignedvector_benchmark.cc +++ /dev/null @@ -1,60 +0,0 @@ -// Copyright 2021 Google LLC -// -// 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. - -#include- -#include "benchmark/benchmark.h" -#include "sparse_matmul/vector/cache_aligned_vector.h" - -// A simple benchmark for CacheAlignedVector. -// -// Running on x86: -// As written, it's not representative of x86 performance since ReducingSample -// is used on x86 and not Sample. -// -// Running on arm64: -// bazel build -c opt --dynamic_mode=off --copt=-gmlt \ -// --copt=-DUSE_FIXED32 --config=android_arm64 \ -// sparse_matmul/vector:cachealignedvector_benchmark -namespace csrblocksparse { - -#ifdef USE_BFLOAT16 -using ComputeType = csrblocksparse::bfloat16; -#elif defined USE_FIXED32 -using ComputeType = csrblocksparse::fixed32<11>; // kGruMatMulOutBits -#else -using ComputeType = float; -#endif // USE_BFLOAT16 - -#if defined(USE_FIXED32) && defined(__aarch64__) -using ScratchType = int; -#else -using ScratchType = float; -#endif // defined(USE_FIXED32) && defined(__aarch64__) - -void BM_Sample(benchmark::State& state) { - constexpr int kVectorSize = 16384; // A large vector. - std::minstd_rand generator; - - CacheAlignedVector values(kVectorSize); - CacheAlignedVector scratch(kVectorSize); - values.FillRandom(); - - for (auto _ : state) { - values.Sample(/*temperature=*/0.98f, &generator, &scratch); - } -} -BENCHMARK(BM_Sample); - -} // namespace csrblocksparse diff --git a/spaces/nyanko7/sd-diffusers-webui/modules/safe.py b/spaces/nyanko7/sd-diffusers-webui/modules/safe.py deleted file mode 100644 index 532c7dab3f60f5a68b068299d2adc0b776a423f9..0000000000000000000000000000000000000000 --- a/spaces/nyanko7/sd-diffusers-webui/modules/safe.py +++ /dev/null @@ -1,188 +0,0 @@ -# this code is adapted from the script contributed by anon from /h/ -# modified, from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/6cff4401824299a983c8e13424018efc347b4a2b/modules/safe.py - -import io -import pickle -import collections -import sys -import traceback - -import torch -import numpy -import _codecs -import zipfile -import re - - -# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage -TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage - - -def encode(*args): - out = _codecs.encode(*args) - return out - - -class RestrictedUnpickler(pickle.Unpickler): - extra_handler = None - - def persistent_load(self, saved_id): - assert saved_id[0] == 'storage' - return TypedStorage() - - def find_class(self, module, name): - if self.extra_handler is not None: - res = self.extra_handler(module, name) - if res is not None: - return res - - if module == 'collections' and name == 'OrderedDict': - return getattr(collections, name) - if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter', '_rebuild_device_tensor_from_numpy']: - return getattr(torch._utils, name) - if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage', 'float32']: - return getattr(torch, name) - if module == 'torch.nn.modules.container' and name in ['ParameterDict']: - return getattr(torch.nn.modules.container, name) - if module == 'numpy.core.multiarray' and name in ['scalar', '_reconstruct']: - return getattr(numpy.core.multiarray, name) - if module == 'numpy' and name in ['dtype', 'ndarray']: - return getattr(numpy, name) - if module == '_codecs' and name == 'encode': - return encode - if module == "pytorch_lightning.callbacks" and name == 'model_checkpoint': - import pytorch_lightning.callbacks - return pytorch_lightning.callbacks.model_checkpoint - if module == "pytorch_lightning.callbacks.model_checkpoint" and name == 'ModelCheckpoint': - import pytorch_lightning.callbacks.model_checkpoint - return pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint - if module == "__builtin__" and name == 'set': - return set - - # Forbid everything else. - raise Exception(f"global '{module}/{name}' is forbidden") - - -# Regular expression that accepts 'dirname/version', 'dirname/data.pkl', and 'dirname/data/ ' -allowed_zip_names_re = re.compile(r"^([^/]+)/((data/\d+)|version|(data\.pkl))$") -data_pkl_re = re.compile(r"^([^/]+)/data\.pkl$") - -def check_zip_filenames(filename, names): - for name in names: - if allowed_zip_names_re.match(name): - continue - - raise Exception(f"bad file inside {filename}: {name}") - - -def check_pt(filename, extra_handler): - try: - - # new pytorch format is a zip file - with zipfile.ZipFile(filename) as z: - check_zip_filenames(filename, z.namelist()) - - # find filename of data.pkl in zip file: ' /data.pkl' - data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)] - if len(data_pkl_filenames) == 0: - raise Exception(f"data.pkl not found in {filename}") - if len(data_pkl_filenames) > 1: - raise Exception(f"Multiple data.pkl found in {filename}") - with z.open(data_pkl_filenames[0]) as file: - unpickler = RestrictedUnpickler(file) - unpickler.extra_handler = extra_handler - unpickler.load() - - except zipfile.BadZipfile: - - # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle - with open(filename, "rb") as file: - unpickler = RestrictedUnpickler(file) - unpickler.extra_handler = extra_handler - for i in range(5): - unpickler.load() - - -def load(filename, *args, **kwargs): - return load_with_extra(filename, extra_handler=global_extra_handler, *args, **kwargs) - - -def load_with_extra(filename, extra_handler=None, *args, **kwargs): - """ - this function is intended to be used by extensions that want to load models with - some extra classes in them that the usual unpickler would find suspicious. - - Use the extra_handler argument to specify a function that takes module and field name as text, - and returns that field's value: - - ```python - def extra(module, name): - if module == 'collections' and name == 'OrderedDict': - return collections.OrderedDict - - return None - - safe.load_with_extra('model.pt', extra_handler=extra) - ``` - - The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is - definitely unsafe. - """ - - try: - check_pt(filename, extra_handler) - - except pickle.UnpicklingError: - print(f"Error verifying pickled file from {filename}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - print("The file is most likely corrupted.", file=sys.stderr) - return None - - except Exception: - print(f"Error verifying pickled file from {filename}:", file=sys.stderr) - print(traceback.format_exc(), file=sys.stderr) - print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr) - print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr) - return None - - return unsafe_torch_load(filename, *args, **kwargs) - - -class Extra: - """ - A class for temporarily setting the global handler for when you can't explicitly call load_with_extra - (because it's not your code making the torch.load call). The intended use is like this: - -``` -import torch -from modules import safe - -def handler(module, name): - if module == 'torch' and name in ['float64', 'float16']: - return getattr(torch, name) - - return None - -with safe.Extra(handler): - x = torch.load('model.pt') -``` - """ - - def __init__(self, handler): - self.handler = handler - - def __enter__(self): - global global_extra_handler - - assert global_extra_handler is None, 'already inside an Extra() block' - global_extra_handler = self.handler - - def __exit__(self, exc_type, exc_val, exc_tb): - global global_extra_handler - - global_extra_handler = None - - -unsafe_torch_load = torch.load -torch.load = load -global_extra_handler = None diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/ddpm.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/ddpm.md deleted file mode 100644 index 3efa603d1cae45daf9390454c9dcbeb9bf2f86cf..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/ddpm.md +++ /dev/null @@ -1,35 +0,0 @@ - - -# DDPM - -[Denoising Diffusion Probabilistic Models](https://huggingface.co/papers/2006.11239) (DDPM) by Jonathan Ho, Ajay Jain and Pieter Abbeel proposes a diffusion based model of the same name. In the 🤗 Diffusers library, DDPM refers to the *discrete denoising scheduler* from the paper as well as the pipeline. - -The abstract from the paper is: - -*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* - -The original codebase can be found at [hohonathanho/diffusion](https://github.com/hojonathanho/diffusion). - - - -Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines. - - - -# DDPMPipeline -[[autodoc]] DDPMPipeline - - all - - __call__ - -## ImagePipelineOutput -[[autodoc]] pipelines.ImagePipelineOutput diff --git a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/tool/makesample.py b/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/tool/makesample.py deleted file mode 100644 index 36276267677360d8238a8dbf71e9753dcc327681..0000000000000000000000000000000000000000 --- a/spaces/paulengstler/interpretable-vertebral-fracture-diagnosis/netdissect/tool/makesample.py +++ /dev/null @@ -1,169 +0,0 @@ -''' -A simple tool to generate sample of output of a GAN, -subject to filtering, sorting, or intervention. -''' - -import torch, numpy, os, argparse, numbers, sys, shutil -from PIL import Image -from torch.utils.data import TensorDataset -from netdissect.zdataset import standard_z_sample -from netdissect.progress import default_progress, verbose_progress -from netdissect.autoeval import autoimport_eval -from netdissect.workerpool import WorkerBase, WorkerPool -from netdissect.nethook import edit_layers, retain_layers - -def main(): - parser = argparse.ArgumentParser(description='GAN sample making utility') - parser.add_argument('--model', type=str, default=None, - help='constructor for the model to test') - parser.add_argument('--pthfile', type=str, default=None, - help='filename of .pth file for the model') - parser.add_argument('--outdir', type=str, default='images', - help='directory for image output') - parser.add_argument('--size', type=int, default=100, - help='number of images to output') - parser.add_argument('--test_size', type=int, default=None, - help='number of images to test') - parser.add_argument('--layer', type=str, default=None, - help='layer to inspect') - parser.add_argument('--seed', type=int, default=1, - help='seed') - parser.add_argument('--maximize_units', type=int, nargs='+', default=None, - help='units to maximize') - parser.add_argument('--ablate_units', type=int, nargs='+', default=None, - help='units to ablate') - parser.add_argument('--quiet', action='store_true', default=False, - help='silences console output') - if len(sys.argv) == 1: - parser.print_usage(sys.stderr) - sys.exit(1) - args = parser.parse_args() - verbose_progress(not args.quiet) - - # Instantiate the model - model = autoimport_eval(args.model) - if args.pthfile is not None: - data = torch.load(args.pthfile) - if 'state_dict' in data: - meta = {} - for key in data: - if isinstance(data[key], numbers.Number): - meta[key] = data[key] - data = data['state_dict'] - model.load_state_dict(data) - # Unwrap any DataParallel-wrapped model - if isinstance(model, torch.nn.DataParallel): - model = next(model.children()) - # Examine first conv in model to determine input feature size. - first_layer = [c for c in model.modules() - if isinstance(c, (torch.nn.Conv2d, torch.nn.ConvTranspose2d, - torch.nn.Linear))][0] - # 4d input if convolutional, 2d input if first layer is linear. - if isinstance(first_layer, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)): - z_channels = first_layer.in_channels - spatialdims = (1, 1) - else: - z_channels = first_layer.in_features - spatialdims = () - # Instrument the model if needed - if args.maximize_units is not None: - retain_layers(model, [args.layer]) - model.cuda() - - # Get the sample of z vectors - if args.maximize_units is None: - indexes = torch.arange(args.size) - z_sample = standard_z_sample(args.size, z_channels, seed=args.seed) - z_sample = z_sample.view(tuple(z_sample.shape) + spatialdims) - else: - # By default, if maximizing units, get a 'top 5%' sample. - if args.test_size is None: - args.test_size = args.size * 20 - z_universe = standard_z_sample(args.test_size, z_channels, - seed=args.seed) - z_universe = z_universe.view(tuple(z_universe.shape) + spatialdims) - indexes = get_highest_znums(model, z_universe, args.maximize_units, - args.size, seed=args.seed) - z_sample = z_universe[indexes] - - if args.ablate_units: - edit_layers(model, [args.layer]) - dims = max(2, max(args.ablate_units) + 1) # >=2 to avoid broadcast - model.ablation[args.layer] = torch.zeros(dims) - model.ablation[args.layer][args.ablate_units] = 1 - - save_znum_images(args.outdir, model, z_sample, indexes, - args.layer, args.ablate_units) - copy_lightbox_to(args.outdir) - - -def get_highest_znums(model, z_universe, max_units, size, - batch_size=100, seed=1): - # The model should have been instrumented already - retained_items = list(model.retained.items()) - assert len(retained_items) == 1 - layer = retained_items[0][0] - # By default, a 10% sample - progress = default_progress() - num_units = None - with torch.no_grad(): - # Pass 1: collect max activation stats - z_loader = torch.utils.data.DataLoader(TensorDataset(z_universe), - batch_size=batch_size, num_workers=2, - pin_memory=True) - scores = [] - for [z] in progress(z_loader, desc='Finding max activations'): - z = z.cuda() - model(z) - feature = model.retained[layer] - num_units = feature.shape[1] - max_feature = feature[:, max_units, ...].view( - feature.shape[0], len(max_units), -1).max(2)[0] - total_feature = max_feature.sum(1) - scores.append(total_feature.cpu()) - scores = torch.cat(scores, 0) - highest = (-scores).sort(0)[1][:size].sort(0)[0] - return highest - - -def save_znum_images(dirname, model, z_sample, indexes, layer, ablated_units, - name_template="image_{}.png", lightbox=False, batch_size=100, seed=1): - progress = default_progress() - os.makedirs(dirname, exist_ok=True) - with torch.no_grad(): - # Pass 2: now generate images - z_loader = torch.utils.data.DataLoader(TensorDataset(z_sample), - batch_size=batch_size, num_workers=2, - pin_memory=True) - saver = WorkerPool(SaveImageWorker) - if ablated_units is not None: - dims = max(2, max(ablated_units) + 1) # >=2 to avoid broadcast - mask = torch.zeros(dims) - mask[ablated_units] = 1 - model.ablation[layer] = mask[None,:,None,None].cuda() - for batch_num, [z] in enumerate(progress(z_loader, - desc='Saving images')): - z = z.cuda() - start_index = batch_num * batch_size - im = ((model(z) + 1) / 2 * 255).clamp(0, 255).byte().permute( - 0, 2, 3, 1).cpu() - for i in range(len(im)): - index = i + start_index - if indexes is not None: - index = indexes[index].item() - filename = os.path.join(dirname, name_template.format(index)) - saver.add(im[i].numpy(), filename) - saver.join() - -def copy_lightbox_to(dirname): - srcdir = os.path.realpath( - os.path.join(os.getcwd(), os.path.dirname(__file__))) - shutil.copy(os.path.join(srcdir, 'lightbox.html'), - os.path.join(dirname, '+lightbox.html')) - -class SaveImageWorker(WorkerBase): - def work(self, data, filename): - Image.fromarray(data).save(filename, optimize=True, quality=100) - -if __name__ == '__main__': - main() diff --git a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Bing.py b/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Bing.py deleted file mode 100644 index 87e04ac82293c7e22068af431ac407bdee435a1b..0000000000000000000000000000000000000000 --- a/spaces/pikto/Elite-freegpt-webui/g4f/Provider/Providers/Bing.py +++ /dev/null @@ -1,349 +0,0 @@ -import os -import json -import random -import json -import os -import uuid -import ssl -import certifi -import aiohttp -import asyncio - -import requests -from ...typing import sha256, Dict, get_type_hints - -url = 'https://bing.com/chat' -model = ['gpt-4'] -supports_stream = True -needs_auth = False - -ssl_context = ssl.create_default_context() -ssl_context.load_verify_locations(certifi.where()) - - -class optionsSets: - optionSet: dict = { - 'tone': str, - 'optionsSets': list - } - - jailbreak: dict = { - "optionsSets": [ - 'saharasugg', - 'enablenewsfc', - 'clgalileo', - 'gencontentv3', - "nlu_direct_response_filter", - "deepleo", - "disable_emoji_spoken_text", - "responsible_ai_policy_235", - "enablemm", - "h3precise" - # "harmonyv3", - "dtappid", - "cricinfo", - "cricinfov2", - "dv3sugg", - "nojbfedge" - ] - } - - -class Defaults: - delimiter = '\x1e' - ip_address = f'13.{random.randint(104, 107)}.{random.randint(0, 255)}.{random.randint(0, 255)}' - - allowedMessageTypes = [ - 'Chat', - 'Disengaged', - 'AdsQuery', - 'SemanticSerp', - 'GenerateContentQuery', - 'SearchQuery', - 'ActionRequest', - 'Context', - 'Progress', - 'AdsQuery', - 'SemanticSerp' - ] - - sliceIds = [ - - # "222dtappid", - # "225cricinfo", - # "224locals0" - - 'winmuid3tf', - 'osbsdusgreccf', - 'ttstmout', - 'crchatrev', - 'winlongmsgtf', - 'ctrlworkpay', - 'norespwtf', - 'tempcacheread', - 'temptacache', - '505scss0', - '508jbcars0', - '515enbotdets0', - '5082tsports', - '515vaoprvs', - '424dagslnv1s0', - 'kcimgattcf', - '427startpms0' - ] - - location = { - 'locale': 'en-US', - 'market': 'en-US', - 'region': 'US', - 'locationHints': [ - { - 'country': 'United States', - 'state': 'California', - 'city': 'Los Angeles', - 'timezoneoffset': 8, - 'countryConfidence': 8, - 'Center': { - 'Latitude': 34.0536909, - 'Longitude': -118.242766 - }, - 'RegionType': 2, - 'SourceType': 1 - } - ], - } - - -def _format(msg: dict) -> str: - return json.dumps(msg, ensure_ascii=False) + Defaults.delimiter - - -async def create_conversation(): - for _ in range(5): - create = requests.get('https://www.bing.com/turing/conversation/create', - headers={ - 'authority': 'edgeservices.bing.com', - 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7', - 'accept-language': 'en-US,en;q=0.9', - 'cache-control': 'max-age=0', - 'sec-ch-ua': '"Chromium";v="110", "Not A(Brand";v="24", "Microsoft Edge";v="110"', - 'sec-ch-ua-arch': '"x86"', - 'sec-ch-ua-bitness': '"64"', - 'sec-ch-ua-full-version': '"110.0.1587.69"', - 'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"', - 'sec-ch-ua-mobile': '?0', - 'sec-ch-ua-model': '""', - 'sec-ch-ua-platform': '"Windows"', - 'sec-ch-ua-platform-version': '"15.0.0"', - 'sec-fetch-dest': 'document', - 'sec-fetch-mode': 'navigate', - 'sec-fetch-site': 'none', - 'sec-fetch-user': '?1', - 'upgrade-insecure-requests': '1', - 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110.0.0.0 Safari/537.36 Edg/110.0.1587.69', - 'x-edge-shopping-flag': '1', - 'x-forwarded-for': Defaults.ip_address - }) - - conversationId = create.json().get('conversationId') - clientId = create.json().get('clientId') - conversationSignature = create.json().get('conversationSignature') - - if not conversationId or not clientId or not conversationSignature and _ == 4: - raise Exception('Failed to create conversation.') - - return conversationId, clientId, conversationSignature - - -async def stream_generate(prompt: str, mode: optionsSets.optionSet = optionsSets.jailbreak, context: bool or str = False): - timeout = aiohttp.ClientTimeout(total=900) - session = aiohttp.ClientSession(timeout=timeout) - - conversationId, clientId, conversationSignature = await create_conversation() - - wss = await session.ws_connect('wss://sydney.bing.com/sydney/ChatHub', ssl=ssl_context, autoping=False, - headers={ - 'accept': 'application/json', - 'accept-language': 'en-US,en;q=0.9', - 'content-type': 'application/json', - 'sec-ch-ua': '"Not_A Brand";v="99", "Microsoft Edge";v="110", "Chromium";v="110"', - 'sec-ch-ua-arch': '"x86"', - 'sec-ch-ua-bitness': '"64"', - 'sec-ch-ua-full-version': '"109.0.1518.78"', - 'sec-ch-ua-full-version-list': '"Chromium";v="110.0.5481.192", "Not A(Brand";v="24.0.0.0", "Microsoft Edge";v="110.0.1587.69"', - 'sec-ch-ua-mobile': '?0', - 'sec-ch-ua-model': '', - 'sec-ch-ua-platform': '"Windows"', - 'sec-ch-ua-platform-version': '"15.0.0"', - 'sec-fetch-dest': 'empty', - 'sec-fetch-mode': 'cors', - 'sec-fetch-site': 'same-origin', - 'x-ms-client-request-id': str(uuid.uuid4()), - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - 'Referer': 'https://www.bing.com/search?q=Bing+AI&showconv=1&FORM=hpcodx', - 'Referrer-Policy': 'origin-when-cross-origin', - 'x-forwarded-for': Defaults.ip_address - }) - - await wss.send_str(_format({'protocol': 'json', 'version': 1})) - await wss.receive(timeout=900) - - struct = { - 'arguments': [ - { - **mode, - 'source': 'cib', - 'allowedMessageTypes': Defaults.allowedMessageTypes, - 'sliceIds': Defaults.sliceIds, - 'traceId': os.urandom(16).hex(), - 'isStartOfSession': True, - 'message': Defaults.location | { - 'author': 'user', - 'inputMethod': 'Keyboard', - 'text': prompt, - 'messageType': 'Chat' - }, - 'conversationSignature': conversationSignature, - 'participant': { - 'id': clientId - }, - 'conversationId': conversationId - } - ], - 'invocationId': '0', - 'target': 'chat', - 'type': 4 - } - - if context: - struct['arguments'][0]['previousMessages'] = [ - { - "author": "user", - "description": context, - "contextType": "WebPage", - "messageType": "Context", - "messageId": "discover-web--page-ping-mriduna-----" - } - ] - - await wss.send_str(_format(struct)) - - final = False - draw = False - resp_txt = '' - result_text = '' - resp_txt_no_link = '' - cache_text = '' - - while not final: - msg = await wss.receive(timeout=900) - objects = msg.data.split(Defaults.delimiter) - - for obj in objects: - if obj is None or not obj: - continue - - response = json.loads(obj) - if response.get('type') == 1 and response['arguments'][0].get('messages',): - if not draw: - if (response['arguments'][0]['messages'][0]['contentOrigin'] != 'Apology') and not draw: - resp_txt = result_text + \ - response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0].get( - 'text', '') - resp_txt_no_link = result_text + \ - response['arguments'][0]['messages'][0].get( - 'text', '') - - if response['arguments'][0]['messages'][0].get('messageType',): - resp_txt = ( - resp_txt - + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text') - + '\n' - ) - result_text = ( - result_text - + response['arguments'][0]['messages'][0]['adaptiveCards'][0]['body'][0]['inlines'][0].get('text') - + '\n' - ) - - if cache_text.endswith(' '): - final = True - if wss and not wss.closed: - await wss.close() - if session and not session.closed: - await session.close() - - yield (resp_txt.replace(cache_text, '')) - cache_text = resp_txt - - elif response.get('type') == 2: - if response['item']['result'].get('error'): - if wss and not wss.closed: - await wss.close() - if session and not session.closed: - await session.close() - - raise Exception( - f"{response['item']['result']['value']}: {response['item']['result']['message']}") - - if draw: - cache = response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] - response['item']['messages'][1]['adaptiveCards'][0]['body'][0]['text'] = ( - cache + resp_txt) - - if (response['item']['messages'][-1]['contentOrigin'] == 'Apology' and resp_txt): - response['item']['messages'][-1]['text'] = resp_txt_no_link - response['item']['messages'][-1]['adaptiveCards'][0]['body'][0]['text'] = resp_txt - - # print('Preserved the message from being deleted', file=sys.stderr) - - final = True - if wss and not wss.closed: - await wss.close() - if session and not session.closed: - await session.close() - - -def run(generator): - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - gen = generator.__aiter__() - - while True: - try: - next_val = loop.run_until_complete(gen.__anext__()) - yield next_val - - except StopAsyncIteration: - break - #print('Done') - -def convert(messages): - context = "" - - for message in messages: - context += "[%s](#message)\n%s\n\n" % (message['role'], - message['content']) - - return context - - -def _create_completion(model: str, messages: list, stream: bool, **kwargs): - if len(messages) < 2: - prompt = messages[0]['content'] - context = False - - else: - prompt = messages[-1]['content'] - context = convert(messages[:-1]) - - response = run(stream_generate(prompt, optionsSets.jailbreak, context)) - for token in response: - yield (token) - - #print('Done') - - -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]]) diff --git a/spaces/pkiage/time_series_decomposition_demo/src/visualization/visualize.py b/spaces/pkiage/time_series_decomposition_demo/src/visualization/visualize.py deleted file mode 100644 index 0062796a93e9419254afc835d85e5f9d8a3f69cb..0000000000000000000000000000000000000000 --- a/spaces/pkiage/time_series_decomposition_demo/src/visualization/visualize.py +++ /dev/null @@ -1,94 +0,0 @@ -import pandas as pd -import streamlit as st -import plotly.express as px - - -def streamlit_2columns_metrics_df_shape(df: pd.DataFrame): - ( - column1name, - column2name, - ) = st.columns(2) - - with column1name: - st.metric( - label="Rows", - value=df.shape[0], - delta=None, - delta_color="normal", - ) - - with column2name: - st.metric( - label="Columns", - value=df.shape[1], - delta=None, - delta_color="normal", - ) - - -def show_inputted_dataframe(data): - with st.expander("Input Dataframe:"): - st.dataframe(data) - streamlit_2columns_metrics_df_shape(data) - - -def standard_decomposition_plot(decomposition): - - fig = decomposition.plot() - - (xsize_standard_decomp, ysize_standard_decomp) = streamlit_chart_setting_height_width( - "Chart Size:", 5, 5, "xsize_standard_decomp", "ysize_standard_decomp") - - fig.set_size_inches(xsize_standard_decomp, ysize_standard_decomp) - - st.pyplot(fig) - - -def time_series_line_plot(data): - fig = px.line( - data - ) - st.plotly_chart(fig, use_container_width=True) - - -def time_series_scatter_plot(data): - fig = px.scatter(data, trendline="ols") - st.plotly_chart(fig, use_container_width=True) - - -def time_series_box_plot(data): - fig = px.box(data, hover_data=['Date'], points="all") - st.plotly_chart(fig, use_container_width=True) - - -def time_series_violin_and_box_plot(graph_data): - fig = px.histogram(graph_data, - marginal="violin") - st.plotly_chart(fig, use_container_width=True) - - -def streamlit_chart_setting_height_width( - title: str, - default_widthvalue: int, - default_heightvalue: int, - widthkey: str, - heightkey: str, -): - with st.expander(title): - - lbarx_col, lbary_col = st.columns(2) - - with lbarx_col: - width_size = st.number_input( - label="Width in inches:", - value=default_widthvalue, - key=widthkey, - ) - - with lbary_col: - height_size = st.number_input( - label="Height in inches:", - value=default_heightvalue, - key=heightkey, - ) - return width_size, height_size diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/color_triplet.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/color_triplet.py deleted file mode 100644 index 02cab328251af9bfa809981aaa44933c407e2cd7..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/color_triplet.py +++ /dev/null @@ -1,38 +0,0 @@ -from typing import NamedTuple, Tuple - - -class ColorTriplet(NamedTuple): - """The red, green, and blue components of a color.""" - - red: int - """Red component in 0 to 255 range.""" - green: int - """Green component in 0 to 255 range.""" - blue: int - """Blue component in 0 to 255 range.""" - - @property - def hex(self) -> str: - """get the color triplet in CSS style.""" - red, green, blue = self - return f"#{red:02x}{green:02x}{blue:02x}" - - @property - def rgb(self) -> str: - """The color in RGB format. - - Returns: - str: An rgb color, e.g. ``"rgb(100,23,255)"``. - """ - red, green, blue = self - return f"rgb({red},{green},{blue})" - - @property - def normalized(self) -> Tuple[float, float, float]: - """Convert components into floats between 0 and 1. - - Returns: - Tuple[float, float, float]: A tuple of three normalized colour components. - """ - red, green, blue = self - return red / 255.0, green / 255.0, blue / 255.0 diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/measure.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/measure.py deleted file mode 100644 index a508ffa80bd715b47c190ed9d747dbc388fa5b19..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/rich/measure.py +++ /dev/null @@ -1,151 +0,0 @@ -from operator import itemgetter -from typing import TYPE_CHECKING, Callable, NamedTuple, Optional, Sequence - -from . import errors -from .protocol import is_renderable, rich_cast - -if TYPE_CHECKING: - from .console import Console, ConsoleOptions, RenderableType - - -class Measurement(NamedTuple): - """Stores the minimum and maximum widths (in characters) required to render an object.""" - - minimum: int - """Minimum number of cells required to render.""" - maximum: int - """Maximum number of cells required to render.""" - - @property - def span(self) -> int: - """Get difference between maximum and minimum.""" - return self.maximum - self.minimum - - def normalize(self) -> "Measurement": - """Get measurement that ensures that minimum <= maximum and minimum >= 0 - - Returns: - Measurement: A normalized measurement. - """ - minimum, maximum = self - minimum = min(max(0, minimum), maximum) - return Measurement(max(0, minimum), max(0, max(minimum, maximum))) - - def with_maximum(self, width: int) -> "Measurement": - """Get a RenderableWith where the widths are <= width. - - Args: - width (int): Maximum desired width. - - Returns: - Measurement: New Measurement object. - """ - minimum, maximum = self - return Measurement(min(minimum, width), min(maximum, width)) - - def with_minimum(self, width: int) -> "Measurement": - """Get a RenderableWith where the widths are >= width. - - Args: - width (int): Minimum desired width. - - Returns: - Measurement: New Measurement object. - """ - minimum, maximum = self - width = max(0, width) - return Measurement(max(minimum, width), max(maximum, width)) - - def clamp( - self, min_width: Optional[int] = None, max_width: Optional[int] = None - ) -> "Measurement": - """Clamp a measurement within the specified range. - - Args: - min_width (int): Minimum desired width, or ``None`` for no minimum. Defaults to None. - max_width (int): Maximum desired width, or ``None`` for no maximum. Defaults to None. - - Returns: - Measurement: New Measurement object. - """ - measurement = self - if min_width is not None: - measurement = measurement.with_minimum(min_width) - if max_width is not None: - measurement = measurement.with_maximum(max_width) - return measurement - - @classmethod - def get( - cls, console: "Console", options: "ConsoleOptions", renderable: "RenderableType" - ) -> "Measurement": - """Get a measurement for a renderable. - - Args: - console (~rich.console.Console): Console instance. - options (~rich.console.ConsoleOptions): Console options. - renderable (RenderableType): An object that may be rendered with Rich. - - Raises: - errors.NotRenderableError: If the object is not renderable. - - Returns: - Measurement: Measurement object containing range of character widths required to render the object. - """ - _max_width = options.max_width - if _max_width < 1: - return Measurement(0, 0) - if isinstance(renderable, str): - renderable = console.render_str( - renderable, markup=options.markup, highlight=False - ) - renderable = rich_cast(renderable) - if is_renderable(renderable): - get_console_width: Optional[ - Callable[["Console", "ConsoleOptions"], "Measurement"] - ] = getattr(renderable, "__rich_measure__", None) - if get_console_width is not None: - render_width = ( - get_console_width(console, options) - .normalize() - .with_maximum(_max_width) - ) - if render_width.maximum < 1: - return Measurement(0, 0) - return render_width.normalize() - else: - return Measurement(0, _max_width) - else: - raise errors.NotRenderableError( - f"Unable to get render width for {renderable!r}; " - "a str, Segment, or object with __rich_console__ method is required" - ) - - -def measure_renderables( - console: "Console", - options: "ConsoleOptions", - renderables: Sequence["RenderableType"], -) -> "Measurement": - """Get a measurement that would fit a number of renderables. - - Args: - console (~rich.console.Console): Console instance. - options (~rich.console.ConsoleOptions): Console options. - renderables (Iterable[RenderableType]): One or more renderable objects. - - Returns: - Measurement: Measurement object containing range of character widths required to - contain all given renderables. - """ - if not renderables: - return Measurement(0, 0) - get_measurement = Measurement.get - measurements = [ - get_measurement(console, options, renderable) for renderable in renderables - ] - measured_width = Measurement( - max(measurements, key=itemgetter(0)).minimum, - max(measurements, key=itemgetter(1)).maximum, - ) - return measured_width diff --git a/spaces/praveen-reddy/PDP/app.py b/spaces/praveen-reddy/PDP/app.py deleted file mode 100644 index 18cd5808a16955d19399896c8a3462110d157dbc..0000000000000000000000000000000000000000 --- a/spaces/praveen-reddy/PDP/app.py +++ /dev/null @@ -1,130 +0,0 @@ -import pickle -import streamlit as st -import numpy as np -import pandas as pd -from sklearn.preprocessing import StandardScaler - - -lr_model = "models/LR_model.pkl" -knn_model = "models/KNN_model.pkl" -svm_model = "models/SVM_model.pkl" -abc_model = "models/ABC_model.pkl" -bc_model = "models/BC_model.pkl" -dt_model = "models/DT_model.pkl" -lgbm_model = "models/LGBM_model.pkl" -nb_model = "models/NB_model.pkl" -rf_model = "models/RF_model.pkl" -xg_model = "models/XG_model.pkl" - -with open(lr_model, 'rb') as file: - LR_model = pickle.load(file) - -with open(knn_model, 'rb') as file: - KNN_model = pickle.load(file) - -with open(svm_model, 'rb') as file: - SVM_model = pickle.load(file) - -with open(abc_model, 'rb') as file: - ABC_model = pickle.load(file) - -with open(bc_model, 'rb') as file: - BC_model = pickle.load(file) - -with open(dt_model, 'rb') as file: - DT_model = pickle.load(file) - -with open(lgbm_model, 'rb') as file: - LGBM_model = pickle.load(file) - -with open(nb_model, 'rb') as file: - NB_model = pickle.load(file) - -with open(rf_model, 'rb') as file: - RF_model = pickle.load(file) - -with open(xg_model, 'rb') as file: - XG_model = pickle.load(file) - - -st.title("Parkinson's Disease Prediction Using Machine Learning") - -st.header('Fill the form and press the predict button to see the result', - divider='rainbow') - -# test_value = [[ 5.29249395e-01, -1.03309592e-01, 1.11583374e+00, -# -5.23716022e-01, -6.82179904e-01, -4.29193964e-01, -# -4.66158519e-01, -4.28206106e-01, -6.17874540e-01, -# -6.05206208e-01, -6.44461440e-01, -5.48107644e-01, -# -5.58034900e-01, -6.44471776e-01, -5.39840575e-01, -# 7.18861885e-01, -1.49332475e+00, 1.18499869e+00, -# -3.23304568e-01, -3.76742214e-01, 3.78931110e-01, -# -3.93143882e-01]] - - -# Initial values -initial_values = { - 'MDVP:Fo(Hz)': 119.99200, - 'MDVP:Fhi(Hz)': 157.30200, - 'MDVP:Flo(Hz)': 74.99700, - 'MDVP:Jitter(%)': 0.00784, - 'MDVP:Jitter(Abs)': 0.00007, - 'MDVP:RAP': 0.00370, - 'MDVP:PPQ': 0.00554, - 'Jitter:DDP': 0.01109, - 'MDVP:Shimmer': 0.04374, - 'MDVP:Shimmer(dB)': 0.42600, - 'Shimmer:APQ3': 0.02182, - 'Shimmer:APQ5': 0.03130, - 'MDVP:APQ': 0.02971, - 'Shimmer:DDA': 0.06545, - 'NHR': 0.02211, - 'HNR': 21.03300, - 'RPDE': 0.414783, - 'DFA': 0.815285, - 'spread1': -4.813031, - 'spread2': 0.266482, - 'D2': 2.301442, - 'PPE': 0.284654 -} - - -for i, (key, value) in enumerate(initial_values.items()): - initial_values[key] = st.text_input(key, value=value, key=i) - -# print(list(enumerate(initial_values.items()))) - -if st.button('Predict', type="primary"): - # Get the values from the text input fields - # values = [float(st.text_input(key, value=value)) for key, value in initial_values.items()] - values = [float(value) for value in initial_values.values()] - - # Convert the list of values to a numpy array and store it in 'test_value' - test_value = np.array(values) - sc = StandardScaler() - - # Print 'test_value' to the console - st.write("Logistic Regression", str( - LR_model.predict(sc.fit_transform([test_value])))) - st.write("KNN", str(KNN_model.predict(sc.fit_transform([test_value])))) - st.write("SVM", str(SVM_model.predict(sc.fit_transform([test_value])))) - st.write("AdaBoost Classifier", str( - ABC_model.predict(sc.fit_transform([test_value])))) - st.write("Bagging Classifier", str( - BC_model.predict(sc.fit_transform([test_value])))) - st.write("Decision Tree", str( - DT_model.predict(sc.fit_transform([test_value])))) - st.write("LightGBM", str(LGBM_model.predict( - sc.fit_transform([test_value])))) - st.write("Naive Bayes", str( - NB_model.predict(sc.fit_transform([test_value])))) - st.write("Random Forest", str( - RF_model.predict(sc.fit_transform([test_value])))) - st.write("XGBoost", str(XG_model.predict(sc.fit_transform([test_value])))) - - st.write("Provided Data: ", test_value) - -dataset = pd.read_csv("dataset/parkinsons.data") - -st.subheader('Dataset Sample:', divider='rainbow') -st.write(dataset.head()) diff --git a/spaces/prerna9811/Chord/portaudio/src/hostapi/coreaudio/pa_mac_core_internal.h b/spaces/prerna9811/Chord/portaudio/src/hostapi/coreaudio/pa_mac_core_internal.h deleted file mode 100644 index d4a97e0c46e86001db8fe57e3212ef3e142d6c37..0000000000000000000000000000000000000000 --- a/spaces/prerna9811/Chord/portaudio/src/hostapi/coreaudio/pa_mac_core_internal.h +++ /dev/null @@ -1,193 +0,0 @@ -/* - * Internal interfaces for PortAudio Apple AUHAL implementation - * - * PortAudio Portable Real-Time Audio Library - * Latest Version at: http://www.portaudio.com - * - * Written by Bjorn Roche of XO Audio LLC, from PA skeleton code. - * Portions copied from code by Dominic Mazzoni (who wrote a HAL implementation) - * - * Dominic's code was based on code by Phil Burk, Darren Gibbs, - * Gord Peters, Stephane Letz, and Greg Pfiel. - * - * The following people also deserve acknowledgements: - * - * Olivier Tristan for feedback and testing - * Glenn Zelniker and Z-Systems engineering for sponsoring the Blocking I/O - * interface. - * - * - * Based on the Open Source API proposed by Ross Bencina - * Copyright (c) 1999-2002 Ross Bencina, Phil Burk - * - * Permission is hereby granted, free of charge, to any person obtaining - * a copy of this software and associated documentation files - * (the "Software"), to deal in the Software without restriction, - * including without limitation the rights to use, copy, modify, merge, - * publish, distribute, sublicense, and/or sell copies of the Software, - * and to permit persons to whom the Software is furnished to do so, - * subject to the following conditions: - * - * The above copyright notice and this permission notice shall be - * included in all copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, - * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF - * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. - * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR - * ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF - * CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION - * WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. - */ - -/* - * The text above constitutes the entire PortAudio license; however, - * the PortAudio community also makes the following non-binding requests: - * - * Any person wishing to distribute modifications to the Software is - * requested to send the modifications to the original developer so that - * they can be incorporated into the canonical version. It is also - * requested that these non-binding requests be included along with the - * license above. - */ - -/** - @file pa_mac_core - @ingroup hostapi_src - @author Bjorn Roche - @brief AUHAL implementation of PortAudio -*/ - -#ifndef PA_MAC_CORE_INTERNAL_H__ -#define PA_MAC_CORE_INTERNAL_H__ - -#include-#include -#include -#include - -#include "portaudio.h" -#include "pa_util.h" -#include "pa_hostapi.h" -#include "pa_stream.h" -#include "pa_allocation.h" -#include "pa_cpuload.h" -#include "pa_process.h" -#include "pa_ringbuffer.h" - -#include "pa_mac_core_blocking.h" - -/* function prototypes */ - -#ifdef __cplusplus -extern "C" -{ -#endif /* __cplusplus */ - -PaError PaMacCore_Initialize( PaUtilHostApiRepresentation **hostApi, PaHostApiIndex index ); - -#ifdef __cplusplus -} -#endif /* __cplusplus */ - -#define RING_BUFFER_ADVANCE_DENOMINATOR (4) - -PaError ReadStream( PaStream* stream, void *buffer, unsigned long frames ); -PaError WriteStream( PaStream* stream, const void *buffer, unsigned long frames ); -signed long GetStreamReadAvailable( PaStream* stream ); -signed long GetStreamWriteAvailable( PaStream* stream ); -/* PaMacAUHAL - host api datastructure specific to this implementation */ -typedef struct -{ - PaUtilHostApiRepresentation inheritedHostApiRep; - PaUtilStreamInterface callbackStreamInterface; - PaUtilStreamInterface blockingStreamInterface; - - PaUtilAllocationGroup *allocations; - - /* implementation specific data goes here */ - long devCount; - AudioDeviceID *devIds; /*array of all audio devices*/ - AudioDeviceID defaultIn; - AudioDeviceID defaultOut; -} -PaMacAUHAL; - -typedef struct PaMacCoreDeviceProperties -{ - /* Values in Frames from property queries. */ - UInt32 safetyOffset; - UInt32 bufferFrameSize; - // UInt32 streamLatency; // Seems to be the same as deviceLatency!? - UInt32 deviceLatency; - /* Current device sample rate. May change! - These are initialized to the nominal device sample rate, - and updated with the actual sample rate, when/where available. - Note that these are the *device* sample rates, prior to any required - SR conversion. */ - Float64 sampleRate; - Float64 samplePeriod; // reciprocal -} -PaMacCoreDeviceProperties; - -/* stream data structure specifically for this implementation */ -typedef struct PaMacCoreStream -{ - PaUtilStreamRepresentation streamRepresentation; - PaUtilCpuLoadMeasurer cpuLoadMeasurer; - PaUtilBufferProcessor bufferProcessor; - - /* implementation specific data goes here */ - bool bufferProcessorIsInitialized; - AudioUnit inputUnit; - AudioUnit outputUnit; - AudioDeviceID inputDevice; - AudioDeviceID outputDevice; - size_t userInChan; - size_t userOutChan; - size_t inputFramesPerBuffer; - size_t outputFramesPerBuffer; - PaMacBlio blio; - /* We use this ring buffer when input and out devs are different. */ - PaUtilRingBuffer inputRingBuffer; - /* We may need to do SR conversion on input. */ - AudioConverterRef inputSRConverter; - /* We need to preallocate an inputBuffer for reading data. */ - AudioBufferList inputAudioBufferList; - AudioTimeStamp startTime; - /* FIXME: instead of volatile, these should be properly memory barriered */ - volatile uint32_t xrunFlags; /*PaStreamCallbackFlags*/ - volatile enum { - STOPPED = 0, /* playback is completely stopped, - and the user has called StopStream(). */ - CALLBACK_STOPPED = 1, /* callback has requested stop, - but user has not yet called StopStream(). */ - STOPPING = 2, /* The stream is in the process of closing - because the user has called StopStream. - This state is just used internally; - externally it is indistinguishable from - ACTIVE.*/ - ACTIVE = 3 /* The stream is active and running. */ - } state; - double sampleRate; - PaMacCoreDeviceProperties inputProperties; - PaMacCoreDeviceProperties outputProperties; - - /* data updated by main thread and notifications, protected by timingInformationMutex */ - int timingInformationMutexIsInitialized; - pthread_mutex_t timingInformationMutex; - - /* These are written by the PA thread or from CoreAudio callbacks. Protected by the mutex. */ - Float64 timestampOffsetCombined; - Float64 timestampOffsetInputDevice; - Float64 timestampOffsetOutputDevice; - - /* Offsets in seconds to be applied to Apple timestamps to convert them to PA timestamps. - * While the io proc is active, the following values are only accessed and manipulated by the ioproc */ - Float64 timestampOffsetCombined_ioProcCopy; - Float64 timestampOffsetInputDevice_ioProcCopy; - Float64 timestampOffsetOutputDevice_ioProcCopy; -} -PaMacCoreStream; - -#endif /* PA_MAC_CORE_INTERNAL_H__ */ diff --git a/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/mingw-include/sal.h b/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/mingw-include/sal.h deleted file mode 100644 index 3f99ab9a9227d04508b064d3ca7e305b80d2473e..0000000000000000000000000000000000000000 --- a/spaces/prerna9811/Chord/portaudio/src/hostapi/wasapi/mingw-include/sal.h +++ /dev/null @@ -1,252 +0,0 @@ -#pragma once - -#if __GNUC__ >=3 -#pragma GCC system_header -#endif - -/*#define __null*/ // << Conflicts with GCC internal type __null -#define __notnull -#define __maybenull -#define __readonly -#define __notreadonly -#define __maybereadonly -#define __valid -#define __notvalid -#define __maybevalid -#define __readableTo(extent) -#define __elem_readableTo(size) -#define __byte_readableTo(size) -#define __writableTo(size) -#define __elem_writableTo(size) -#define __byte_writableTo(size) -#define __deref -#define __pre -#define __post -#define __precond(expr) -#define __postcond(expr) -#define __exceptthat -#define __execeptthat -#define __inner_success(expr) -#define __inner_checkReturn -#define __inner_typefix(ctype) -#define __inner_override -#define __inner_callback -#define __inner_blocksOn(resource) -#define __inner_fallthrough_dec -#define __inner_fallthrough -#define __refparam -#define __inner_control_entrypoint(category) -#define __inner_data_entrypoint(category) - -#define __ecount(size) -#define __bcount(size) -#define __in -#define __in_ecount(size) -#define __in_bcount(size) -#define __in_z -#define __in_ecount_z(size) -#define __in_bcount_z(size) -#define __in_nz -#define __in_ecount_nz(size) -#define __in_bcount_nz(size) -#define __out -#define __out_ecount(size) -#define __out_bcount(size) -#define __out_ecount_part(size,length) -#define __out_bcount_part(size,length) -#define __out_ecount_full(size) -#define __out_bcount_full(size) -#define __out_z -#define __out_z_opt -#define __out_ecount_z(size) -#define __out_bcount_z(size) -#define __out_ecount_part_z(size,length) -#define __out_bcount_part_z(size,length) -#define __out_ecount_full_z(size) -#define __out_bcount_full_z(size) -#define __out_nz -#define __out_nz_opt -#define __out_ecount_nz(size) -#define __out_bcount_nz(size) -#define __inout -#define __inout_ecount(size) -#define __inout_bcount(size) -#define __inout_ecount_part(size,length) -#define __inout_bcount_part(size,length) -#define __inout_ecount_full(size) -#define __inout_bcount_full(size) -#define __inout_z -#define __inout_ecount_z(size) -#define __inout_bcount_z(size) -#define __inout_nz -#define __inout_ecount_nz(size) -#define __inout_bcount_nz(size) -#define __ecount_opt(size) -#define __bcount_opt(size) -#define __in_opt -#define __in_ecount_opt(size) -#define __in_bcount_opt(size) -#define __in_z_opt -#define __in_ecount_z_opt(size) -#define __in_bcount_z_opt(size) -#define __in_nz_opt -#define __in_ecount_nz_opt(size) -#define __in_bcount_nz_opt(size) -#define __out_opt -#define __out_ecount_opt(size) -#define __out_bcount_opt(size) -#define __out_ecount_part_opt(size,length) -#define __out_bcount_part_opt(size,length) -#define __out_ecount_full_opt(size) -#define __out_bcount_full_opt(size) -#define __out_ecount_z_opt(size) -#define __out_bcount_z_opt(size) -#define __out_ecount_part_z_opt(size,length) -#define __out_bcount_part_z_opt(size,length) -#define __out_ecount_full_z_opt(size) -#define __out_bcount_full_z_opt(size) -#define __out_ecount_nz_opt(size) -#define __out_bcount_nz_opt(size) -#define __inout_opt -#define __inout_ecount_opt(size) -#define __inout_bcount_opt(size) -#define __inout_ecount_part_opt(size,length) -#define __inout_bcount_part_opt(size,length) -#define __inout_ecount_full_opt(size) -#define __inout_bcount_full_opt(size) -#define __inout_z_opt -#define __inout_ecount_z_opt(size) -#define __inout_ecount_z_opt(size) -#define __inout_bcount_z_opt(size) -#define __inout_nz_opt -#define __inout_ecount_nz_opt(size) -#define __inout_bcount_nz_opt(size) -#define __deref_ecount(size) -#define __deref_bcount(size) -#define __deref_out -#define __deref_out_ecount(size) -#define __deref_out_bcount(size) -#define __deref_out_ecount_part(size,length) -#define __deref_out_bcount_part(size,length) -#define __deref_out_ecount_full(size) -#define __deref_out_bcount_full(size) -#define __deref_out_z -#define __deref_out_ecount_z(size) -#define __deref_out_bcount_z(size) -#define __deref_out_nz -#define __deref_out_ecount_nz(size) -#define __deref_out_bcount_nz(size) -#define __deref_inout -#define __deref_inout_z -#define __deref_inout_ecount(size) -#define __deref_inout_bcount(size) -#define __deref_inout_ecount_part(size,length) -#define __deref_inout_bcount_part(size,length) -#define __deref_inout_ecount_full(size) -#define __deref_inout_bcount_full(size) -#define __deref_inout_z -#define __deref_inout_ecount_z(size) -#define __deref_inout_bcount_z(size) -#define __deref_inout_nz -#define __deref_inout_ecount_nz(size) -#define __deref_inout_bcount_nz(size) -#define __deref_ecount_opt(size) -#define __deref_bcount_opt(size) -#define __deref_out_opt -#define __deref_out_ecount_opt(size) -#define __deref_out_bcount_opt(size) -#define __deref_out_ecount_part_opt(size,length) -#define __deref_out_bcount_part_opt(size,length) -#define __deref_out_ecount_full_opt(size) -#define __deref_out_bcount_full_opt(size) -#define __deref_out_z_opt -#define __deref_out_ecount_z_opt(size) -#define __deref_out_bcount_z_opt(size) -#define __deref_out_nz_opt -#define __deref_out_ecount_nz_opt(size) -#define __deref_out_bcount_nz_opt(size) -#define __deref_inout_opt -#define __deref_inout_ecount_opt(size) -#define __deref_inout_bcount_opt(size) -#define __deref_inout_ecount_part_opt(size,length) -#define __deref_inout_bcount_part_opt(size,length) -#define __deref_inout_ecount_full_opt(size) -#define __deref_inout_bcount_full_opt(size) -#define __deref_inout_z_opt -#define __deref_inout_ecount_z_opt(size) -#define __deref_inout_bcount_z_opt(size) -#define __deref_inout_nz_opt -#define __deref_inout_ecount_nz_opt(size) -#define __deref_inout_bcount_nz_opt(size) -#define __deref_opt_ecount(size) -#define __deref_opt_bcount(size) -#define __deref_opt_out -#define __deref_opt_out_z -#define __deref_opt_out_ecount(size) -#define __deref_opt_out_bcount(size) -#define __deref_opt_out_ecount_part(size,length) -#define __deref_opt_out_bcount_part(size,length) -#define __deref_opt_out_ecount_full(size) -#define __deref_opt_out_bcount_full(size) -#define __deref_opt_inout -#define __deref_opt_inout_ecount(size) -#define __deref_opt_inout_bcount(size) -#define __deref_opt_inout_ecount_part(size,length) -#define __deref_opt_inout_bcount_part(size,length) -#define __deref_opt_inout_ecount_full(size) -#define __deref_opt_inout_bcount_full(size) -#define __deref_opt_inout_z -#define __deref_opt_inout_ecount_z(size) -#define __deref_opt_inout_bcount_z(size) -#define __deref_opt_inout_nz -#define __deref_opt_inout_ecount_nz(size) -#define __deref_opt_inout_bcount_nz(size) -#define __deref_opt_ecount_opt(size) -#define __deref_opt_bcount_opt(size) -#define __deref_opt_out_opt -#define __deref_opt_out_ecount_opt(size) -#define __deref_opt_out_bcount_opt(size) -#define __deref_opt_out_ecount_part_opt(size,length) -#define __deref_opt_out_bcount_part_opt(size,length) -#define __deref_opt_out_ecount_full_opt(size) -#define __deref_opt_out_bcount_full_opt(size) -#define __deref_opt_out_z_opt -#define __deref_opt_out_ecount_z_opt(size) -#define __deref_opt_out_bcount_z_opt(size) -#define __deref_opt_out_nz_opt -#define __deref_opt_out_ecount_nz_opt(size) -#define __deref_opt_out_bcount_nz_opt(size) -#define __deref_opt_inout_opt -#define __deref_opt_inout_ecount_opt(size) -#define __deref_opt_inout_bcount_opt(size) -#define __deref_opt_inout_ecount_part_opt(size,length) -#define __deref_opt_inout_bcount_part_opt(size,length) -#define __deref_opt_inout_ecount_full_opt(size) -#define __deref_opt_inout_bcount_full_opt(size) -#define __deref_opt_inout_z_opt -#define __deref_opt_inout_ecount_z_opt(size) -#define __deref_opt_inout_bcount_z_opt(size) -#define __deref_opt_inout_nz_opt -#define __deref_opt_inout_ecount_nz_opt(size) -#define __deref_opt_inout_bcount_nz_opt(size) - -#define __success(expr) -#define __nullterminated -#define __nullnullterminated -#define __reserved -#define __checkReturn -#define __typefix(ctype) -#define __override -#define __callback -#define __format_string -#define __blocksOn(resource) -#define __control_entrypoint(category) -#define __data_entrypoint(category) - -#ifndef __fallthrough - #define __fallthrough __inner_fallthrough -#endif - -#ifndef __analysis_assume - #define __analysis_assume(expr) -#endif diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/datastructures.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/datastructures.py deleted file mode 100644 index ce03e3ce4747a93956877d43dd9b7d3f9166160f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fastapi/datastructures.py +++ /dev/null @@ -1,204 +0,0 @@ -from typing import ( - Any, - BinaryIO, - Callable, - Dict, - Iterable, - Optional, - Type, - TypeVar, - cast, -) - -from fastapi._compat import ( - PYDANTIC_V2, - CoreSchema, - GetJsonSchemaHandler, - JsonSchemaValue, - with_info_plain_validator_function, -) -from starlette.datastructures import URL as URL # noqa: F401 -from starlette.datastructures import Address as Address # noqa: F401 -from starlette.datastructures import FormData as FormData # noqa: F401 -from starlette.datastructures import Headers as Headers # noqa: F401 -from starlette.datastructures import QueryParams as QueryParams # noqa: F401 -from starlette.datastructures import State as State # noqa: F401 -from starlette.datastructures import UploadFile as StarletteUploadFile -from typing_extensions import Annotated, Doc # type: ignore [attr-defined] - - -class UploadFile(StarletteUploadFile): - """ - A file uploaded in a request. - - Define it as a *path operation function* (or dependency) parameter. - - If you are using a regular `def` function, you can use the `upload_file.file` - attribute to access the raw standard Python file (blocking, not async), useful and - needed for non-async code. - - Read more about it in the - [FastAPI docs for Request Files](https://fastapi.tiangolo.com/tutorial/request-files/). - - ## Example - - ```python - from typing import Annotated - - from fastapi import FastAPI, File, UploadFile - - app = FastAPI() - - - @app.post("/files/") - async def create_file(file: Annotated[bytes, File()]): - return {"file_size": len(file)} - - - @app.post("/uploadfile/") - async def create_upload_file(file: UploadFile): - return {"filename": file.filename} - ``` - """ - - file: Annotated[ - BinaryIO, - Doc("The standard Python file object (non-async)."), - ] - filename: Annotated[Optional[str], Doc("The original file name.")] - size: Annotated[Optional[int], Doc("The size of the file in bytes.")] - headers: Annotated[Headers, Doc("The headers of the request.")] - content_type: Annotated[ - Optional[str], Doc("The content type of the request, from the headers.") - ] - - async def write( - self, - data: Annotated[ - bytes, - Doc( - """ - The bytes to write to the file. - """ - ), - ], - ) -> None: - """ - Write some bytes to the file. - - You normally wouldn't use this from a file you read in a request. - - To be awaitable, compatible with async, this is run in threadpool. - """ - return await super().write(data) - - async def read( - self, - size: Annotated[ - int, - Doc( - """ - The number of bytes to read from the file. - """ - ), - ] = -1, - ) -> bytes: - """ - Read some bytes from the file. - - To be awaitable, compatible with async, this is run in threadpool. - """ - return await super().read(size) - - async def seek( - self, - offset: Annotated[ - int, - Doc( - """ - The position in bytes to seek to in the file. - """ - ), - ], - ) -> None: - """ - Move to a position in the file. - - Any next read or write will be done from that position. - - To be awaitable, compatible with async, this is run in threadpool. - """ - return await super().seek(offset) - - async def close(self) -> None: - """ - Close the file. - - To be awaitable, compatible with async, this is run in threadpool. - """ - return await super().close() - - @classmethod - def __get_validators__(cls: Type["UploadFile"]) -> Iterable[Callable[..., Any]]: - yield cls.validate - - @classmethod - def validate(cls: Type["UploadFile"], v: Any) -> Any: - if not isinstance(v, StarletteUploadFile): - raise ValueError(f"Expected UploadFile, received: {type(v)}") - return v - - @classmethod - def _validate(cls, __input_value: Any, _: Any) -> "UploadFile": - if not isinstance(__input_value, StarletteUploadFile): - raise ValueError(f"Expected UploadFile, received: {type(__input_value)}") - return cast(UploadFile, __input_value) - - if not PYDANTIC_V2: - - @classmethod - def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: - field_schema.update({"type": "string", "format": "binary"}) - - @classmethod - def __get_pydantic_json_schema__( - cls, core_schema: CoreSchema, handler: GetJsonSchemaHandler - ) -> JsonSchemaValue: - return {"type": "string", "format": "binary"} - - @classmethod - def __get_pydantic_core_schema__( - cls, source: Type[Any], handler: Callable[[Any], CoreSchema] - ) -> CoreSchema: - return with_info_plain_validator_function(cls._validate) - - -class DefaultPlaceholder: - """ - You shouldn't use this class directly. - - It's used internally to recognize when a default value has been overwritten, even - if the overridden default value was truthy. - """ - - def __init__(self, value: Any): - self.value = value - - def __bool__(self) -> bool: - return bool(self.value) - - def __eq__(self, o: object) -> bool: - return isinstance(o, DefaultPlaceholder) and o.value == self.value - - -DefaultType = TypeVar("DefaultType") - - -def Default(value: DefaultType) -> DefaultType: - """ - You shouldn't use this function directly. - - It's used internally to recognize when a default value has been overwritten, even - if the overridden default value was truthy. - """ - return DefaultPlaceholder(value) # type: ignore diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/H_V_A_R_.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/H_V_A_R_.py deleted file mode 100644 index 094aedaea5ebc5c88b33e448ea8f131563acd3c0..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/H_V_A_R_.py +++ /dev/null @@ -1,5 +0,0 @@ -from .otBase import BaseTTXConverter - - -class table_H_V_A_R_(BaseTTXConverter): - pass diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/otData.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/otData.py deleted file mode 100644 index 56716824ecd7950dda249a159b5b292dbd2a86f7..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/otData.py +++ /dev/null @@ -1,6236 +0,0 @@ -otData = [ - # - # common - # - ("LookupOrder", []), - ( - "ScriptList", - [ - ("uint16", "ScriptCount", None, None, "Number of ScriptRecords"), - ( - "struct", - "ScriptRecord", - "ScriptCount", - 0, - "Array of ScriptRecords -listed alphabetically by ScriptTag", - ), - ], - ), - ( - "ScriptRecord", - [ - ("Tag", "ScriptTag", None, None, "4-byte ScriptTag identifier"), - ( - "Offset", - "Script", - None, - None, - "Offset to Script table-from beginning of ScriptList", - ), - ], - ), - ( - "Script", - [ - ( - "Offset", - "DefaultLangSys", - None, - None, - "Offset to DefaultLangSys table-from beginning of Script table-may be NULL", - ), - ( - "uint16", - "LangSysCount", - None, - None, - "Number of LangSysRecords for this script-excluding the DefaultLangSys", - ), - ( - "struct", - "LangSysRecord", - "LangSysCount", - 0, - "Array of LangSysRecords-listed alphabetically by LangSysTag", - ), - ], - ), - ( - "LangSysRecord", - [ - ("Tag", "LangSysTag", None, None, "4-byte LangSysTag identifier"), - ( - "Offset", - "LangSys", - None, - None, - "Offset to LangSys table-from beginning of Script table", - ), - ], - ), - ( - "LangSys", - [ - ( - "Offset", - "LookupOrder", - None, - None, - "= NULL (reserved for an offset to a reordering table)", - ), - ( - "uint16", - "ReqFeatureIndex", - None, - None, - "Index of a feature required for this language system- if no required features = 0xFFFF", - ), - ( - "uint16", - "FeatureCount", - None, - None, - "Number of FeatureIndex values for this language system-excludes the required feature", - ), - ( - "uint16", - "FeatureIndex", - "FeatureCount", - 0, - "Array of indices into the FeatureList-in arbitrary order", - ), - ], - ), - ( - "FeatureList", - [ - ( - "uint16", - "FeatureCount", - None, - None, - "Number of FeatureRecords in this table", - ), - ( - "struct", - "FeatureRecord", - "FeatureCount", - 0, - "Array of FeatureRecords-zero-based (first feature has FeatureIndex = 0)-listed alphabetically by FeatureTag", - ), - ], - ), - ( - "FeatureRecord", - [ - ("Tag", "FeatureTag", None, None, "4-byte feature identification tag"), - ( - "Offset", - "Feature", - None, - None, - "Offset to Feature table-from beginning of FeatureList", - ), - ], - ), - ( - "Feature", - [ - ( - "Offset", - "FeatureParams", - None, - None, - "= NULL (reserved for offset to FeatureParams)", - ), - ( - "uint16", - "LookupCount", - None, - None, - "Number of LookupList indices for this feature", - ), - ( - "uint16", - "LookupListIndex", - "LookupCount", - 0, - "Array of LookupList indices for this feature -zero-based (first lookup is LookupListIndex = 0)", - ), - ], - ), - ("FeatureParams", []), - ( - "FeatureParamsSize", - [ - ( - "DeciPoints", - "DesignSize", - None, - None, - "The design size in 720/inch units (decipoints).", - ), - ( - "uint16", - "SubfamilyID", - None, - None, - "Serves as an identifier that associates fonts in a subfamily.", - ), - ("NameID", "SubfamilyNameID", None, None, "Subfamily NameID."), - ( - "DeciPoints", - "RangeStart", - None, - None, - "Small end of recommended usage range (exclusive) in 720/inch units.", - ), - ( - "DeciPoints", - "RangeEnd", - None, - None, - "Large end of recommended usage range (inclusive) in 720/inch units.", - ), - ], - ), - ( - "FeatureParamsStylisticSet", - [ - ("uint16", "Version", None, None, "Set to 0."), - ("NameID", "UINameID", None, None, "UI NameID."), - ], - ), - ( - "FeatureParamsCharacterVariants", - [ - ("uint16", "Format", None, None, "Set to 0."), - ("NameID", "FeatUILabelNameID", None, None, "Feature UI label NameID."), - ( - "NameID", - "FeatUITooltipTextNameID", - None, - None, - "Feature UI tooltip text NameID.", - ), - ("NameID", "SampleTextNameID", None, None, "Sample text NameID."), - ("uint16", "NumNamedParameters", None, None, "Number of named parameters."), - ( - "NameID", - "FirstParamUILabelNameID", - None, - None, - "First NameID of UI feature parameters.", - ), - ( - "uint16", - "CharCount", - None, - None, - "Count of characters this feature provides glyph variants for.", - ), - ( - "uint24", - "Character", - "CharCount", - 0, - "Unicode characters for which this feature provides glyph variants.", - ), - ], - ), - ( - "LookupList", - [ - ("uint16", "LookupCount", None, None, "Number of lookups in this table"), - ( - "Offset", - "Lookup", - "LookupCount", - 0, - "Array of offsets to Lookup tables-from beginning of LookupList -zero based (first lookup is Lookup index = 0)", - ), - ], - ), - ( - "Lookup", - [ - ( - "uint16", - "LookupType", - None, - None, - "Different enumerations for GSUB and GPOS", - ), - ("LookupFlag", "LookupFlag", None, None, "Lookup qualifiers"), - ( - "uint16", - "SubTableCount", - None, - None, - "Number of SubTables for this lookup", - ), - ( - "Offset", - "SubTable", - "SubTableCount", - 0, - "Array of offsets to SubTables-from beginning of Lookup table", - ), - ( - "uint16", - "MarkFilteringSet", - None, - "LookupFlag & 0x0010", - "If set, indicates that the lookup table structure is followed by a MarkFilteringSet field. The layout engine skips over all mark glyphs not in the mark filtering set indicated.", - ), - ], - ), - ( - "CoverageFormat1", - [ - ("uint16", "CoverageFormat", None, None, "Format identifier-format = 1"), - ("uint16", "GlyphCount", None, None, "Number of glyphs in the GlyphArray"), - ( - "GlyphID", - "GlyphArray", - "GlyphCount", - 0, - "Array of GlyphIDs-in numerical order", - ), - ], - ), - ( - "CoverageFormat2", - [ - ("uint16", "CoverageFormat", None, None, "Format identifier-format = 2"), - ("uint16", "RangeCount", None, None, "Number of RangeRecords"), - ( - "struct", - "RangeRecord", - "RangeCount", - 0, - "Array of glyph ranges-ordered by Start GlyphID", - ), - ], - ), - ( - "RangeRecord", - [ - ("GlyphID", "Start", None, None, "First GlyphID in the range"), - ("GlyphID", "End", None, None, "Last GlyphID in the range"), - ( - "uint16", - "StartCoverageIndex", - None, - None, - "Coverage Index of first GlyphID in range", - ), - ], - ), - ( - "ClassDefFormat1", - [ - ("uint16", "ClassFormat", None, None, "Format identifier-format = 1"), - ( - "GlyphID", - "StartGlyph", - None, - None, - "First GlyphID of the ClassValueArray", - ), - ("uint16", "GlyphCount", None, None, "Size of the ClassValueArray"), - ( - "uint16", - "ClassValueArray", - "GlyphCount", - 0, - "Array of Class Values-one per GlyphID", - ), - ], - ), - ( - "ClassDefFormat2", - [ - ("uint16", "ClassFormat", None, None, "Format identifier-format = 2"), - ("uint16", "ClassRangeCount", None, None, "Number of ClassRangeRecords"), - ( - "struct", - "ClassRangeRecord", - "ClassRangeCount", - 0, - "Array of ClassRangeRecords-ordered by Start GlyphID", - ), - ], - ), - ( - "ClassRangeRecord", - [ - ("GlyphID", "Start", None, None, "First GlyphID in the range"), - ("GlyphID", "End", None, None, "Last GlyphID in the range"), - ("uint16", "Class", None, None, "Applied to all glyphs in the range"), - ], - ), - ( - "Device", - [ - ("uint16", "StartSize", None, None, "Smallest size to correct-in ppem"), - ("uint16", "EndSize", None, None, "Largest size to correct-in ppem"), - ( - "uint16", - "DeltaFormat", - None, - None, - "Format of DeltaValue array data: 1, 2, or 3", - ), - ( - "DeltaValue", - "DeltaValue", - "", - "DeltaFormat in (1,2,3)", - "Array of compressed data", - ), - ], - ), - # - # gpos - # - ( - "GPOS", - [ - ( - "Version", - "Version", - None, - None, - "Version of the GPOS table- 0x00010000 or 0x00010001", - ), - ( - "Offset", - "ScriptList", - None, - None, - "Offset to ScriptList table-from beginning of GPOS table", - ), - ( - "Offset", - "FeatureList", - None, - None, - "Offset to FeatureList table-from beginning of GPOS table", - ), - ( - "Offset", - "LookupList", - None, - None, - "Offset to LookupList table-from beginning of GPOS table", - ), - ( - "LOffset", - "FeatureVariations", - None, - "Version >= 0x00010001", - "Offset to FeatureVariations table-from beginning of GPOS table", - ), - ], - ), - ( - "SinglePosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of SinglePos subtable", - ), - ( - "uint16", - "ValueFormat", - None, - None, - "Defines the types of data in the ValueRecord", - ), - ( - "ValueRecord", - "Value", - None, - None, - "Defines positioning value(s)-applied to all glyphs in the Coverage table", - ), - ], - ), - ( - "SinglePosFormat2", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of SinglePos subtable", - ), - ( - "uint16", - "ValueFormat", - None, - None, - "Defines the types of data in the ValueRecord", - ), - ("uint16", "ValueCount", None, None, "Number of ValueRecords"), - ( - "ValueRecord", - "Value", - "ValueCount", - 0, - "Array of ValueRecords-positioning values applied to glyphs", - ), - ], - ), - ( - "PairPosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of PairPos subtable-only the first glyph in each pair", - ), - ( - "uint16", - "ValueFormat1", - None, - None, - "Defines the types of data in ValueRecord1-for the first glyph in the pair -may be zero (0)", - ), - ( - "uint16", - "ValueFormat2", - None, - None, - "Defines the types of data in ValueRecord2-for the second glyph in the pair -may be zero (0)", - ), - ("uint16", "PairSetCount", None, None, "Number of PairSet tables"), - ( - "Offset", - "PairSet", - "PairSetCount", - 0, - "Array of offsets to PairSet tables-from beginning of PairPos subtable-ordered by Coverage Index", - ), - ], - ), - ( - "PairSet", - [ - ("uint16", "PairValueCount", None, None, "Number of PairValueRecords"), - ( - "struct", - "PairValueRecord", - "PairValueCount", - 0, - "Array of PairValueRecords-ordered by GlyphID of the second glyph", - ), - ], - ), - ( - "PairValueRecord", - [ - ( - "GlyphID", - "SecondGlyph", - None, - None, - "GlyphID of second glyph in the pair-first glyph is listed in the Coverage table", - ), - ( - "ValueRecord", - "Value1", - None, - None, - "Positioning data for the first glyph in the pair", - ), - ( - "ValueRecord", - "Value2", - None, - None, - "Positioning data for the second glyph in the pair", - ), - ], - ), - ( - "PairPosFormat2", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of PairPos subtable-for the first glyph of the pair", - ), - ( - "uint16", - "ValueFormat1", - None, - None, - "ValueRecord definition-for the first glyph of the pair-may be zero (0)", - ), - ( - "uint16", - "ValueFormat2", - None, - None, - "ValueRecord definition-for the second glyph of the pair-may be zero (0)", - ), - ( - "Offset", - "ClassDef1", - None, - None, - "Offset to ClassDef table-from beginning of PairPos subtable-for the first glyph of the pair", - ), - ( - "Offset", - "ClassDef2", - None, - None, - "Offset to ClassDef table-from beginning of PairPos subtable-for the second glyph of the pair", - ), - ( - "uint16", - "Class1Count", - None, - None, - "Number of classes in ClassDef1 table-includes Class0", - ), - ( - "uint16", - "Class2Count", - None, - None, - "Number of classes in ClassDef2 table-includes Class0", - ), - ( - "struct", - "Class1Record", - "Class1Count", - 0, - "Array of Class1 records-ordered by Class1", - ), - ], - ), - ( - "Class1Record", - [ - ( - "struct", - "Class2Record", - "Class2Count", - 0, - "Array of Class2 records-ordered by Class2", - ), - ], - ), - ( - "Class2Record", - [ - ( - "ValueRecord", - "Value1", - None, - None, - "Positioning for first glyph-empty if ValueFormat1 = 0", - ), - ( - "ValueRecord", - "Value2", - None, - None, - "Positioning for second glyph-empty if ValueFormat2 = 0", - ), - ], - ), - ( - "CursivePosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of CursivePos subtable", - ), - ("uint16", "EntryExitCount", None, None, "Number of EntryExit records"), - ( - "struct", - "EntryExitRecord", - "EntryExitCount", - 0, - "Array of EntryExit records-in Coverage Index order", - ), - ], - ), - ( - "EntryExitRecord", - [ - ( - "Offset", - "EntryAnchor", - None, - None, - "Offset to EntryAnchor table-from beginning of CursivePos subtable-may be NULL", - ), - ( - "Offset", - "ExitAnchor", - None, - None, - "Offset to ExitAnchor table-from beginning of CursivePos subtable-may be NULL", - ), - ], - ), - ( - "MarkBasePosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "MarkCoverage", - None, - None, - "Offset to MarkCoverage table-from beginning of MarkBasePos subtable", - ), - ( - "Offset", - "BaseCoverage", - None, - None, - "Offset to BaseCoverage table-from beginning of MarkBasePos subtable", - ), - ("uint16", "ClassCount", None, None, "Number of classes defined for marks"), - ( - "Offset", - "MarkArray", - None, - None, - "Offset to MarkArray table-from beginning of MarkBasePos subtable", - ), - ( - "Offset", - "BaseArray", - None, - None, - "Offset to BaseArray table-from beginning of MarkBasePos subtable", - ), - ], - ), - ( - "BaseArray", - [ - ("uint16", "BaseCount", None, None, "Number of BaseRecords"), - ( - "struct", - "BaseRecord", - "BaseCount", - 0, - "Array of BaseRecords-in order of BaseCoverage Index", - ), - ], - ), - ( - "BaseRecord", - [ - ( - "Offset", - "BaseAnchor", - "ClassCount", - 0, - "Array of offsets (one per class) to Anchor tables-from beginning of BaseArray table-ordered by class-zero-based", - ), - ], - ), - ( - "MarkLigPosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "MarkCoverage", - None, - None, - "Offset to Mark Coverage table-from beginning of MarkLigPos subtable", - ), - ( - "Offset", - "LigatureCoverage", - None, - None, - "Offset to Ligature Coverage table-from beginning of MarkLigPos subtable", - ), - ("uint16", "ClassCount", None, None, "Number of defined mark classes"), - ( - "Offset", - "MarkArray", - None, - None, - "Offset to MarkArray table-from beginning of MarkLigPos subtable", - ), - ( - "Offset", - "LigatureArray", - None, - None, - "Offset to LigatureArray table-from beginning of MarkLigPos subtable", - ), - ], - ), - ( - "LigatureArray", - [ - ( - "uint16", - "LigatureCount", - None, - None, - "Number of LigatureAttach table offsets", - ), - ( - "Offset", - "LigatureAttach", - "LigatureCount", - 0, - "Array of offsets to LigatureAttach tables-from beginning of LigatureArray table-ordered by LigatureCoverage Index", - ), - ], - ), - ( - "LigatureAttach", - [ - ( - "uint16", - "ComponentCount", - None, - None, - "Number of ComponentRecords in this ligature", - ), - ( - "struct", - "ComponentRecord", - "ComponentCount", - 0, - "Array of Component records-ordered in writing direction", - ), - ], - ), - ( - "ComponentRecord", - [ - ( - "Offset", - "LigatureAnchor", - "ClassCount", - 0, - "Array of offsets (one per class) to Anchor tables-from beginning of LigatureAttach table-ordered by class-NULL if a component does not have an attachment for a class-zero-based array", - ), - ], - ), - ( - "MarkMarkPosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Mark1Coverage", - None, - None, - "Offset to Combining Mark Coverage table-from beginning of MarkMarkPos subtable", - ), - ( - "Offset", - "Mark2Coverage", - None, - None, - "Offset to Base Mark Coverage table-from beginning of MarkMarkPos subtable", - ), - ( - "uint16", - "ClassCount", - None, - None, - "Number of Combining Mark classes defined", - ), - ( - "Offset", - "Mark1Array", - None, - None, - "Offset to MarkArray table for Mark1-from beginning of MarkMarkPos subtable", - ), - ( - "Offset", - "Mark2Array", - None, - None, - "Offset to Mark2Array table for Mark2-from beginning of MarkMarkPos subtable", - ), - ], - ), - ( - "Mark2Array", - [ - ("uint16", "Mark2Count", None, None, "Number of Mark2 records"), - ( - "struct", - "Mark2Record", - "Mark2Count", - 0, - "Array of Mark2 records-in Coverage order", - ), - ], - ), - ( - "Mark2Record", - [ - ( - "Offset", - "Mark2Anchor", - "ClassCount", - 0, - "Array of offsets (one per class) to Anchor tables-from beginning of Mark2Array table-zero-based array", - ), - ], - ), - ( - "PosLookupRecord", - [ - ( - "uint16", - "SequenceIndex", - None, - None, - "Index to input glyph sequence-first glyph = 0", - ), - ( - "uint16", - "LookupListIndex", - None, - None, - "Lookup to apply to that position-zero-based", - ), - ], - ), - ( - "ContextPosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of ContextPos subtable", - ), - ("uint16", "PosRuleSetCount", None, None, "Number of PosRuleSet tables"), - ( - "Offset", - "PosRuleSet", - "PosRuleSetCount", - 0, - "Array of offsets to PosRuleSet tables-from beginning of ContextPos subtable-ordered by Coverage Index", - ), - ], - ), - ( - "PosRuleSet", - [ - ("uint16", "PosRuleCount", None, None, "Number of PosRule tables"), - ( - "Offset", - "PosRule", - "PosRuleCount", - 0, - "Array of offsets to PosRule tables-from beginning of PosRuleSet-ordered by preference", - ), - ], - ), - ( - "PosRule", - [ - ( - "uint16", - "GlyphCount", - None, - None, - "Number of glyphs in the Input glyph sequence", - ), - ("uint16", "PosCount", None, None, "Number of PosLookupRecords"), - ( - "GlyphID", - "Input", - "GlyphCount", - -1, - "Array of input GlyphIDs-starting with the second glyph", - ), - ( - "struct", - "PosLookupRecord", - "PosCount", - 0, - "Array of positioning lookups-in design order", - ), - ], - ), - ( - "ContextPosFormat2", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of ContextPos subtable", - ), - ( - "Offset", - "ClassDef", - None, - None, - "Offset to ClassDef table-from beginning of ContextPos subtable", - ), - ("uint16", "PosClassSetCount", None, None, "Number of PosClassSet tables"), - ( - "Offset", - "PosClassSet", - "PosClassSetCount", - 0, - "Array of offsets to PosClassSet tables-from beginning of ContextPos subtable-ordered by class-may be NULL", - ), - ], - ), - ( - "PosClassSet", - [ - ( - "uint16", - "PosClassRuleCount", - None, - None, - "Number of PosClassRule tables", - ), - ( - "Offset", - "PosClassRule", - "PosClassRuleCount", - 0, - "Array of offsets to PosClassRule tables-from beginning of PosClassSet-ordered by preference", - ), - ], - ), - ( - "PosClassRule", - [ - ("uint16", "GlyphCount", None, None, "Number of glyphs to be matched"), - ("uint16", "PosCount", None, None, "Number of PosLookupRecords"), - ( - "uint16", - "Class", - "GlyphCount", - -1, - "Array of classes-beginning with the second class-to be matched to the input glyph sequence", - ), - ( - "struct", - "PosLookupRecord", - "PosCount", - 0, - "Array of positioning lookups-in design order", - ), - ], - ), - ( - "ContextPosFormat3", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 3"), - ( - "uint16", - "GlyphCount", - None, - None, - "Number of glyphs in the input sequence", - ), - ("uint16", "PosCount", None, None, "Number of PosLookupRecords"), - ( - "Offset", - "Coverage", - "GlyphCount", - 0, - "Array of offsets to Coverage tables-from beginning of ContextPos subtable", - ), - ( - "struct", - "PosLookupRecord", - "PosCount", - 0, - "Array of positioning lookups-in design order", - ), - ], - ), - ( - "ChainContextPosFormat1", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of ContextPos subtable", - ), - ( - "uint16", - "ChainPosRuleSetCount", - None, - None, - "Number of ChainPosRuleSet tables", - ), - ( - "Offset", - "ChainPosRuleSet", - "ChainPosRuleSetCount", - 0, - "Array of offsets to ChainPosRuleSet tables-from beginning of ContextPos subtable-ordered by Coverage Index", - ), - ], - ), - ( - "ChainPosRuleSet", - [ - ( - "uint16", - "ChainPosRuleCount", - None, - None, - "Number of ChainPosRule tables", - ), - ( - "Offset", - "ChainPosRule", - "ChainPosRuleCount", - 0, - "Array of offsets to ChainPosRule tables-from beginning of ChainPosRuleSet-ordered by preference", - ), - ], - ), - ( - "ChainPosRule", - [ - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)", - ), - ( - "GlyphID", - "Backtrack", - "BacktrackGlyphCount", - 0, - "Array of backtracking GlyphID's (to be matched before the input sequence)", - ), - ( - "uint16", - "InputGlyphCount", - None, - None, - "Total number of glyphs in the input sequence (includes the first glyph)", - ), - ( - "GlyphID", - "Input", - "InputGlyphCount", - -1, - "Array of input GlyphIDs (start with second glyph)", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Total number of glyphs in the look ahead sequence (number of glyphs to be matched after the input sequence)", - ), - ( - "GlyphID", - "LookAhead", - "LookAheadGlyphCount", - 0, - "Array of lookahead GlyphID's (to be matched after the input sequence)", - ), - ("uint16", "PosCount", None, None, "Number of PosLookupRecords"), - ( - "struct", - "PosLookupRecord", - "PosCount", - 0, - "Array of PosLookupRecords (in design order)", - ), - ], - ), - ( - "ChainContextPosFormat2", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of ChainContextPos subtable", - ), - ( - "Offset", - "BacktrackClassDef", - None, - None, - "Offset to ClassDef table containing backtrack sequence context-from beginning of ChainContextPos subtable", - ), - ( - "Offset", - "InputClassDef", - None, - None, - "Offset to ClassDef table containing input sequence context-from beginning of ChainContextPos subtable", - ), - ( - "Offset", - "LookAheadClassDef", - None, - None, - "Offset to ClassDef table containing lookahead sequence context-from beginning of ChainContextPos subtable", - ), - ( - "uint16", - "ChainPosClassSetCount", - None, - None, - "Number of ChainPosClassSet tables", - ), - ( - "Offset", - "ChainPosClassSet", - "ChainPosClassSetCount", - 0, - "Array of offsets to ChainPosClassSet tables-from beginning of ChainContextPos subtable-ordered by input class-may be NULL", - ), - ], - ), - ( - "ChainPosClassSet", - [ - ( - "uint16", - "ChainPosClassRuleCount", - None, - None, - "Number of ChainPosClassRule tables", - ), - ( - "Offset", - "ChainPosClassRule", - "ChainPosClassRuleCount", - 0, - "Array of offsets to ChainPosClassRule tables-from beginning of ChainPosClassSet-ordered by preference", - ), - ], - ), - ( - "ChainPosClassRule", - [ - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)", - ), - ( - "uint16", - "Backtrack", - "BacktrackGlyphCount", - 0, - "Array of backtracking classes(to be matched before the input sequence)", - ), - ( - "uint16", - "InputGlyphCount", - None, - None, - "Total number of classes in the input sequence (includes the first class)", - ), - ( - "uint16", - "Input", - "InputGlyphCount", - -1, - "Array of input classes(start with second class; to be matched with the input glyph sequence)", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Total number of classes in the look ahead sequence (number of classes to be matched after the input sequence)", - ), - ( - "uint16", - "LookAhead", - "LookAheadGlyphCount", - 0, - "Array of lookahead classes(to be matched after the input sequence)", - ), - ("uint16", "PosCount", None, None, "Number of PosLookupRecords"), - ( - "struct", - "PosLookupRecord", - "PosCount", - 0, - "Array of PosLookupRecords (in design order)", - ), - ], - ), - ( - "ChainContextPosFormat3", - [ - ("uint16", "PosFormat", None, None, "Format identifier-format = 3"), - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Number of glyphs in the backtracking sequence", - ), - ( - "Offset", - "BacktrackCoverage", - "BacktrackGlyphCount", - 0, - "Array of offsets to coverage tables in backtracking sequence, in glyph sequence order", - ), - ( - "uint16", - "InputGlyphCount", - None, - None, - "Number of glyphs in input sequence", - ), - ( - "Offset", - "InputCoverage", - "InputGlyphCount", - 0, - "Array of offsets to coverage tables in input sequence, in glyph sequence order", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Number of glyphs in lookahead sequence", - ), - ( - "Offset", - "LookAheadCoverage", - "LookAheadGlyphCount", - 0, - "Array of offsets to coverage tables in lookahead sequence, in glyph sequence order", - ), - ("uint16", "PosCount", None, None, "Number of PosLookupRecords"), - ( - "struct", - "PosLookupRecord", - "PosCount", - 0, - "Array of PosLookupRecords,in design order", - ), - ], - ), - ( - "ExtensionPosFormat1", - [ - ("uint16", "ExtFormat", None, None, "Format identifier. Set to 1."), - ( - "uint16", - "ExtensionLookupType", - None, - None, - "Lookup type of subtable referenced by ExtensionOffset (i.e. the extension subtable).", - ), - ("LOffset", "ExtSubTable", None, None, "Offset to SubTable"), - ], - ), - # ('ValueRecord', [ - # ('int16', 'XPlacement', None, None, 'Horizontal adjustment for placement-in design units'), - # ('int16', 'YPlacement', None, None, 'Vertical adjustment for placement-in design units'), - # ('int16', 'XAdvance', None, None, 'Horizontal adjustment for advance-in design units (only used for horizontal writing)'), - # ('int16', 'YAdvance', None, None, 'Vertical adjustment for advance-in design units (only used for vertical writing)'), - # ('Offset', 'XPlaDevice', None, None, 'Offset to Device table for horizontal placement-measured from beginning of PosTable (may be NULL)'), - # ('Offset', 'YPlaDevice', None, None, 'Offset to Device table for vertical placement-measured from beginning of PosTable (may be NULL)'), - # ('Offset', 'XAdvDevice', None, None, 'Offset to Device table for horizontal advance-measured from beginning of PosTable (may be NULL)'), - # ('Offset', 'YAdvDevice', None, None, 'Offset to Device table for vertical advance-measured from beginning of PosTable (may be NULL)'), - # ]), - ( - "AnchorFormat1", - [ - ("uint16", "AnchorFormat", None, None, "Format identifier-format = 1"), - ("int16", "XCoordinate", None, None, "Horizontal value-in design units"), - ("int16", "YCoordinate", None, None, "Vertical value-in design units"), - ], - ), - ( - "AnchorFormat2", - [ - ("uint16", "AnchorFormat", None, None, "Format identifier-format = 2"), - ("int16", "XCoordinate", None, None, "Horizontal value-in design units"), - ("int16", "YCoordinate", None, None, "Vertical value-in design units"), - ("uint16", "AnchorPoint", None, None, "Index to glyph contour point"), - ], - ), - ( - "AnchorFormat3", - [ - ("uint16", "AnchorFormat", None, None, "Format identifier-format = 3"), - ("int16", "XCoordinate", None, None, "Horizontal value-in design units"), - ("int16", "YCoordinate", None, None, "Vertical value-in design units"), - ( - "Offset", - "XDeviceTable", - None, - None, - "Offset to Device table for X coordinate- from beginning of Anchor table (may be NULL)", - ), - ( - "Offset", - "YDeviceTable", - None, - None, - "Offset to Device table for Y coordinate- from beginning of Anchor table (may be NULL)", - ), - ], - ), - ( - "MarkArray", - [ - ("uint16", "MarkCount", None, None, "Number of MarkRecords"), - ( - "struct", - "MarkRecord", - "MarkCount", - 0, - "Array of MarkRecords-in Coverage order", - ), - ], - ), - ( - "MarkRecord", - [ - ("uint16", "Class", None, None, "Class defined for this mark"), - ( - "Offset", - "MarkAnchor", - None, - None, - "Offset to Anchor table-from beginning of MarkArray table", - ), - ], - ), - # - # gsub - # - ( - "GSUB", - [ - ( - "Version", - "Version", - None, - None, - "Version of the GSUB table- 0x00010000 or 0x00010001", - ), - ( - "Offset", - "ScriptList", - None, - None, - "Offset to ScriptList table-from beginning of GSUB table", - ), - ( - "Offset", - "FeatureList", - None, - None, - "Offset to FeatureList table-from beginning of GSUB table", - ), - ( - "Offset", - "LookupList", - None, - None, - "Offset to LookupList table-from beginning of GSUB table", - ), - ( - "LOffset", - "FeatureVariations", - None, - "Version >= 0x00010001", - "Offset to FeatureVariations table-from beginning of GSUB table", - ), - ], - ), - ( - "SingleSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "uint16", - "DeltaGlyphID", - None, - None, - "Add to original GlyphID modulo 65536 to get substitute GlyphID", - ), - ], - ), - ( - "SingleSubstFormat2", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "uint16", - "GlyphCount", - None, - None, - "Number of GlyphIDs in the Substitute array", - ), - ( - "GlyphID", - "Substitute", - "GlyphCount", - 0, - "Array of substitute GlyphIDs-ordered by Coverage Index", - ), - ], - ), - ( - "MultipleSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "uint16", - "SequenceCount", - None, - None, - "Number of Sequence table offsets in the Sequence array", - ), - ( - "Offset", - "Sequence", - "SequenceCount", - 0, - "Array of offsets to Sequence tables-from beginning of Substitution table-ordered by Coverage Index", - ), - ], - ), - ( - "Sequence", - [ - ( - "uint16", - "GlyphCount", - None, - None, - "Number of GlyphIDs in the Substitute array. This should always be greater than 0.", - ), - ( - "GlyphID", - "Substitute", - "GlyphCount", - 0, - "String of GlyphIDs to substitute", - ), - ], - ), - ( - "AlternateSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "uint16", - "AlternateSetCount", - None, - None, - "Number of AlternateSet tables", - ), - ( - "Offset", - "AlternateSet", - "AlternateSetCount", - 0, - "Array of offsets to AlternateSet tables-from beginning of Substitution table-ordered by Coverage Index", - ), - ], - ), - ( - "AlternateSet", - [ - ( - "uint16", - "GlyphCount", - None, - None, - "Number of GlyphIDs in the Alternate array", - ), - ( - "GlyphID", - "Alternate", - "GlyphCount", - 0, - "Array of alternate GlyphIDs-in arbitrary order", - ), - ], - ), - ( - "LigatureSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ("uint16", "LigSetCount", None, None, "Number of LigatureSet tables"), - ( - "Offset", - "LigatureSet", - "LigSetCount", - 0, - "Array of offsets to LigatureSet tables-from beginning of Substitution table-ordered by Coverage Index", - ), - ], - ), - ( - "LigatureSet", - [ - ("uint16", "LigatureCount", None, None, "Number of Ligature tables"), - ( - "Offset", - "Ligature", - "LigatureCount", - 0, - "Array of offsets to Ligature tables-from beginning of LigatureSet table-ordered by preference", - ), - ], - ), - ( - "Ligature", - [ - ("GlyphID", "LigGlyph", None, None, "GlyphID of ligature to substitute"), - ("uint16", "CompCount", None, None, "Number of components in the ligature"), - ( - "GlyphID", - "Component", - "CompCount", - -1, - "Array of component GlyphIDs-start with the second component-ordered in writing direction", - ), - ], - ), - ( - "SubstLookupRecord", - [ - ( - "uint16", - "SequenceIndex", - None, - None, - "Index into current glyph sequence-first glyph = 0", - ), - ( - "uint16", - "LookupListIndex", - None, - None, - "Lookup to apply to that position-zero-based", - ), - ], - ), - ( - "ContextSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "uint16", - "SubRuleSetCount", - None, - None, - "Number of SubRuleSet tables-must equal GlyphCount in Coverage table", - ), - ( - "Offset", - "SubRuleSet", - "SubRuleSetCount", - 0, - "Array of offsets to SubRuleSet tables-from beginning of Substitution table-ordered by Coverage Index", - ), - ], - ), - ( - "SubRuleSet", - [ - ("uint16", "SubRuleCount", None, None, "Number of SubRule tables"), - ( - "Offset", - "SubRule", - "SubRuleCount", - 0, - "Array of offsets to SubRule tables-from beginning of SubRuleSet table-ordered by preference", - ), - ], - ), - ( - "SubRule", - [ - ( - "uint16", - "GlyphCount", - None, - None, - "Total number of glyphs in input glyph sequence-includes the first glyph", - ), - ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"), - ( - "GlyphID", - "Input", - "GlyphCount", - -1, - "Array of input GlyphIDs-start with second glyph", - ), - ( - "struct", - "SubstLookupRecord", - "SubstCount", - 0, - "Array of SubstLookupRecords-in design order", - ), - ], - ), - ( - "ContextSubstFormat2", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "Offset", - "ClassDef", - None, - None, - "Offset to glyph ClassDef table-from beginning of Substitution table", - ), - ("uint16", "SubClassSetCount", None, None, "Number of SubClassSet tables"), - ( - "Offset", - "SubClassSet", - "SubClassSetCount", - 0, - "Array of offsets to SubClassSet tables-from beginning of Substitution table-ordered by class-may be NULL", - ), - ], - ), - ( - "SubClassSet", - [ - ( - "uint16", - "SubClassRuleCount", - None, - None, - "Number of SubClassRule tables", - ), - ( - "Offset", - "SubClassRule", - "SubClassRuleCount", - 0, - "Array of offsets to SubClassRule tables-from beginning of SubClassSet-ordered by preference", - ), - ], - ), - ( - "SubClassRule", - [ - ( - "uint16", - "GlyphCount", - None, - None, - "Total number of classes specified for the context in the rule-includes the first class", - ), - ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"), - ( - "uint16", - "Class", - "GlyphCount", - -1, - "Array of classes-beginning with the second class-to be matched to the input glyph class sequence", - ), - ( - "struct", - "SubstLookupRecord", - "SubstCount", - 0, - "Array of Substitution lookups-in design order", - ), - ], - ), - ( - "ContextSubstFormat3", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 3"), - ( - "uint16", - "GlyphCount", - None, - None, - "Number of glyphs in the input glyph sequence", - ), - ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"), - ( - "Offset", - "Coverage", - "GlyphCount", - 0, - "Array of offsets to Coverage table-from beginning of Substitution table-in glyph sequence order", - ), - ( - "struct", - "SubstLookupRecord", - "SubstCount", - 0, - "Array of SubstLookupRecords-in design order", - ), - ], - ), - ( - "ChainContextSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "uint16", - "ChainSubRuleSetCount", - None, - None, - "Number of ChainSubRuleSet tables-must equal GlyphCount in Coverage table", - ), - ( - "Offset", - "ChainSubRuleSet", - "ChainSubRuleSetCount", - 0, - "Array of offsets to ChainSubRuleSet tables-from beginning of Substitution table-ordered by Coverage Index", - ), - ], - ), - ( - "ChainSubRuleSet", - [ - ( - "uint16", - "ChainSubRuleCount", - None, - None, - "Number of ChainSubRule tables", - ), - ( - "Offset", - "ChainSubRule", - "ChainSubRuleCount", - 0, - "Array of offsets to ChainSubRule tables-from beginning of ChainSubRuleSet table-ordered by preference", - ), - ], - ), - ( - "ChainSubRule", - [ - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)", - ), - ( - "GlyphID", - "Backtrack", - "BacktrackGlyphCount", - 0, - "Array of backtracking GlyphID's (to be matched before the input sequence)", - ), - ( - "uint16", - "InputGlyphCount", - None, - None, - "Total number of glyphs in the input sequence (includes the first glyph)", - ), - ( - "GlyphID", - "Input", - "InputGlyphCount", - -1, - "Array of input GlyphIDs (start with second glyph)", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Total number of glyphs in the look ahead sequence (number of glyphs to be matched after the input sequence)", - ), - ( - "GlyphID", - "LookAhead", - "LookAheadGlyphCount", - 0, - "Array of lookahead GlyphID's (to be matched after the input sequence)", - ), - ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"), - ( - "struct", - "SubstLookupRecord", - "SubstCount", - 0, - "Array of SubstLookupRecords (in design order)", - ), - ], - ), - ( - "ChainContextSubstFormat2", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 2"), - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table-from beginning of Substitution table", - ), - ( - "Offset", - "BacktrackClassDef", - None, - None, - "Offset to glyph ClassDef table containing backtrack sequence data-from beginning of Substitution table", - ), - ( - "Offset", - "InputClassDef", - None, - None, - "Offset to glyph ClassDef table containing input sequence data-from beginning of Substitution table", - ), - ( - "Offset", - "LookAheadClassDef", - None, - None, - "Offset to glyph ClassDef table containing lookahead sequence data-from beginning of Substitution table", - ), - ( - "uint16", - "ChainSubClassSetCount", - None, - None, - "Number of ChainSubClassSet tables", - ), - ( - "Offset", - "ChainSubClassSet", - "ChainSubClassSetCount", - 0, - "Array of offsets to ChainSubClassSet tables-from beginning of Substitution table-ordered by input class-may be NULL", - ), - ], - ), - ( - "ChainSubClassSet", - [ - ( - "uint16", - "ChainSubClassRuleCount", - None, - None, - "Number of ChainSubClassRule tables", - ), - ( - "Offset", - "ChainSubClassRule", - "ChainSubClassRuleCount", - 0, - "Array of offsets to ChainSubClassRule tables-from beginning of ChainSubClassSet-ordered by preference", - ), - ], - ), - ( - "ChainSubClassRule", - [ - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Total number of glyphs in the backtrack sequence (number of glyphs to be matched before the first glyph)", - ), - ( - "uint16", - "Backtrack", - "BacktrackGlyphCount", - 0, - "Array of backtracking classes(to be matched before the input sequence)", - ), - ( - "uint16", - "InputGlyphCount", - None, - None, - "Total number of classes in the input sequence (includes the first class)", - ), - ( - "uint16", - "Input", - "InputGlyphCount", - -1, - "Array of input classes(start with second class; to be matched with the input glyph sequence)", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Total number of classes in the look ahead sequence (number of classes to be matched after the input sequence)", - ), - ( - "uint16", - "LookAhead", - "LookAheadGlyphCount", - 0, - "Array of lookahead classes(to be matched after the input sequence)", - ), - ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"), - ( - "struct", - "SubstLookupRecord", - "SubstCount", - 0, - "Array of SubstLookupRecords (in design order)", - ), - ], - ), - ( - "ChainContextSubstFormat3", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 3"), - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Number of glyphs in the backtracking sequence", - ), - ( - "Offset", - "BacktrackCoverage", - "BacktrackGlyphCount", - 0, - "Array of offsets to coverage tables in backtracking sequence, in glyph sequence order", - ), - ( - "uint16", - "InputGlyphCount", - None, - None, - "Number of glyphs in input sequence", - ), - ( - "Offset", - "InputCoverage", - "InputGlyphCount", - 0, - "Array of offsets to coverage tables in input sequence, in glyph sequence order", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Number of glyphs in lookahead sequence", - ), - ( - "Offset", - "LookAheadCoverage", - "LookAheadGlyphCount", - 0, - "Array of offsets to coverage tables in lookahead sequence, in glyph sequence order", - ), - ("uint16", "SubstCount", None, None, "Number of SubstLookupRecords"), - ( - "struct", - "SubstLookupRecord", - "SubstCount", - 0, - "Array of SubstLookupRecords, in design order", - ), - ], - ), - ( - "ExtensionSubstFormat1", - [ - ("uint16", "ExtFormat", None, None, "Format identifier. Set to 1."), - ( - "uint16", - "ExtensionLookupType", - None, - None, - "Lookup type of subtable referenced by ExtensionOffset (i.e. the extension subtable).", - ), - ( - "LOffset", - "ExtSubTable", - None, - None, - "Array of offsets to Lookup tables-from beginning of LookupList -zero based (first lookup is Lookup index = 0)", - ), - ], - ), - ( - "ReverseChainSingleSubstFormat1", - [ - ("uint16", "SubstFormat", None, None, "Format identifier-format = 1"), - ( - "Offset", - "Coverage", - None, - 0, - "Offset to Coverage table - from beginning of Substitution table", - ), - ( - "uint16", - "BacktrackGlyphCount", - None, - None, - "Number of glyphs in the backtracking sequence", - ), - ( - "Offset", - "BacktrackCoverage", - "BacktrackGlyphCount", - 0, - "Array of offsets to coverage tables in backtracking sequence, in glyph sequence order", - ), - ( - "uint16", - "LookAheadGlyphCount", - None, - None, - "Number of glyphs in lookahead sequence", - ), - ( - "Offset", - "LookAheadCoverage", - "LookAheadGlyphCount", - 0, - "Array of offsets to coverage tables in lookahead sequence, in glyph sequence order", - ), - ( - "uint16", - "GlyphCount", - None, - None, - "Number of GlyphIDs in the Substitute array", - ), - ( - "GlyphID", - "Substitute", - "GlyphCount", - 0, - "Array of substitute GlyphIDs-ordered by Coverage index", - ), - ], - ), - # - # gdef - # - ( - "GDEF", - [ - ( - "Version", - "Version", - None, - None, - "Version of the GDEF table- 0x00010000, 0x00010002, or 0x00010003", - ), - ( - "Offset", - "GlyphClassDef", - None, - None, - "Offset to class definition table for glyph type-from beginning of GDEF header (may be NULL)", - ), - ( - "Offset", - "AttachList", - None, - None, - "Offset to list of glyphs with attachment points-from beginning of GDEF header (may be NULL)", - ), - ( - "Offset", - "LigCaretList", - None, - None, - "Offset to list of positioning points for ligature carets-from beginning of GDEF header (may be NULL)", - ), - ( - "Offset", - "MarkAttachClassDef", - None, - None, - "Offset to class definition table for mark attachment type-from beginning of GDEF header (may be NULL)", - ), - ( - "Offset", - "MarkGlyphSetsDef", - None, - "Version >= 0x00010002", - "Offset to the table of mark set definitions-from beginning of GDEF header (may be NULL)", - ), - ( - "LOffset", - "VarStore", - None, - "Version >= 0x00010003", - "Offset to variation store (may be NULL)", - ), - ], - ), - ( - "AttachList", - [ - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table - from beginning of AttachList table", - ), - ( - "uint16", - "GlyphCount", - None, - None, - "Number of glyphs with attachment points", - ), - ( - "Offset", - "AttachPoint", - "GlyphCount", - 0, - "Array of offsets to AttachPoint tables-from beginning of AttachList table-in Coverage Index order", - ), - ], - ), - ( - "AttachPoint", - [ - ( - "uint16", - "PointCount", - None, - None, - "Number of attachment points on this glyph", - ), - ( - "uint16", - "PointIndex", - "PointCount", - 0, - "Array of contour point indices -in increasing numerical order", - ), - ], - ), - ( - "LigCaretList", - [ - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table - from beginning of LigCaretList table", - ), - ("uint16", "LigGlyphCount", None, None, "Number of ligature glyphs"), - ( - "Offset", - "LigGlyph", - "LigGlyphCount", - 0, - "Array of offsets to LigGlyph tables-from beginning of LigCaretList table-in Coverage Index order", - ), - ], - ), - ( - "LigGlyph", - [ - ( - "uint16", - "CaretCount", - None, - None, - "Number of CaretValues for this ligature (components - 1)", - ), - ( - "Offset", - "CaretValue", - "CaretCount", - 0, - "Array of offsets to CaretValue tables-from beginning of LigGlyph table-in increasing coordinate order", - ), - ], - ), - ( - "CaretValueFormat1", - [ - ("uint16", "CaretValueFormat", None, None, "Format identifier-format = 1"), - ("int16", "Coordinate", None, None, "X or Y value, in design units"), - ], - ), - ( - "CaretValueFormat2", - [ - ("uint16", "CaretValueFormat", None, None, "Format identifier-format = 2"), - ("uint16", "CaretValuePoint", None, None, "Contour point index on glyph"), - ], - ), - ( - "CaretValueFormat3", - [ - ("uint16", "CaretValueFormat", None, None, "Format identifier-format = 3"), - ("int16", "Coordinate", None, None, "X or Y value, in design units"), - ( - "Offset", - "DeviceTable", - None, - None, - "Offset to Device table for X or Y value-from beginning of CaretValue table", - ), - ], - ), - ( - "MarkGlyphSetsDef", - [ - ("uint16", "MarkSetTableFormat", None, None, "Format identifier == 1"), - ("uint16", "MarkSetCount", None, None, "Number of mark sets defined"), - ( - "LOffset", - "Coverage", - "MarkSetCount", - 0, - "Array of offsets to mark set coverage tables.", - ), - ], - ), - # - # base - # - ( - "BASE", - [ - ( - "Version", - "Version", - None, - None, - "Version of the BASE table-initially 0x00010000", - ), - ( - "Offset", - "HorizAxis", - None, - None, - "Offset to horizontal Axis table-from beginning of BASE table-may be NULL", - ), - ( - "Offset", - "VertAxis", - None, - None, - "Offset to vertical Axis table-from beginning of BASE table-may be NULL", - ), - ( - "LOffset", - "VarStore", - None, - "Version >= 0x00010001", - "Offset to variation store (may be NULL)", - ), - ], - ), - ( - "Axis", - [ - ( - "Offset", - "BaseTagList", - None, - None, - "Offset to BaseTagList table-from beginning of Axis table-may be NULL", - ), - ( - "Offset", - "BaseScriptList", - None, - None, - "Offset to BaseScriptList table-from beginning of Axis table", - ), - ], - ), - ( - "BaseTagList", - [ - ( - "uint16", - "BaseTagCount", - None, - None, - "Number of baseline identification tags in this text direction-may be zero (0)", - ), - ( - "Tag", - "BaselineTag", - "BaseTagCount", - 0, - "Array of 4-byte baseline identification tags-must be in alphabetical order", - ), - ], - ), - ( - "BaseScriptList", - [ - ( - "uint16", - "BaseScriptCount", - None, - None, - "Number of BaseScriptRecords defined", - ), - ( - "struct", - "BaseScriptRecord", - "BaseScriptCount", - 0, - "Array of BaseScriptRecords-in alphabetical order by BaseScriptTag", - ), - ], - ), - ( - "BaseScriptRecord", - [ - ("Tag", "BaseScriptTag", None, None, "4-byte script identification tag"), - ( - "Offset", - "BaseScript", - None, - None, - "Offset to BaseScript table-from beginning of BaseScriptList", - ), - ], - ), - ( - "BaseScript", - [ - ( - "Offset", - "BaseValues", - None, - None, - "Offset to BaseValues table-from beginning of BaseScript table-may be NULL", - ), - ( - "Offset", - "DefaultMinMax", - None, - None, - "Offset to MinMax table- from beginning of BaseScript table-may be NULL", - ), - ( - "uint16", - "BaseLangSysCount", - None, - None, - "Number of BaseLangSysRecords defined-may be zero (0)", - ), - ( - "struct", - "BaseLangSysRecord", - "BaseLangSysCount", - 0, - "Array of BaseLangSysRecords-in alphabetical order by BaseLangSysTag", - ), - ], - ), - ( - "BaseLangSysRecord", - [ - ( - "Tag", - "BaseLangSysTag", - None, - None, - "4-byte language system identification tag", - ), - ( - "Offset", - "MinMax", - None, - None, - "Offset to MinMax table-from beginning of BaseScript table", - ), - ], - ), - ( - "BaseValues", - [ - ( - "uint16", - "DefaultIndex", - None, - None, - "Index number of default baseline for this script-equals index position of baseline tag in BaselineArray of the BaseTagList", - ), - ( - "uint16", - "BaseCoordCount", - None, - None, - "Number of BaseCoord tables defined-should equal BaseTagCount in the BaseTagList", - ), - ( - "Offset", - "BaseCoord", - "BaseCoordCount", - 0, - "Array of offsets to BaseCoord-from beginning of BaseValues table-order matches BaselineTag array in the BaseTagList", - ), - ], - ), - ( - "MinMax", - [ - ( - "Offset", - "MinCoord", - None, - None, - "Offset to BaseCoord table-defines minimum extent value-from the beginning of MinMax table-may be NULL", - ), - ( - "Offset", - "MaxCoord", - None, - None, - "Offset to BaseCoord table-defines maximum extent value-from the beginning of MinMax table-may be NULL", - ), - ( - "uint16", - "FeatMinMaxCount", - None, - None, - "Number of FeatMinMaxRecords-may be zero (0)", - ), - ( - "struct", - "FeatMinMaxRecord", - "FeatMinMaxCount", - 0, - "Array of FeatMinMaxRecords-in alphabetical order, by FeatureTableTag", - ), - ], - ), - ( - "FeatMinMaxRecord", - [ - ( - "Tag", - "FeatureTableTag", - None, - None, - "4-byte feature identification tag-must match FeatureTag in FeatureList", - ), - ( - "Offset", - "MinCoord", - None, - None, - "Offset to BaseCoord table-defines minimum extent value-from beginning of MinMax table-may be NULL", - ), - ( - "Offset", - "MaxCoord", - None, - None, - "Offset to BaseCoord table-defines maximum extent value-from beginning of MinMax table-may be NULL", - ), - ], - ), - ( - "BaseCoordFormat1", - [ - ("uint16", "BaseCoordFormat", None, None, "Format identifier-format = 1"), - ("int16", "Coordinate", None, None, "X or Y value, in design units"), - ], - ), - ( - "BaseCoordFormat2", - [ - ("uint16", "BaseCoordFormat", None, None, "Format identifier-format = 2"), - ("int16", "Coordinate", None, None, "X or Y value, in design units"), - ("GlyphID", "ReferenceGlyph", None, None, "GlyphID of control glyph"), - ( - "uint16", - "BaseCoordPoint", - None, - None, - "Index of contour point on the ReferenceGlyph", - ), - ], - ), - ( - "BaseCoordFormat3", - [ - ("uint16", "BaseCoordFormat", None, None, "Format identifier-format = 3"), - ("int16", "Coordinate", None, None, "X or Y value, in design units"), - ( - "Offset", - "DeviceTable", - None, - None, - "Offset to Device table for X or Y value", - ), - ], - ), - # - # jstf - # - ( - "JSTF", - [ - ( - "Version", - "Version", - None, - None, - "Version of the JSTF table-initially set to 0x00010000", - ), - ( - "uint16", - "JstfScriptCount", - None, - None, - "Number of JstfScriptRecords in this table", - ), - ( - "struct", - "JstfScriptRecord", - "JstfScriptCount", - 0, - "Array of JstfScriptRecords-in alphabetical order, by JstfScriptTag", - ), - ], - ), - ( - "JstfScriptRecord", - [ - ("Tag", "JstfScriptTag", None, None, "4-byte JstfScript identification"), - ( - "Offset", - "JstfScript", - None, - None, - "Offset to JstfScript table-from beginning of JSTF Header", - ), - ], - ), - ( - "JstfScript", - [ - ( - "Offset", - "ExtenderGlyph", - None, - None, - "Offset to ExtenderGlyph table-from beginning of JstfScript table-may be NULL", - ), - ( - "Offset", - "DefJstfLangSys", - None, - None, - "Offset to Default JstfLangSys table-from beginning of JstfScript table-may be NULL", - ), - ( - "uint16", - "JstfLangSysCount", - None, - None, - "Number of JstfLangSysRecords in this table- may be zero (0)", - ), - ( - "struct", - "JstfLangSysRecord", - "JstfLangSysCount", - 0, - "Array of JstfLangSysRecords-in alphabetical order, by JstfLangSysTag", - ), - ], - ), - ( - "JstfLangSysRecord", - [ - ("Tag", "JstfLangSysTag", None, None, "4-byte JstfLangSys identifier"), - ( - "Offset", - "JstfLangSys", - None, - None, - "Offset to JstfLangSys table-from beginning of JstfScript table", - ), - ], - ), - ( - "ExtenderGlyph", - [ - ( - "uint16", - "GlyphCount", - None, - None, - "Number of Extender Glyphs in this script", - ), - ( - "GlyphID", - "ExtenderGlyph", - "GlyphCount", - 0, - "GlyphIDs-in increasing numerical order", - ), - ], - ), - ( - "JstfLangSys", - [ - ( - "uint16", - "JstfPriorityCount", - None, - None, - "Number of JstfPriority tables", - ), - ( - "Offset", - "JstfPriority", - "JstfPriorityCount", - 0, - "Array of offsets to JstfPriority tables-from beginning of JstfLangSys table-in priority order", - ), - ], - ), - ( - "JstfPriority", - [ - ( - "Offset", - "ShrinkageEnableGSUB", - None, - None, - "Offset to Shrinkage Enable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL", - ), - ( - "Offset", - "ShrinkageDisableGSUB", - None, - None, - "Offset to Shrinkage Disable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL", - ), - ( - "Offset", - "ShrinkageEnableGPOS", - None, - None, - "Offset to Shrinkage Enable JstfGPOSModList table-from beginning of JstfPriority table-may be NULL", - ), - ( - "Offset", - "ShrinkageDisableGPOS", - None, - None, - "Offset to Shrinkage Disable JstfGPOSModList table-from beginning of JstfPriority table-may be NULL", - ), - ( - "Offset", - "ShrinkageJstfMax", - None, - None, - "Offset to Shrinkage JstfMax table-from beginning of JstfPriority table -may be NULL", - ), - ( - "Offset", - "ExtensionEnableGSUB", - None, - None, - "Offset to Extension Enable JstfGSUBModList table-may be NULL", - ), - ( - "Offset", - "ExtensionDisableGSUB", - None, - None, - "Offset to Extension Disable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL", - ), - ( - "Offset", - "ExtensionEnableGPOS", - None, - None, - "Offset to Extension Enable JstfGSUBModList table-may be NULL", - ), - ( - "Offset", - "ExtensionDisableGPOS", - None, - None, - "Offset to Extension Disable JstfGSUBModList table-from beginning of JstfPriority table-may be NULL", - ), - ( - "Offset", - "ExtensionJstfMax", - None, - None, - "Offset to Extension JstfMax table-from beginning of JstfPriority table -may be NULL", - ), - ], - ), - ( - "JstfGSUBModList", - [ - ( - "uint16", - "LookupCount", - None, - None, - "Number of lookups for this modification", - ), - ( - "uint16", - "GSUBLookupIndex", - "LookupCount", - 0, - "Array of LookupIndex identifiers in GSUB-in increasing numerical order", - ), - ], - ), - ( - "JstfGPOSModList", - [ - ( - "uint16", - "LookupCount", - None, - None, - "Number of lookups for this modification", - ), - ( - "uint16", - "GPOSLookupIndex", - "LookupCount", - 0, - "Array of LookupIndex identifiers in GPOS-in increasing numerical order", - ), - ], - ), - ( - "JstfMax", - [ - ( - "uint16", - "LookupCount", - None, - None, - "Number of lookup Indices for this modification", - ), - ( - "Offset", - "Lookup", - "LookupCount", - 0, - "Array of offsets to GPOS-type lookup tables-from beginning of JstfMax table-in design order", - ), - ], - ), - # - # STAT - # - ( - "STAT", - [ - ( - "Version", - "Version", - None, - None, - "Version of the table-initially set to 0x00010000, currently 0x00010002.", - ), - ( - "uint16", - "DesignAxisRecordSize", - None, - None, - "Size in bytes of each design axis record", - ), - ("uint16", "DesignAxisCount", None, None, "Number of design axis records"), - ( - "LOffsetTo(AxisRecordArray)", - "DesignAxisRecord", - None, - None, - "Offset in bytes from the beginning of the STAT table to the start of the design axes array", - ), - ("uint16", "AxisValueCount", None, None, "Number of axis value tables"), - ( - "LOffsetTo(AxisValueArray)", - "AxisValueArray", - None, - None, - "Offset in bytes from the beginning of the STAT table to the start of the axes value offset array", - ), - ( - "NameID", - "ElidedFallbackNameID", - None, - "Version >= 0x00010001", - "NameID to use when all style attributes are elided.", - ), - ], - ), - ( - "AxisRecordArray", - [ - ("AxisRecord", "Axis", "DesignAxisCount", 0, "Axis records"), - ], - ), - ( - "AxisRecord", - [ - ( - "Tag", - "AxisTag", - None, - None, - "A tag identifying the axis of design variation", - ), - ( - "NameID", - "AxisNameID", - None, - None, - 'The name ID for entries in the "name" table that provide a display string for this axis', - ), - ( - "uint16", - "AxisOrdering", - None, - None, - "A value that applications can use to determine primary sorting of face names, or for ordering of descriptors when composing family or face names", - ), - ( - "uint8", - "MoreBytes", - "DesignAxisRecordSize", - -8, - "Extra bytes. Set to empty array.", - ), - ], - ), - ( - "AxisValueArray", - [ - ("Offset", "AxisValue", "AxisValueCount", 0, "Axis values"), - ], - ), - ( - "AxisValueFormat1", - [ - ("uint16", "Format", None, None, "Format, = 1"), - ( - "uint16", - "AxisIndex", - None, - None, - "Index into the axis record array identifying the axis of design variation to which the axis value record applies.", - ), - ("STATFlags", "Flags", None, None, "Flags."), - ("NameID", "ValueNameID", None, None, ""), - ("Fixed", "Value", None, None, ""), - ], - ), - ( - "AxisValueFormat2", - [ - ("uint16", "Format", None, None, "Format, = 2"), - ( - "uint16", - "AxisIndex", - None, - None, - "Index into the axis record array identifying the axis of design variation to which the axis value record applies.", - ), - ("STATFlags", "Flags", None, None, "Flags."), - ("NameID", "ValueNameID", None, None, ""), - ("Fixed", "NominalValue", None, None, ""), - ("Fixed", "RangeMinValue", None, None, ""), - ("Fixed", "RangeMaxValue", None, None, ""), - ], - ), - ( - "AxisValueFormat3", - [ - ("uint16", "Format", None, None, "Format, = 3"), - ( - "uint16", - "AxisIndex", - None, - None, - "Index into the axis record array identifying the axis of design variation to which the axis value record applies.", - ), - ("STATFlags", "Flags", None, None, "Flags."), - ("NameID", "ValueNameID", None, None, ""), - ("Fixed", "Value", None, None, ""), - ("Fixed", "LinkedValue", None, None, ""), - ], - ), - ( - "AxisValueFormat4", - [ - ("uint16", "Format", None, None, "Format, = 4"), - ( - "uint16", - "AxisCount", - None, - None, - "The total number of axes contributing to this axis-values combination.", - ), - ("STATFlags", "Flags", None, None, "Flags."), - ("NameID", "ValueNameID", None, None, ""), - ( - "struct", - "AxisValueRecord", - "AxisCount", - 0, - "Array of AxisValue records that provide the combination of axis values, one for each contributing axis. ", - ), - ], - ), - ( - "AxisValueRecord", - [ - ( - "uint16", - "AxisIndex", - None, - None, - "Index into the axis record array identifying the axis of design variation to which the axis value record applies.", - ), - ("Fixed", "Value", None, None, "A numeric value for this attribute value."), - ], - ), - # - # Variation fonts - # - # GSUB/GPOS FeatureVariations - ( - "FeatureVariations", - [ - ( - "Version", - "Version", - None, - None, - "Version of the table-initially set to 0x00010000", - ), - ( - "uint32", - "FeatureVariationCount", - None, - None, - "Number of records in the FeatureVariationRecord array", - ), - ( - "struct", - "FeatureVariationRecord", - "FeatureVariationCount", - 0, - "Array of FeatureVariationRecord", - ), - ], - ), - ( - "FeatureVariationRecord", - [ - ( - "LOffset", - "ConditionSet", - None, - None, - "Offset to a ConditionSet table, from beginning of the FeatureVariations table.", - ), - ( - "LOffset", - "FeatureTableSubstitution", - None, - None, - "Offset to a FeatureTableSubstitution table, from beginning of the FeatureVariations table", - ), - ], - ), - ( - "ConditionSet", - [ - ( - "uint16", - "ConditionCount", - None, - None, - "Number of condition tables in the ConditionTable array", - ), - ( - "LOffset", - "ConditionTable", - "ConditionCount", - 0, - "Array of condition tables.", - ), - ], - ), - ( - "ConditionTableFormat1", - [ - ("uint16", "Format", None, None, "Format, = 1"), - ( - "uint16", - "AxisIndex", - None, - None, - "Index for the variation axis within the fvar table, base 0.", - ), - ( - "F2Dot14", - "FilterRangeMinValue", - None, - None, - "Minimum normalized axis value of the font variation instances that satisfy this condition.", - ), - ( - "F2Dot14", - "FilterRangeMaxValue", - None, - None, - "Maximum value that satisfies this condition.", - ), - ], - ), - ( - "FeatureTableSubstitution", - [ - ( - "Version", - "Version", - None, - None, - "Version of the table-initially set to 0x00010000", - ), - ( - "uint16", - "SubstitutionCount", - None, - None, - "Number of records in the FeatureVariationRecords array", - ), - ( - "FeatureTableSubstitutionRecord", - "SubstitutionRecord", - "SubstitutionCount", - 0, - "Array of FeatureTableSubstitutionRecord", - ), - ], - ), - ( - "FeatureTableSubstitutionRecord", - [ - ("uint16", "FeatureIndex", None, None, "The feature table index to match."), - ( - "LOffset", - "Feature", - None, - None, - "Offset to an alternate feature table, from start of the FeatureTableSubstitution table.", - ), - ], - ), - # VariationStore - ( - "VarRegionAxis", - [ - ("F2Dot14", "StartCoord", None, None, ""), - ("F2Dot14", "PeakCoord", None, None, ""), - ("F2Dot14", "EndCoord", None, None, ""), - ], - ), - ( - "VarRegion", - [ - ("struct", "VarRegionAxis", "RegionAxisCount", 0, ""), - ], - ), - ( - "VarRegionList", - [ - ("uint16", "RegionAxisCount", None, None, ""), - ("uint16", "RegionCount", None, None, ""), - ("VarRegion", "Region", "RegionCount", 0, ""), - ], - ), - ( - "VarData", - [ - ("uint16", "ItemCount", None, None, ""), - ("uint16", "NumShorts", None, None, ""), - ("uint16", "VarRegionCount", None, None, ""), - ("uint16", "VarRegionIndex", "VarRegionCount", 0, ""), - ("VarDataValue", "Item", "ItemCount", 0, ""), - ], - ), - ( - "VarStore", - [ - ("uint16", "Format", None, None, "Set to 1."), - ("LOffset", "VarRegionList", None, None, ""), - ("uint16", "VarDataCount", None, None, ""), - ("LOffset", "VarData", "VarDataCount", 0, ""), - ], - ), - # Variation helpers - ( - "VarIdxMap", - [ - ("uint16", "EntryFormat", None, None, ""), # Automatically computed - ("uint16", "MappingCount", None, None, ""), # Automatically computed - ("VarIdxMapValue", "mapping", "", 0, "Array of compressed data"), - ], - ), - ( - "DeltaSetIndexMapFormat0", - [ - ("uint8", "Format", None, None, "Format of the DeltaSetIndexMap = 0"), - ("uint8", "EntryFormat", None, None, ""), # Automatically computed - ("uint16", "MappingCount", None, None, ""), # Automatically computed - ("VarIdxMapValue", "mapping", "", 0, "Array of compressed data"), - ], - ), - ( - "DeltaSetIndexMapFormat1", - [ - ("uint8", "Format", None, None, "Format of the DeltaSetIndexMap = 1"), - ("uint8", "EntryFormat", None, None, ""), # Automatically computed - ("uint32", "MappingCount", None, None, ""), # Automatically computed - ("VarIdxMapValue", "mapping", "", 0, "Array of compressed data"), - ], - ), - # Glyph advance variations - ( - "HVAR", - [ - ( - "Version", - "Version", - None, - None, - "Version of the HVAR table-initially = 0x00010000", - ), - ("LOffset", "VarStore", None, None, ""), - ("LOffsetTo(VarIdxMap)", "AdvWidthMap", None, None, ""), - ("LOffsetTo(VarIdxMap)", "LsbMap", None, None, ""), - ("LOffsetTo(VarIdxMap)", "RsbMap", None, None, ""), - ], - ), - ( - "VVAR", - [ - ( - "Version", - "Version", - None, - None, - "Version of the VVAR table-initially = 0x00010000", - ), - ("LOffset", "VarStore", None, None, ""), - ("LOffsetTo(VarIdxMap)", "AdvHeightMap", None, None, ""), - ("LOffsetTo(VarIdxMap)", "TsbMap", None, None, ""), - ("LOffsetTo(VarIdxMap)", "BsbMap", None, None, ""), - ("LOffsetTo(VarIdxMap)", "VOrgMap", None, None, "Vertical origin mapping."), - ], - ), - # Font-wide metrics variations - ( - "MetricsValueRecord", - [ - ("Tag", "ValueTag", None, None, "4-byte font-wide measure identifier"), - ("uint32", "VarIdx", None, None, "Combined outer-inner variation index"), - ( - "uint8", - "MoreBytes", - "ValueRecordSize", - -8, - "Extra bytes. Set to empty array.", - ), - ], - ), - ( - "MVAR", - [ - ( - "Version", - "Version", - None, - None, - "Version of the MVAR table-initially = 0x00010000", - ), - ("uint16", "Reserved", None, None, "Set to 0"), - ("uint16", "ValueRecordSize", None, None, ""), - ("uint16", "ValueRecordCount", None, None, ""), - ("Offset", "VarStore", None, None, ""), - ("MetricsValueRecord", "ValueRecord", "ValueRecordCount", 0, ""), - ], - ), - # - # math - # - ( - "MATH", - [ - ( - "Version", - "Version", - None, - None, - "Version of the MATH table-initially set to 0x00010000.", - ), - ( - "Offset", - "MathConstants", - None, - None, - "Offset to MathConstants table - from the beginning of MATH table.", - ), - ( - "Offset", - "MathGlyphInfo", - None, - None, - "Offset to MathGlyphInfo table - from the beginning of MATH table.", - ), - ( - "Offset", - "MathVariants", - None, - None, - "Offset to MathVariants table - from the beginning of MATH table.", - ), - ], - ), - ( - "MathValueRecord", - [ - ("int16", "Value", None, None, "The X or Y value in design units."), - ( - "Offset", - "DeviceTable", - None, - None, - "Offset to the device table - from the beginning of parent table. May be NULL. Suggested format for device table is 1.", - ), - ], - ), - ( - "MathConstants", - [ - ( - "int16", - "ScriptPercentScaleDown", - None, - None, - "Percentage of scaling down for script level 1. Suggested value: 80%.", - ), - ( - "int16", - "ScriptScriptPercentScaleDown", - None, - None, - "Percentage of scaling down for script level 2 (ScriptScript). Suggested value: 60%.", - ), - ( - "uint16", - "DelimitedSubFormulaMinHeight", - None, - None, - "Minimum height required for a delimited expression to be treated as a subformula. Suggested value: normal line height x1.5.", - ), - ( - "uint16", - "DisplayOperatorMinHeight", - None, - None, - "Minimum height of n-ary operators (such as integral and summation) for formulas in display mode.", - ), - ( - "MathValueRecord", - "MathLeading", - None, - None, - "White space to be left between math formulas to ensure proper line spacing. For example, for applications that treat line gap as a part of line ascender, formulas with ink going above (os2.sTypoAscender + os2.sTypoLineGap - MathLeading) or with ink going below os2.sTypoDescender will result in increasing line height.", - ), - ("MathValueRecord", "AxisHeight", None, None, "Axis height of the font."), - ( - "MathValueRecord", - "AccentBaseHeight", - None, - None, - "Maximum (ink) height of accent base that does not require raising the accents. Suggested: x-height of the font (os2.sxHeight) plus any possible overshots.", - ), - ( - "MathValueRecord", - "FlattenedAccentBaseHeight", - None, - None, - "Maximum (ink) height of accent base that does not require flattening the accents. Suggested: cap height of the font (os2.sCapHeight).", - ), - ( - "MathValueRecord", - "SubscriptShiftDown", - None, - None, - "The standard shift down applied to subscript elements. Positive for moving in the downward direction. Suggested: os2.ySubscriptYOffset.", - ), - ( - "MathValueRecord", - "SubscriptTopMax", - None, - None, - "Maximum allowed height of the (ink) top of subscripts that does not require moving subscripts further down. Suggested: 4/5 x-height.", - ), - ( - "MathValueRecord", - "SubscriptBaselineDropMin", - None, - None, - "Minimum allowed drop of the baseline of subscripts relative to the (ink) bottom of the base. Checked for bases that are treated as a box or extended shape. Positive for subscript baseline dropped below the base bottom.", - ), - ( - "MathValueRecord", - "SuperscriptShiftUp", - None, - None, - "Standard shift up applied to superscript elements. Suggested: os2.ySuperscriptYOffset.", - ), - ( - "MathValueRecord", - "SuperscriptShiftUpCramped", - None, - None, - "Standard shift of superscripts relative to the base, in cramped style.", - ), - ( - "MathValueRecord", - "SuperscriptBottomMin", - None, - None, - "Minimum allowed height of the (ink) bottom of superscripts that does not require moving subscripts further up. Suggested: 1/4 x-height.", - ), - ( - "MathValueRecord", - "SuperscriptBaselineDropMax", - None, - None, - "Maximum allowed drop of the baseline of superscripts relative to the (ink) top of the base. Checked for bases that are treated as a box or extended shape. Positive for superscript baseline below the base top.", - ), - ( - "MathValueRecord", - "SubSuperscriptGapMin", - None, - None, - "Minimum gap between the superscript and subscript ink. Suggested: 4x default rule thickness.", - ), - ( - "MathValueRecord", - "SuperscriptBottomMaxWithSubscript", - None, - None, - "The maximum level to which the (ink) bottom of superscript can be pushed to increase the gap between superscript and subscript, before subscript starts being moved down. Suggested: 4/5 x-height.", - ), - ( - "MathValueRecord", - "SpaceAfterScript", - None, - None, - "Extra white space to be added after each subscript and superscript. Suggested: 0.5pt for a 12 pt font.", - ), - ( - "MathValueRecord", - "UpperLimitGapMin", - None, - None, - "Minimum gap between the (ink) bottom of the upper limit, and the (ink) top of the base operator.", - ), - ( - "MathValueRecord", - "UpperLimitBaselineRiseMin", - None, - None, - "Minimum distance between baseline of upper limit and (ink) top of the base operator.", - ), - ( - "MathValueRecord", - "LowerLimitGapMin", - None, - None, - "Minimum gap between (ink) top of the lower limit, and (ink) bottom of the base operator.", - ), - ( - "MathValueRecord", - "LowerLimitBaselineDropMin", - None, - None, - "Minimum distance between baseline of the lower limit and (ink) bottom of the base operator.", - ), - ( - "MathValueRecord", - "StackTopShiftUp", - None, - None, - "Standard shift up applied to the top element of a stack.", - ), - ( - "MathValueRecord", - "StackTopDisplayStyleShiftUp", - None, - None, - "Standard shift up applied to the top element of a stack in display style.", - ), - ( - "MathValueRecord", - "StackBottomShiftDown", - None, - None, - "Standard shift down applied to the bottom element of a stack. Positive for moving in the downward direction.", - ), - ( - "MathValueRecord", - "StackBottomDisplayStyleShiftDown", - None, - None, - "Standard shift down applied to the bottom element of a stack in display style. Positive for moving in the downward direction.", - ), - ( - "MathValueRecord", - "StackGapMin", - None, - None, - "Minimum gap between (ink) bottom of the top element of a stack, and the (ink) top of the bottom element. Suggested: 3x default rule thickness.", - ), - ( - "MathValueRecord", - "StackDisplayStyleGapMin", - None, - None, - "Minimum gap between (ink) bottom of the top element of a stack, and the (ink) top of the bottom element in display style. Suggested: 7x default rule thickness.", - ), - ( - "MathValueRecord", - "StretchStackTopShiftUp", - None, - None, - "Standard shift up applied to the top element of the stretch stack.", - ), - ( - "MathValueRecord", - "StretchStackBottomShiftDown", - None, - None, - "Standard shift down applied to the bottom element of the stretch stack. Positive for moving in the downward direction.", - ), - ( - "MathValueRecord", - "StretchStackGapAboveMin", - None, - None, - "Minimum gap between the ink of the stretched element, and the (ink) bottom of the element above. Suggested: UpperLimitGapMin", - ), - ( - "MathValueRecord", - "StretchStackGapBelowMin", - None, - None, - "Minimum gap between the ink of the stretched element, and the (ink) top of the element below. Suggested: LowerLimitGapMin.", - ), - ( - "MathValueRecord", - "FractionNumeratorShiftUp", - None, - None, - "Standard shift up applied to the numerator.", - ), - ( - "MathValueRecord", - "FractionNumeratorDisplayStyleShiftUp", - None, - None, - "Standard shift up applied to the numerator in display style. Suggested: StackTopDisplayStyleShiftUp.", - ), - ( - "MathValueRecord", - "FractionDenominatorShiftDown", - None, - None, - "Standard shift down applied to the denominator. Positive for moving in the downward direction.", - ), - ( - "MathValueRecord", - "FractionDenominatorDisplayStyleShiftDown", - None, - None, - "Standard shift down applied to the denominator in display style. Positive for moving in the downward direction. Suggested: StackBottomDisplayStyleShiftDown.", - ), - ( - "MathValueRecord", - "FractionNumeratorGapMin", - None, - None, - "Minimum tolerated gap between the (ink) bottom of the numerator and the ink of the fraction bar. Suggested: default rule thickness", - ), - ( - "MathValueRecord", - "FractionNumDisplayStyleGapMin", - None, - None, - "Minimum tolerated gap between the (ink) bottom of the numerator and the ink of the fraction bar in display style. Suggested: 3x default rule thickness.", - ), - ( - "MathValueRecord", - "FractionRuleThickness", - None, - None, - "Thickness of the fraction bar. Suggested: default rule thickness.", - ), - ( - "MathValueRecord", - "FractionDenominatorGapMin", - None, - None, - "Minimum tolerated gap between the (ink) top of the denominator and the ink of the fraction bar. Suggested: default rule thickness", - ), - ( - "MathValueRecord", - "FractionDenomDisplayStyleGapMin", - None, - None, - "Minimum tolerated gap between the (ink) top of the denominator and the ink of the fraction bar in display style. Suggested: 3x default rule thickness.", - ), - ( - "MathValueRecord", - "SkewedFractionHorizontalGap", - None, - None, - "Horizontal distance between the top and bottom elements of a skewed fraction.", - ), - ( - "MathValueRecord", - "SkewedFractionVerticalGap", - None, - None, - "Vertical distance between the ink of the top and bottom elements of a skewed fraction.", - ), - ( - "MathValueRecord", - "OverbarVerticalGap", - None, - None, - "Distance between the overbar and the (ink) top of he base. Suggested: 3x default rule thickness.", - ), - ( - "MathValueRecord", - "OverbarRuleThickness", - None, - None, - "Thickness of overbar. Suggested: default rule thickness.", - ), - ( - "MathValueRecord", - "OverbarExtraAscender", - None, - None, - "Extra white space reserved above the overbar. Suggested: default rule thickness.", - ), - ( - "MathValueRecord", - "UnderbarVerticalGap", - None, - None, - "Distance between underbar and (ink) bottom of the base. Suggested: 3x default rule thickness.", - ), - ( - "MathValueRecord", - "UnderbarRuleThickness", - None, - None, - "Thickness of underbar. Suggested: default rule thickness.", - ), - ( - "MathValueRecord", - "UnderbarExtraDescender", - None, - None, - "Extra white space reserved below the underbar. Always positive. Suggested: default rule thickness.", - ), - ( - "MathValueRecord", - "RadicalVerticalGap", - None, - None, - "Space between the (ink) top of the expression and the bar over it. Suggested: 1 1/4 default rule thickness.", - ), - ( - "MathValueRecord", - "RadicalDisplayStyleVerticalGap", - None, - None, - "Space between the (ink) top of the expression and the bar over it. Suggested: default rule thickness + 1/4 x-height.", - ), - ( - "MathValueRecord", - "RadicalRuleThickness", - None, - None, - "Thickness of the radical rule. This is the thickness of the rule in designed or constructed radical signs. Suggested: default rule thickness.", - ), - ( - "MathValueRecord", - "RadicalExtraAscender", - None, - None, - "Extra white space reserved above the radical. Suggested: RadicalRuleThickness.", - ), - ( - "MathValueRecord", - "RadicalKernBeforeDegree", - None, - None, - "Extra horizontal kern before the degree of a radical, if such is present. Suggested: 5/18 of em.", - ), - ( - "MathValueRecord", - "RadicalKernAfterDegree", - None, - None, - "Negative kern after the degree of a radical, if such is present. Suggested: 10/18 of em.", - ), - ( - "uint16", - "RadicalDegreeBottomRaisePercent", - None, - None, - "Height of the bottom of the radical degree, if such is present, in proportion to the ascender of the radical sign. Suggested: 60%.", - ), - ], - ), - ( - "MathGlyphInfo", - [ - ( - "Offset", - "MathItalicsCorrectionInfo", - None, - None, - "Offset to MathItalicsCorrectionInfo table - from the beginning of MathGlyphInfo table.", - ), - ( - "Offset", - "MathTopAccentAttachment", - None, - None, - "Offset to MathTopAccentAttachment table - from the beginning of MathGlyphInfo table.", - ), - ( - "Offset", - "ExtendedShapeCoverage", - None, - None, - "Offset to coverage table for Extended Shape glyphs - from the beginning of MathGlyphInfo table. When the left or right glyph of a box is an extended shape variant, the (ink) box (and not the default position defined by values in MathConstants table) should be used for vertical positioning purposes. May be NULL.", - ), - ( - "Offset", - "MathKernInfo", - None, - None, - "Offset to MathKernInfo table - from the beginning of MathGlyphInfo table.", - ), - ], - ), - ( - "MathItalicsCorrectionInfo", - [ - ( - "Offset", - "Coverage", - None, - None, - "Offset to Coverage table - from the beginning of MathItalicsCorrectionInfo table.", - ), - ( - "uint16", - "ItalicsCorrectionCount", - None, - None, - "Number of italics correction values. Should coincide with the number of covered glyphs.", - ), - ( - "MathValueRecord", - "ItalicsCorrection", - "ItalicsCorrectionCount", - 0, - "Array of MathValueRecords defining italics correction values for each covered glyph.", - ), - ], - ), - ( - "MathTopAccentAttachment", - [ - ( - "Offset", - "TopAccentCoverage", - None, - None, - "Offset to Coverage table - from the beginning of MathTopAccentAttachment table.", - ), - ( - "uint16", - "TopAccentAttachmentCount", - None, - None, - "Number of top accent attachment point values. Should coincide with the number of covered glyphs", - ), - ( - "MathValueRecord", - "TopAccentAttachment", - "TopAccentAttachmentCount", - 0, - "Array of MathValueRecords defining top accent attachment points for each covered glyph", - ), - ], - ), - ( - "MathKernInfo", - [ - ( - "Offset", - "MathKernCoverage", - None, - None, - "Offset to Coverage table - from the beginning of the MathKernInfo table.", - ), - ("uint16", "MathKernCount", None, None, "Number of MathKernInfoRecords."), - ( - "MathKernInfoRecord", - "MathKernInfoRecords", - "MathKernCount", - 0, - "Array of MathKernInfoRecords, per-glyph information for mathematical positioning of subscripts and superscripts.", - ), - ], - ), - ( - "MathKernInfoRecord", - [ - ( - "Offset", - "TopRightMathKern", - None, - None, - "Offset to MathKern table for top right corner - from the beginning of MathKernInfo table. May be NULL.", - ), - ( - "Offset", - "TopLeftMathKern", - None, - None, - "Offset to MathKern table for the top left corner - from the beginning of MathKernInfo table. May be NULL.", - ), - ( - "Offset", - "BottomRightMathKern", - None, - None, - "Offset to MathKern table for bottom right corner - from the beginning of MathKernInfo table. May be NULL.", - ), - ( - "Offset", - "BottomLeftMathKern", - None, - None, - "Offset to MathKern table for bottom left corner - from the beginning of MathKernInfo table. May be NULL.", - ), - ], - ), - ( - "MathKern", - [ - ( - "uint16", - "HeightCount", - None, - None, - "Number of heights on which the kern value changes.", - ), - ( - "MathValueRecord", - "CorrectionHeight", - "HeightCount", - 0, - "Array of correction heights at which the kern value changes. Sorted by the height value in design units.", - ), - ( - "MathValueRecord", - "KernValue", - "HeightCount", - 1, - "Array of kern values corresponding to heights. First value is the kern value for all heights less or equal than the first height in this table.Last value is the value to be applied for all heights greater than the last height in this table. Negative values are interpreted as move glyphs closer to each other.", - ), - ], - ), - ( - "MathVariants", - [ - ( - "uint16", - "MinConnectorOverlap", - None, - None, - "Minimum overlap of connecting glyphs during glyph construction, in design units.", - ), - ( - "Offset", - "VertGlyphCoverage", - None, - None, - "Offset to Coverage table - from the beginning of MathVariants table.", - ), - ( - "Offset", - "HorizGlyphCoverage", - None, - None, - "Offset to Coverage table - from the beginning of MathVariants table.", - ), - ( - "uint16", - "VertGlyphCount", - None, - None, - "Number of glyphs for which information is provided for vertically growing variants.", - ), - ( - "uint16", - "HorizGlyphCount", - None, - None, - "Number of glyphs for which information is provided for horizontally growing variants.", - ), - ( - "Offset", - "VertGlyphConstruction", - "VertGlyphCount", - 0, - "Array of offsets to MathGlyphConstruction tables - from the beginning of the MathVariants table, for shapes growing in vertical direction.", - ), - ( - "Offset", - "HorizGlyphConstruction", - "HorizGlyphCount", - 0, - "Array of offsets to MathGlyphConstruction tables - from the beginning of the MathVariants table, for shapes growing in horizontal direction.", - ), - ], - ), - ( - "MathGlyphConstruction", - [ - ( - "Offset", - "GlyphAssembly", - None, - None, - "Offset to GlyphAssembly table for this shape - from the beginning of MathGlyphConstruction table. May be NULL", - ), - ( - "uint16", - "VariantCount", - None, - None, - "Count of glyph growing variants for this glyph.", - ), - ( - "MathGlyphVariantRecord", - "MathGlyphVariantRecord", - "VariantCount", - 0, - "MathGlyphVariantRecords for alternative variants of the glyphs.", - ), - ], - ), - ( - "MathGlyphVariantRecord", - [ - ("GlyphID", "VariantGlyph", None, None, "Glyph ID for the variant."), - ( - "uint16", - "AdvanceMeasurement", - None, - None, - "Advance width/height, in design units, of the variant, in the direction of requested glyph extension.", - ), - ], - ), - ( - "GlyphAssembly", - [ - ( - "MathValueRecord", - "ItalicsCorrection", - None, - None, - "Italics correction of this GlyphAssembly. Should not depend on the assembly size.", - ), - ("uint16", "PartCount", None, None, "Number of parts in this assembly."), - ( - "GlyphPartRecord", - "PartRecords", - "PartCount", - 0, - "Array of part records, from left to right and bottom to top.", - ), - ], - ), - ( - "GlyphPartRecord", - [ - ("GlyphID", "glyph", None, None, "Glyph ID for the part."), - ( - "uint16", - "StartConnectorLength", - None, - None, - "Advance width/ height of the straight bar connector material, in design units, is at the beginning of the glyph, in the direction of the extension.", - ), - ( - "uint16", - "EndConnectorLength", - None, - None, - "Advance width/ height of the straight bar connector material, in design units, is at the end of the glyph, in the direction of the extension.", - ), - ( - "uint16", - "FullAdvance", - None, - None, - "Full advance width/height for this part, in the direction of the extension. In design units.", - ), - ( - "uint16", - "PartFlags", - None, - None, - "Part qualifiers. PartFlags enumeration currently uses only one bit: 0x0001 fExtender: If set, the part can be skipped or repeated. 0xFFFE Reserved", - ), - ], - ), - ## - ## Apple Advanced Typography (AAT) tables - ## - ( - "AATLookupSegment", - [ - ("uint16", "lastGlyph", None, None, "Last glyph index in this segment."), - ("uint16", "firstGlyph", None, None, "First glyph index in this segment."), - ( - "uint16", - "value", - None, - None, - "A 16-bit offset from the start of the table to the data.", - ), - ], - ), - # - # ankr - # - ( - "ankr", - [ - ("struct", "AnchorPoints", None, None, "Anchor points table."), - ], - ), - ( - "AnchorPointsFormat0", - [ - ("uint16", "Format", None, None, "Format of the anchor points table, = 0."), - ("uint16", "Flags", None, None, "Flags. Currenty unused, set to zero."), - ( - "AATLookupWithDataOffset(AnchorGlyphData)", - "Anchors", - None, - None, - "Table of with anchor overrides for each glyph.", - ), - ], - ), - ( - "AnchorGlyphData", - [ - ( - "uint32", - "AnchorPointCount", - None, - None, - "Number of anchor points for this glyph.", - ), - ( - "struct", - "AnchorPoint", - "AnchorPointCount", - 0, - "Individual anchor points.", - ), - ], - ), - ( - "AnchorPoint", - [ - ("int16", "XCoordinate", None, None, "X coordinate of this anchor point."), - ("int16", "YCoordinate", None, None, "Y coordinate of this anchor point."), - ], - ), - # - # bsln - # - ( - "bsln", - [ - ( - "Version", - "Version", - None, - None, - "Version number of the AAT baseline table (0x00010000 for the initial version).", - ), - ("struct", "Baseline", None, None, "Baseline table."), - ], - ), - ( - "BaselineFormat0", - [ - ("uint16", "Format", None, None, "Format of the baseline table, = 0."), - ( - "uint16", - "DefaultBaseline", - None, - None, - "Default baseline value for all glyphs. This value can be from 0 through 31.", - ), - ( - "uint16", - "Delta", - 32, - 0, - "These are the FUnit distance deltas from the font’s natural baseline to the other baselines used in the font. A total of 32 deltas must be assigned.", - ), - ], - ), - ( - "BaselineFormat1", - [ - ("uint16", "Format", None, None, "Format of the baseline table, = 1."), - ( - "uint16", - "DefaultBaseline", - None, - None, - "Default baseline value for all glyphs. This value can be from 0 through 31.", - ), - ( - "uint16", - "Delta", - 32, - 0, - "These are the FUnit distance deltas from the font’s natural baseline to the other baselines used in the font. A total of 32 deltas must be assigned.", - ), - ( - "AATLookup(uint16)", - "BaselineValues", - None, - None, - "Lookup table that maps glyphs to their baseline values.", - ), - ], - ), - ( - "BaselineFormat2", - [ - ("uint16", "Format", None, None, "Format of the baseline table, = 1."), - ( - "uint16", - "DefaultBaseline", - None, - None, - "Default baseline value for all glyphs. This value can be from 0 through 31.", - ), - ( - "GlyphID", - "StandardGlyph", - None, - None, - "Glyph index of the glyph in this font to be used to set the baseline values. This glyph must contain a set of control points (whose numbers are contained in the following field) that determines baseline distances.", - ), - ( - "uint16", - "ControlPoint", - 32, - 0, - "Array of 32 control point numbers, associated with the standard glyph. A value of 0xFFFF means there is no corresponding control point in the standard glyph.", - ), - ], - ), - ( - "BaselineFormat3", - [ - ("uint16", "Format", None, None, "Format of the baseline table, = 1."), - ( - "uint16", - "DefaultBaseline", - None, - None, - "Default baseline value for all glyphs. This value can be from 0 through 31.", - ), - ( - "GlyphID", - "StandardGlyph", - None, - None, - "Glyph index of the glyph in this font to be used to set the baseline values. This glyph must contain a set of control points (whose numbers are contained in the following field) that determines baseline distances.", - ), - ( - "uint16", - "ControlPoint", - 32, - 0, - "Array of 32 control point numbers, associated with the standard glyph. A value of 0xFFFF means there is no corresponding control point in the standard glyph.", - ), - ( - "AATLookup(uint16)", - "BaselineValues", - None, - None, - "Lookup table that maps glyphs to their baseline values.", - ), - ], - ), - # - # cidg - # - ( - "cidg", - [ - ("struct", "CIDGlyphMapping", None, None, "CID-to-glyph mapping table."), - ], - ), - ( - "CIDGlyphMappingFormat0", - [ - ( - "uint16", - "Format", - None, - None, - "Format of the CID-to-glyph mapping table, = 0.", - ), - ("uint16", "DataFormat", None, None, "Currenty unused, set to zero."), - ("uint32", "StructLength", None, None, "Size of the table in bytes."), - ("uint16", "Registry", None, None, "The registry ID."), - ( - "char64", - "RegistryName", - None, - None, - "The registry name in ASCII; unused bytes should be set to 0.", - ), - ("uint16", "Order", None, None, "The order ID."), - ( - "char64", - "OrderName", - None, - None, - "The order name in ASCII; unused bytes should be set to 0.", - ), - ("uint16", "SupplementVersion", None, None, "The supplement version."), - ( - "CIDGlyphMap", - "Mapping", - None, - None, - "A mapping from CIDs to the glyphs in the font, starting with CID 0. If a CID from the identified collection has no glyph in the font, 0xFFFF is used", - ), - ], - ), - # - # feat - # - ( - "feat", - [ - ( - "Version", - "Version", - None, - None, - "Version of the feat table-initially set to 0x00010000.", - ), - ("FeatureNames", "FeatureNames", None, None, "The feature names."), - ], - ), - ( - "FeatureNames", - [ - ( - "uint16", - "FeatureNameCount", - None, - None, - "Number of entries in the feature name array.", - ), - ("uint16", "Reserved1", None, None, "Reserved (set to zero)."), - ("uint32", "Reserved2", None, None, "Reserved (set to zero)."), - ( - "FeatureName", - "FeatureName", - "FeatureNameCount", - 0, - "The feature name array.", - ), - ], - ), - ( - "FeatureName", - [ - ("uint16", "FeatureType", None, None, "Feature type."), - ( - "uint16", - "SettingsCount", - None, - None, - "The number of records in the setting name array.", - ), - ( - "LOffset", - "Settings", - None, - None, - "Offset to setting table for this feature.", - ), - ( - "uint16", - "FeatureFlags", - None, - None, - "Single-bit flags associated with the feature type.", - ), - ( - "NameID", - "FeatureNameID", - None, - None, - "The name table index for the feature name.", - ), - ], - ), - ( - "Settings", - [ - ("Setting", "Setting", "SettingsCount", 0, "The setting array."), - ], - ), - ( - "Setting", - [ - ("uint16", "SettingValue", None, None, "The setting."), - ( - "NameID", - "SettingNameID", - None, - None, - "The name table index for the setting name.", - ), - ], - ), - # - # gcid - # - ( - "gcid", - [ - ("struct", "GlyphCIDMapping", None, None, "Glyph to CID mapping table."), - ], - ), - ( - "GlyphCIDMappingFormat0", - [ - ( - "uint16", - "Format", - None, - None, - "Format of the glyph-to-CID mapping table, = 0.", - ), - ("uint16", "DataFormat", None, None, "Currenty unused, set to zero."), - ("uint32", "StructLength", None, None, "Size of the table in bytes."), - ("uint16", "Registry", None, None, "The registry ID."), - ( - "char64", - "RegistryName", - None, - None, - "The registry name in ASCII; unused bytes should be set to 0.", - ), - ("uint16", "Order", None, None, "The order ID."), - ( - "char64", - "OrderName", - None, - None, - "The order name in ASCII; unused bytes should be set to 0.", - ), - ("uint16", "SupplementVersion", None, None, "The supplement version."), - ( - "GlyphCIDMap", - "Mapping", - None, - None, - "The CIDs for the glyphs in the font, starting with glyph 0. If a glyph does not correspond to a CID in the identified collection, 0xFFFF is used", - ), - ], - ), - # - # lcar - # - ( - "lcar", - [ - ( - "Version", - "Version", - None, - None, - "Version number of the ligature caret table (0x00010000 for the initial version).", - ), - ("struct", "LigatureCarets", None, None, "Ligature carets table."), - ], - ), - ( - "LigatureCaretsFormat0", - [ - ( - "uint16", - "Format", - None, - None, - "Format of the ligature caret table. Format 0 indicates division points are distances in font units, Format 1 indicates division points are indexes of control points.", - ), - ( - "AATLookup(LigCaretDistances)", - "Carets", - None, - None, - "Lookup table associating ligature glyphs with their caret positions, in font unit distances.", - ), - ], - ), - ( - "LigatureCaretsFormat1", - [ - ( - "uint16", - "Format", - None, - None, - "Format of the ligature caret table. Format 0 indicates division points are distances in font units, Format 1 indicates division points are indexes of control points.", - ), - ( - "AATLookup(LigCaretPoints)", - "Carets", - None, - None, - "Lookup table associating ligature glyphs with their caret positions, as control points.", - ), - ], - ), - ( - "LigCaretDistances", - [ - ("uint16", "DivsionPointCount", None, None, "Number of division points."), - ( - "int16", - "DivisionPoint", - "DivsionPointCount", - 0, - "Distance in font units through which a subdivision is made orthogonally to the baseline.", - ), - ], - ), - ( - "LigCaretPoints", - [ - ("uint16", "DivsionPointCount", None, None, "Number of division points."), - ( - "int16", - "DivisionPoint", - "DivsionPointCount", - 0, - "The number of the control point through which a subdivision is made orthogonally to the baseline.", - ), - ], - ), - # - # mort - # - ( - "mort", - [ - ("Version", "Version", None, None, "Version of the mort table."), - ( - "uint32", - "MorphChainCount", - None, - None, - "Number of metamorphosis chains.", - ), - ( - "MortChain", - "MorphChain", - "MorphChainCount", - 0, - "Array of metamorphosis chains.", - ), - ], - ), - ( - "MortChain", - [ - ( - "Flags32", - "DefaultFlags", - None, - None, - "The default specification for subtables.", - ), - ( - "uint32", - "StructLength", - None, - None, - "Total byte count, including this header; must be a multiple of 4.", - ), - ( - "uint16", - "MorphFeatureCount", - None, - None, - "Number of metamorphosis feature entries.", - ), - ( - "uint16", - "MorphSubtableCount", - None, - None, - "The number of subtables in the chain.", - ), - ( - "struct", - "MorphFeature", - "MorphFeatureCount", - 0, - "Array of metamorphosis features.", - ), - ( - "MortSubtable", - "MorphSubtable", - "MorphSubtableCount", - 0, - "Array of metamorphosis subtables.", - ), - ], - ), - ( - "MortSubtable", - [ - ( - "uint16", - "StructLength", - None, - None, - "Total subtable length, including this header.", - ), - ( - "uint8", - "CoverageFlags", - None, - None, - "Most significant byte of coverage flags.", - ), - ("uint8", "MorphType", None, None, "Subtable type."), - ( - "Flags32", - "SubFeatureFlags", - None, - None, - "The 32-bit mask identifying which subtable this is (the subtable being executed if the AND of this value and the processed defaultFlags is nonzero).", - ), - ("SubStruct", "SubStruct", None, None, "SubTable."), - ], - ), - # - # morx - # - ( - "morx", - [ - ("uint16", "Version", None, None, "Version of the morx table."), - ("uint16", "Reserved", None, None, "Reserved (set to zero)."), - ( - "uint32", - "MorphChainCount", - None, - None, - "Number of extended metamorphosis chains.", - ), - ( - "MorxChain", - "MorphChain", - "MorphChainCount", - 0, - "Array of extended metamorphosis chains.", - ), - ], - ), - ( - "MorxChain", - [ - ( - "Flags32", - "DefaultFlags", - None, - None, - "The default specification for subtables.", - ), - ( - "uint32", - "StructLength", - None, - None, - "Total byte count, including this header; must be a multiple of 4.", - ), - ( - "uint32", - "MorphFeatureCount", - None, - None, - "Number of feature subtable entries.", - ), - ( - "uint32", - "MorphSubtableCount", - None, - None, - "The number of subtables in the chain.", - ), - ( - "MorphFeature", - "MorphFeature", - "MorphFeatureCount", - 0, - "Array of metamorphosis features.", - ), - ( - "MorxSubtable", - "MorphSubtable", - "MorphSubtableCount", - 0, - "Array of extended metamorphosis subtables.", - ), - ], - ), - ( - "MorphFeature", - [ - ("uint16", "FeatureType", None, None, "The type of feature."), - ( - "uint16", - "FeatureSetting", - None, - None, - "The feature's setting (aka selector).", - ), - ( - "Flags32", - "EnableFlags", - None, - None, - "Flags for the settings that this feature and setting enables.", - ), - ( - "Flags32", - "DisableFlags", - None, - None, - "Complement of flags for the settings that this feature and setting disable.", - ), - ], - ), - # Apple TrueType Reference Manual, chapter “The ‘morx’ table”, - # section “Metamorphosis Subtables”. - # https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6morx.html - ( - "MorxSubtable", - [ - ( - "uint32", - "StructLength", - None, - None, - "Total subtable length, including this header.", - ), - ( - "uint8", - "CoverageFlags", - None, - None, - "Most significant byte of coverage flags.", - ), - ("uint16", "Reserved", None, None, "Unused."), - ("uint8", "MorphType", None, None, "Subtable type."), - ( - "Flags32", - "SubFeatureFlags", - None, - None, - "The 32-bit mask identifying which subtable this is (the subtable being executed if the AND of this value and the processed defaultFlags is nonzero).", - ), - ("SubStruct", "SubStruct", None, None, "SubTable."), - ], - ), - ( - "StateHeader", - [ - ( - "uint32", - "ClassCount", - None, - None, - "Number of classes, which is the number of 16-bit entry indices in a single line in the state array.", - ), - ( - "uint32", - "MorphClass", - None, - None, - "Offset from the start of this state table header to the start of the class table.", - ), - ( - "uint32", - "StateArrayOffset", - None, - None, - "Offset from the start of this state table header to the start of the state array.", - ), - ( - "uint32", - "EntryTableOffset", - None, - None, - "Offset from the start of this state table header to the start of the entry table.", - ), - ], - ), - ( - "RearrangementMorph", - [ - ( - "STXHeader(RearrangementMorphAction)", - "StateTable", - None, - None, - "Finite-state transducer table for indic rearrangement.", - ), - ], - ), - ( - "ContextualMorph", - [ - ( - "STXHeader(ContextualMorphAction)", - "StateTable", - None, - None, - "Finite-state transducer for contextual glyph substitution.", - ), - ], - ), - ( - "LigatureMorph", - [ - ( - "STXHeader(LigatureMorphAction)", - "StateTable", - None, - None, - "Finite-state transducer for ligature substitution.", - ), - ], - ), - ( - "NoncontextualMorph", - [ - ( - "AATLookup(GlyphID)", - "Substitution", - None, - None, - "The noncontextual glyph substitution table.", - ), - ], - ), - ( - "InsertionMorph", - [ - ( - "STXHeader(InsertionMorphAction)", - "StateTable", - None, - None, - "Finite-state transducer for glyph insertion.", - ), - ], - ), - ( - "MorphClass", - [ - ( - "uint16", - "FirstGlyph", - None, - None, - "Glyph index of the first glyph in the class table.", - ), - # ('uint16', 'GlyphCount', None, None, 'Number of glyphs in class table.'), - # ('uint8', 'GlyphClass', 'GlyphCount', 0, 'The class codes (indexed by glyph index minus firstGlyph). Class codes range from 0 to the value of stateSize minus 1.'), - ], - ), - # If the 'morx' table version is 3 or greater, then the last subtable in the chain is followed by a subtableGlyphCoverageArray, as described below. - # ('Offset', 'MarkGlyphSetsDef', None, 'round(Version*0x10000) >= 0x00010002', 'Offset to the table of mark set definitions-from beginning of GDEF header (may be NULL)'), - # - # prop - # - ( - "prop", - [ - ( - "Fixed", - "Version", - None, - None, - "Version number of the AAT glyphs property table. Version 1.0 is the initial table version. Version 2.0, which is recognized by macOS 8.5 and later, adds support for the “attaches on right” bit. Version 3.0, which gets recognized by macOS X and iOS, adds support for the additional directional properties defined in Unicode 3.0.", - ), - ("struct", "GlyphProperties", None, None, "Glyph properties."), - ], - ), - ( - "GlyphPropertiesFormat0", - [ - ("uint16", "Format", None, None, "Format, = 0."), - ( - "uint16", - "DefaultProperties", - None, - None, - "Default properties applied to a glyph. Since there is no lookup table in prop format 0, the default properties get applied to every glyph in the font.", - ), - ], - ), - ( - "GlyphPropertiesFormat1", - [ - ("uint16", "Format", None, None, "Format, = 1."), - ( - "uint16", - "DefaultProperties", - None, - None, - "Default properties applied to a glyph if that glyph is not present in the Properties lookup table.", - ), - ( - "AATLookup(uint16)", - "Properties", - None, - None, - "Lookup data associating glyphs with their properties.", - ), - ], - ), - # - # opbd - # - ( - "opbd", - [ - ( - "Version", - "Version", - None, - None, - "Version number of the optical bounds table (0x00010000 for the initial version).", - ), - ("struct", "OpticalBounds", None, None, "Optical bounds table."), - ], - ), - ( - "OpticalBoundsFormat0", - [ - ( - "uint16", - "Format", - None, - None, - "Format of the optical bounds table, = 0.", - ), - ( - "AATLookup(OpticalBoundsDeltas)", - "OpticalBoundsDeltas", - None, - None, - "Lookup table associating glyphs with their optical bounds, given as deltas in font units.", - ), - ], - ), - ( - "OpticalBoundsFormat1", - [ - ( - "uint16", - "Format", - None, - None, - "Format of the optical bounds table, = 1.", - ), - ( - "AATLookup(OpticalBoundsPoints)", - "OpticalBoundsPoints", - None, - None, - "Lookup table associating glyphs with their optical bounds, given as references to control points.", - ), - ], - ), - ( - "OpticalBoundsDeltas", - [ - ( - "int16", - "Left", - None, - None, - "Delta value for the left-side optical edge.", - ), - ("int16", "Top", None, None, "Delta value for the top-side optical edge."), - ( - "int16", - "Right", - None, - None, - "Delta value for the right-side optical edge.", - ), - ( - "int16", - "Bottom", - None, - None, - "Delta value for the bottom-side optical edge.", - ), - ], - ), - ( - "OpticalBoundsPoints", - [ - ( - "int16", - "Left", - None, - None, - "Control point index for the left-side optical edge, or -1 if this glyph has none.", - ), - ( - "int16", - "Top", - None, - None, - "Control point index for the top-side optical edge, or -1 if this glyph has none.", - ), - ( - "int16", - "Right", - None, - None, - "Control point index for the right-side optical edge, or -1 if this glyph has none.", - ), - ( - "int16", - "Bottom", - None, - None, - "Control point index for the bottom-side optical edge, or -1 if this glyph has none.", - ), - ], - ), - # - # TSIC - # - ( - "TSIC", - [ - ( - "Version", - "Version", - None, - None, - "Version of table initially set to 0x00010000.", - ), - ("uint16", "Flags", None, None, "TSIC flags - set to 0"), - ("uint16", "AxisCount", None, None, "Axis count from fvar"), - ("uint16", "RecordCount", None, None, "TSIC record count"), - ("uint16", "Reserved", None, None, "Set to 0"), - ("Tag", "AxisArray", "AxisCount", 0, "Array of axis tags in fvar order"), - ( - "LocationRecord", - "RecordLocations", - "RecordCount", - 0, - "Location in variation space of TSIC record", - ), - ("TSICRecord", "Record", "RecordCount", 0, "Array of TSIC records"), - ], - ), - ( - "LocationRecord", - [ - ("F2Dot14", "Axis", "AxisCount", 0, "Axis record"), - ], - ), - ( - "TSICRecord", - [ - ("uint16", "Flags", None, None, "Record flags - set to 0"), - ("uint16", "NumCVTEntries", None, None, "Number of CVT number value pairs"), - ("uint16", "NameLength", None, None, "Length of optional user record name"), - ("uint16", "NameArray", "NameLength", 0, "Unicode 16 name"), - ("uint16", "CVTArray", "NumCVTEntries", 0, "CVT number array"), - ("int16", "CVTValueArray", "NumCVTEntries", 0, "CVT value"), - ], - ), - # - # COLR - # - ( - "COLR", - [ - ("uint16", "Version", None, None, "Table version number (starts at 0)."), - ( - "uint16", - "BaseGlyphRecordCount", - None, - None, - "Number of Base Glyph Records.", - ), - ( - "LOffset", - "BaseGlyphRecordArray", - None, - None, - "Offset (from beginning of COLR table) to Base Glyph records.", - ), - ( - "LOffset", - "LayerRecordArray", - None, - None, - "Offset (from beginning of COLR table) to Layer Records.", - ), - ("uint16", "LayerRecordCount", None, None, "Number of Layer Records."), - ( - "LOffset", - "BaseGlyphList", - None, - "Version >= 1", - "Offset (from beginning of COLR table) to array of Version-1 Base Glyph records.", - ), - ( - "LOffset", - "LayerList", - None, - "Version >= 1", - "Offset (from beginning of COLR table) to LayerList.", - ), - ( - "LOffset", - "ClipList", - None, - "Version >= 1", - "Offset to ClipList table (may be NULL)", - ), - ( - "LOffsetTo(DeltaSetIndexMap)", - "VarIndexMap", - None, - "Version >= 1", - "Offset to DeltaSetIndexMap table (may be NULL)", - ), - ( - "LOffset", - "VarStore", - None, - "Version >= 1", - "Offset to variation store (may be NULL)", - ), - ], - ), - ( - "BaseGlyphRecordArray", - [ - ( - "BaseGlyphRecord", - "BaseGlyphRecord", - "BaseGlyphRecordCount", - 0, - "Base Glyph records.", - ), - ], - ), - ( - "BaseGlyphRecord", - [ - ( - "GlyphID", - "BaseGlyph", - None, - None, - "Glyph ID of reference glyph. This glyph is for reference only and is not rendered for color.", - ), - ( - "uint16", - "FirstLayerIndex", - None, - None, - "Index (from beginning of the Layer Records) to the layer record. There will be numLayers consecutive entries for this base glyph.", - ), - ( - "uint16", - "NumLayers", - None, - None, - "Number of color layers associated with this glyph.", - ), - ], - ), - ( - "LayerRecordArray", - [ - ("LayerRecord", "LayerRecord", "LayerRecordCount", 0, "Layer records."), - ], - ), - ( - "LayerRecord", - [ - ( - "GlyphID", - "LayerGlyph", - None, - None, - "Glyph ID of layer glyph (must be in z-order from bottom to top).", - ), - ( - "uint16", - "PaletteIndex", - None, - None, - "Index value to use with a selected color palette.", - ), - ], - ), - ( - "BaseGlyphList", - [ - ( - "uint32", - "BaseGlyphCount", - None, - None, - "Number of Version-1 Base Glyph records", - ), - ( - "struct", - "BaseGlyphPaintRecord", - "BaseGlyphCount", - 0, - "Array of Version-1 Base Glyph records", - ), - ], - ), - ( - "BaseGlyphPaintRecord", - [ - ("GlyphID", "BaseGlyph", None, None, "Glyph ID of reference glyph."), - ( - "LOffset", - "Paint", - None, - None, - "Offset (from beginning of BaseGlyphPaintRecord) to Paint, typically a PaintColrLayers.", - ), - ], - ), - ( - "LayerList", - [ - ("uint32", "LayerCount", None, None, "Number of Version-1 Layers"), - ( - "LOffset", - "Paint", - "LayerCount", - 0, - "Array of offsets to Paint tables, from the start of the LayerList table.", - ), - ], - ), - ( - "ClipListFormat1", - [ - ( - "uint8", - "Format", - None, - None, - "Format for ClipList with 16bit glyph IDs: 1", - ), - ("uint32", "ClipCount", None, None, "Number of Clip records."), - ( - "struct", - "ClipRecord", - "ClipCount", - 0, - "Array of Clip records sorted by glyph ID.", - ), - ], - ), - ( - "ClipRecord", - [ - ("uint16", "StartGlyphID", None, None, "First glyph ID in the range."), - ("uint16", "EndGlyphID", None, None, "Last glyph ID in the range."), - ("Offset24", "ClipBox", None, None, "Offset to a ClipBox table."), - ], - ), - ( - "ClipBoxFormat1", - [ - ( - "uint8", - "Format", - None, - None, - "Format for ClipBox without variation: set to 1.", - ), - ("int16", "xMin", None, None, "Minimum x of clip box."), - ("int16", "yMin", None, None, "Minimum y of clip box."), - ("int16", "xMax", None, None, "Maximum x of clip box."), - ("int16", "yMax", None, None, "Maximum y of clip box."), - ], - ), - ( - "ClipBoxFormat2", - [ - ("uint8", "Format", None, None, "Format for variable ClipBox: set to 2."), - ("int16", "xMin", None, None, "Minimum x of clip box. VarIndexBase + 0."), - ("int16", "yMin", None, None, "Minimum y of clip box. VarIndexBase + 1."), - ("int16", "xMax", None, None, "Maximum x of clip box. VarIndexBase + 2."), - ("int16", "yMax", None, None, "Maximum y of clip box. VarIndexBase + 3."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # COLRv1 Affine2x3 uses the same column-major order to serialize a 2D - # Affine Transformation as the one used by fontTools.misc.transform. - # However, for historical reasons, the labels 'xy' and 'yx' are swapped. - # Their fundamental meaning is the same though. - # COLRv1 Affine2x3 follows the names found in FreeType and Cairo. - # In all case, the second element in the 6-tuple correspond to the - # y-part of the x basis vector, and the third to the x-part of the y - # basis vector. - # See https://github.com/googlefonts/colr-gradients-spec/pull/85 - ( - "Affine2x3", - [ - ("Fixed", "xx", None, None, "x-part of x basis vector"), - ("Fixed", "yx", None, None, "y-part of x basis vector"), - ("Fixed", "xy", None, None, "x-part of y basis vector"), - ("Fixed", "yy", None, None, "y-part of y basis vector"), - ("Fixed", "dx", None, None, "Translation in x direction"), - ("Fixed", "dy", None, None, "Translation in y direction"), - ], - ), - ( - "VarAffine2x3", - [ - ("Fixed", "xx", None, None, "x-part of x basis vector. VarIndexBase + 0."), - ("Fixed", "yx", None, None, "y-part of x basis vector. VarIndexBase + 1."), - ("Fixed", "xy", None, None, "x-part of y basis vector. VarIndexBase + 2."), - ("Fixed", "yy", None, None, "y-part of y basis vector. VarIndexBase + 3."), - ( - "Fixed", - "dx", - None, - None, - "Translation in x direction. VarIndexBase + 4.", - ), - ( - "Fixed", - "dy", - None, - None, - "Translation in y direction. VarIndexBase + 5.", - ), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - ( - "ColorStop", - [ - ("F2Dot14", "StopOffset", None, None, ""), - ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."), - ("F2Dot14", "Alpha", None, None, "Values outsided [0.,1.] reserved"), - ], - ), - ( - "VarColorStop", - [ - ("F2Dot14", "StopOffset", None, None, "VarIndexBase + 0."), - ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."), - ( - "F2Dot14", - "Alpha", - None, - None, - "Values outsided [0.,1.] reserved. VarIndexBase + 1.", - ), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - ( - "ColorLine", - [ - ( - "ExtendMode", - "Extend", - None, - None, - "Enum {PAD = 0, REPEAT = 1, REFLECT = 2}", - ), - ("uint16", "StopCount", None, None, "Number of Color stops."), - ("ColorStop", "ColorStop", "StopCount", 0, "Array of Color stops."), - ], - ), - ( - "VarColorLine", - [ - ( - "ExtendMode", - "Extend", - None, - None, - "Enum {PAD = 0, REPEAT = 1, REFLECT = 2}", - ), - ("uint16", "StopCount", None, None, "Number of Color stops."), - ("VarColorStop", "ColorStop", "StopCount", 0, "Array of Color stops."), - ], - ), - # PaintColrLayers - ( - "PaintFormat1", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 1"), - ( - "uint8", - "NumLayers", - None, - None, - "Number of offsets to Paint to read from LayerList.", - ), - ("uint32", "FirstLayerIndex", None, None, "Index into LayerList."), - ], - ), - # PaintSolid - ( - "PaintFormat2", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 2"), - ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."), - ("F2Dot14", "Alpha", None, None, "Values outsided [0.,1.] reserved"), - ], - ), - # PaintVarSolid - ( - "PaintFormat3", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 3"), - ("uint16", "PaletteIndex", None, None, "Index for a CPAL palette entry."), - ( - "F2Dot14", - "Alpha", - None, - None, - "Values outsided [0.,1.] reserved. VarIndexBase + 0.", - ), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintLinearGradient - ( - "PaintFormat4", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 4"), - ( - "Offset24", - "ColorLine", - None, - None, - "Offset (from beginning of PaintLinearGradient table) to ColorLine subtable.", - ), - ("int16", "x0", None, None, ""), - ("int16", "y0", None, None, ""), - ("int16", "x1", None, None, ""), - ("int16", "y1", None, None, ""), - ("int16", "x2", None, None, ""), - ("int16", "y2", None, None, ""), - ], - ), - # PaintVarLinearGradient - ( - "PaintFormat5", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 5"), - ( - "LOffset24To(VarColorLine)", - "ColorLine", - None, - None, - "Offset (from beginning of PaintVarLinearGradient table) to VarColorLine subtable.", - ), - ("int16", "x0", None, None, "VarIndexBase + 0."), - ("int16", "y0", None, None, "VarIndexBase + 1."), - ("int16", "x1", None, None, "VarIndexBase + 2."), - ("int16", "y1", None, None, "VarIndexBase + 3."), - ("int16", "x2", None, None, "VarIndexBase + 4."), - ("int16", "y2", None, None, "VarIndexBase + 5."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintRadialGradient - ( - "PaintFormat6", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 6"), - ( - "Offset24", - "ColorLine", - None, - None, - "Offset (from beginning of PaintRadialGradient table) to ColorLine subtable.", - ), - ("int16", "x0", None, None, ""), - ("int16", "y0", None, None, ""), - ("uint16", "r0", None, None, ""), - ("int16", "x1", None, None, ""), - ("int16", "y1", None, None, ""), - ("uint16", "r1", None, None, ""), - ], - ), - # PaintVarRadialGradient - ( - "PaintFormat7", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 7"), - ( - "LOffset24To(VarColorLine)", - "ColorLine", - None, - None, - "Offset (from beginning of PaintVarRadialGradient table) to VarColorLine subtable.", - ), - ("int16", "x0", None, None, "VarIndexBase + 0."), - ("int16", "y0", None, None, "VarIndexBase + 1."), - ("uint16", "r0", None, None, "VarIndexBase + 2."), - ("int16", "x1", None, None, "VarIndexBase + 3."), - ("int16", "y1", None, None, "VarIndexBase + 4."), - ("uint16", "r1", None, None, "VarIndexBase + 5."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintSweepGradient - ( - "PaintFormat8", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 8"), - ( - "Offset24", - "ColorLine", - None, - None, - "Offset (from beginning of PaintSweepGradient table) to ColorLine subtable.", - ), - ("int16", "centerX", None, None, "Center x coordinate."), - ("int16", "centerY", None, None, "Center y coordinate."), - ( - "BiasedAngle", - "startAngle", - None, - None, - "Start of the angular range of the gradient.", - ), - ( - "BiasedAngle", - "endAngle", - None, - None, - "End of the angular range of the gradient.", - ), - ], - ), - # PaintVarSweepGradient - ( - "PaintFormat9", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 9"), - ( - "LOffset24To(VarColorLine)", - "ColorLine", - None, - None, - "Offset (from beginning of PaintVarSweepGradient table) to VarColorLine subtable.", - ), - ("int16", "centerX", None, None, "Center x coordinate. VarIndexBase + 0."), - ("int16", "centerY", None, None, "Center y coordinate. VarIndexBase + 1."), - ( - "BiasedAngle", - "startAngle", - None, - None, - "Start of the angular range of the gradient. VarIndexBase + 2.", - ), - ( - "BiasedAngle", - "endAngle", - None, - None, - "End of the angular range of the gradient. VarIndexBase + 3.", - ), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintGlyph - ( - "PaintFormat10", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 10"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintGlyph table) to Paint subtable.", - ), - ("GlyphID", "Glyph", None, None, "Glyph ID for the source outline."), - ], - ), - # PaintColrGlyph - ( - "PaintFormat11", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 11"), - ( - "GlyphID", - "Glyph", - None, - None, - "Virtual glyph ID for a BaseGlyphList base glyph.", - ), - ], - ), - # PaintTransform - ( - "PaintFormat12", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 12"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintTransform table) to Paint subtable.", - ), - ( - "LOffset24To(Affine2x3)", - "Transform", - None, - None, - "2x3 matrix for 2D affine transformations.", - ), - ], - ), - # PaintVarTransform - ( - "PaintFormat13", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 13"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarTransform table) to Paint subtable.", - ), - ( - "LOffset24To(VarAffine2x3)", - "Transform", - None, - None, - "2x3 matrix for 2D affine transformations.", - ), - ], - ), - # PaintTranslate - ( - "PaintFormat14", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 14"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintTranslate table) to Paint subtable.", - ), - ("int16", "dx", None, None, "Translation in x direction."), - ("int16", "dy", None, None, "Translation in y direction."), - ], - ), - # PaintVarTranslate - ( - "PaintFormat15", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 15"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarTranslate table) to Paint subtable.", - ), - ( - "int16", - "dx", - None, - None, - "Translation in x direction. VarIndexBase + 0.", - ), - ( - "int16", - "dy", - None, - None, - "Translation in y direction. VarIndexBase + 1.", - ), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintScale - ( - "PaintFormat16", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 16"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintScale table) to Paint subtable.", - ), - ("F2Dot14", "scaleX", None, None, ""), - ("F2Dot14", "scaleY", None, None, ""), - ], - ), - # PaintVarScale - ( - "PaintFormat17", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 17"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarScale table) to Paint subtable.", - ), - ("F2Dot14", "scaleX", None, None, "VarIndexBase + 0."), - ("F2Dot14", "scaleY", None, None, "VarIndexBase + 1."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintScaleAroundCenter - ( - "PaintFormat18", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 18"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintScaleAroundCenter table) to Paint subtable.", - ), - ("F2Dot14", "scaleX", None, None, ""), - ("F2Dot14", "scaleY", None, None, ""), - ("int16", "centerX", None, None, ""), - ("int16", "centerY", None, None, ""), - ], - ), - # PaintVarScaleAroundCenter - ( - "PaintFormat19", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 19"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarScaleAroundCenter table) to Paint subtable.", - ), - ("F2Dot14", "scaleX", None, None, "VarIndexBase + 0."), - ("F2Dot14", "scaleY", None, None, "VarIndexBase + 1."), - ("int16", "centerX", None, None, "VarIndexBase + 2."), - ("int16", "centerY", None, None, "VarIndexBase + 3."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintScaleUniform - ( - "PaintFormat20", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 20"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintScaleUniform table) to Paint subtable.", - ), - ("F2Dot14", "scale", None, None, ""), - ], - ), - # PaintVarScaleUniform - ( - "PaintFormat21", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 21"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarScaleUniform table) to Paint subtable.", - ), - ("F2Dot14", "scale", None, None, "VarIndexBase + 0."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintScaleUniformAroundCenter - ( - "PaintFormat22", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 22"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintScaleUniformAroundCenter table) to Paint subtable.", - ), - ("F2Dot14", "scale", None, None, ""), - ("int16", "centerX", None, None, ""), - ("int16", "centerY", None, None, ""), - ], - ), - # PaintVarScaleUniformAroundCenter - ( - "PaintFormat23", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 23"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarScaleUniformAroundCenter table) to Paint subtable.", - ), - ("F2Dot14", "scale", None, None, "VarIndexBase + 0"), - ("int16", "centerX", None, None, "VarIndexBase + 1"), - ("int16", "centerY", None, None, "VarIndexBase + 2"), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintRotate - ( - "PaintFormat24", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 24"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintRotate table) to Paint subtable.", - ), - ("Angle", "angle", None, None, ""), - ], - ), - # PaintVarRotate - ( - "PaintFormat25", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 25"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarRotate table) to Paint subtable.", - ), - ("Angle", "angle", None, None, "VarIndexBase + 0."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintRotateAroundCenter - ( - "PaintFormat26", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 26"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintRotateAroundCenter table) to Paint subtable.", - ), - ("Angle", "angle", None, None, ""), - ("int16", "centerX", None, None, ""), - ("int16", "centerY", None, None, ""), - ], - ), - # PaintVarRotateAroundCenter - ( - "PaintFormat27", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 27"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarRotateAroundCenter table) to Paint subtable.", - ), - ("Angle", "angle", None, None, "VarIndexBase + 0."), - ("int16", "centerX", None, None, "VarIndexBase + 1."), - ("int16", "centerY", None, None, "VarIndexBase + 2."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintSkew - ( - "PaintFormat28", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 28"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintSkew table) to Paint subtable.", - ), - ("Angle", "xSkewAngle", None, None, ""), - ("Angle", "ySkewAngle", None, None, ""), - ], - ), - # PaintVarSkew - ( - "PaintFormat29", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 29"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarSkew table) to Paint subtable.", - ), - ("Angle", "xSkewAngle", None, None, "VarIndexBase + 0."), - ("Angle", "ySkewAngle", None, None, "VarIndexBase + 1."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintSkewAroundCenter - ( - "PaintFormat30", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 30"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintSkewAroundCenter table) to Paint subtable.", - ), - ("Angle", "xSkewAngle", None, None, ""), - ("Angle", "ySkewAngle", None, None, ""), - ("int16", "centerX", None, None, ""), - ("int16", "centerY", None, None, ""), - ], - ), - # PaintVarSkewAroundCenter - ( - "PaintFormat31", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 31"), - ( - "Offset24", - "Paint", - None, - None, - "Offset (from beginning of PaintVarSkewAroundCenter table) to Paint subtable.", - ), - ("Angle", "xSkewAngle", None, None, "VarIndexBase + 0."), - ("Angle", "ySkewAngle", None, None, "VarIndexBase + 1."), - ("int16", "centerX", None, None, "VarIndexBase + 2."), - ("int16", "centerY", None, None, "VarIndexBase + 3."), - ( - "VarIndex", - "VarIndexBase", - None, - None, - "Base index into DeltaSetIndexMap.", - ), - ], - ), - # PaintComposite - ( - "PaintFormat32", - [ - ("uint8", "PaintFormat", None, None, "Format identifier-format = 32"), - ( - "LOffset24To(Paint)", - "SourcePaint", - None, - None, - "Offset (from beginning of PaintComposite table) to source Paint subtable.", - ), - ( - "CompositeMode", - "CompositeMode", - None, - None, - "A CompositeMode enumeration value.", - ), - ( - "LOffset24To(Paint)", - "BackdropPaint", - None, - None, - "Offset (from beginning of PaintComposite table) to backdrop Paint subtable.", - ), - ], - ), - # - # avar - # - ( - "AxisValueMap", - [ - ( - "F2Dot14", - "FromCoordinate", - None, - None, - "A normalized coordinate value obtained using default normalization", - ), - ( - "F2Dot14", - "ToCoordinate", - None, - None, - "The modified, normalized coordinate value", - ), - ], - ), - ( - "AxisSegmentMap", - [ - ( - "uint16", - "PositionMapCount", - None, - None, - "The number of correspondence pairs for this axis", - ), - ( - "AxisValueMap", - "AxisValueMap", - "PositionMapCount", - 0, - "The array of axis value map records for this axis", - ), - ], - ), - ( - "avar", - [ - ( - "Version", - "Version", - None, - None, - "Version of the avar table- 0x00010000 or 0x00020000", - ), - ("uint16", "Reserved", None, None, "Permanently reserved; set to zero"), - ( - "uint16", - "AxisCount", - None, - None, - 'The number of variation axes for this font. This must be the same number as axisCount in the "fvar" table', - ), - ( - "AxisSegmentMap", - "AxisSegmentMap", - "AxisCount", - 0, - 'The segment maps array — one segment map for each axis, in the order of axes specified in the "fvar" table', - ), - ( - "LOffsetTo(DeltaSetIndexMap)", - "VarIdxMap", - None, - "Version >= 0x00020000", - "", - ), - ("LOffset", "VarStore", None, "Version >= 0x00020000", ""), - ], - ), -] diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/runtime/svelte-hooks.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/runtime/svelte-hooks.js deleted file mode 100644 index 82ca3e37ab99982f4c029519b1d6a5cc30e4b290..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/node/dev/files/runtime/svelte-hooks.js +++ /dev/null @@ -1,347 +0,0 @@ -/** - * Emulates forthcoming HMR hooks in Svelte. - * - * All references to private component state ($$) are now isolated in this - * module. - */ -import { - current_component, - get_current_component, - set_current_component, -} from 'svelte/internal' - -const captureState = cmp => { - // sanity check: propper behaviour here is to crash noisily so that - // user knows that they're looking at something broken - if (!cmp) { - throw new Error('Missing component') - } - if (!cmp.$$) { - throw new Error('Invalid component') - } - - const { - $$: { callbacks, bound, ctx, props }, - } = cmp - - const state = cmp.$capture_state() - - // capturing current value of props (or we'll recreate the component with the - // initial prop values, that may have changed -- and would not be reflected in - // options.props) - const hmr_props_values = {} - Object.keys(cmp.$$.props).forEach(prop => { - hmr_props_values[prop] = ctx[props[prop]] - }) - - return { - ctx, - props, - callbacks, - bound, - state, - hmr_props_values, - } -} - -// remapping all existing bindings (including hmr_future_foo ones) to the -// new version's props indexes, and refresh them with the new value from -// context -const restoreBound = (cmp, restore) => { - // reverse prop:ctxIndex in $$.props to ctxIndex:prop - // - // ctxIndex can be either a regular index in $$.ctx or a hmr_future_ prop - // - const propsByIndex = {} - for (const [name, i] of Object.entries(restore.props)) { - propsByIndex[i] = name - } - - // NOTE $$.bound cannot change in the HMR lifetime of a component, because - // if bindings changes, that means the parent component has changed, - // which means the child (current) component will be wholly recreated - for (const [oldIndex, updateBinding] of Object.entries(restore.bound)) { - // can be either regular prop, or future_hmr_ prop - const propName = propsByIndex[oldIndex] - - // this should never happen if remembering of future props is enabled... - // in any case, there's nothing we can do about it if we have lost prop - // name knowledge at this point - if (propName == null) continue - - // NOTE $$.props[propName] also propagates knowledge of a possible - // future prop to the new $$.props (via $$.props being a Proxy) - const newIndex = cmp.$$.props[propName] - cmp.$$.bound[newIndex] = updateBinding - - // NOTE if the prop doesn't exist or doesn't exist anymore in the new - // version of the component, clearing the binding is the expected - // behaviour (since that's what would happen in non HMR code) - const newValue = cmp.$$.ctx[newIndex] - updateBinding(newValue) - } -} - -// restoreState -// -// It is too late to restore context at this point because component instance -// function has already been called (and so context has already been read). -// Instead, we rely on setting current_component to the same value it has when -// the component was first rendered -- which fix support for context, and is -// also generally more respectful of normal operation. -// -const restoreState = (cmp, restore) => { - if (!restore) return - - if (restore.callbacks) { - cmp.$$.callbacks = restore.callbacks - } - - if (restore.bound) { - restoreBound(cmp, restore) - } - - // props, props.$$slots are restored at component creation (works - // better -- well, at all actually) -} - -const get_current_component_safe = () => { - // NOTE relying on dynamic bindings (current_component) makes us dependent on - // bundler config (and apparently it does not work in demo-svelte-nollup) - try { - // unfortunately, unlike current_component, get_current_component() can - // crash in the normal path (when there is really no parent) - return get_current_component() - } catch (err) { - // ... so we need to consider that this error means that there is no parent - // - // that makes us tightly coupled to the error message but, at least, we - // won't mute an unexpected error, which is quite a horrible thing to do - if (err.message === 'Function called outside component initialization') { - // who knows... - return current_component - } else { - throw err - } - } -} - -export const createProxiedComponent = ( - Component, - initialOptions, - { allowLiveBinding, onInstance, onMount, onDestroy } -) => { - let cmp - let options = initialOptions - - const isCurrent = _cmp => cmp === _cmp - - const assignOptions = (target, anchor, restore, preserveLocalState) => { - const props = Object.assign({}, options.props) - - // Filtering props to avoid "unexpected prop" warning - // NOTE this is based on props present in initial options, but it should - // always works, because props that are passed from the parent can't - // change without a code change to the parent itself -- hence, the - // child component will be fully recreated, and initial options should - // always represent props that are currnetly passed by the parent - if (options.props && restore.hmr_props_values) { - for (const prop of Object.keys(options.props)) { - if (restore.hmr_props_values.hasOwnProperty(prop)) { - props[prop] = restore.hmr_props_values[prop] - } - } - } - - if (preserveLocalState && restore.state) { - if (Array.isArray(preserveLocalState)) { - // form ['a', 'b'] => preserve only 'a' and 'b' - props.$$inject = {} - for (const key of preserveLocalState) { - props.$$inject[key] = restore.state[key] - } - } else { - props.$$inject = restore.state - } - } else { - delete props.$$inject - } - options = Object.assign({}, initialOptions, { - target, - anchor, - props, - hydrate: false, - }) - } - - // Preserving knowledge of "future props" -- very hackish version (maybe - // there should be an option to opt out of this) - // - // The use case is bind:something where something doesn't exist yet in the - // target component, but comes to exist later, after a HMR update. - // - // If Svelte can't map a prop in the current version of the component, it - // will just completely discard it: - // https://github.com/sveltejs/svelte/blob/1632bca34e4803d6b0e0b0abd652ab5968181860/src/runtime/internal/Component.ts#L46 - // - const rememberFutureProps = cmp => { - if (typeof Proxy === 'undefined') return - - cmp.$$.props = new Proxy(cmp.$$.props, { - get(target, name) { - if (target[name] === undefined) { - target[name] = 'hmr_future_' + name - } - return target[name] - }, - set(target, name, value) { - target[name] = value - }, - }) - } - - const instrument = targetCmp => { - const createComponent = (Component, restore, previousCmp) => { - set_current_component(parentComponent || previousCmp) - const comp = new Component(options) - // NOTE must be instrumented before restoreState, because restoring - // bindings relies on hacked $$.props - instrument(comp) - restoreState(comp, restore) - return comp - } - - rememberFutureProps(targetCmp) - - targetCmp.$$.on_hmr = [] - - // `conservative: true` means we want to be sure that the new component has - // actually been successfuly created before destroying the old instance. - // This could be useful for preventing runtime errors in component init to - // bring down the whole HMR. Unfortunately the implementation bellow is - // broken (FIXME), but that remains an interesting target for when HMR hooks - // will actually land in Svelte itself. - // - // The goal would be to render an error inplace in case of error, to avoid - // losing the navigation stack (especially annoying in native, that is not - // based on URL navigation, so we lose the current page on each error). - // - targetCmp.$replace = ( - Component, - { - target = options.target, - anchor = options.anchor, - preserveLocalState, - conservative = false, - } - ) => { - const restore = captureState(targetCmp) - assignOptions( - target || options.target, - anchor, - restore, - preserveLocalState - ) - - const callbacks = cmp ? cmp.$$.on_hmr : [] - - const afterCallbacks = callbacks.map(fn => fn(cmp)).filter(Boolean) - - const previous = cmp - if (conservative) { - try { - const next = createComponent(Component, restore, previous) - // prevents on_destroy from firing on non-final cmp instance - cmp = null - previous.$destroy() - cmp = next - } catch (err) { - cmp = previous - throw err - } - } else { - // prevents on_destroy from firing on non-final cmp instance - cmp = null - if (previous) { - // previous can be null if last constructor has crashed - previous.$destroy() - } - cmp = createComponent(Component, restore, cmp) - } - - cmp.$$.hmr_cmp = cmp - - for (const fn of afterCallbacks) { - fn(cmp) - } - - cmp.$$.on_hmr = callbacks - - return cmp - } - - // NOTE onMount must provide target & anchor (for us to be able to determinate - // actual DOM insertion point) - // - // And also, to support keyed list, it needs to be called each time the - // component is moved (same as $$.fragment.m) - if (onMount) { - const m = targetCmp.$$.fragment.m - targetCmp.$$.fragment.m = (...args) => { - const result = m(...args) - onMount(...args) - return result - } - } - - // NOTE onDestroy must be called even if the call doesn't pass through the - // component's $destroy method (that we can hook onto by ourselves, since - // it's public API) -- this happens a lot in svelte's internals, that - // manipulates cmp.$$.fragment directly, often binding to fragment.d, - // for example - if (onDestroy) { - targetCmp.$$.on_destroy.push(() => { - if (isCurrent(targetCmp)) { - onDestroy() - } - }) - } - - if (onInstance) { - onInstance(targetCmp) - } - - // Svelte 3 creates and mount components from their constructor if - // options.target is present. - // - // This means that at this point, the component's `fragment.c` and, - // most notably, `fragment.m` will already have been called _from inside - // createComponent_. That is: before we have a chance to hook on it. - // - // Proxy's constructor - // -> createComponent - // -> component constructor - // -> component.$$.fragment.c(...) (or l, if hydrate:true) - // -> component.$$.fragment.m(...) - // - // -> you are here <- - // - if (onMount) { - const { target, anchor } = options - if (target) { - onMount(target, anchor) - } - } - } - - const parentComponent = allowLiveBinding - ? current_component - : get_current_component_safe() - - cmp = new Component(options) - cmp.$$.hmr_cmp = cmp - - instrument(cmp) - - return cmp -} diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jinja2/runtime.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jinja2/runtime.py deleted file mode 100644 index 985842b284270bcd52855029f13d3da19d718349..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/jinja2/runtime.py +++ /dev/null @@ -1,1053 +0,0 @@ -"""The runtime functions and state used by compiled templates.""" -import functools -import sys -import typing as t -from collections import abc -from itertools import chain - -from markupsafe import escape # noqa: F401 -from markupsafe import Markup -from markupsafe import soft_str - -from .async_utils import auto_aiter -from .async_utils import auto_await # noqa: F401 -from .exceptions import TemplateNotFound # noqa: F401 -from .exceptions import TemplateRuntimeError # noqa: F401 -from .exceptions import UndefinedError -from .nodes import EvalContext -from .utils import _PassArg -from .utils import concat -from .utils import internalcode -from .utils import missing -from .utils import Namespace # noqa: F401 -from .utils import object_type_repr -from .utils import pass_eval_context - -V = t.TypeVar("V") -F = t.TypeVar("F", bound=t.Callable[..., t.Any]) - -if t.TYPE_CHECKING: - import logging - import typing_extensions as te - from .environment import Environment - - class LoopRenderFunc(te.Protocol): - def __call__( - self, - reciter: t.Iterable[V], - loop_render_func: "LoopRenderFunc", - depth: int = 0, - ) -> str: - ... - - -# these variables are exported to the template runtime -exported = [ - "LoopContext", - "TemplateReference", - "Macro", - "Markup", - "TemplateRuntimeError", - "missing", - "escape", - "markup_join", - "str_join", - "identity", - "TemplateNotFound", - "Namespace", - "Undefined", - "internalcode", -] -async_exported = [ - "AsyncLoopContext", - "auto_aiter", - "auto_await", -] - - -def identity(x: V) -> V: - """Returns its argument. Useful for certain things in the - environment. - """ - return x - - -def markup_join(seq: t.Iterable[t.Any]) -> str: - """Concatenation that escapes if necessary and converts to string.""" - buf = [] - iterator = map(soft_str, seq) - for arg in iterator: - buf.append(arg) - if hasattr(arg, "__html__"): - return Markup("").join(chain(buf, iterator)) - return concat(buf) - - -def str_join(seq: t.Iterable[t.Any]) -> str: - """Simple args to string conversion and concatenation.""" - return concat(map(str, seq)) - - -def new_context( - environment: "Environment", - template_name: t.Optional[str], - blocks: t.Dict[str, t.Callable[["Context"], t.Iterator[str]]], - vars: t.Optional[t.Dict[str, t.Any]] = None, - shared: bool = False, - globals: t.Optional[t.MutableMapping[str, t.Any]] = None, - locals: t.Optional[t.Mapping[str, t.Any]] = None, -) -> "Context": - """Internal helper for context creation.""" - if vars is None: - vars = {} - if shared: - parent = vars - else: - parent = dict(globals or (), **vars) - if locals: - # if the parent is shared a copy should be created because - # we don't want to modify the dict passed - if shared: - parent = dict(parent) - for key, value in locals.items(): - if value is not missing: - parent[key] = value - return environment.context_class( - environment, parent, template_name, blocks, globals=globals - ) - - -class TemplateReference: - """The `self` in templates.""" - - def __init__(self, context: "Context") -> None: - self.__context = context - - def __getitem__(self, name: str) -> t.Any: - blocks = self.__context.blocks[name] - return BlockReference(name, self.__context, blocks, 0) - - def __repr__(self) -> str: - return f"<{type(self).__name__} {self.__context.name!r}>" - - -def _dict_method_all(dict_method: F) -> F: - @functools.wraps(dict_method) - def f_all(self: "Context") -> t.Any: - return dict_method(self.get_all()) - - return t.cast(F, f_all) - - -@abc.Mapping.register -class Context: - """The template context holds the variables of a template. It stores the - values passed to the template and also the names the template exports. - Creating instances is neither supported nor useful as it's created - automatically at various stages of the template evaluation and should not - be created by hand. - - The context is immutable. Modifications on :attr:`parent` **must not** - happen and modifications on :attr:`vars` are allowed from generated - template code only. Template filters and global functions marked as - :func:`pass_context` get the active context passed as first argument - and are allowed to access the context read-only. - - The template context supports read only dict operations (`get`, - `keys`, `values`, `items`, `iterkeys`, `itervalues`, `iteritems`, - `__getitem__`, `__contains__`). Additionally there is a :meth:`resolve` - method that doesn't fail with a `KeyError` but returns an - :class:`Undefined` object for missing variables. - """ - - def __init__( - self, - environment: "Environment", - parent: t.Dict[str, t.Any], - name: t.Optional[str], - blocks: t.Dict[str, t.Callable[["Context"], t.Iterator[str]]], - globals: t.Optional[t.MutableMapping[str, t.Any]] = None, - ): - self.parent = parent - self.vars: t.Dict[str, t.Any] = {} - self.environment: "Environment" = environment - self.eval_ctx = EvalContext(self.environment, name) - self.exported_vars: t.Set[str] = set() - self.name = name - self.globals_keys = set() if globals is None else set(globals) - - # create the initial mapping of blocks. Whenever template inheritance - # takes place the runtime will update this mapping with the new blocks - # from the template. - self.blocks = {k: [v] for k, v in blocks.items()} - - def super( - self, name: str, current: t.Callable[["Context"], t.Iterator[str]] - ) -> t.Union["BlockReference", "Undefined"]: - """Render a parent block.""" - try: - blocks = self.blocks[name] - index = blocks.index(current) + 1 - blocks[index] - except LookupError: - return self.environment.undefined( - f"there is no parent block called {name!r}.", name="super" - ) - return BlockReference(name, self, blocks, index) - - def get(self, key: str, default: t.Any = None) -> t.Any: - """Look up a variable by name, or return a default if the key is - not found. - - :param key: The variable name to look up. - :param default: The value to return if the key is not found. - """ - try: - return self[key] - except KeyError: - return default - - def resolve(self, key: str) -> t.Union[t.Any, "Undefined"]: - """Look up a variable by name, or return an :class:`Undefined` - object if the key is not found. - - If you need to add custom behavior, override - :meth:`resolve_or_missing`, not this method. The various lookup - functions use that method, not this one. - - :param key: The variable name to look up. - """ - rv = self.resolve_or_missing(key) - - if rv is missing: - return self.environment.undefined(name=key) - - return rv - - def resolve_or_missing(self, key: str) -> t.Any: - """Look up a variable by name, or return a ``missing`` sentinel - if the key is not found. - - Override this method to add custom lookup behavior. - :meth:`resolve`, :meth:`get`, and :meth:`__getitem__` use this - method. Don't call this method directly. - - :param key: The variable name to look up. - """ - if key in self.vars: - return self.vars[key] - - if key in self.parent: - return self.parent[key] - - return missing - - def get_exported(self) -> t.Dict[str, t.Any]: - """Get a new dict with the exported variables.""" - return {k: self.vars[k] for k in self.exported_vars} - - def get_all(self) -> t.Dict[str, t.Any]: - """Return the complete context as dict including the exported - variables. For optimizations reasons this might not return an - actual copy so be careful with using it. - """ - if not self.vars: - return self.parent - if not self.parent: - return self.vars - return dict(self.parent, **self.vars) - - @internalcode - def call( - __self, __obj: t.Callable, *args: t.Any, **kwargs: t.Any # noqa: B902 - ) -> t.Union[t.Any, "Undefined"]: - """Call the callable with the arguments and keyword arguments - provided but inject the active context or environment as first - argument if the callable has :func:`pass_context` or - :func:`pass_environment`. - """ - if __debug__: - __traceback_hide__ = True # noqa - - # Allow callable classes to take a context - if ( - hasattr(__obj, "__call__") # noqa: B004 - and _PassArg.from_obj(__obj.__call__) is not None # type: ignore - ): - __obj = __obj.__call__ # type: ignore - - pass_arg = _PassArg.from_obj(__obj) - - if pass_arg is _PassArg.context: - # the active context should have access to variables set in - # loops and blocks without mutating the context itself - if kwargs.get("_loop_vars"): - __self = __self.derived(kwargs["_loop_vars"]) - if kwargs.get("_block_vars"): - __self = __self.derived(kwargs["_block_vars"]) - args = (__self,) + args - elif pass_arg is _PassArg.eval_context: - args = (__self.eval_ctx,) + args - elif pass_arg is _PassArg.environment: - args = (__self.environment,) + args - - kwargs.pop("_block_vars", None) - kwargs.pop("_loop_vars", None) - - try: - return __obj(*args, **kwargs) - except StopIteration: - return __self.environment.undefined( - "value was undefined because a callable raised a" - " StopIteration exception" - ) - - def derived(self, locals: t.Optional[t.Dict[str, t.Any]] = None) -> "Context": - """Internal helper function to create a derived context. This is - used in situations where the system needs a new context in the same - template that is independent. - """ - context = new_context( - self.environment, self.name, {}, self.get_all(), True, None, locals - ) - context.eval_ctx = self.eval_ctx - context.blocks.update((k, list(v)) for k, v in self.blocks.items()) - return context - - keys = _dict_method_all(dict.keys) - values = _dict_method_all(dict.values) - items = _dict_method_all(dict.items) - - def __contains__(self, name: str) -> bool: - return name in self.vars or name in self.parent - - def __getitem__(self, key: str) -> t.Any: - """Look up a variable by name with ``[]`` syntax, or raise a - ``KeyError`` if the key is not found. - """ - item = self.resolve_or_missing(key) - - if item is missing: - raise KeyError(key) - - return item - - def __repr__(self) -> str: - return f"<{type(self).__name__} {self.get_all()!r} of {self.name!r}>" - - -class BlockReference: - """One block on a template reference.""" - - def __init__( - self, - name: str, - context: "Context", - stack: t.List[t.Callable[["Context"], t.Iterator[str]]], - depth: int, - ) -> None: - self.name = name - self._context = context - self._stack = stack - self._depth = depth - - @property - def super(self) -> t.Union["BlockReference", "Undefined"]: - """Super the block.""" - if self._depth + 1 >= len(self._stack): - return self._context.environment.undefined( - f"there is no parent block called {self.name!r}.", name="super" - ) - return BlockReference(self.name, self._context, self._stack, self._depth + 1) - - @internalcode - async def _async_call(self) -> str: - rv = concat( - [x async for x in self._stack[self._depth](self._context)] # type: ignore - ) - - if self._context.eval_ctx.autoescape: - return Markup(rv) - - return rv - - @internalcode - def __call__(self) -> str: - if self._context.environment.is_async: - return self._async_call() # type: ignore - - rv = concat(self._stack[self._depth](self._context)) - - if self._context.eval_ctx.autoescape: - return Markup(rv) - - return rv - - -class LoopContext: - """A wrapper iterable for dynamic ``for`` loops, with information - about the loop and iteration. - """ - - #: Current iteration of the loop, starting at 0. - index0 = -1 - - _length: t.Optional[int] = None - _after: t.Any = missing - _current: t.Any = missing - _before: t.Any = missing - _last_changed_value: t.Any = missing - - def __init__( - self, - iterable: t.Iterable[V], - undefined: t.Type["Undefined"], - recurse: t.Optional["LoopRenderFunc"] = None, - depth0: int = 0, - ) -> None: - """ - :param iterable: Iterable to wrap. - :param undefined: :class:`Undefined` class to use for next and - previous items. - :param recurse: The function to render the loop body when the - loop is marked recursive. - :param depth0: Incremented when looping recursively. - """ - self._iterable = iterable - self._iterator = self._to_iterator(iterable) - self._undefined = undefined - self._recurse = recurse - #: How many levels deep a recursive loop currently is, starting at 0. - self.depth0 = depth0 - - @staticmethod - def _to_iterator(iterable: t.Iterable[V]) -> t.Iterator[V]: - return iter(iterable) - - @property - def length(self) -> int: - """Length of the iterable. - - If the iterable is a generator or otherwise does not have a - size, it is eagerly evaluated to get a size. - """ - if self._length is not None: - return self._length - - try: - self._length = len(self._iterable) # type: ignore - except TypeError: - iterable = list(self._iterator) - self._iterator = self._to_iterator(iterable) - self._length = len(iterable) + self.index + (self._after is not missing) - - return self._length - - def __len__(self) -> int: - return self.length - - @property - def depth(self) -> int: - """How many levels deep a recursive loop currently is, starting at 1.""" - return self.depth0 + 1 - - @property - def index(self) -> int: - """Current iteration of the loop, starting at 1.""" - return self.index0 + 1 - - @property - def revindex0(self) -> int: - """Number of iterations from the end of the loop, ending at 0. - - Requires calculating :attr:`length`. - """ - return self.length - self.index - - @property - def revindex(self) -> int: - """Number of iterations from the end of the loop, ending at 1. - - Requires calculating :attr:`length`. - """ - return self.length - self.index0 - - @property - def first(self) -> bool: - """Whether this is the first iteration of the loop.""" - return self.index0 == 0 - - def _peek_next(self) -> t.Any: - """Return the next element in the iterable, or :data:`missing` - if the iterable is exhausted. Only peeks one item ahead, caching - the result in :attr:`_last` for use in subsequent checks. The - cache is reset when :meth:`__next__` is called. - """ - if self._after is not missing: - return self._after - - self._after = next(self._iterator, missing) - return self._after - - @property - def last(self) -> bool: - """Whether this is the last iteration of the loop. - - Causes the iterable to advance early. See - :func:`itertools.groupby` for issues this can cause. - The :func:`groupby` filter avoids that issue. - """ - return self._peek_next() is missing - - @property - def previtem(self) -> t.Union[t.Any, "Undefined"]: - """The item in the previous iteration. Undefined during the - first iteration. - """ - if self.first: - return self._undefined("there is no previous item") - - return self._before - - @property - def nextitem(self) -> t.Union[t.Any, "Undefined"]: - """The item in the next iteration. Undefined during the last - iteration. - - Causes the iterable to advance early. See - :func:`itertools.groupby` for issues this can cause. - The :func:`jinja-filters.groupby` filter avoids that issue. - """ - rv = self._peek_next() - - if rv is missing: - return self._undefined("there is no next item") - - return rv - - def cycle(self, *args: V) -> V: - """Return a value from the given args, cycling through based on - the current :attr:`index0`. - - :param args: One or more values to cycle through. - """ - if not args: - raise TypeError("no items for cycling given") - - return args[self.index0 % len(args)] - - def changed(self, *value: t.Any) -> bool: - """Return ``True`` if previously called with a different value - (including when called for the first time). - - :param value: One or more values to compare to the last call. - """ - if self._last_changed_value != value: - self._last_changed_value = value - return True - - return False - - def __iter__(self) -> "LoopContext": - return self - - def __next__(self) -> t.Tuple[t.Any, "LoopContext"]: - if self._after is not missing: - rv = self._after - self._after = missing - else: - rv = next(self._iterator) - - self.index0 += 1 - self._before = self._current - self._current = rv - return rv, self - - @internalcode - def __call__(self, iterable: t.Iterable[V]) -> str: - """When iterating over nested data, render the body of the loop - recursively with the given inner iterable data. - - The loop must have the ``recursive`` marker for this to work. - """ - if self._recurse is None: - raise TypeError( - "The loop must have the 'recursive' marker to be called recursively." - ) - - return self._recurse(iterable, self._recurse, depth=self.depth) - - def __repr__(self) -> str: - return f"<{type(self).__name__} {self.index}/{self.length}>" - - -class AsyncLoopContext(LoopContext): - _iterator: t.AsyncIterator[t.Any] # type: ignore - - @staticmethod - def _to_iterator( # type: ignore - iterable: t.Union[t.Iterable[V], t.AsyncIterable[V]] - ) -> t.AsyncIterator[V]: - return auto_aiter(iterable) - - @property - async def length(self) -> int: # type: ignore - if self._length is not None: - return self._length - - try: - self._length = len(self._iterable) # type: ignore - except TypeError: - iterable = [x async for x in self._iterator] - self._iterator = self._to_iterator(iterable) - self._length = len(iterable) + self.index + (self._after is not missing) - - return self._length - - @property - async def revindex0(self) -> int: # type: ignore - return await self.length - self.index - - @property - async def revindex(self) -> int: # type: ignore - return await self.length - self.index0 - - async def _peek_next(self) -> t.Any: - if self._after is not missing: - return self._after - - try: - self._after = await self._iterator.__anext__() - except StopAsyncIteration: - self._after = missing - - return self._after - - @property - async def last(self) -> bool: # type: ignore - return await self._peek_next() is missing - - @property - async def nextitem(self) -> t.Union[t.Any, "Undefined"]: - rv = await self._peek_next() - - if rv is missing: - return self._undefined("there is no next item") - - return rv - - def __aiter__(self) -> "AsyncLoopContext": - return self - - async def __anext__(self) -> t.Tuple[t.Any, "AsyncLoopContext"]: - if self._after is not missing: - rv = self._after - self._after = missing - else: - rv = await self._iterator.__anext__() - - self.index0 += 1 - self._before = self._current - self._current = rv - return rv, self - - -class Macro: - """Wraps a macro function.""" - - def __init__( - self, - environment: "Environment", - func: t.Callable[..., str], - name: str, - arguments: t.List[str], - catch_kwargs: bool, - catch_varargs: bool, - caller: bool, - default_autoescape: t.Optional[bool] = None, - ): - self._environment = environment - self._func = func - self._argument_count = len(arguments) - self.name = name - self.arguments = arguments - self.catch_kwargs = catch_kwargs - self.catch_varargs = catch_varargs - self.caller = caller - self.explicit_caller = "caller" in arguments - - if default_autoescape is None: - if callable(environment.autoescape): - default_autoescape = environment.autoescape(None) - else: - default_autoescape = environment.autoescape - - self._default_autoescape = default_autoescape - - @internalcode - @pass_eval_context - def __call__(self, *args: t.Any, **kwargs: t.Any) -> str: - # This requires a bit of explanation, In the past we used to - # decide largely based on compile-time information if a macro is - # safe or unsafe. While there was a volatile mode it was largely - # unused for deciding on escaping. This turns out to be - # problematic for macros because whether a macro is safe depends not - # on the escape mode when it was defined, but rather when it was used. - # - # Because however we export macros from the module system and - # there are historic callers that do not pass an eval context (and - # will continue to not pass one), we need to perform an instance - # check here. - # - # This is considered safe because an eval context is not a valid - # argument to callables otherwise anyway. Worst case here is - # that if no eval context is passed we fall back to the compile - # time autoescape flag. - if args and isinstance(args[0], EvalContext): - autoescape = args[0].autoescape - args = args[1:] - else: - autoescape = self._default_autoescape - - # try to consume the positional arguments - arguments = list(args[: self._argument_count]) - off = len(arguments) - - # For information why this is necessary refer to the handling - # of caller in the `macro_body` handler in the compiler. - found_caller = False - - # if the number of arguments consumed is not the number of - # arguments expected we start filling in keyword arguments - # and defaults. - if off != self._argument_count: - for name in self.arguments[len(arguments) :]: - try: - value = kwargs.pop(name) - except KeyError: - value = missing - if name == "caller": - found_caller = True - arguments.append(value) - else: - found_caller = self.explicit_caller - - # it's important that the order of these arguments does not change - # if not also changed in the compiler's `function_scoping` method. - # the order is caller, keyword arguments, positional arguments! - if self.caller and not found_caller: - caller = kwargs.pop("caller", None) - if caller is None: - caller = self._environment.undefined("No caller defined", name="caller") - arguments.append(caller) - - if self.catch_kwargs: - arguments.append(kwargs) - elif kwargs: - if "caller" in kwargs: - raise TypeError( - f"macro {self.name!r} was invoked with two values for the special" - " caller argument. This is most likely a bug." - ) - raise TypeError( - f"macro {self.name!r} takes no keyword argument {next(iter(kwargs))!r}" - ) - if self.catch_varargs: - arguments.append(args[self._argument_count :]) - elif len(args) > self._argument_count: - raise TypeError( - f"macro {self.name!r} takes not more than" - f" {len(self.arguments)} argument(s)" - ) - - return self._invoke(arguments, autoescape) - - async def _async_invoke(self, arguments: t.List[t.Any], autoescape: bool) -> str: - rv = await self._func(*arguments) # type: ignore - - if autoescape: - return Markup(rv) - - return rv # type: ignore - - def _invoke(self, arguments: t.List[t.Any], autoescape: bool) -> str: - if self._environment.is_async: - return self._async_invoke(arguments, autoescape) # type: ignore - - rv = self._func(*arguments) - - if autoescape: - rv = Markup(rv) - - return rv - - def __repr__(self) -> str: - name = "anonymous" if self.name is None else repr(self.name) - return f"<{type(self).__name__} {name}>" - - -class Undefined: - """The default undefined type. This undefined type can be printed and - iterated over, but every other access will raise an :exc:`UndefinedError`: - - >>> foo = Undefined(name='foo') - >>> str(foo) - '' - >>> not foo - True - >>> foo + 42 - Traceback (most recent call last): - ... - jinja2.exceptions.UndefinedError: 'foo' is undefined - """ - - __slots__ = ( - "_undefined_hint", - "_undefined_obj", - "_undefined_name", - "_undefined_exception", - ) - - def __init__( - self, - hint: t.Optional[str] = None, - obj: t.Any = missing, - name: t.Optional[str] = None, - exc: t.Type[TemplateRuntimeError] = UndefinedError, - ) -> None: - self._undefined_hint = hint - self._undefined_obj = obj - self._undefined_name = name - self._undefined_exception = exc - - @property - def _undefined_message(self) -> str: - """Build a message about the undefined value based on how it was - accessed. - """ - if self._undefined_hint: - return self._undefined_hint - - if self._undefined_obj is missing: - return f"{self._undefined_name!r} is undefined" - - if not isinstance(self._undefined_name, str): - return ( - f"{object_type_repr(self._undefined_obj)} has no" - f" element {self._undefined_name!r}" - ) - - return ( - f"{object_type_repr(self._undefined_obj)!r} has no" - f" attribute {self._undefined_name!r}" - ) - - @internalcode - def _fail_with_undefined_error( - self, *args: t.Any, **kwargs: t.Any - ) -> "te.NoReturn": - """Raise an :exc:`UndefinedError` when operations are performed - on the undefined value. - """ - raise self._undefined_exception(self._undefined_message) - - @internalcode - def __getattr__(self, name: str) -> t.Any: - if name[:2] == "__": - raise AttributeError(name) - - return self._fail_with_undefined_error() - - __add__ = __radd__ = __sub__ = __rsub__ = _fail_with_undefined_error - __mul__ = __rmul__ = __div__ = __rdiv__ = _fail_with_undefined_error - __truediv__ = __rtruediv__ = _fail_with_undefined_error - __floordiv__ = __rfloordiv__ = _fail_with_undefined_error - __mod__ = __rmod__ = _fail_with_undefined_error - __pos__ = __neg__ = _fail_with_undefined_error - __call__ = __getitem__ = _fail_with_undefined_error - __lt__ = __le__ = __gt__ = __ge__ = _fail_with_undefined_error - __int__ = __float__ = __complex__ = _fail_with_undefined_error - __pow__ = __rpow__ = _fail_with_undefined_error - - def __eq__(self, other: t.Any) -> bool: - return type(self) is type(other) - - def __ne__(self, other: t.Any) -> bool: - return not self.__eq__(other) - - def __hash__(self) -> int: - return id(type(self)) - - def __str__(self) -> str: - return "" - - def __len__(self) -> int: - return 0 - - def __iter__(self) -> t.Iterator[t.Any]: - yield from () - - async def __aiter__(self) -> t.AsyncIterator[t.Any]: - for _ in (): - yield - - def __bool__(self) -> bool: - return False - - def __repr__(self) -> str: - return "Undefined" - - -def make_logging_undefined( - logger: t.Optional["logging.Logger"] = None, base: t.Type[Undefined] = Undefined -) -> t.Type[Undefined]: - """Given a logger object this returns a new undefined class that will - log certain failures. It will log iterations and printing. If no - logger is given a default logger is created. - - Example:: - - logger = logging.getLogger(__name__) - LoggingUndefined = make_logging_undefined( - logger=logger, - base=Undefined - ) - - .. versionadded:: 2.8 - - :param logger: the logger to use. If not provided, a default logger - is created. - :param base: the base class to add logging functionality to. This - defaults to :class:`Undefined`. - """ - if logger is None: - import logging - - logger = logging.getLogger(__name__) - logger.addHandler(logging.StreamHandler(sys.stderr)) - - def _log_message(undef: Undefined) -> None: - logger.warning( # type: ignore - "Template variable warning: %s", undef._undefined_message - ) - - class LoggingUndefined(base): # type: ignore - __slots__ = () - - def _fail_with_undefined_error( # type: ignore - self, *args: t.Any, **kwargs: t.Any - ) -> "te.NoReturn": - try: - super()._fail_with_undefined_error(*args, **kwargs) - except self._undefined_exception as e: - logger.error("Template variable error: %s", e) # type: ignore - raise e - - def __str__(self) -> str: - _log_message(self) - return super().__str__() # type: ignore - - def __iter__(self) -> t.Iterator[t.Any]: - _log_message(self) - return super().__iter__() # type: ignore - - def __bool__(self) -> bool: - _log_message(self) - return super().__bool__() # type: ignore - - return LoggingUndefined - - -class ChainableUndefined(Undefined): - """An undefined that is chainable, where both ``__getattr__`` and - ``__getitem__`` return itself rather than raising an - :exc:`UndefinedError`. - - >>> foo = ChainableUndefined(name='foo') - >>> str(foo.bar['baz']) - '' - >>> foo.bar['baz'] + 42 - Traceback (most recent call last): - ... - jinja2.exceptions.UndefinedError: 'foo' is undefined - - .. versionadded:: 2.11.0 - """ - - __slots__ = () - - def __html__(self) -> str: - return str(self) - - def __getattr__(self, _: str) -> "ChainableUndefined": - return self - - __getitem__ = __getattr__ # type: ignore - - -class DebugUndefined(Undefined): - """An undefined that returns the debug info when printed. - - >>> foo = DebugUndefined(name='foo') - >>> str(foo) - '{{ foo }}' - >>> not foo - True - >>> foo + 42 - Traceback (most recent call last): - ... - jinja2.exceptions.UndefinedError: 'foo' is undefined - """ - - __slots__ = () - - def __str__(self) -> str: - if self._undefined_hint: - message = f"undefined value printed: {self._undefined_hint}" - - elif self._undefined_obj is missing: - message = self._undefined_name # type: ignore - - else: - message = ( - f"no such element: {object_type_repr(self._undefined_obj)}" - f"[{self._undefined_name!r}]" - ) - - return f"{{{{ {message} }}}}" - - -class StrictUndefined(Undefined): - """An undefined that barks on print and iteration as well as boolean - tests and all kinds of comparisons. In other words: you can do nothing - with it except checking if it's defined using the `defined` test. - - >>> foo = StrictUndefined(name='foo') - >>> str(foo) - Traceback (most recent call last): - ... - jinja2.exceptions.UndefinedError: 'foo' is undefined - >>> not foo - Traceback (most recent call last): - ... - jinja2.exceptions.UndefinedError: 'foo' is undefined - >>> foo + 42 - Traceback (most recent call last): - ... - jinja2.exceptions.UndefinedError: 'foo' is undefined - """ - - __slots__ = () - __iter__ = __str__ = __len__ = Undefined._fail_with_undefined_error - __eq__ = __ne__ = __bool__ = __hash__ = Undefined._fail_with_undefined_error - __contains__ = Undefined._fail_with_undefined_error - - -# Remove slots attributes, after the metaclass is applied they are -# unneeded and contain wrong data for subclasses. -del ( - Undefined.__slots__, - ChainableUndefined.__slots__, - DebugUndefined.__slots__, - StrictUndefined.__slots__, -) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/rules_inline/emphasis.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/rules_inline/emphasis.py deleted file mode 100644 index 9a98f9e216c94db0217e986270aaaa72fcc99f7f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/rules_inline/emphasis.py +++ /dev/null @@ -1,102 +0,0 @@ -# Process *this* and _that_ -# -from __future__ import annotations - -from .state_inline import Delimiter, StateInline - - -def tokenize(state: StateInline, silent: bool) -> bool: - """Insert each marker as a separate text token, and add it to delimiter list""" - start = state.pos - marker = state.src[start] - - if silent: - return False - - if marker not in ("_", "*"): - return False - - scanned = state.scanDelims(state.pos, marker == "*") - - for _ in range(scanned.length): - token = state.push("text", "", 0) - token.content = marker - state.delimiters.append( - Delimiter( - marker=ord(marker), - length=scanned.length, - token=len(state.tokens) - 1, - end=-1, - open=scanned.can_open, - close=scanned.can_close, - ) - ) - - state.pos += scanned.length - - return True - - -def _postProcess(state: StateInline, delimiters: list[Delimiter]) -> None: - i = len(delimiters) - 1 - while i >= 0: - startDelim = delimiters[i] - - # /* _ */ /* * */ - if startDelim.marker != 0x5F and startDelim.marker != 0x2A: - i -= 1 - continue - - # Process only opening markers - if startDelim.end == -1: - i -= 1 - continue - - endDelim = delimiters[startDelim.end] - - # If the previous delimiter has the same marker and is adjacent to this one, - # merge those into one strong delimiter. - # - # `whatever` -> `whatever` - # - isStrong = ( - i > 0 - and delimiters[i - 1].end == startDelim.end + 1 - # check that first two markers match and adjacent - and delimiters[i - 1].marker == startDelim.marker - and delimiters[i - 1].token == startDelim.token - 1 - # check that last two markers are adjacent (we can safely assume they match) - and delimiters[startDelim.end + 1].token == endDelim.token + 1 - ) - - ch = chr(startDelim.marker) - - token = state.tokens[startDelim.token] - token.type = "strong_open" if isStrong else "em_open" - token.tag = "strong" if isStrong else "em" - token.nesting = 1 - token.markup = ch + ch if isStrong else ch - token.content = "" - - token = state.tokens[endDelim.token] - token.type = "strong_close" if isStrong else "em_close" - token.tag = "strong" if isStrong else "em" - token.nesting = -1 - token.markup = ch + ch if isStrong else ch - token.content = "" - - if isStrong: - state.tokens[delimiters[i - 1].token].content = "" - state.tokens[delimiters[startDelim.end + 1].token].content = "" - i -= 1 - - i -= 1 - - -def postProcess(state: StateInline) -> None: - """Walk through delimiter list and replace text tokens with tags.""" - _postProcess(state, state.delimiters) - - for token in state.tokens_meta: - if token and "delimiters" in token: - _postProcess(state, token["delimiters"]) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_pyinstaller/pyinstaller-smoke.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_pyinstaller/pyinstaller-smoke.py deleted file mode 100644 index eb28070e38baf80223fe0178ac0a7c0f5732a2c8..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/_pyinstaller/pyinstaller-smoke.py +++ /dev/null @@ -1,32 +0,0 @@ -"""A crude *bit of everything* smoke test to verify PyInstaller compatibility. - -PyInstaller typically goes wrong by forgetting to package modules, extension -modules or shared libraries. This script should aim to touch as many of those -as possible in an attempt to trip a ModuleNotFoundError or a DLL load failure -due to an uncollected resource. Missing resources are unlikely to lead to -arithmetic errors so there's generally no need to verify any calculation's -output - merely that it made it to the end OK. This script should not -explicitly import any of numpy's submodules as that gives PyInstaller undue -hints that those submodules exist and should be collected (accessing implicitly -loaded submodules is OK). - -""" -import numpy as np - -a = np.arange(1., 10.).reshape((3, 3)) % 5 -np.linalg.det(a) -a @ a -a @ a.T -np.linalg.inv(a) -np.sin(np.exp(a)) -np.linalg.svd(a) -np.linalg.eigh(a) - -np.unique(np.random.randint(0, 10, 100)) -np.sort(np.random.uniform(0, 10, 100)) - -np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8)) -np.ma.masked_array(np.arange(10), np.random.rand(10) < .5).sum() -np.polynomial.Legendre([7, 8, 9]).roots() - -print("I made it!") diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/extra_vsx4_mma.c b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/extra_vsx4_mma.c deleted file mode 100644 index a70b2a9f6f95408eb7cfe59c056f114cc363869b..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/checks/extra_vsx4_mma.c +++ /dev/null @@ -1,21 +0,0 @@ -#ifndef __VSX__ - #error "VSX is not supported" -#endif -#include - -typedef __vector float fv4sf_t; -typedef __vector unsigned char vec_t; - -int main(void) -{ - __vector_quad acc0; - float a[4] = {0,1,2,3}; - float b[4] = {0,1,2,3}; - vec_t *va = (vec_t *) a; - vec_t *vb = (vec_t *) b; - __builtin_mma_xvf32ger(&acc0, va[0], vb[0]); - fv4sf_t result[4]; - __builtin_mma_disassemble_acc((void *)result, &acc0); - fv4sf_t c0 = result[0]; - return (int)((float*)&c0)[0]; -} diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/copy_view/test_internals.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/copy_view/test_internals.py deleted file mode 100644 index a727331307d7e9086144aa8d27f70ffa83973620..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/copy_view/test_internals.py +++ /dev/null @@ -1,151 +0,0 @@ -import numpy as np -import pytest - -import pandas.util._test_decorators as td - -import pandas as pd -from pandas import DataFrame -import pandas._testing as tm -from pandas.tests.copy_view.util import get_array - - -@td.skip_array_manager_invalid_test -def test_consolidate(using_copy_on_write): - # create unconsolidated DataFrame - df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) - df["c"] = [4, 5, 6] - - # take a viewing subset - subset = df[:] - - # each block of subset references a block of df - assert all(blk.refs.has_reference() for blk in subset._mgr.blocks) - - # consolidate the two int64 blocks - subset._consolidate_inplace() - - # the float64 block still references the parent one because it still a view - assert subset._mgr.blocks[0].refs.has_reference() - # equivalent of assert np.shares_memory(df["b"].values, subset["b"].values) - # but avoids caching df["b"] - assert np.shares_memory(get_array(df, "b"), get_array(subset, "b")) - - # the new consolidated int64 block does not reference another - assert not subset._mgr.blocks[1].refs.has_reference() - - # the parent dataframe now also only is linked for the float column - assert not df._mgr.blocks[0].refs.has_reference() - assert df._mgr.blocks[1].refs.has_reference() - assert not df._mgr.blocks[2].refs.has_reference() - - # and modifying subset still doesn't modify parent - if using_copy_on_write: - subset.iloc[0, 1] = 0.0 - assert not df._mgr.blocks[1].refs.has_reference() - assert df.loc[0, "b"] == 0.1 - - -@pytest.mark.single_cpu -@td.skip_array_manager_invalid_test -def test_switch_options(): - # ensure we can switch the value of the option within one session - # (assuming data is constructed after switching) - - # using the option_context to ensure we set back to global option value - # after running the test - with pd.option_context("mode.copy_on_write", False): - df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) - subset = df[:] - subset.iloc[0, 0] = 0 - # df updated with CoW disabled - assert df.iloc[0, 0] == 0 - - pd.options.mode.copy_on_write = True - df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) - subset = df[:] - subset.iloc[0, 0] = 0 - # df not updated with CoW enabled - assert df.iloc[0, 0] == 1 - - pd.options.mode.copy_on_write = False - df = DataFrame({"a": [1, 2, 3], "b": [0.1, 0.2, 0.3]}) - subset = df[:] - subset.iloc[0, 0] = 0 - # df updated with CoW disabled - assert df.iloc[0, 0] == 0 - - -@td.skip_array_manager_invalid_test -@pytest.mark.parametrize("dtype", [np.intp, np.int8]) -@pytest.mark.parametrize( - "locs, arr", - [ - ([0], np.array([-1, -2, -3])), - ([1], np.array([-1, -2, -3])), - ([5], np.array([-1, -2, -3])), - ([0, 1], np.array([[-1, -2, -3], [-4, -5, -6]]).T), - ([0, 2], np.array([[-1, -2, -3], [-4, -5, -6]]).T), - ([0, 1, 2], np.array([[-1, -2, -3], [-4, -5, -6], [-4, -5, -6]]).T), - ([1, 2], np.array([[-1, -2, -3], [-4, -5, -6]]).T), - ([1, 3], np.array([[-1, -2, -3], [-4, -5, -6]]).T), - ([1, 3], np.array([[-1, -2, -3], [-4, -5, -6]]).T), - ], -) -def test_iset_splits_blocks_inplace(using_copy_on_write, locs, arr, dtype): - # Nothing currently calls iset with - # more than 1 loc with inplace=True (only happens with inplace=False) - # but ensure that it works - df = DataFrame( - { - "a": [1, 2, 3], - "b": [4, 5, 6], - "c": [7, 8, 9], - "d": [10, 11, 12], - "e": [13, 14, 15], - "f": ["a", "b", "c"], - }, - ) - arr = arr.astype(dtype) - df_orig = df.copy() - df2 = df.copy(deep=None) # Trigger a CoW (if enabled, otherwise makes copy) - df2._mgr.iset(locs, arr, inplace=True) - - tm.assert_frame_equal(df, df_orig) - - if using_copy_on_write: - for i, col in enumerate(df.columns): - if i not in locs: - assert np.shares_memory(get_array(df, col), get_array(df2, col)) - else: - for col in df.columns: - assert not np.shares_memory(get_array(df, col), get_array(df2, col)) - - -def test_exponential_backoff(): - # GH#55518 - df = DataFrame({"a": [1, 2, 3]}) - for i in range(490): - df.copy(deep=False) - - assert len(df._mgr.blocks[0].refs.referenced_blocks) == 491 - - df = DataFrame({"a": [1, 2, 3]}) - dfs = [df.copy(deep=False) for i in range(510)] - - for i in range(20): - df.copy(deep=False) - assert len(df._mgr.blocks[0].refs.referenced_blocks) == 531 - assert df._mgr.blocks[0].refs.clear_counter == 1000 - - for i in range(500): - df.copy(deep=False) - - # Don't reduce since we still have over 500 objects alive - assert df._mgr.blocks[0].refs.clear_counter == 1000 - - dfs = dfs[:300] - for i in range(500): - df.copy(deep=False) - - # Reduce since there are less than 500 objects alive - assert df._mgr.blocks[0].refs.clear_counter == 500 diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/reshaping.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/reshaping.py deleted file mode 100644 index 5d9c03e1b25691ac9a4b1243f81e27de9c2deb41..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/reshaping.py +++ /dev/null @@ -1,374 +0,0 @@ -import itertools - -import numpy as np -import pytest - -import pandas as pd -import pandas._testing as tm -from pandas.api.extensions import ExtensionArray -from pandas.core.internals.blocks import EABackedBlock - - -class BaseReshapingTests: - """Tests for reshaping and concatenation.""" - - @pytest.mark.parametrize("in_frame", [True, False]) - def test_concat(self, data, in_frame): - wrapped = pd.Series(data) - if in_frame: - wrapped = pd.DataFrame(wrapped) - result = pd.concat([wrapped, wrapped], ignore_index=True) - - assert len(result) == len(data) * 2 - - if in_frame: - dtype = result.dtypes[0] - else: - dtype = result.dtype - - assert dtype == data.dtype - if hasattr(result._mgr, "blocks"): - assert isinstance(result._mgr.blocks[0], EABackedBlock) - assert isinstance(result._mgr.arrays[0], ExtensionArray) - - @pytest.mark.parametrize("in_frame", [True, False]) - def test_concat_all_na_block(self, data_missing, in_frame): - valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1]) - na_block = pd.Series(data_missing.take([0, 0]), index=[2, 3]) - if in_frame: - valid_block = pd.DataFrame({"a": valid_block}) - na_block = pd.DataFrame({"a": na_block}) - result = pd.concat([valid_block, na_block]) - if in_frame: - expected = pd.DataFrame({"a": data_missing.take([1, 1, 0, 0])}) - tm.assert_frame_equal(result, expected) - else: - expected = pd.Series(data_missing.take([1, 1, 0, 0])) - tm.assert_series_equal(result, expected) - - def test_concat_mixed_dtypes(self, data): - # https://github.com/pandas-dev/pandas/issues/20762 - df1 = pd.DataFrame({"A": data[:3]}) - df2 = pd.DataFrame({"A": [1, 2, 3]}) - df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category") - dfs = [df1, df2, df3] - - # dataframes - result = pd.concat(dfs) - expected = pd.concat([x.astype(object) for x in dfs]) - tm.assert_frame_equal(result, expected) - - # series - result = pd.concat([x["A"] for x in dfs]) - expected = pd.concat([x["A"].astype(object) for x in dfs]) - tm.assert_series_equal(result, expected) - - # simple test for just EA and one other - result = pd.concat([df1, df2.astype(object)]) - expected = pd.concat([df1.astype("object"), df2.astype("object")]) - tm.assert_frame_equal(result, expected) - - result = pd.concat([df1["A"], df2["A"].astype(object)]) - expected = pd.concat([df1["A"].astype("object"), df2["A"].astype("object")]) - tm.assert_series_equal(result, expected) - - def test_concat_columns(self, data, na_value): - df1 = pd.DataFrame({"A": data[:3]}) - df2 = pd.DataFrame({"B": [1, 2, 3]}) - - expected = pd.DataFrame({"A": data[:3], "B": [1, 2, 3]}) - result = pd.concat([df1, df2], axis=1) - tm.assert_frame_equal(result, expected) - result = pd.concat([df1["A"], df2["B"]], axis=1) - tm.assert_frame_equal(result, expected) - - # non-aligned - df2 = pd.DataFrame({"B": [1, 2, 3]}, index=[1, 2, 3]) - expected = pd.DataFrame( - { - "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype), - "B": [np.nan, 1, 2, 3], - } - ) - - result = pd.concat([df1, df2], axis=1) - tm.assert_frame_equal(result, expected) - result = pd.concat([df1["A"], df2["B"]], axis=1) - tm.assert_frame_equal(result, expected) - - def test_concat_extension_arrays_copy_false(self, data, na_value): - # GH 20756 - df1 = pd.DataFrame({"A": data[:3]}) - df2 = pd.DataFrame({"B": data[3:7]}) - expected = pd.DataFrame( - { - "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype), - "B": data[3:7], - } - ) - result = pd.concat([df1, df2], axis=1, copy=False) - tm.assert_frame_equal(result, expected) - - def test_concat_with_reindex(self, data): - # GH-33027 - a = pd.DataFrame({"a": data[:5]}) - b = pd.DataFrame({"b": data[:5]}) - result = pd.concat([a, b], ignore_index=True) - expected = pd.DataFrame( - { - "a": data.take(list(range(5)) + ([-1] * 5), allow_fill=True), - "b": data.take(([-1] * 5) + list(range(5)), allow_fill=True), - } - ) - tm.assert_frame_equal(result, expected) - - def test_align(self, data, na_value): - a = data[:3] - b = data[2:5] - r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3])) - - # Assumes that the ctor can take a list of scalars of the type - e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype)) - e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype)) - tm.assert_series_equal(r1, e1) - tm.assert_series_equal(r2, e2) - - def test_align_frame(self, data, na_value): - a = data[:3] - b = data[2:5] - r1, r2 = pd.DataFrame({"A": a}).align(pd.DataFrame({"A": b}, index=[1, 2, 3])) - - # Assumes that the ctor can take a list of scalars of the type - e1 = pd.DataFrame( - {"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)} - ) - e2 = pd.DataFrame( - {"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)} - ) - tm.assert_frame_equal(r1, e1) - tm.assert_frame_equal(r2, e2) - - def test_align_series_frame(self, data, na_value): - # https://github.com/pandas-dev/pandas/issues/20576 - ser = pd.Series(data, name="a") - df = pd.DataFrame({"col": np.arange(len(ser) + 1)}) - r1, r2 = ser.align(df) - - e1 = pd.Series( - data._from_sequence(list(data) + [na_value], dtype=data.dtype), - name=ser.name, - ) - - tm.assert_series_equal(r1, e1) - tm.assert_frame_equal(r2, df) - - def test_set_frame_expand_regular_with_extension(self, data): - df = pd.DataFrame({"A": [1] * len(data)}) - df["B"] = data - expected = pd.DataFrame({"A": [1] * len(data), "B": data}) - tm.assert_frame_equal(df, expected) - - def test_set_frame_expand_extension_with_regular(self, data): - df = pd.DataFrame({"A": data}) - df["B"] = [1] * len(data) - expected = pd.DataFrame({"A": data, "B": [1] * len(data)}) - tm.assert_frame_equal(df, expected) - - def test_set_frame_overwrite_object(self, data): - # https://github.com/pandas-dev/pandas/issues/20555 - df = pd.DataFrame({"A": [1] * len(data)}, dtype=object) - df["A"] = data - assert df.dtypes["A"] == data.dtype - - def test_merge(self, data, na_value): - # GH-20743 - df1 = pd.DataFrame({"ext": data[:3], "int1": [1, 2, 3], "key": [0, 1, 2]}) - df2 = pd.DataFrame({"int2": [1, 2, 3, 4], "key": [0, 0, 1, 3]}) - - res = pd.merge(df1, df2) - exp = pd.DataFrame( - { - "int1": [1, 1, 2], - "int2": [1, 2, 3], - "key": [0, 0, 1], - "ext": data._from_sequence( - [data[0], data[0], data[1]], dtype=data.dtype - ), - } - ) - tm.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]]) - - res = pd.merge(df1, df2, how="outer") - exp = pd.DataFrame( - { - "int1": [1, 1, 2, 3, np.nan], - "int2": [1, 2, 3, np.nan, 4], - "key": [0, 0, 1, 2, 3], - "ext": data._from_sequence( - [data[0], data[0], data[1], data[2], na_value], dtype=data.dtype - ), - } - ) - tm.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]]) - - def test_merge_on_extension_array(self, data): - # GH 23020 - a, b = data[:2] - key = type(data)._from_sequence([a, b], dtype=data.dtype) - - df = pd.DataFrame({"key": key, "val": [1, 2]}) - result = pd.merge(df, df, on="key") - expected = pd.DataFrame({"key": key, "val_x": [1, 2], "val_y": [1, 2]}) - tm.assert_frame_equal(result, expected) - - # order - result = pd.merge(df.iloc[[1, 0]], df, on="key") - expected = expected.iloc[[1, 0]].reset_index(drop=True) - tm.assert_frame_equal(result, expected) - - def test_merge_on_extension_array_duplicates(self, data): - # GH 23020 - a, b = data[:2] - key = type(data)._from_sequence([a, b, a], dtype=data.dtype) - df1 = pd.DataFrame({"key": key, "val": [1, 2, 3]}) - df2 = pd.DataFrame({"key": key, "val": [1, 2, 3]}) - - result = pd.merge(df1, df2, on="key") - expected = pd.DataFrame( - { - "key": key.take([0, 0, 0, 0, 1]), - "val_x": [1, 1, 3, 3, 2], - "val_y": [1, 3, 1, 3, 2], - } - ) - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize( - "columns", - [ - ["A", "B"], - pd.MultiIndex.from_tuples( - [("A", "a"), ("A", "b")], names=["outer", "inner"] - ), - ], - ) - @pytest.mark.parametrize("future_stack", [True, False]) - def test_stack(self, data, columns, future_stack): - df = pd.DataFrame({"A": data[:5], "B": data[:5]}) - df.columns = columns - result = df.stack(future_stack=future_stack) - expected = df.astype(object).stack(future_stack=future_stack) - # we need a second astype(object), in case the constructor inferred - # object -> specialized, as is done for period. - expected = expected.astype(object) - - if isinstance(expected, pd.Series): - assert result.dtype == df.iloc[:, 0].dtype - else: - assert all(result.dtypes == df.iloc[:, 0].dtype) - - result = result.astype(object) - tm.assert_equal(result, expected) - - @pytest.mark.parametrize( - "index", - [ - # Two levels, uniform. - pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"]), - # non-uniform - pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "b")]), - # three levels, non-uniform - pd.MultiIndex.from_product([("A", "B"), ("a", "b", "c"), (0, 1, 2)]), - pd.MultiIndex.from_tuples( - [ - ("A", "a", 1), - ("A", "b", 0), - ("A", "a", 0), - ("B", "a", 0), - ("B", "c", 1), - ] - ), - ], - ) - @pytest.mark.parametrize("obj", ["series", "frame"]) - def test_unstack(self, data, index, obj): - data = data[: len(index)] - if obj == "series": - ser = pd.Series(data, index=index) - else: - ser = pd.DataFrame({"A": data, "B": data}, index=index) - - n = index.nlevels - levels = list(range(n)) - # [0, 1, 2] - # [(0,), (1,), (2,), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)] - combinations = itertools.chain.from_iterable( - itertools.permutations(levels, i) for i in range(1, n) - ) - - for level in combinations: - result = ser.unstack(level=level) - assert all( - isinstance(result[col].array, type(data)) for col in result.columns - ) - - if obj == "series": - # We should get the same result with to_frame+unstack+droplevel - df = ser.to_frame() - - alt = df.unstack(level=level).droplevel(0, axis=1) - tm.assert_frame_equal(result, alt) - - obj_ser = ser.astype(object) - - expected = obj_ser.unstack(level=level, fill_value=data.dtype.na_value) - if obj == "series": - assert (expected.dtypes == object).all() - - result = result.astype(object) - tm.assert_frame_equal(result, expected) - - def test_ravel(self, data): - # as long as EA is 1D-only, ravel is a no-op - result = data.ravel() - assert type(result) == type(data) - - if data.dtype._is_immutable: - pytest.skip("test_ravel assumes mutability") - - # Check that we have a view, not a copy - result[0] = result[1] - assert data[0] == data[1] - - def test_transpose(self, data): - result = data.transpose() - assert type(result) == type(data) - - # check we get a new object - assert result is not data - - # If we ever _did_ support 2D, shape should be reversed - assert result.shape == data.shape[::-1] - - if data.dtype._is_immutable: - pytest.skip("test_transpose assumes mutability") - - # Check that we have a view, not a copy - result[0] = result[1] - assert data[0] == data[1] - - def test_transpose_frame(self, data): - df = pd.DataFrame({"A": data[:4], "B": data[:4]}, index=["a", "b", "c", "d"]) - result = df.T - expected = pd.DataFrame( - { - "a": type(data)._from_sequence([data[0]] * 2, dtype=data.dtype), - "b": type(data)._from_sequence([data[1]] * 2, dtype=data.dtype), - "c": type(data)._from_sequence([data[2]] * 2, dtype=data.dtype), - "d": type(data)._from_sequence([data[3]] * 2, dtype=data.dtype), - }, - index=["A", "B"], - ) - tm.assert_frame_equal(result, expected) - tm.assert_frame_equal(np.transpose(np.transpose(df)), df) - tm.assert_frame_equal(np.transpose(np.transpose(df[["A"]])), df[["A"]]) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_pandas.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_pandas.py deleted file mode 100644 index 663d0a195fa55044a2c243f1ad436842454962e3..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/json/test_pandas.py +++ /dev/null @@ -1,2115 +0,0 @@ -import datetime -from datetime import timedelta -from decimal import Decimal -from io import StringIO -import json -import os -import sys -import time - -import numpy as np -import pytest - -from pandas.compat import IS64 -import pandas.util._test_decorators as td - -import pandas as pd -from pandas import ( - NA, - DataFrame, - DatetimeIndex, - Series, - Timestamp, - read_json, -) -import pandas._testing as tm -from pandas.core.arrays import ( - ArrowStringArray, - StringArray, -) - - -def test_literal_json_deprecation(): - # PR 53409 - expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"]) - - jsonl = """{"a": 1, "b": 2} - {"a": 3, "b": 4} - {"a": 5, "b": 6} - {"a": 7, "b": 8}""" - - msg = ( - "Passing literal json to 'read_json' is deprecated and " - "will be removed in a future version. To read from a " - "literal string, wrap it in a 'StringIO' object." - ) - - with tm.assert_produces_warning(FutureWarning, match=msg): - try: - read_json(jsonl, lines=False) - except ValueError: - pass - - with tm.assert_produces_warning(FutureWarning, match=msg): - read_json(expected.to_json(), lines=False) - - with tm.assert_produces_warning(FutureWarning, match=msg): - result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=True) - tm.assert_frame_equal(result, expected) - - with tm.assert_produces_warning(FutureWarning, match=msg): - try: - result = read_json( - '{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n', - lines=False, - ) - except ValueError: - pass - - with tm.assert_produces_warning(FutureWarning, match=msg): - try: - result = read_json('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n', lines=False) - except ValueError: - pass - tm.assert_frame_equal(result, expected) - - -def assert_json_roundtrip_equal(result, expected, orient): - if orient in ("records", "values"): - expected = expected.reset_index(drop=True) - if orient == "values": - expected.columns = range(len(expected.columns)) - tm.assert_frame_equal(result, expected) - - -class TestPandasContainer: - @pytest.fixture - def categorical_frame(self): - _seriesd = tm.getSeriesData() - - _cat_frame = DataFrame(_seriesd) - - cat = ["bah"] * 5 + ["bar"] * 5 + ["baz"] * 5 + ["foo"] * (len(_cat_frame) - 15) - _cat_frame.index = pd.CategoricalIndex(cat, name="E") - _cat_frame["E"] = list(reversed(cat)) - _cat_frame["sort"] = np.arange(len(_cat_frame), dtype="int64") - return _cat_frame - - @pytest.fixture - def datetime_series(self): - # Same as usual datetime_series, but with index freq set to None, - # since that doesn't round-trip, see GH#33711 - ser = tm.makeTimeSeries() - ser.name = "ts" - ser.index = ser.index._with_freq(None) - return ser - - @pytest.fixture - def datetime_frame(self): - # Same as usual datetime_frame, but with index freq set to None, - # since that doesn't round-trip, see GH#33711 - df = DataFrame(tm.getTimeSeriesData()) - df.index = df.index._with_freq(None) - return df - - def test_frame_double_encoded_labels(self, orient): - df = DataFrame( - [["a", "b"], ["c", "d"]], - index=['index " 1', "index / 2"], - columns=["a \\ b", "y / z"], - ) - - data = StringIO(df.to_json(orient=orient)) - result = read_json(data, orient=orient) - expected = df.copy() - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("orient", ["split", "records", "values"]) - def test_frame_non_unique_index(self, orient): - df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"]) - data = StringIO(df.to_json(orient=orient)) - result = read_json(data, orient=orient) - expected = df.copy() - - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("orient", ["index", "columns"]) - def test_frame_non_unique_index_raises(self, orient): - df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 1], columns=["x", "y"]) - msg = f"DataFrame index must be unique for orient='{orient}'" - with pytest.raises(ValueError, match=msg): - df.to_json(orient=orient) - - @pytest.mark.parametrize("orient", ["split", "values"]) - @pytest.mark.parametrize( - "data", - [ - [["a", "b"], ["c", "d"]], - [[1.5, 2.5], [3.5, 4.5]], - [[1, 2.5], [3, 4.5]], - [[Timestamp("20130101"), 3.5], [Timestamp("20130102"), 4.5]], - ], - ) - def test_frame_non_unique_columns(self, orient, data): - df = DataFrame(data, index=[1, 2], columns=["x", "x"]) - - result = read_json( - StringIO(df.to_json(orient=orient)), orient=orient, convert_dates=["x"] - ) - if orient == "values": - expected = DataFrame(data) - if expected.iloc[:, 0].dtype == "datetime64[ns]": - # orient == "values" by default will write Timestamp objects out - # in milliseconds; these are internally stored in nanosecond, - # so divide to get where we need - # TODO: a to_epoch method would also solve; see GH 14772 - expected.iloc[:, 0] = expected.iloc[:, 0].view(np.int64) // 1000000 - elif orient == "split": - expected = df - expected.columns = ["x", "x.1"] - - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("orient", ["index", "columns", "records"]) - def test_frame_non_unique_columns_raises(self, orient): - df = DataFrame([["a", "b"], ["c", "d"]], index=[1, 2], columns=["x", "x"]) - - msg = f"DataFrame columns must be unique for orient='{orient}'" - with pytest.raises(ValueError, match=msg): - df.to_json(orient=orient) - - def test_frame_default_orient(self, float_frame): - assert float_frame.to_json() == float_frame.to_json(orient="columns") - - @pytest.mark.parametrize("dtype", [False, float]) - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_simple(self, orient, convert_axes, dtype, float_frame): - data = StringIO(float_frame.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype) - - expected = float_frame - - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("dtype", [False, np.int64]) - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_intframe(self, orient, convert_axes, dtype, int_frame): - data = StringIO(int_frame.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype) - expected = int_frame - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("dtype", [None, np.float64, int, "U3"]) - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_str_axes(self, orient, convert_axes, dtype): - df = DataFrame( - np.zeros((200, 4)), - columns=[str(i) for i in range(4)], - index=[str(i) for i in range(200)], - dtype=dtype, - ) - - data = StringIO(df.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes, dtype=dtype) - - expected = df.copy() - if not dtype: - expected = expected.astype(np.int64) - - # index columns, and records orients cannot fully preserve the string - # dtype for axes as the index and column labels are used as keys in - # JSON objects. JSON keys are by definition strings, so there's no way - # to disambiguate whether those keys actually were strings or numeric - # beforehand and numeric wins out. - if convert_axes and (orient in ("index", "columns")): - expected.columns = expected.columns.astype(np.int64) - expected.index = expected.index.astype(np.int64) - elif orient == "records" and convert_axes: - expected.columns = expected.columns.astype(np.int64) - elif convert_axes and orient == "split": - expected.columns = expected.columns.astype(np.int64) - - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_categorical( - self, request, orient, categorical_frame, convert_axes - ): - # TODO: create a better frame to test with and improve coverage - if orient in ("index", "columns"): - request.node.add_marker( - pytest.mark.xfail( - reason=f"Can't have duplicate index values for orient '{orient}')" - ) - ) - - data = StringIO(categorical_frame.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes) - - expected = categorical_frame.copy() - expected.index = expected.index.astype(str) # Categorical not preserved - expected.index.name = None # index names aren't preserved in JSON - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_empty(self, orient, convert_axes): - empty_frame = DataFrame() - data = StringIO(empty_frame.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes) - if orient == "split": - idx = pd.Index([], dtype=(float if convert_axes else object)) - expected = DataFrame(index=idx, columns=idx) - elif orient in ["index", "columns"]: - expected = DataFrame() - else: - expected = empty_frame.copy() - - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_timestamp(self, orient, convert_axes, datetime_frame): - # TODO: improve coverage with date_format parameter - data = StringIO(datetime_frame.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes) - expected = datetime_frame.copy() - - if not convert_axes: # one off for ts handling - # DTI gets converted to epoch values - idx = expected.index.view(np.int64) // 1000000 - if orient != "split": # TODO: handle consistently across orients - idx = idx.astype(str) - - expected.index = idx - - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_roundtrip_mixed(self, orient, convert_axes): - index = pd.Index(["a", "b", "c", "d", "e"]) - values = { - "A": [0.0, 1.0, 2.0, 3.0, 4.0], - "B": [0.0, 1.0, 0.0, 1.0, 0.0], - "C": ["foo1", "foo2", "foo3", "foo4", "foo5"], - "D": [True, False, True, False, True], - } - - df = DataFrame(data=values, index=index) - - data = StringIO(df.to_json(orient=orient)) - result = read_json(data, orient=orient, convert_axes=convert_axes) - - expected = df.copy() - expected = expected.assign(**expected.select_dtypes("number").astype(np.int64)) - - assert_json_roundtrip_equal(result, expected, orient) - - @pytest.mark.xfail( - reason="#50456 Column multiindex is stored and loaded differently", - raises=AssertionError, - ) - @pytest.mark.parametrize( - "columns", - [ - [["2022", "2022"], ["JAN", "FEB"]], - [["2022", "2023"], ["JAN", "JAN"]], - [["2022", "2022"], ["JAN", "JAN"]], - ], - ) - def test_roundtrip_multiindex(self, columns): - df = DataFrame( - [[1, 2], [3, 4]], - columns=pd.MultiIndex.from_arrays(columns), - ) - data = StringIO(df.to_json(orient="split")) - result = read_json(data, orient="split") - tm.assert_frame_equal(result, df) - - @pytest.mark.parametrize( - "data,msg,orient", - [ - ('{"key":b:a:d}', "Expected object or value", "columns"), - # too few indices - ( - '{"columns":["A","B"],' - '"index":["2","3"],' - '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}', - "|".join( - [ - r"Length of values \(3\) does not match length of index \(2\)", - ] - ), - "split", - ), - # too many columns - ( - '{"columns":["A","B","C"],' - '"index":["1","2","3"],' - '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}', - "3 columns passed, passed data had 2 columns", - "split", - ), - # bad key - ( - '{"badkey":["A","B"],' - '"index":["2","3"],' - '"data":[[1.0,"1"],[2.0,"2"],[null,"3"]]}', - r"unexpected key\(s\): badkey", - "split", - ), - ], - ) - def test_frame_from_json_bad_data_raises(self, data, msg, orient): - with pytest.raises(ValueError, match=msg): - read_json(StringIO(data), orient=orient) - - @pytest.mark.parametrize("dtype", [True, False]) - @pytest.mark.parametrize("convert_axes", [True, False]) - def test_frame_from_json_missing_data(self, orient, convert_axes, dtype): - num_df = DataFrame([[1, 2], [4, 5, 6]]) - - result = read_json( - StringIO(num_df.to_json(orient=orient)), - orient=orient, - convert_axes=convert_axes, - dtype=dtype, - ) - assert np.isnan(result.iloc[0, 2]) - - obj_df = DataFrame([["1", "2"], ["4", "5", "6"]]) - result = read_json( - StringIO(obj_df.to_json(orient=orient)), - orient=orient, - convert_axes=convert_axes, - dtype=dtype, - ) - assert np.isnan(result.iloc[0, 2]) - - @pytest.mark.parametrize("dtype", [True, False]) - def test_frame_read_json_dtype_missing_value(self, dtype): - # GH28501 Parse missing values using read_json with dtype=False - # to NaN instead of None - result = read_json(StringIO("[null]"), dtype=dtype) - expected = DataFrame([np.nan]) - - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("inf", [np.inf, -np.inf]) - @pytest.mark.parametrize("dtype", [True, False]) - def test_frame_infinity(self, inf, dtype): - # infinities get mapped to nulls which get mapped to NaNs during - # deserialisation - df = DataFrame([[1, 2], [4, 5, 6]]) - df.loc[0, 2] = inf - - data = StringIO(df.to_json()) - result = read_json(data, dtype=dtype) - assert np.isnan(result.iloc[0, 2]) - - @pytest.mark.skipif(not IS64, reason="not compliant on 32-bit, xref #15865") - @pytest.mark.parametrize( - "value,precision,expected_val", - [ - (0.95, 1, 1.0), - (1.95, 1, 2.0), - (-1.95, 1, -2.0), - (0.995, 2, 1.0), - (0.9995, 3, 1.0), - (0.99999999999999944, 15, 1.0), - ], - ) - def test_frame_to_json_float_precision(self, value, precision, expected_val): - df = DataFrame([{"a_float": value}]) - encoded = df.to_json(double_precision=precision) - assert encoded == f'{{"a_float":{{"0":{expected_val}}}}}' - - def test_frame_to_json_except(self): - df = DataFrame([1, 2, 3]) - msg = "Invalid value 'garbage' for option 'orient'" - with pytest.raises(ValueError, match=msg): - df.to_json(orient="garbage") - - def test_frame_empty(self): - df = DataFrame(columns=["jim", "joe"]) - assert not df._is_mixed_type - - data = StringIO(df.to_json()) - result = read_json(data, dtype=dict(df.dtypes)) - tm.assert_frame_equal(result, df, check_index_type=False) - - def test_frame_empty_to_json(self): - # GH 7445 - df = DataFrame({"test": []}, index=[]) - result = df.to_json(orient="columns") - expected = '{"test":{}}' - assert result == expected - - def test_frame_empty_mixedtype(self): - # mixed type - df = DataFrame(columns=["jim", "joe"]) - df["joe"] = df["joe"].astype("i8") - assert df._is_mixed_type - data = df.to_json() - tm.assert_frame_equal( - read_json(StringIO(data), dtype=dict(df.dtypes)), - df, - check_index_type=False, - ) - - def test_frame_mixedtype_orient(self): # GH10289 - vals = [ - [10, 1, "foo", 0.1, 0.01], - [20, 2, "bar", 0.2, 0.02], - [30, 3, "baz", 0.3, 0.03], - [40, 4, "qux", 0.4, 0.04], - ] - - df = DataFrame( - vals, index=list("abcd"), columns=["1st", "2nd", "3rd", "4th", "5th"] - ) - - assert df._is_mixed_type - right = df.copy() - - for orient in ["split", "index", "columns"]: - inp = StringIO(df.to_json(orient=orient)) - left = read_json(inp, orient=orient, convert_axes=False) - tm.assert_frame_equal(left, right) - - right.index = pd.RangeIndex(len(df)) - inp = StringIO(df.to_json(orient="records")) - left = read_json(inp, orient="records", convert_axes=False) - tm.assert_frame_equal(left, right) - - right.columns = pd.RangeIndex(df.shape[1]) - inp = StringIO(df.to_json(orient="values")) - left = read_json(inp, orient="values", convert_axes=False) - tm.assert_frame_equal(left, right) - - def test_v12_compat(self, datapath): - dti = pd.date_range("2000-01-03", "2000-01-07") - # freq doesn't roundtrip - dti = DatetimeIndex(np.asarray(dti), freq=None) - df = DataFrame( - [ - [1.56808523, 0.65727391, 1.81021139, -0.17251653], - [-0.2550111, -0.08072427, -0.03202878, -0.17581665], - [1.51493992, 0.11805825, 1.629455, -1.31506612], - [-0.02765498, 0.44679743, 0.33192641, -0.27885413], - [0.05951614, -2.69652057, 1.28163262, 0.34703478], - ], - columns=["A", "B", "C", "D"], - index=dti, - ) - df["date"] = Timestamp("19920106 18:21:32.12").as_unit("ns") - df.iloc[3, df.columns.get_loc("date")] = Timestamp("20130101") - df["modified"] = df["date"] - df.iloc[1, df.columns.get_loc("modified")] = pd.NaT - - dirpath = datapath("io", "json", "data") - v12_json = os.path.join(dirpath, "tsframe_v012.json") - df_unser = read_json(v12_json) - tm.assert_frame_equal(df, df_unser) - - df_iso = df.drop(["modified"], axis=1) - v12_iso_json = os.path.join(dirpath, "tsframe_iso_v012.json") - df_unser_iso = read_json(v12_iso_json) - tm.assert_frame_equal(df_iso, df_unser_iso) - - def test_blocks_compat_GH9037(self): - index = pd.date_range("20000101", periods=10, freq="H") - # freq doesn't round-trip - index = DatetimeIndex(list(index), freq=None) - - df_mixed = DataFrame( - { - "float_1": [ - -0.92077639, - 0.77434435, - 1.25234727, - 0.61485564, - -0.60316077, - 0.24653374, - 0.28668979, - -2.51969012, - 0.95748401, - -1.02970536, - ], - "int_1": [ - 19680418, - 75337055, - 99973684, - 65103179, - 79373900, - 40314334, - 21290235, - 4991321, - 41903419, - 16008365, - ], - "str_1": [ - "78c608f1", - "64a99743", - "13d2ff52", - "ca7f4af2", - "97236474", - "bde7e214", - "1a6bde47", - "b1190be5", - "7a669144", - "8d64d068", - ], - "float_2": [ - -0.0428278, - -1.80872357, - 3.36042349, - -0.7573685, - -0.48217572, - 0.86229683, - 1.08935819, - 0.93898739, - -0.03030452, - 1.43366348, - ], - "str_2": [ - "14f04af9", - "d085da90", - "4bcfac83", - "81504caf", - "2ffef4a9", - "08e2f5c4", - "07e1af03", - "addbd4a7", - "1f6a09ba", - "4bfc4d87", - ], - "int_2": [ - 86967717, - 98098830, - 51927505, - 20372254, - 12601730, - 20884027, - 34193846, - 10561746, - 24867120, - 76131025, - ], - }, - index=index, - ) - - # JSON deserialisation always creates unicode strings - df_mixed.columns = df_mixed.columns.astype(np.str_) - data = StringIO(df_mixed.to_json(orient="split")) - df_roundtrip = read_json(data, orient="split") - tm.assert_frame_equal( - df_mixed, - df_roundtrip, - check_index_type=True, - check_column_type=True, - by_blocks=True, - check_exact=True, - ) - - def test_frame_nonprintable_bytes(self): - # GH14256: failing column caused segfaults, if it is not the last one - - class BinaryThing: - def __init__(self, hexed) -> None: - self.hexed = hexed - self.binary = bytes.fromhex(hexed) - - def __str__(self) -> str: - return self.hexed - - hexed = "574b4454ba8c5eb4f98a8f45" - binthing = BinaryThing(hexed) - - # verify the proper conversion of printable content - df_printable = DataFrame({"A": [binthing.hexed]}) - assert df_printable.to_json() == f'{{"A":{{"0":"{hexed}"}}}}' - - # check if non-printable content throws appropriate Exception - df_nonprintable = DataFrame({"A": [binthing]}) - msg = "Unsupported UTF-8 sequence length when encoding string" - with pytest.raises(OverflowError, match=msg): - df_nonprintable.to_json() - - # the same with multiple columns threw segfaults - df_mixed = DataFrame({"A": [binthing], "B": [1]}, columns=["A", "B"]) - with pytest.raises(OverflowError, match=msg): - df_mixed.to_json() - - # default_handler should resolve exceptions for non-string types - result = df_nonprintable.to_json(default_handler=str) - expected = f'{{"A":{{"0":"{hexed}"}}}}' - assert result == expected - assert ( - df_mixed.to_json(default_handler=str) - == f'{{"A":{{"0":"{hexed}"}},"B":{{"0":1}}}}' - ) - - def test_label_overflow(self): - # GH14256: buffer length not checked when writing label - result = DataFrame({"bar" * 100000: [1], "foo": [1337]}).to_json() - expected = f'{{"{"bar" * 100000}":{{"0":1}},"foo":{{"0":1337}}}}' - assert result == expected - - def test_series_non_unique_index(self): - s = Series(["a", "b"], index=[1, 1]) - - msg = "Series index must be unique for orient='index'" - with pytest.raises(ValueError, match=msg): - s.to_json(orient="index") - - tm.assert_series_equal( - s, - read_json( - StringIO(s.to_json(orient="split")), orient="split", typ="series" - ), - ) - unserialized = read_json( - StringIO(s.to_json(orient="records")), orient="records", typ="series" - ) - tm.assert_numpy_array_equal(s.values, unserialized.values) - - def test_series_default_orient(self, string_series): - assert string_series.to_json() == string_series.to_json(orient="index") - - def test_series_roundtrip_simple(self, orient, string_series): - data = StringIO(string_series.to_json(orient=orient)) - result = read_json(data, typ="series", orient=orient) - - expected = string_series - if orient in ("values", "records"): - expected = expected.reset_index(drop=True) - if orient != "split": - expected.name = None - - tm.assert_series_equal(result, expected) - - @pytest.mark.parametrize("dtype", [False, None]) - def test_series_roundtrip_object(self, orient, dtype, object_series): - data = StringIO(object_series.to_json(orient=orient)) - result = read_json(data, typ="series", orient=orient, dtype=dtype) - - expected = object_series - if orient in ("values", "records"): - expected = expected.reset_index(drop=True) - if orient != "split": - expected.name = None - - tm.assert_series_equal(result, expected) - - def test_series_roundtrip_empty(self, orient): - empty_series = Series([], index=[], dtype=np.float64) - data = StringIO(empty_series.to_json(orient=orient)) - result = read_json(data, typ="series", orient=orient) - - expected = empty_series.reset_index(drop=True) - if orient in ("split"): - expected.index = expected.index.astype(np.float64) - - tm.assert_series_equal(result, expected) - - def test_series_roundtrip_timeseries(self, orient, datetime_series): - data = StringIO(datetime_series.to_json(orient=orient)) - result = read_json(data, typ="series", orient=orient) - - expected = datetime_series - if orient in ("values", "records"): - expected = expected.reset_index(drop=True) - if orient != "split": - expected.name = None - - tm.assert_series_equal(result, expected) - - @pytest.mark.parametrize("dtype", [np.float64, int]) - def test_series_roundtrip_numeric(self, orient, dtype): - s = Series(range(6), index=["a", "b", "c", "d", "e", "f"]) - data = StringIO(s.to_json(orient=orient)) - result = read_json(data, typ="series", orient=orient) - - expected = s.copy() - if orient in ("values", "records"): - expected = expected.reset_index(drop=True) - - tm.assert_series_equal(result, expected) - - def test_series_to_json_except(self): - s = Series([1, 2, 3]) - msg = "Invalid value 'garbage' for option 'orient'" - with pytest.raises(ValueError, match=msg): - s.to_json(orient="garbage") - - def test_series_from_json_precise_float(self): - s = Series([4.56, 4.56, 4.56]) - result = read_json(StringIO(s.to_json()), typ="series", precise_float=True) - tm.assert_series_equal(result, s, check_index_type=False) - - def test_series_with_dtype(self): - # GH 21986 - s = Series([4.56, 4.56, 4.56]) - result = read_json(StringIO(s.to_json()), typ="series", dtype=np.int64) - expected = Series([4] * 3) - tm.assert_series_equal(result, expected) - - @pytest.mark.parametrize( - "dtype,expected", - [ - (True, Series(["2000-01-01"], dtype="datetime64[ns]")), - (False, Series([946684800000])), - ], - ) - def test_series_with_dtype_datetime(self, dtype, expected): - s = Series(["2000-01-01"], dtype="datetime64[ns]") - data = StringIO(s.to_json()) - result = read_json(data, typ="series", dtype=dtype) - tm.assert_series_equal(result, expected) - - def test_frame_from_json_precise_float(self): - df = DataFrame([[4.56, 4.56, 4.56], [4.56, 4.56, 4.56]]) - result = read_json(StringIO(df.to_json()), precise_float=True) - tm.assert_frame_equal(result, df) - - def test_typ(self): - s = Series(range(6), index=["a", "b", "c", "d", "e", "f"], dtype="int64") - result = read_json(StringIO(s.to_json()), typ=None) - tm.assert_series_equal(result, s) - - def test_reconstruction_index(self): - df = DataFrame([[1, 2, 3], [4, 5, 6]]) - result = read_json(StringIO(df.to_json())) - tm.assert_frame_equal(result, df) - - df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, index=["A", "B", "C"]) - result = read_json(StringIO(df.to_json())) - tm.assert_frame_equal(result, df) - - def test_path(self, float_frame, int_frame, datetime_frame): - with tm.ensure_clean("test.json") as path: - for df in [float_frame, int_frame, datetime_frame]: - df.to_json(path) - read_json(path) - - def test_axis_dates(self, datetime_series, datetime_frame): - # frame - json = StringIO(datetime_frame.to_json()) - result = read_json(json) - tm.assert_frame_equal(result, datetime_frame) - - # series - json = StringIO(datetime_series.to_json()) - result = read_json(json, typ="series") - tm.assert_series_equal(result, datetime_series, check_names=False) - assert result.name is None - - def test_convert_dates(self, datetime_series, datetime_frame): - # frame - df = datetime_frame - df["date"] = Timestamp("20130101").as_unit("ns") - - json = StringIO(df.to_json()) - result = read_json(json) - tm.assert_frame_equal(result, df) - - df["foo"] = 1.0 - json = StringIO(df.to_json(date_unit="ns")) - - result = read_json(json, convert_dates=False) - expected = df.copy() - expected["date"] = expected["date"].values.view("i8") - expected["foo"] = expected["foo"].astype("int64") - tm.assert_frame_equal(result, expected) - - # series - ts = Series(Timestamp("20130101").as_unit("ns"), index=datetime_series.index) - json = StringIO(ts.to_json()) - result = read_json(json, typ="series") - tm.assert_series_equal(result, ts) - - @pytest.mark.parametrize("date_format", ["epoch", "iso"]) - @pytest.mark.parametrize("as_object", [True, False]) - @pytest.mark.parametrize("date_typ", [datetime.date, datetime.datetime, Timestamp]) - def test_date_index_and_values(self, date_format, as_object, date_typ): - data = [date_typ(year=2020, month=1, day=1), pd.NaT] - if as_object: - data.append("a") - - ser = Series(data, index=data) - result = ser.to_json(date_format=date_format) - - if date_format == "epoch": - expected = '{"1577836800000":1577836800000,"null":null}' - else: - expected = ( - '{"2020-01-01T00:00:00.000":"2020-01-01T00:00:00.000","null":null}' - ) - - if as_object: - expected = expected.replace("}", ',"a":"a"}') - - assert result == expected - - @pytest.mark.parametrize( - "infer_word", - [ - "trade_time", - "date", - "datetime", - "sold_at", - "modified", - "timestamp", - "timestamps", - ], - ) - def test_convert_dates_infer(self, infer_word): - # GH10747 - from pandas.io.json import dumps - - data = [{"id": 1, infer_word: 1036713600000}, {"id": 2}] - expected = DataFrame( - [[1, Timestamp("2002-11-08")], [2, pd.NaT]], columns=["id", infer_word] - ) - - result = read_json(StringIO(dumps(data)))[["id", infer_word]] - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize( - "date,date_unit", - [ - ("20130101 20:43:42.123", None), - ("20130101 20:43:42", "s"), - ("20130101 20:43:42.123", "ms"), - ("20130101 20:43:42.123456", "us"), - ("20130101 20:43:42.123456789", "ns"), - ], - ) - def test_date_format_frame(self, date, date_unit, datetime_frame): - df = datetime_frame - - df["date"] = Timestamp(date).as_unit("ns") - df.iloc[1, df.columns.get_loc("date")] = pd.NaT - df.iloc[5, df.columns.get_loc("date")] = pd.NaT - if date_unit: - json = df.to_json(date_format="iso", date_unit=date_unit) - else: - json = df.to_json(date_format="iso") - - result = read_json(StringIO(json)) - expected = df.copy() - tm.assert_frame_equal(result, expected) - - def test_date_format_frame_raises(self, datetime_frame): - df = datetime_frame - msg = "Invalid value 'foo' for option 'date_unit'" - with pytest.raises(ValueError, match=msg): - df.to_json(date_format="iso", date_unit="foo") - - @pytest.mark.parametrize( - "date,date_unit", - [ - ("20130101 20:43:42.123", None), - ("20130101 20:43:42", "s"), - ("20130101 20:43:42.123", "ms"), - ("20130101 20:43:42.123456", "us"), - ("20130101 20:43:42.123456789", "ns"), - ], - ) - def test_date_format_series(self, date, date_unit, datetime_series): - ts = Series(Timestamp(date).as_unit("ns"), index=datetime_series.index) - ts.iloc[1] = pd.NaT - ts.iloc[5] = pd.NaT - if date_unit: - json = ts.to_json(date_format="iso", date_unit=date_unit) - else: - json = ts.to_json(date_format="iso") - - result = read_json(StringIO(json), typ="series") - expected = ts.copy() - tm.assert_series_equal(result, expected) - - def test_date_format_series_raises(self, datetime_series): - ts = Series(Timestamp("20130101 20:43:42.123"), index=datetime_series.index) - msg = "Invalid value 'foo' for option 'date_unit'" - with pytest.raises(ValueError, match=msg): - ts.to_json(date_format="iso", date_unit="foo") - - @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"]) - def test_date_unit(self, unit, datetime_frame): - df = datetime_frame - df["date"] = Timestamp("20130101 20:43:42").as_unit("ns") - dl = df.columns.get_loc("date") - df.iloc[1, dl] = Timestamp("19710101 20:43:42") - df.iloc[2, dl] = Timestamp("21460101 20:43:42") - df.iloc[4, dl] = pd.NaT - - json = df.to_json(date_format="epoch", date_unit=unit) - - # force date unit - result = read_json(StringIO(json), date_unit=unit) - tm.assert_frame_equal(result, df) - - # detect date unit - result = read_json(StringIO(json), date_unit=None) - tm.assert_frame_equal(result, df) - - @pytest.mark.parametrize("unit", ["s", "ms", "us"]) - def test_iso_non_nano_datetimes(self, unit): - # Test that numpy datetimes - # in an Index or a column with non-nano resolution can be serialized - # correctly - # GH53686 - index = DatetimeIndex( - [np.datetime64("2023-01-01T11:22:33.123456", unit)], - dtype=f"datetime64[{unit}]", - ) - df = DataFrame( - { - "date": Series( - [np.datetime64("2022-01-01T11:22:33.123456", unit)], - dtype=f"datetime64[{unit}]", - index=index, - ), - "date_obj": Series( - [np.datetime64("2023-01-01T11:22:33.123456", unit)], - dtype=object, - index=index, - ), - }, - ) - - buf = StringIO() - df.to_json(buf, date_format="iso", date_unit=unit) - buf.seek(0) - - # read_json always reads datetimes in nanosecond resolution - # TODO: check_dtype/check_index_type should be removable - # once read_json gets non-nano support - tm.assert_frame_equal( - read_json(buf, convert_dates=["date", "date_obj"]), - df, - check_index_type=False, - check_dtype=False, - ) - - def test_weird_nested_json(self): - # this used to core dump the parser - s = r"""{ - "status": "success", - "data": { - "posts": [ - { - "id": 1, - "title": "A blog post", - "body": "Some useful content" - }, - { - "id": 2, - "title": "Another blog post", - "body": "More content" - } - ] - } - }""" - read_json(StringIO(s)) - - def test_doc_example(self): - dfj2 = DataFrame( - np.random.default_rng(2).standard_normal((5, 2)), columns=list("AB") - ) - dfj2["date"] = Timestamp("20130101") - dfj2["ints"] = range(5) - dfj2["bools"] = True - dfj2.index = pd.date_range("20130101", periods=5) - - json = StringIO(dfj2.to_json()) - result = read_json(json, dtype={"ints": np.int64, "bools": np.bool_}) - tm.assert_frame_equal(result, result) - - def test_round_trip_exception(self, datapath): - # GH 3867 - path = datapath("io", "json", "data", "teams.csv") - df = pd.read_csv(path) - s = df.to_json() - - result = read_json(StringIO(s)) - res = result.reindex(index=df.index, columns=df.columns) - msg = "The 'downcast' keyword in fillna is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - res = res.fillna(np.nan, downcast=False) - tm.assert_frame_equal(res, df) - - @pytest.mark.network - @pytest.mark.single_cpu - @pytest.mark.parametrize( - "field,dtype", - [ - ["created_at", pd.DatetimeTZDtype(tz="UTC")], - ["closed_at", "datetime64[ns]"], - ["updated_at", pd.DatetimeTZDtype(tz="UTC")], - ], - ) - def test_url(self, field, dtype, httpserver): - data = '{"created_at": ["2023-06-23T18:21:36Z"], "closed_at": ["2023-06-23T18:21:36"], "updated_at": ["2023-06-23T18:21:36Z"]}\n' # noqa: E501 - httpserver.serve_content(content=data) - result = read_json(httpserver.url, convert_dates=True) - assert result[field].dtype == dtype - - def test_timedelta(self): - converter = lambda x: pd.to_timedelta(x, unit="ms") - - ser = Series([timedelta(23), timedelta(seconds=5)]) - assert ser.dtype == "timedelta64[ns]" - - result = read_json(StringIO(ser.to_json()), typ="series").apply(converter) - tm.assert_series_equal(result, ser) - - ser = Series([timedelta(23), timedelta(seconds=5)], index=pd.Index([0, 1])) - assert ser.dtype == "timedelta64[ns]" - result = read_json(StringIO(ser.to_json()), typ="series").apply(converter) - tm.assert_series_equal(result, ser) - - frame = DataFrame([timedelta(23), timedelta(seconds=5)]) - assert frame[0].dtype == "timedelta64[ns]" - tm.assert_frame_equal( - frame, read_json(StringIO(frame.to_json())).apply(converter) - ) - - def test_timedelta2(self): - frame = DataFrame( - { - "a": [timedelta(days=23), timedelta(seconds=5)], - "b": [1, 2], - "c": pd.date_range(start="20130101", periods=2), - } - ) - data = StringIO(frame.to_json(date_unit="ns")) - result = read_json(data) - result["a"] = pd.to_timedelta(result.a, unit="ns") - result["c"] = pd.to_datetime(result.c) - tm.assert_frame_equal(frame, result) - - def test_mixed_timedelta_datetime(self): - td = timedelta(23) - ts = Timestamp("20130101") - frame = DataFrame({"a": [td, ts]}, dtype=object) - - expected = DataFrame( - {"a": [pd.Timedelta(td).as_unit("ns")._value, ts.as_unit("ns")._value]} - ) - data = StringIO(frame.to_json(date_unit="ns")) - result = read_json(data, dtype={"a": "int64"}) - tm.assert_frame_equal(result, expected, check_index_type=False) - - @pytest.mark.parametrize("as_object", [True, False]) - @pytest.mark.parametrize("date_format", ["iso", "epoch"]) - @pytest.mark.parametrize("timedelta_typ", [pd.Timedelta, timedelta]) - def test_timedelta_to_json(self, as_object, date_format, timedelta_typ): - # GH28156: to_json not correctly formatting Timedelta - data = [timedelta_typ(days=1), timedelta_typ(days=2), pd.NaT] - if as_object: - data.append("a") - - ser = Series(data, index=data) - if date_format == "iso": - expected = ( - '{"P1DT0H0M0S":"P1DT0H0M0S","P2DT0H0M0S":"P2DT0H0M0S","null":null}' - ) - else: - expected = '{"86400000":86400000,"172800000":172800000,"null":null}' - - if as_object: - expected = expected.replace("}", ',"a":"a"}') - - result = ser.to_json(date_format=date_format) - assert result == expected - - def test_default_handler(self): - value = object() - frame = DataFrame({"a": [7, value]}) - expected = DataFrame({"a": [7, str(value)]}) - result = read_json(StringIO(frame.to_json(default_handler=str))) - tm.assert_frame_equal(expected, result, check_index_type=False) - - def test_default_handler_indirect(self): - from pandas.io.json import dumps - - def default(obj): - if isinstance(obj, complex): - return [("mathjs", "Complex"), ("re", obj.real), ("im", obj.imag)] - return str(obj) - - df_list = [ - 9, - DataFrame( - {"a": [1, "STR", complex(4, -5)], "b": [float("nan"), None, "N/A"]}, - columns=["a", "b"], - ), - ] - expected = ( - '[9,[[1,null],["STR",null],[[["mathjs","Complex"],' - '["re",4.0],["im",-5.0]],"N\\/A"]]]' - ) - assert dumps(df_list, default_handler=default, orient="values") == expected - - def test_default_handler_numpy_unsupported_dtype(self): - # GH12554 to_json raises 'Unhandled numpy dtype 15' - df = DataFrame( - {"a": [1, 2.3, complex(4, -5)], "b": [float("nan"), None, complex(1.2, 0)]}, - columns=["a", "b"], - ) - expected = ( - '[["(1+0j)","(nan+0j)"],' - '["(2.3+0j)","(nan+0j)"],' - '["(4-5j)","(1.2+0j)"]]' - ) - assert df.to_json(default_handler=str, orient="values") == expected - - def test_default_handler_raises(self): - msg = "raisin" - - def my_handler_raises(obj): - raise TypeError(msg) - - with pytest.raises(TypeError, match=msg): - DataFrame({"a": [1, 2, object()]}).to_json( - default_handler=my_handler_raises - ) - with pytest.raises(TypeError, match=msg): - DataFrame({"a": [1, 2, complex(4, -5)]}).to_json( - default_handler=my_handler_raises - ) - - def test_categorical(self): - # GH4377 df.to_json segfaults with non-ndarray blocks - df = DataFrame({"A": ["a", "b", "c", "a", "b", "b", "a"]}) - df["B"] = df["A"] - expected = df.to_json() - - df["B"] = df["A"].astype("category") - assert expected == df.to_json() - - s = df["A"] - sc = df["B"] - assert s.to_json() == sc.to_json() - - def test_datetime_tz(self): - # GH4377 df.to_json segfaults with non-ndarray blocks - tz_range = pd.date_range("20130101", periods=3, tz="US/Eastern") - tz_naive = tz_range.tz_convert("utc").tz_localize(None) - - df = DataFrame({"A": tz_range, "B": pd.date_range("20130101", periods=3)}) - - df_naive = df.copy() - df_naive["A"] = tz_naive - expected = df_naive.to_json() - assert expected == df.to_json() - - stz = Series(tz_range) - s_naive = Series(tz_naive) - assert stz.to_json() == s_naive.to_json() - - def test_sparse(self): - # GH4377 df.to_json segfaults with non-ndarray blocks - df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) - df.loc[:8] = np.nan - - sdf = df.astype("Sparse") - expected = df.to_json() - assert expected == sdf.to_json() - - s = Series(np.random.default_rng(2).standard_normal(10)) - s.loc[:8] = np.nan - ss = s.astype("Sparse") - - expected = s.to_json() - assert expected == ss.to_json() - - @pytest.mark.parametrize( - "ts", - [ - Timestamp("2013-01-10 05:00:00Z"), - Timestamp("2013-01-10 00:00:00", tz="US/Eastern"), - Timestamp("2013-01-10 00:00:00-0500"), - ], - ) - def test_tz_is_utc(self, ts): - from pandas.io.json import dumps - - exp = '"2013-01-10T05:00:00.000Z"' - - assert dumps(ts, iso_dates=True) == exp - dt = ts.to_pydatetime() - assert dumps(dt, iso_dates=True) == exp - - def test_tz_is_naive(self): - from pandas.io.json import dumps - - ts = Timestamp("2013-01-10 05:00:00") - exp = '"2013-01-10T05:00:00.000"' - - assert dumps(ts, iso_dates=True) == exp - dt = ts.to_pydatetime() - assert dumps(dt, iso_dates=True) == exp - - @pytest.mark.parametrize( - "tz_range", - [ - pd.date_range("2013-01-01 05:00:00Z", periods=2), - pd.date_range("2013-01-01 00:00:00", periods=2, tz="US/Eastern"), - pd.date_range("2013-01-01 00:00:00-0500", periods=2), - ], - ) - def test_tz_range_is_utc(self, tz_range): - from pandas.io.json import dumps - - exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]' - dfexp = ( - '{"DT":{' - '"0":"2013-01-01T05:00:00.000Z",' - '"1":"2013-01-02T05:00:00.000Z"}}' - ) - - assert dumps(tz_range, iso_dates=True) == exp - dti = DatetimeIndex(tz_range) - # Ensure datetimes in object array are serialized correctly - # in addition to the normal DTI case - assert dumps(dti, iso_dates=True) == exp - assert dumps(dti.astype(object), iso_dates=True) == exp - df = DataFrame({"DT": dti}) - result = dumps(df, iso_dates=True) - assert result == dfexp - assert dumps(df.astype({"DT": object}), iso_dates=True) - - def test_tz_range_is_naive(self): - from pandas.io.json import dumps - - dti = pd.date_range("2013-01-01 05:00:00", periods=2) - - exp = '["2013-01-01T05:00:00.000","2013-01-02T05:00:00.000"]' - dfexp = '{"DT":{"0":"2013-01-01T05:00:00.000","1":"2013-01-02T05:00:00.000"}}' - - # Ensure datetimes in object array are serialized correctly - # in addition to the normal DTI case - assert dumps(dti, iso_dates=True) == exp - assert dumps(dti.astype(object), iso_dates=True) == exp - df = DataFrame({"DT": dti}) - result = dumps(df, iso_dates=True) - assert result == dfexp - assert dumps(df.astype({"DT": object}), iso_dates=True) - - def test_read_inline_jsonl(self): - # GH9180 - - result = read_json(StringIO('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n'), lines=True) - expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"]) - tm.assert_frame_equal(result, expected) - - @pytest.mark.single_cpu - @td.skip_if_not_us_locale - def test_read_s3_jsonl(self, s3_public_bucket_with_data, s3so): - # GH17200 - - result = read_json( - f"s3n://{s3_public_bucket_with_data.name}/items.jsonl", - lines=True, - storage_options=s3so, - ) - expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"]) - tm.assert_frame_equal(result, expected) - - def test_read_local_jsonl(self): - # GH17200 - with tm.ensure_clean("tmp_items.json") as path: - with open(path, "w", encoding="utf-8") as infile: - infile.write('{"a": 1, "b": 2}\n{"b":2, "a" :1}\n') - result = read_json(path, lines=True) - expected = DataFrame([[1, 2], [1, 2]], columns=["a", "b"]) - tm.assert_frame_equal(result, expected) - - def test_read_jsonl_unicode_chars(self): - # GH15132: non-ascii unicode characters - # \u201d == RIGHT DOUBLE QUOTATION MARK - - # simulate file handle - json = '{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n' - json = StringIO(json) - result = read_json(json, lines=True) - expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"]) - tm.assert_frame_equal(result, expected) - - # simulate string - json = StringIO('{"a": "foo”", "b": "bar"}\n{"a": "foo", "b": "bar"}\n') - result = read_json(json, lines=True) - expected = DataFrame([["foo\u201d", "bar"], ["foo", "bar"]], columns=["a", "b"]) - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("bigNum", [sys.maxsize + 1, -(sys.maxsize + 2)]) - def test_to_json_large_numbers(self, bigNum): - # GH34473 - series = Series(bigNum, dtype=object, index=["articleId"]) - json = series.to_json() - expected = '{"articleId":' + str(bigNum) + "}" - assert json == expected - - df = DataFrame(bigNum, dtype=object, index=["articleId"], columns=[0]) - json = df.to_json() - expected = '{"0":{"articleId":' + str(bigNum) + "}}" - assert json == expected - - @pytest.mark.parametrize("bigNum", [-(2**63) - 1, 2**64]) - def test_read_json_large_numbers(self, bigNum): - # GH20599, 26068 - json = StringIO('{"articleId":' + str(bigNum) + "}") - msg = r"Value is too small|Value is too big" - with pytest.raises(ValueError, match=msg): - read_json(json) - - json = StringIO('{"0":{"articleId":' + str(bigNum) + "}}") - with pytest.raises(ValueError, match=msg): - read_json(json) - - def test_read_json_large_numbers2(self): - # GH18842 - json = '{"articleId": "1404366058080022500245"}' - json = StringIO(json) - result = read_json(json, typ="series") - expected = Series(1.404366e21, index=["articleId"]) - tm.assert_series_equal(result, expected) - - json = '{"0": {"articleId": "1404366058080022500245"}}' - json = StringIO(json) - result = read_json(json) - expected = DataFrame(1.404366e21, index=["articleId"], columns=[0]) - tm.assert_frame_equal(result, expected) - - def test_to_jsonl(self): - # GH9180 - df = DataFrame([[1, 2], [1, 2]], columns=["a", "b"]) - result = df.to_json(orient="records", lines=True) - expected = '{"a":1,"b":2}\n{"a":1,"b":2}\n' - assert result == expected - - df = DataFrame([["foo}", "bar"], ['foo"', "bar"]], columns=["a", "b"]) - result = df.to_json(orient="records", lines=True) - expected = '{"a":"foo}","b":"bar"}\n{"a":"foo\\"","b":"bar"}\n' - assert result == expected - tm.assert_frame_equal(read_json(StringIO(result), lines=True), df) - - # GH15096: escaped characters in columns and data - df = DataFrame([["foo\\", "bar"], ['foo"', "bar"]], columns=["a\\", "b"]) - result = df.to_json(orient="records", lines=True) - expected = '{"a\\\\":"foo\\\\","b":"bar"}\n{"a\\\\":"foo\\"","b":"bar"}\n' - assert result == expected - - tm.assert_frame_equal(read_json(StringIO(result), lines=True), df) - - # TODO: there is a near-identical test for pytables; can we share? - @pytest.mark.xfail(reason="GH#13774 encoding kwarg not supported", raises=TypeError) - @pytest.mark.parametrize( - "val", - [ - [b"E\xc9, 17", b"", b"a", b"b", b"c"], - [b"E\xc9, 17", b"a", b"b", b"c"], - [b"EE, 17", b"", b"a", b"b", b"c"], - [b"E\xc9, 17", b"\xf8\xfc", b"a", b"b", b"c"], - [b"", b"a", b"b", b"c"], - [b"\xf8\xfc", b"a", b"b", b"c"], - [b"A\xf8\xfc", b"", b"a", b"b", b"c"], - [np.nan, b"", b"b", b"c"], - [b"A\xf8\xfc", np.nan, b"", b"b", b"c"], - ], - ) - @pytest.mark.parametrize("dtype", ["category", object]) - def test_latin_encoding(self, dtype, val): - # GH 13774 - ser = Series( - [x.decode("latin-1") if isinstance(x, bytes) else x for x in val], - dtype=dtype, - ) - encoding = "latin-1" - with tm.ensure_clean("test.json") as path: - ser.to_json(path, encoding=encoding) - retr = read_json(StringIO(path), encoding=encoding) - tm.assert_series_equal(ser, retr, check_categorical=False) - - def test_data_frame_size_after_to_json(self): - # GH15344 - df = DataFrame({"a": [str(1)]}) - - size_before = df.memory_usage(index=True, deep=True).sum() - df.to_json() - size_after = df.memory_usage(index=True, deep=True).sum() - - assert size_before == size_after - - @pytest.mark.parametrize( - "index", [None, [1, 2], [1.0, 2.0], ["a", "b"], ["1", "2"], ["1.", "2."]] - ) - @pytest.mark.parametrize("columns", [["a", "b"], ["1", "2"], ["1.", "2."]]) - def test_from_json_to_json_table_index_and_columns(self, index, columns): - # GH25433 GH25435 - expected = DataFrame([[1, 2], [3, 4]], index=index, columns=columns) - dfjson = expected.to_json(orient="table") - - result = read_json(StringIO(dfjson), orient="table") - tm.assert_frame_equal(result, expected) - - def test_from_json_to_json_table_dtypes(self): - # GH21345 - expected = DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]}) - dfjson = expected.to_json(orient="table") - result = read_json(StringIO(dfjson), orient="table") - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("orient", ["split", "records", "index", "columns"]) - def test_to_json_from_json_columns_dtypes(self, orient): - # GH21892 GH33205 - expected = DataFrame.from_dict( - { - "Integer": Series([1, 2, 3], dtype="int64"), - "Float": Series([None, 2.0, 3.0], dtype="float64"), - "Object": Series([None, "", "c"], dtype="object"), - "Bool": Series([True, False, True], dtype="bool"), - "Category": Series(["a", "b", None], dtype="category"), - "Datetime": Series( - ["2020-01-01", None, "2020-01-03"], dtype="datetime64[ns]" - ), - } - ) - dfjson = expected.to_json(orient=orient) - - result = read_json( - StringIO(dfjson), - orient=orient, - dtype={ - "Integer": "int64", - "Float": "float64", - "Object": "object", - "Bool": "bool", - "Category": "category", - "Datetime": "datetime64[ns]", - }, - ) - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("dtype", [True, {"b": int, "c": int}]) - def test_read_json_table_dtype_raises(self, dtype): - # GH21345 - df = DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["5", "6"]}) - dfjson = df.to_json(orient="table") - msg = "cannot pass both dtype and orient='table'" - with pytest.raises(ValueError, match=msg): - read_json(dfjson, orient="table", dtype=dtype) - - @pytest.mark.parametrize("orient", ["index", "columns", "records", "values"]) - def test_read_json_table_empty_axes_dtype(self, orient): - # GH28558 - - expected = DataFrame() - result = read_json(StringIO("{}"), orient=orient, convert_axes=True) - tm.assert_index_equal(result.index, expected.index) - tm.assert_index_equal(result.columns, expected.columns) - - def test_read_json_table_convert_axes_raises(self): - # GH25433 GH25435 - df = DataFrame([[1, 2], [3, 4]], index=[1.0, 2.0], columns=["1.", "2."]) - dfjson = df.to_json(orient="table") - msg = "cannot pass both convert_axes and orient='table'" - with pytest.raises(ValueError, match=msg): - read_json(dfjson, orient="table", convert_axes=True) - - @pytest.mark.parametrize( - "data, expected", - [ - ( - DataFrame([[1, 2], [4, 5]], columns=["a", "b"]), - {"columns": ["a", "b"], "data": [[1, 2], [4, 5]]}, - ), - ( - DataFrame([[1, 2], [4, 5]], columns=["a", "b"]).rename_axis("foo"), - {"columns": ["a", "b"], "data": [[1, 2], [4, 5]]}, - ), - ( - DataFrame( - [[1, 2], [4, 5]], columns=["a", "b"], index=[["a", "b"], ["c", "d"]] - ), - {"columns": ["a", "b"], "data": [[1, 2], [4, 5]]}, - ), - (Series([1, 2, 3], name="A"), {"name": "A", "data": [1, 2, 3]}), - ( - Series([1, 2, 3], name="A").rename_axis("foo"), - {"name": "A", "data": [1, 2, 3]}, - ), - ( - Series([1, 2], name="A", index=[["a", "b"], ["c", "d"]]), - {"name": "A", "data": [1, 2]}, - ), - ], - ) - def test_index_false_to_json_split(self, data, expected): - # GH 17394 - # Testing index=False in to_json with orient='split' - - result = data.to_json(orient="split", index=False) - result = json.loads(result) - - assert result == expected - - @pytest.mark.parametrize( - "data", - [ - (DataFrame([[1, 2], [4, 5]], columns=["a", "b"])), - (DataFrame([[1, 2], [4, 5]], columns=["a", "b"]).rename_axis("foo")), - ( - DataFrame( - [[1, 2], [4, 5]], columns=["a", "b"], index=[["a", "b"], ["c", "d"]] - ) - ), - (Series([1, 2, 3], name="A")), - (Series([1, 2, 3], name="A").rename_axis("foo")), - (Series([1, 2], name="A", index=[["a", "b"], ["c", "d"]])), - ], - ) - def test_index_false_to_json_table(self, data): - # GH 17394 - # Testing index=False in to_json with orient='table' - - result = data.to_json(orient="table", index=False) - result = json.loads(result) - - expected = { - "schema": pd.io.json.build_table_schema(data, index=False), - "data": DataFrame(data).to_dict(orient="records"), - } - - assert result == expected - - @pytest.mark.parametrize("orient", ["index", "columns"]) - def test_index_false_error_to_json(self, orient): - # GH 17394, 25513 - # Testing error message from to_json with index=False - - df = DataFrame([[1, 2], [4, 5]], columns=["a", "b"]) - - msg = ( - "'index=False' is only valid when 'orient' is 'split', " - "'table', 'records', or 'values'" - ) - with pytest.raises(ValueError, match=msg): - df.to_json(orient=orient, index=False) - - @pytest.mark.parametrize("orient", ["records", "values"]) - def test_index_true_error_to_json(self, orient): - # GH 25513 - # Testing error message from to_json with index=True - - df = DataFrame([[1, 2], [4, 5]], columns=["a", "b"]) - - msg = ( - "'index=True' is only valid when 'orient' is 'split', " - "'table', 'index', or 'columns'" - ) - with pytest.raises(ValueError, match=msg): - df.to_json(orient=orient, index=True) - - @pytest.mark.parametrize("orient", ["split", "table"]) - @pytest.mark.parametrize("index", [True, False]) - def test_index_false_from_json_to_json(self, orient, index): - # GH25170 - # Test index=False in from_json to_json - expected = DataFrame({"a": [1, 2], "b": [3, 4]}) - dfjson = expected.to_json(orient=orient, index=index) - result = read_json(StringIO(dfjson), orient=orient) - tm.assert_frame_equal(result, expected) - - def test_read_timezone_information(self): - # GH 25546 - result = read_json( - StringIO('{"2019-01-01T11:00:00.000Z":88}'), typ="series", orient="index" - ) - expected = Series([88], index=DatetimeIndex(["2019-01-01 11:00:00"], tz="UTC")) - tm.assert_series_equal(result, expected) - - @pytest.mark.parametrize( - "url", - [ - "s3://example-fsspec/", - "gcs://another-fsspec/file.json", - "https://example-site.com/data", - "some-protocol://data.txt", - ], - ) - def test_read_json_with_url_value(self, url): - # GH 36271 - result = read_json(StringIO(f'{{"url":{{"0":"{url}"}}}}')) - expected = DataFrame({"url": [url]}) - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize( - "compression", - ["", ".gz", ".bz2", ".tar"], - ) - def test_read_json_with_very_long_file_path(self, compression): - # GH 46718 - long_json_path = f'{"a" * 1000}.json{compression}' - with pytest.raises( - FileNotFoundError, match=f"File {long_json_path} does not exist" - ): - # path too long for Windows is handled in file_exists() but raises in - # _get_data_from_filepath() - read_json(long_json_path) - - @pytest.mark.parametrize( - "date_format,key", [("epoch", 86400000), ("iso", "P1DT0H0M0S")] - ) - def test_timedelta_as_label(self, date_format, key): - df = DataFrame([[1]], columns=[pd.Timedelta("1D")]) - expected = f'{{"{key}":{{"0":1}}}}' - result = df.to_json(date_format=date_format) - - assert result == expected - - @pytest.mark.parametrize( - "orient,expected", - [ - ("index", "{\"('a', 'b')\":{\"('c', 'd')\":1}}"), - ("columns", "{\"('c', 'd')\":{\"('a', 'b')\":1}}"), - # TODO: the below have separate encoding procedures - pytest.param( - "split", - "", - marks=pytest.mark.xfail( - reason="Produces JSON but not in a consistent manner" - ), - ), - pytest.param( - "table", - "", - marks=pytest.mark.xfail( - reason="Produces JSON but not in a consistent manner" - ), - ), - ], - ) - def test_tuple_labels(self, orient, expected): - # GH 20500 - df = DataFrame([[1]], index=[("a", "b")], columns=[("c", "d")]) - result = df.to_json(orient=orient) - assert result == expected - - @pytest.mark.parametrize("indent", [1, 2, 4]) - def test_to_json_indent(self, indent): - # GH 12004 - df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"]) - - result = df.to_json(indent=indent) - spaces = " " * indent - expected = f"""{{ -{spaces}"a":{{ -{spaces}{spaces}"0":"foo", -{spaces}{spaces}"1":"baz" -{spaces}}}, -{spaces}"b":{{ -{spaces}{spaces}"0":"bar", -{spaces}{spaces}"1":"qux" -{spaces}}} -}}""" - - assert result == expected - - @pytest.mark.parametrize( - "orient,expected", - [ - ( - "split", - """{ - "columns":[ - "a", - "b" - ], - "index":[ - 0, - 1 - ], - "data":[ - [ - "foo", - "bar" - ], - [ - "baz", - "qux" - ] - ] -}""", - ), - ( - "records", - """[ - { - "a":"foo", - "b":"bar" - }, - { - "a":"baz", - "b":"qux" - } -]""", - ), - ( - "index", - """{ - "0":{ - "a":"foo", - "b":"bar" - }, - "1":{ - "a":"baz", - "b":"qux" - } -}""", - ), - ( - "columns", - """{ - "a":{ - "0":"foo", - "1":"baz" - }, - "b":{ - "0":"bar", - "1":"qux" - } -}""", - ), - ( - "values", - """[ - [ - "foo", - "bar" - ], - [ - "baz", - "qux" - ] -]""", - ), - ( - "table", - """{ - "schema":{ - "fields":[ - { - "name":"index", - "type":"integer" - }, - { - "name":"a", - "type":"string" - }, - { - "name":"b", - "type":"string" - } - ], - "primaryKey":[ - "index" - ], - "pandas_version":"1.4.0" - }, - "data":[ - { - "index":0, - "a":"foo", - "b":"bar" - }, - { - "index":1, - "a":"baz", - "b":"qux" - } - ] -}""", - ), - ], - ) - def test_json_indent_all_orients(self, orient, expected): - # GH 12004 - df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["a", "b"]) - result = df.to_json(orient=orient, indent=4) - assert result == expected - - def test_json_negative_indent_raises(self): - with pytest.raises(ValueError, match="must be a nonnegative integer"): - DataFrame().to_json(indent=-1) - - def test_emca_262_nan_inf_support(self): - # GH 12213 - data = StringIO( - '["a", NaN, "NaN", Infinity, "Infinity", -Infinity, "-Infinity"]' - ) - result = read_json(data) - expected = DataFrame( - ["a", None, "NaN", np.inf, "Infinity", -np.inf, "-Infinity"] - ) - tm.assert_frame_equal(result, expected) - - def test_frame_int_overflow(self): - # GH 30320 - encoded_json = json.dumps([{"col": "31900441201190696999"}, {"col": "Text"}]) - expected = DataFrame({"col": ["31900441201190696999", "Text"]}) - result = read_json(StringIO(encoded_json)) - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize( - "dataframe,expected", - [ - ( - DataFrame({"x": [1, 2, 3], "y": ["a", "b", "c"]}), - '{"(0, \'x\')":1,"(0, \'y\')":"a","(1, \'x\')":2,' - '"(1, \'y\')":"b","(2, \'x\')":3,"(2, \'y\')":"c"}', - ) - ], - ) - def test_json_multiindex(self, dataframe, expected): - series = dataframe.stack(future_stack=True) - result = series.to_json(orient="index") - assert result == expected - - @pytest.mark.single_cpu - def test_to_s3(self, s3_public_bucket, s3so): - # GH 28375 - mock_bucket_name, target_file = s3_public_bucket.name, "test.json" - df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]}) - df.to_json(f"s3://{mock_bucket_name}/{target_file}", storage_options=s3so) - timeout = 5 - while True: - if target_file in (obj.key for obj in s3_public_bucket.objects.all()): - break - time.sleep(0.1) - timeout -= 0.1 - assert timeout > 0, "Timed out waiting for file to appear on moto" - - def test_json_pandas_nulls(self, nulls_fixture, request): - # GH 31615 - if isinstance(nulls_fixture, Decimal): - mark = pytest.mark.xfail(reason="not implemented") - request.node.add_marker(mark) - - result = DataFrame([[nulls_fixture]]).to_json() - assert result == '{"0":{"0":null}}' - - def test_readjson_bool_series(self): - # GH31464 - result = read_json(StringIO("[true, true, false]"), typ="series") - expected = Series([True, True, False]) - tm.assert_series_equal(result, expected) - - def test_to_json_multiindex_escape(self): - # GH 15273 - df = DataFrame( - True, - index=pd.date_range("2017-01-20", "2017-01-23"), - columns=["foo", "bar"], - ).stack(future_stack=True) - result = df.to_json() - expected = ( - "{\"(Timestamp('2017-01-20 00:00:00'), 'foo')\":true," - "\"(Timestamp('2017-01-20 00:00:00'), 'bar')\":true," - "\"(Timestamp('2017-01-21 00:00:00'), 'foo')\":true," - "\"(Timestamp('2017-01-21 00:00:00'), 'bar')\":true," - "\"(Timestamp('2017-01-22 00:00:00'), 'foo')\":true," - "\"(Timestamp('2017-01-22 00:00:00'), 'bar')\":true," - "\"(Timestamp('2017-01-23 00:00:00'), 'foo')\":true," - "\"(Timestamp('2017-01-23 00:00:00'), 'bar')\":true}" - ) - assert result == expected - - def test_to_json_series_of_objects(self): - class _TestObject: - def __init__(self, a, b, _c, d) -> None: - self.a = a - self.b = b - self._c = _c - self.d = d - - def e(self): - return 5 - - # JSON keys should be all non-callable non-underscore attributes, see GH-42768 - series = Series([_TestObject(a=1, b=2, _c=3, d=4)]) - assert json.loads(series.to_json()) == {"0": {"a": 1, "b": 2, "d": 4}} - - @pytest.mark.parametrize( - "data,expected", - [ - ( - Series({0: -6 + 8j, 1: 0 + 1j, 2: 9 - 5j}), - '{"0":{"imag":8.0,"real":-6.0},' - '"1":{"imag":1.0,"real":0.0},' - '"2":{"imag":-5.0,"real":9.0}}', - ), - ( - Series({0: -9.39 + 0.66j, 1: 3.95 + 9.32j, 2: 4.03 - 0.17j}), - '{"0":{"imag":0.66,"real":-9.39},' - '"1":{"imag":9.32,"real":3.95},' - '"2":{"imag":-0.17,"real":4.03}}', - ), - ( - DataFrame([[-2 + 3j, -1 - 0j], [4 - 3j, -0 - 10j]]), - '{"0":{"0":{"imag":3.0,"real":-2.0},' - '"1":{"imag":-3.0,"real":4.0}},' - '"1":{"0":{"imag":0.0,"real":-1.0},' - '"1":{"imag":-10.0,"real":0.0}}}', - ), - ( - DataFrame( - [[-0.28 + 0.34j, -1.08 - 0.39j], [0.41 - 0.34j, -0.78 - 1.35j]] - ), - '{"0":{"0":{"imag":0.34,"real":-0.28},' - '"1":{"imag":-0.34,"real":0.41}},' - '"1":{"0":{"imag":-0.39,"real":-1.08},' - '"1":{"imag":-1.35,"real":-0.78}}}', - ), - ], - ) - def test_complex_data_tojson(self, data, expected): - # GH41174 - result = data.to_json() - assert result == expected - - def test_json_uint64(self): - # GH21073 - expected = ( - '{"columns":["col1"],"index":[0,1],' - '"data":[[13342205958987758245],[12388075603347835679]]}' - ) - df = DataFrame(data={"col1": [13342205958987758245, 12388075603347835679]}) - result = df.to_json(orient="split") - assert result == expected - - @pytest.mark.parametrize( - "orient", ["split", "records", "values", "index", "columns"] - ) - def test_read_json_dtype_backend(self, string_storage, dtype_backend, orient): - # GH#50750 - pa = pytest.importorskip("pyarrow") - df = DataFrame( - { - "a": Series([1, np.nan, 3], dtype="Int64"), - "b": Series([1, 2, 3], dtype="Int64"), - "c": Series([1.5, np.nan, 2.5], dtype="Float64"), - "d": Series([1.5, 2.0, 2.5], dtype="Float64"), - "e": [True, False, None], - "f": [True, False, True], - "g": ["a", "b", "c"], - "h": ["a", "b", None], - } - ) - - if string_storage == "python": - string_array = StringArray(np.array(["a", "b", "c"], dtype=np.object_)) - string_array_na = StringArray(np.array(["a", "b", NA], dtype=np.object_)) - - else: - string_array = ArrowStringArray(pa.array(["a", "b", "c"])) - string_array_na = ArrowStringArray(pa.array(["a", "b", None])) - - out = df.to_json(orient=orient) - with pd.option_context("mode.string_storage", string_storage): - result = read_json( - StringIO(out), dtype_backend=dtype_backend, orient=orient - ) - - expected = DataFrame( - { - "a": Series([1, np.nan, 3], dtype="Int64"), - "b": Series([1, 2, 3], dtype="Int64"), - "c": Series([1.5, np.nan, 2.5], dtype="Float64"), - "d": Series([1.5, 2.0, 2.5], dtype="Float64"), - "e": Series([True, False, NA], dtype="boolean"), - "f": Series([True, False, True], dtype="boolean"), - "g": string_array, - "h": string_array_na, - } - ) - - if dtype_backend == "pyarrow": - from pandas.arrays import ArrowExtensionArray - - expected = DataFrame( - { - col: ArrowExtensionArray(pa.array(expected[col], from_pandas=True)) - for col in expected.columns - } - ) - - if orient == "values": - expected.columns = list(range(0, 8)) - - tm.assert_frame_equal(result, expected) - - @pytest.mark.parametrize("orient", ["split", "records", "index"]) - def test_read_json_nullable_series(self, string_storage, dtype_backend, orient): - # GH#50750 - pa = pytest.importorskip("pyarrow") - ser = Series([1, np.nan, 3], dtype="Int64") - - out = ser.to_json(orient=orient) - with pd.option_context("mode.string_storage", string_storage): - result = read_json( - StringIO(out), dtype_backend=dtype_backend, orient=orient, typ="series" - ) - - expected = Series([1, np.nan, 3], dtype="Int64") - - if dtype_backend == "pyarrow": - from pandas.arrays import ArrowExtensionArray - - expected = Series(ArrowExtensionArray(pa.array(expected, from_pandas=True))) - - tm.assert_series_equal(result, expected) - - def test_invalid_dtype_backend(self): - msg = ( - "dtype_backend numpy is invalid, only 'numpy_nullable' and " - "'pyarrow' are allowed." - ) - with pytest.raises(ValueError, match=msg): - read_json("test", dtype_backend="numpy") - 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- diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/layers_123821KB.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/layers_123821KB.py deleted file mode 100644 index b82f06bb4993cd63f076e68d7e24185269b1bc42..0000000000000000000000000000000000000000 --- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/layers_123821KB.py +++ /dev/null @@ -1,118 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from . import spec_utils - - -class Conv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(Conv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nout, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - bias=False, - ), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class SeperableConv2DBNActiv(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): - super(SeperableConv2DBNActiv, self).__init__() - self.conv = nn.Sequential( - nn.Conv2d( - nin, - nin, - kernel_size=ksize, - stride=stride, - padding=pad, - dilation=dilation, - groups=nin, - bias=False, - ), - nn.Conv2d(nin, nout, kernel_size=1, bias=False), - nn.BatchNorm2d(nout), - activ(), - ) - - def __call__(self, x): - return self.conv(x) - - -class Encoder(nn.Module): - def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): - super(Encoder, self).__init__() - self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ) - - def __call__(self, x): - skip = self.conv1(x) - h = self.conv2(skip) - - return h, skip - - -class Decoder(nn.Module): - def __init__( - self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False - ): - super(Decoder, self).__init__() - self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) - self.dropout = nn.Dropout2d(0.1) if dropout else None - - def __call__(self, x, skip=None): - x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True) - if skip is not None: - skip = spec_utils.crop_center(skip, x) - x = torch.cat([x, skip], dim=1) - h = self.conv(x) - - if self.dropout is not None: - h = self.dropout(h) - - return h - - -class ASPPModule(nn.Module): - def __init__(self, nin, nout, dilations=(4, 8, 16), activ=nn.ReLU): - super(ASPPModule, self).__init__() - self.conv1 = nn.Sequential( - nn.AdaptiveAvgPool2d((1, None)), - Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ), - ) - self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ) - self.conv3 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[0], dilations[0], activ=activ - ) - self.conv4 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[1], dilations[1], activ=activ - ) - self.conv5 = SeperableConv2DBNActiv( - nin, nin, 3, 1, dilations[2], dilations[2], activ=activ - ) - self.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1) - ) - - def forward(self, x): - _, _, h, w = x.size() - feat1 = F.interpolate( - self.conv1(x), size=(h, w), mode="bilinear", align_corners=True - ) - feat2 = self.conv2(x) - feat3 = self.conv3(x) - feat4 = self.conv4(x) - feat5 = self.conv5(x) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py b/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py deleted file mode 100644 index becbfae85683a13bbb19d3ea6c840da24e61e01e..0000000000000000000000000000000000000000 --- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py +++ /dev/null @@ -1,122 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F - -from . import layers_123821KB as layers - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 32) - self.stg1_high_band_net = BaseASPPNet(2, 32) - - self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(16, 32) - - self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(32, 64) - - self.out = nn.Conv2d(64, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(32, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(32, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/r3gm/RVC_HF/demucs/utils.py b/spaces/r3gm/RVC_HF/demucs/utils.py deleted file mode 100644 index 4364184059b1afe3c8379c77793a8e76dccf9699..0000000000000000000000000000000000000000 --- a/spaces/r3gm/RVC_HF/demucs/utils.py +++ /dev/null @@ -1,323 +0,0 @@ -# Copyright (c) Facebook, Inc. and its 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. - -import errno -import functools -import hashlib -import inspect -import io -import os -import random -import socket -import tempfile -import warnings -import zlib -from contextlib import contextmanager - -from diffq import UniformQuantizer, DiffQuantizer -import torch as th -import tqdm -from torch import distributed -from torch.nn import functional as F - - -def center_trim(tensor, reference): - """ - Center trim `tensor` with respect to `reference`, along the last dimension. - `reference` can also be a number, representing the length to trim to. - If the size difference != 0 mod 2, the extra sample is removed on the right side. - """ - if hasattr(reference, "size"): - reference = reference.size(-1) - delta = tensor.size(-1) - reference - if delta < 0: - raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.") - if delta: - tensor = tensor[..., delta // 2:-(delta - delta // 2)] - return tensor - - -def average_metric(metric, count=1.): - """ - Average `metric` which should be a float across all hosts. `count` should be - the weight for this particular host (i.e. number of examples). - """ - metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda') - distributed.all_reduce(metric, op=distributed.ReduceOp.SUM) - return metric[1].item() / metric[0].item() - - -def free_port(host='', low=20000, high=40000): - """ - Return a port number that is most likely free. - This could suffer from a race condition although - it should be quite rare. - """ - sock = socket.socket() - while True: - port = random.randint(low, high) - try: - sock.bind((host, port)) - except OSError as error: - if error.errno == errno.EADDRINUSE: - continue - raise - return port - - -def sizeof_fmt(num, suffix='B'): - """ - Given `num` bytes, return human readable size. - Taken from https://stackoverflow.com/a/1094933 - """ - for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']: - if abs(num) < 1024.0: - return "%3.1f%s%s" % (num, unit, suffix) - num /= 1024.0 - return "%.1f%s%s" % (num, 'Yi', suffix) - - -def human_seconds(seconds, display='.2f'): - """ - Given `seconds` seconds, return human readable duration. - """ - value = seconds * 1e6 - ratios = [1e3, 1e3, 60, 60, 24] - names = ['us', 'ms', 's', 'min', 'hrs', 'days'] - last = names.pop(0) - for name, ratio in zip(names, ratios): - if value / ratio < 0.3: - break - value /= ratio - last = name - return f"{format(value, display)} {last}" - - -class TensorChunk: - def __init__(self, tensor, offset=0, length=None): - total_length = tensor.shape[-1] - assert offset >= 0 - assert offset < total_length - - if length is None: - length = total_length - offset - else: - length = min(total_length - offset, length) - - self.tensor = tensor - self.offset = offset - self.length = length - self.device = tensor.device - - @property - def shape(self): - shape = list(self.tensor.shape) - shape[-1] = self.length - return shape - - def padded(self, target_length): - delta = target_length - self.length - total_length = self.tensor.shape[-1] - assert delta >= 0 - - start = self.offset - delta // 2 - end = start + target_length - - correct_start = max(0, start) - correct_end = min(total_length, end) - - pad_left = correct_start - start - pad_right = end - correct_end - - out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right)) - assert out.shape[-1] == target_length - return out - - -def tensor_chunk(tensor_or_chunk): - if isinstance(tensor_or_chunk, TensorChunk): - return tensor_or_chunk - else: - assert isinstance(tensor_or_chunk, th.Tensor) - return TensorChunk(tensor_or_chunk) - - -def apply_model(model, mix, shifts=None, split=False, - overlap=0.25, transition_power=1., progress=False): - """ - Apply model to a given mixture. - - Args: - shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec - and apply the oppositve shift to the output. This is repeated `shifts` time and - all predictions are averaged. This effectively makes the model time equivariant - and improves SDR by up to 0.2 points. - split (bool): if True, the input will be broken down in 8 seconds extracts - and predictions will be performed individually on each and concatenated. - Useful for model with large memory footprint like Tasnet. - progress (bool): if True, show a progress bar (requires split=True) - """ - assert transition_power >= 1, "transition_power < 1 leads to weird behavior." - device = mix.device - channels, length = mix.shape - if split: - out = th.zeros(len(model.sources), channels, length, device=device) - sum_weight = th.zeros(length, device=device) - segment = model.segment_length - stride = int((1 - overlap) * segment) - offsets = range(0, length, stride) - scale = stride / model.samplerate - if progress: - offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds') - # We start from a triangle shaped weight, with maximal weight in the middle - # of the segment. Then we normalize and take to the power `transition_power`. - # Large values of transition power will lead to sharper transitions. - weight = th.cat([th.arange(1, segment // 2 + 1), - th.arange(segment - segment // 2, 0, -1)]).to(device) - assert len(weight) == segment - # If the overlap < 50%, this will translate to linear transition when - # transition_power is 1. - weight = (weight / weight.max())**transition_power - for offset in offsets: - chunk = TensorChunk(mix, offset, segment) - chunk_out = apply_model(model, chunk, shifts=shifts) - chunk_length = chunk_out.shape[-1] - out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out - sum_weight[offset:offset + segment] += weight[:chunk_length] - offset += segment - assert sum_weight.min() > 0 - out /= sum_weight - return out - elif shifts: - max_shift = int(0.5 * model.samplerate) - mix = tensor_chunk(mix) - padded_mix = mix.padded(length + 2 * max_shift) - out = 0 - for _ in range(shifts): - offset = random.randint(0, max_shift) - shifted = TensorChunk(padded_mix, offset, length + max_shift - offset) - shifted_out = apply_model(model, shifted) - out += shifted_out[..., max_shift - offset:] - out /= shifts - return out - else: - valid_length = model.valid_length(length) - mix = tensor_chunk(mix) - padded_mix = mix.padded(valid_length) - with th.no_grad(): - out = model(padded_mix.unsqueeze(0))[0] - return center_trim(out, length) - - -@contextmanager -def temp_filenames(count, delete=True): - names = [] - try: - for _ in range(count): - names.append(tempfile.NamedTemporaryFile(delete=False).name) - yield names - finally: - if delete: - for name in names: - os.unlink(name) - - -def get_quantizer(model, args, optimizer=None): - quantizer = None - if args.diffq: - quantizer = DiffQuantizer( - model, min_size=args.q_min_size, group_size=8) - if optimizer is not None: - quantizer.setup_optimizer(optimizer) - elif args.qat: - quantizer = UniformQuantizer( - model, bits=args.qat, min_size=args.q_min_size) - return quantizer - - -def load_model(path, strict=False): - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - load_from = path - package = th.load(load_from, 'cpu') - - klass = package["klass"] - args = package["args"] - kwargs = package["kwargs"] - - if strict: - model = klass(*args, **kwargs) - else: - sig = inspect.signature(klass) - for key in list(kwargs): - if key not in sig.parameters: - warnings.warn("Dropping inexistant parameter " + key) - del kwargs[key] - model = klass(*args, **kwargs) - - state = package["state"] - training_args = package["training_args"] - quantizer = get_quantizer(model, training_args) - - set_state(model, quantizer, state) - return model - - -def get_state(model, quantizer): - if quantizer is None: - state = {k: p.data.to('cpu') for k, p in model.state_dict().items()} - else: - state = quantizer.get_quantized_state() - buf = io.BytesIO() - th.save(state, buf) - state = {'compressed': zlib.compress(buf.getvalue())} - return state - - -def set_state(model, quantizer, state): - if quantizer is None: - model.load_state_dict(state) - else: - buf = io.BytesIO(zlib.decompress(state["compressed"])) - state = th.load(buf, "cpu") - quantizer.restore_quantized_state(state) - - return state - - -def save_state(state, path): - buf = io.BytesIO() - th.save(state, buf) - sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8] - - path = path.parent / (path.stem + "-" + sig + path.suffix) - path.write_bytes(buf.getvalue()) - - -def save_model(model, quantizer, training_args, path): - args, kwargs = model._init_args_kwargs - klass = model.__class__ - - state = get_state(model, quantizer) - - save_to = path - package = { - 'klass': klass, - 'args': args, - 'kwargs': kwargs, - 'state': state, - 'training_args': training_args, - } - th.save(package, save_to) - - -def capture_init(init): - @functools.wraps(init) - def __init__(self, *args, **kwargs): - self._init_args_kwargs = (args, kwargs) - init(self, *args, **kwargs) - - return __init__ diff --git a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/inceptionv4.py b/spaces/rachana219/MODT2/trackers/strongsort/deep/models/inceptionv4.py deleted file mode 100644 index b14916f140712298866c943ebdb4ebad67d72fc4..0000000000000000000000000000000000000000 --- a/spaces/rachana219/MODT2/trackers/strongsort/deep/models/inceptionv4.py +++ /dev/null @@ -1,381 +0,0 @@ -from __future__ import division, absolute_import -import torch -import torch.nn as nn -import torch.utils.model_zoo as model_zoo - -__all__ = ['inceptionv4'] -""" -Code imported from https://github.com/Cadene/pretrained-models.pytorch -""" - -pretrained_settings = { - 'inceptionv4': { - 'imagenet': { - 'url': - 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth', - 'input_space': 'RGB', - 'input_size': [3, 299, 299], - 'input_range': [0, 1], - 'mean': [0.5, 0.5, 0.5], - 'std': [0.5, 0.5, 0.5], - 'num_classes': 1000 - }, - 'imagenet+background': { - 'url': - 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth', - 'input_space': 'RGB', - 'input_size': [3, 299, 299], - 'input_range': [0, 1], - 'mean': [0.5, 0.5, 0.5], - 'std': [0.5, 0.5, 0.5], - 'num_classes': 1001 - } - } -} - - -class BasicConv2d(nn.Module): - - def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): - super(BasicConv2d, self).__init__() - self.conv = nn.Conv2d( - in_planes, - out_planes, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=False - ) # verify bias false - self.bn = nn.BatchNorm2d( - out_planes, - eps=0.001, # value found in tensorflow - momentum=0.1, # default pytorch value - affine=True - ) - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - x = self.conv(x) - x = self.bn(x) - x = self.relu(x) - return x - - -class Mixed_3a(nn.Module): - - def __init__(self): - super(Mixed_3a, self).__init__() - self.maxpool = nn.MaxPool2d(3, stride=2) - self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2) - - def forward(self, x): - x0 = self.maxpool(x) - x1 = self.conv(x) - out = torch.cat((x0, x1), 1) - return out - - -class Mixed_4a(nn.Module): - - def __init__(self): - super(Mixed_4a, self).__init__() - - self.branch0 = nn.Sequential( - BasicConv2d(160, 64, kernel_size=1, stride=1), - BasicConv2d(64, 96, kernel_size=3, stride=1) - ) - - self.branch1 = nn.Sequential( - BasicConv2d(160, 64, kernel_size=1, stride=1), - BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)), - BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)), - BasicConv2d(64, 96, kernel_size=(3, 3), stride=1) - ) - - def forward(self, x): - x0 = self.branch0(x) - x1 = self.branch1(x) - out = torch.cat((x0, x1), 1) - return out - - -class Mixed_5a(nn.Module): - - def __init__(self): - super(Mixed_5a, self).__init__() - self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2) - self.maxpool = nn.MaxPool2d(3, stride=2) - - def forward(self, x): - x0 = self.conv(x) - x1 = self.maxpool(x) - out = torch.cat((x0, x1), 1) - return out - - -class Inception_A(nn.Module): - - def __init__(self): - super(Inception_A, self).__init__() - self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1) - - self.branch1 = nn.Sequential( - BasicConv2d(384, 64, kernel_size=1, stride=1), - BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1) - ) - - self.branch2 = nn.Sequential( - BasicConv2d(384, 64, kernel_size=1, stride=1), - BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), - BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1) - ) - - self.branch3 = nn.Sequential( - nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), - BasicConv2d(384, 96, kernel_size=1, stride=1) - ) - - def forward(self, x): - x0 = self.branch0(x) - x1 = self.branch1(x) - x2 = self.branch2(x) - x3 = self.branch3(x) - out = torch.cat((x0, x1, x2, x3), 1) - return out - - -class Reduction_A(nn.Module): - - def __init__(self): - super(Reduction_A, self).__init__() - self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2) - - self.branch1 = nn.Sequential( - BasicConv2d(384, 192, kernel_size=1, stride=1), - BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1), - BasicConv2d(224, 256, kernel_size=3, stride=2) - ) - - self.branch2 = nn.MaxPool2d(3, stride=2) - - def forward(self, x): - x0 = self.branch0(x) - x1 = self.branch1(x) - x2 = self.branch2(x) - out = torch.cat((x0, x1, x2), 1) - return out - - -class Inception_B(nn.Module): - - def __init__(self): - super(Inception_B, self).__init__() - self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1) - - self.branch1 = nn.Sequential( - BasicConv2d(1024, 192, kernel_size=1, stride=1), - BasicConv2d( - 192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3) - ), - BasicConv2d( - 224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0) - ) - ) - - self.branch2 = nn.Sequential( - BasicConv2d(1024, 192, kernel_size=1, stride=1), - BasicConv2d( - 192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0) - ), - BasicConv2d( - 192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3) - ), - BasicConv2d( - 224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0) - ), - BasicConv2d( - 224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3) - ) - ) - - self.branch3 = nn.Sequential( - nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), - BasicConv2d(1024, 128, kernel_size=1, stride=1) - ) - - def forward(self, x): - x0 = self.branch0(x) - x1 = self.branch1(x) - x2 = self.branch2(x) - x3 = self.branch3(x) - out = torch.cat((x0, x1, x2, x3), 1) - return out - - -class Reduction_B(nn.Module): - - def __init__(self): - super(Reduction_B, self).__init__() - - self.branch0 = nn.Sequential( - BasicConv2d(1024, 192, kernel_size=1, stride=1), - BasicConv2d(192, 192, kernel_size=3, stride=2) - ) - - self.branch1 = nn.Sequential( - BasicConv2d(1024, 256, kernel_size=1, stride=1), - BasicConv2d( - 256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3) - ), - BasicConv2d( - 256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0) - ), BasicConv2d(320, 320, kernel_size=3, stride=2) - ) - - self.branch2 = nn.MaxPool2d(3, stride=2) - - def forward(self, x): - x0 = self.branch0(x) - x1 = self.branch1(x) - x2 = self.branch2(x) - out = torch.cat((x0, x1, x2), 1) - return out - - -class Inception_C(nn.Module): - - def __init__(self): - super(Inception_C, self).__init__() - - self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1) - - self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) - self.branch1_1a = BasicConv2d( - 384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1) - ) - self.branch1_1b = BasicConv2d( - 384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) - ) - - self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) - self.branch2_1 = BasicConv2d( - 384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0) - ) - self.branch2_2 = BasicConv2d( - 448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1) - ) - self.branch2_3a = BasicConv2d( - 512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1) - ) - self.branch2_3b = BasicConv2d( - 512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) - ) - - self.branch3 = nn.Sequential( - nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), - BasicConv2d(1536, 256, kernel_size=1, stride=1) - ) - - def forward(self, x): - x0 = self.branch0(x) - - x1_0 = self.branch1_0(x) - x1_1a = self.branch1_1a(x1_0) - x1_1b = self.branch1_1b(x1_0) - x1 = torch.cat((x1_1a, x1_1b), 1) - - x2_0 = self.branch2_0(x) - x2_1 = self.branch2_1(x2_0) - x2_2 = self.branch2_2(x2_1) - x2_3a = self.branch2_3a(x2_2) - x2_3b = self.branch2_3b(x2_2) - x2 = torch.cat((x2_3a, x2_3b), 1) - - x3 = self.branch3(x) - - out = torch.cat((x0, x1, x2, x3), 1) - return out - - -class InceptionV4(nn.Module): - """Inception-v4. - - Reference: - Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual - Connections on Learning. AAAI 2017. - - Public keys: - - ``inceptionv4``: InceptionV4. - """ - - def __init__(self, num_classes, loss, **kwargs): - super(InceptionV4, self).__init__() - self.loss = loss - - self.features = nn.Sequential( - BasicConv2d(3, 32, kernel_size=3, stride=2), - BasicConv2d(32, 32, kernel_size=3, stride=1), - BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), - Mixed_3a(), - Mixed_4a(), - Mixed_5a(), - Inception_A(), - Inception_A(), - Inception_A(), - Inception_A(), - Reduction_A(), # Mixed_6a - Inception_B(), - Inception_B(), - Inception_B(), - Inception_B(), - Inception_B(), - Inception_B(), - Inception_B(), - Reduction_B(), # Mixed_7a - Inception_C(), - Inception_C(), - Inception_C() - ) - self.global_avgpool = nn.AdaptiveAvgPool2d(1) - self.classifier = nn.Linear(1536, num_classes) - - def forward(self, x): - f = self.features(x) - v = self.global_avgpool(f) - v = v.view(v.size(0), -1) - - if not self.training: - return v - - y = self.classifier(v) - - if self.loss == 'softmax': - return y - elif self.loss == 'triplet': - return y, v - else: - raise KeyError('Unsupported loss: {}'.format(self.loss)) - - -def init_pretrained_weights(model, model_url): - """Initializes model with pretrained weights. - - Layers that don't match with pretrained layers in name or size are kept unchanged. - """ - pretrain_dict = model_zoo.load_url(model_url) - model_dict = model.state_dict() - pretrain_dict = { - k: v - for k, v in pretrain_dict.items() - if k in model_dict and model_dict[k].size() == v.size() - } - model_dict.update(pretrain_dict) - model.load_state_dict(model_dict) - - -def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs): - model = InceptionV4(num_classes, loss, **kwargs) - if pretrained: - model_url = pretrained_settings['inceptionv4']['imagenet']['url'] - init_pretrained_weights(model, model_url) - return model diff --git a/spaces/raedeXanto/academic-chatgpt-beta/1001bit Pro 2.2 How to Install and Use this Plugin for Sketchup.md b/spaces/raedeXanto/academic-chatgpt-beta/1001bit Pro 2.2 How to Install and Use this Plugin for Sketchup.md deleted file mode 100644 index 883ba7dcea8b6d80101103c8318ff1a38d3873fa..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/1001bit Pro 2.2 How to Install and Use this Plugin for Sketchup.md +++ /dev/null @@ -1,147 +0,0 @@ -
-1001bit Pro 2.2 Plugin for Sketchup: A Review
-If you are a Sketchup user who loves to create architectural models, you might have heard of 1001bit Pro, a collection of plugins that can help you achieve more while doing less. In this article, we will review the latest version of this plugin, 1001bit Pro 2.2, and show you how it can enhance your modeling experience and output.
-1001bit.Pro.2.2..Plugin.for.Sketchup..epub
Download File >> https://tinourl.com/2uL1l6
-1001bit Pro is a set of Ruby scripts that are dedicated for architectural works. It was developed by Goh Chun Hee, an architect based in Singapore. With this plugin, you can create parametric architectural elements such as staircases, windows, doors, roofs, louvres, floor joists, and more within minutes. You can also save commonly used parameters for easy selection in future. Moreover, you can use various editing tools to modify your models quickly and easily.
-In this review, we will cover the following aspects of 1001bit Pro:
--
-
- Features of 1001bit Pro -
- Benefits of using 1001bit Pro -
- How to install and activate 1001bit Pro -
- How to use 1001bit Pro -
Features of 1001bit Pro
-1001bit Pro has many features that can help you create stunning architectural models in Sketchup. Here are some of the main features:
-Architectural Tools
-With these tools, you can create various architectural elements with just a few clicks. You can specify the dimensions, materials, styles, and other parameters of each element. Some of the architectural tools are:
--
-
- Staircases: You can create straight or spiral staircases with different types of handrails, balusters, landings, and steps. -
- Windows: You can create different types of windows such as casement, sliding, bay, arch, louver, and more. -
- Doors: You can create different types of doors such as swing, sliding, bi-fold, pocket, and more. -
- Roofs: You can create different types of roofs such as gable, hip, mansard, gambrel, shed, dome, and more. -
- Louvres: You can create different types of louvres such as horizontal, vertical, fixed, operable, and more. -
- Floor joists: You can create floor joists with different sizes, spacing, and directions. -
- And more: You can also create other elements such as walls, columns, beams, railings, fences, ramps, etc. -
Editing Tools
-With these tools, you can edit your models easily and efficiently. You can perform various operations such as extending, trimming, filleting, chamfering, offsetting, rotating,
Editing Tools
-With these tools, you can edit your models easily and efficiently. You can perform various operations such as extending, trimming, filleting, chamfering, offsetting, rotating, scaling, and more. Some of the editing tools are:
-How to install 1001bit Pro 2.2 plugin for Sketchup
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-How to create stairs with 1001bit Pro 2.2 plugin for Sketchup
-How to create roofs with 1001bit Pro 2.2 plugin for Sketchup
-How to create windows with 1001bit Pro 2.2 plugin for Sketchup
-How to create doors with 1001bit Pro 2.2 plugin for Sketchup
-How to create walls with 1001bit Pro 2.2 plugin for Sketchup
-How to create columns with 1001bit Pro 2.2 plugin for Sketchup
-How to create beams with 1001bit Pro 2.2 plugin for Sketchup
-How to create railings with 1001bit Pro 2.2 plugin for Sketchup
-How to create slabs with 1001bit Pro 2.2 plugin for Sketchup
-How to create foundations with 1001bit Pro 2.2 plugin for Sketchup
-How to create floors with 1001bit Pro 2.2 plugin for Sketchup
-How to create ceilings with 1001bit Pro 2.2 plugin for Sketchup
-How to create furniture with 1001bit Pro 2.2 plugin for Sketchup
-How to create lighting with 1001bit Pro 2.2 plugin for Sketchup
-How to create landscaping with 1001bit Pro 2.2 plugin for Sketchup
-How to create site plans with 1001bit Pro 2.2 plugin for Sketchup
-How to create sections with 1001bit Pro 2.2 plugin for Sketchup
-How to create elevations with 1001bit Pro 2.2 plugin for Sketchup
-How to create perspectives with 1001bit Pro 2.2 plugin for Sketchup
-How to create renderings with 1001bit Pro 2.-
-
- Extend: You can extend edges or faces to meet other edges or faces. -
- Trim: You can trim edges or faces by other edges or faces. -
- Fillet: You can create rounded corners between two edges or faces. -
- Chamfer: You can create beveled corners between two edges or faces. -
- Offset: You can create parallel copies of edges or faces with a specified distance. -
- Rotate 3D: You can rotate objects around any axis. -
- Scale 3D: You can scale objects along any axis. -
- And more: You can also use other tools such as mirror, array, taper, twist, bend, etc. -
Saving and Loading Parameters
-With this feature, you can save the parameters of any architectural element that you create with 1001bit Pro. You can then load the saved parameters for future use. This can save you time and effort when you need to create similar elements again. You can also share your saved parameters with other users.
-Benefits of using 1001bit Pro
-Using 1001bit Pro can bring you many benefits as a Sketchup user. Here are some of the main benefits:
-Faster and Easier Modeling
-With 1001bit Pro, you can create complex architectural elements with just a few clicks. You don't need to draw every detail manually or use multiple plugins. You can also edit your models quickly and easily with the editing tools. This can save you a lot of time and effort in modeling.
-More Creative and Realistic Designs
-With 1001bit Pro, you can create more creative and realistic designs for your architectural models. You can choose from different types, styles, and materials of each element. You can also adjust the parameters and options to suit your preferences and needs. This can enhance the quality and appearance of your models.
-Compatible with Sketchup Versions
-1001bit Pro is compatible with Sketchup 8 and later versions. It also works with both Windows and Mac operating systems. You don't need to worry about compatibility issues when using this plugin.
-How to Install and Activate 1001bit Pro
-If you are interested in using 1001bit Pro, you need to install and activate it first. Here are the steps to do so:
-Downloading the Plugin
-You can download 1001bit Pro from the official website: https://www.1001bit.com/pro/download.html. There are two formats available: rbz and zip. We recommend you use the rbz format as it is easier to install. The current version is 2.2 which fixes some crashing issues with Sketchup 2014.
-Installing the Plugin
-To install 1001bit Pro using the rbz format, follow these steps:
--
-
- Open Sketchup and go to Window > Preferences > Extensions. -
- Click on Install Extension and browse to the rbz file that you downloaded. -
- Select the file and click OK. -
- A message will appear asking you to confirm the installation. Click Yes. -
- A message will appear saying that the plugin has been installed successfully. Click OK. -
- Restart Sketchup to activate the plugin. -
To install 1001bit Pro using the zip format, follow these steps:
--
-
- Extract the zip file that you downloaded to a folder on your computer. -
- Copy all the files in the folder to your Sketchup plugins folder. The location of this folder depends on your Sketchup version and operating system. For example, for Sketchup 2014 on Windows, it is C:\Users\YourName\AppData\Roaming\SketchUp\SketchUp 2014\SketchUp\Plugins. -
- Restart Sketchup to activate the plugin. -
- Go to the official website: https://www.1001bit.com/pro/purchase.html and click on Buy Now. -
- Fill in your details and make the payment via PayPal or credit card. -
- You will receive an email with your activation key and instructions. -
- Open Sketchup and go to Plugins > 1001bit Pro > Activate 1001bit Pro. -
- Enter your activation key and click OK. -
- A message will appear saying that the plugin has been activated successfully. Click OK. -
- You can now use all the features of 1001bit Pro without any limitations. -
- What are the system requirements for 1001bit Pro? -
- How many computers can I activate 1001bit Pro on? -
- How do I update 1001bit Pro? -
- How do I get support for 1001bit Pro? -
- How do I uninstall 1001bit Pro? -
- It features Rajinikanth in a dual role as Dr. Vaseegaran, a brilliant scientist who creates a humanoid robot named Chitti, and as Chitti himself, who develops emotions and falls in love with Vaseegaran's girlfriend Sana (played by Aishwarya Rai Bachchan). -
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-In conclusion, Endhiran is a blockbuster Tamil movie that features Rajinikanth and Aishwarya Rai Bachchan in a sci-fi action thriller. The movie has many features such as star cast and crew, plot and theme, visual effects and music that make it popular among the audience. If you want to watch Endhiran online, you can either stream it on legal platforms or download it from illegal sources. However, downloading Endhiran full movie in Tamil HD 1080p for free has many benefits such as convenience, saving money and time, and avoiding ads and interruptions. It also has many risks such as legal issues and penalties, malware and viruses, and poor quality and fake files.
-Call to action
-Therefore, we recommend that you watch Endhiran online legally and safely by subscribing to any of the platforms mentioned above. You can enjoy the movie in high quality without any trouble or guilt. You can also support the filmmakers and encourage them to make more such movies in the future.
-FAQs
--
-
- Q: When was Endhiran released? -
- A: Endhiran was released on 1 October 2010. -
- Q: Who directed Endhiran? -
- A: S. Shankar directed Endhiran. -
- Q: Who composed the music for Endhiran? -
- A: A.R. Rahman composed the music for Endhiran. -
- Q: How many awards did Endhiran win? -
- A: Endhiran won four National Film Awards, three Filmfare Awards South, two Vijay Awards, two SIIMA Awards, and one Kalaimamani Award. -
- Q: Is there a sequel to Endhiran? -
- A: Yes, there is a sequel to Endhiran called 2.0 (2018), which also stars Rajinikanth along with Akshay Kumar and Amy Jackson. -
-
-
\ No newline at end of file diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/querystring.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/querystring.d.ts deleted file mode 100644 index e1185478461f4b15444b7b2ae114c8a6819a992a..0000000000000000000000000000000000000000 --- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/ts4.8/querystring.d.ts +++ /dev/null @@ -1,131 +0,0 @@ -/** - * The `querystring` module provides utilities for parsing and formatting URL - * query strings. It can be accessed using: - * - * ```js - * const querystring = require('querystring'); - * ``` - * - * `querystring` is more performant than `URLSearchParams` but is not a - * standardized API. Use `URLSearchParams` when performance is not critical - * or when compatibility with browser code is desirable. - * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/querystring.js) - */ -declare module 'querystring' { - interface StringifyOptions { - encodeURIComponent?: ((str: string) => string) | undefined; - } - interface ParseOptions { - maxKeys?: number | undefined; - decodeURIComponent?: ((str: string) => string) | undefined; - } - interface ParsedUrlQuery extends NodeJS.Dict{} - interface ParsedUrlQueryInput extends NodeJS.Dict | ReadonlyArray | ReadonlyArray | null> {} - /** - * The `querystring.stringify()` method produces a URL query string from a - * given `obj` by iterating through the object's "own properties". - * - * It serializes the following types of values passed in `obj`:[string](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#String_type) | - * [number](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Number_type) | - * [bigint](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/BigInt) | - * [boolean](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Boolean_type) | - * [string\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#String_type) | - * [number\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Number_type) | - * [bigint\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/BigInt) | - * [boolean\[\]](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Data_structures#Boolean_type) The numeric values must be finite. Any other input values will be coerced to - * empty strings. - * - * ```js - * querystring.stringify({ foo: 'bar', baz: ['qux', 'quux'], corge: '' }); - * // Returns 'foo=bar&baz=qux&baz=quux&corge=' - * - * querystring.stringify({ foo: 'bar', baz: 'qux' }, ';', ':'); - * // Returns 'foo:bar;baz:qux' - * ``` - * - * By default, characters requiring percent-encoding within the query string will - * be encoded as UTF-8\. If an alternative encoding is required, then an alternative`encodeURIComponent` option will need to be specified: - * - * ```js - * // Assuming gbkEncodeURIComponent function already exists, - * - * querystring.stringify({ w: '中文', foo: 'bar' }, null, null, - * { encodeURIComponent: gbkEncodeURIComponent }); - * ``` - * @since v0.1.25 - * @param obj The object to serialize into a URL query string - * @param [sep='&'] The substring used to delimit key and value pairs in the query string. - * @param [eq='='] . The substring used to delimit keys and values in the query string. - */ - function stringify(obj?: ParsedUrlQueryInput, sep?: string, eq?: string, options?: StringifyOptions): string; - /** - * The `querystring.parse()` method parses a URL query string (`str`) into a - * collection of key and value pairs. - * - * For example, the query string `'foo=bar&abc=xyz&abc=123'` is parsed into: - * - * ```js - * { - * foo: 'bar', - * abc: ['xyz', '123'] - * } - * ``` - * - * The object returned by the `querystring.parse()` method _does not_prototypically inherit from the JavaScript `Object`. This means that typical`Object` methods such as `obj.toString()`, - * `obj.hasOwnProperty()`, and others - * are not defined and _will not work_. - * - * By default, percent-encoded characters within the query string will be assumed - * to use UTF-8 encoding. If an alternative character encoding is used, then an - * alternative `decodeURIComponent` option will need to be specified: - * - * ```js - * // Assuming gbkDecodeURIComponent function already exists... - * - * querystring.parse('w=%D6%D0%CE%C4&foo=bar', null, null, - * { decodeURIComponent: gbkDecodeURIComponent }); - * ``` - * @since v0.1.25 - * @param str The URL query string to parse - * @param [sep='&'] The substring used to delimit key and value pairs in the query string. - * @param [eq='='] . The substring used to delimit keys and values in the query string. - */ - function parse(str: string, sep?: string, eq?: string, options?: ParseOptions): ParsedUrlQuery; - /** - * The querystring.encode() function is an alias for querystring.stringify(). - */ - const encode: typeof stringify; - /** - * The querystring.decode() function is an alias for querystring.parse(). - */ - const decode: typeof parse; - /** - * The `querystring.escape()` method performs URL percent-encoding on the given`str` in a manner that is optimized for the specific requirements of URL - * query strings. - * - * The `querystring.escape()` method is used by `querystring.stringify()` and is - * generally not expected to be used directly. It is exported primarily to allow - * application code to provide a replacement percent-encoding implementation if - * necessary by assigning `querystring.escape` to an alternative function. - * @since v0.1.25 - */ - function escape(str: string): string; - /** - * The `querystring.unescape()` method performs decoding of URL percent-encoded - * characters on the given `str`. - * - * The `querystring.unescape()` method is used by `querystring.parse()` and is - * generally not expected to be used directly. It is exported primarily to allow - * application code to provide a replacement decoding implementation if - * necessary by assigning `querystring.unescape` to an alternative function. - * - * By default, the `querystring.unescape()` method will attempt to use the - * JavaScript built-in `decodeURIComponent()` method to decode. If that fails, - * a safer equivalent that does not throw on malformed URLs will be used. - * @since v0.1.25 - */ - function unescape(str: string): string; -} -declare module 'node:querystring' { - export * from 'querystring'; -} diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/content-disposition/index.js b/spaces/rayan-saleh/whisper2notion/server/node_modules/content-disposition/index.js deleted file mode 100644 index ecec899a992d46f2e903a87475b1c342f2ce4d30..0000000000000000000000000000000000000000 --- a/spaces/rayan-saleh/whisper2notion/server/node_modules/content-disposition/index.js +++ /dev/null @@ -1,458 +0,0 @@ -/*! - * content-disposition - * Copyright(c) 2014-2017 Douglas Christopher Wilson - * MIT Licensed - */ - -'use strict' - -/** - * Module exports. - * @public - */ - -module.exports = contentDisposition -module.exports.parse = parse - -/** - * Module dependencies. - * @private - */ - -var basename = require('path').basename -var Buffer = require('safe-buffer').Buffer - -/** - * RegExp to match non attr-char, *after* encodeURIComponent (i.e. not including "%") - * @private - */ - -var ENCODE_URL_ATTR_CHAR_REGEXP = /[\x00-\x20"'()*,/:;<=>?@[\\\]{}\x7f]/g // eslint-disable-line no-control-regex - -/** - * RegExp to match percent encoding escape. - * @private - */ - -var HEX_ESCAPE_REGEXP = /%[0-9A-Fa-f]{2}/ -var HEX_ESCAPE_REPLACE_REGEXP = /%([0-9A-Fa-f]{2})/g - -/** - * RegExp to match non-latin1 characters. - * @private - */ - -var NON_LATIN1_REGEXP = /[^\x20-\x7e\xa0-\xff]/g - -/** - * RegExp to match quoted-pair in RFC 2616 - * - * quoted-pair = "\" CHAR - * CHAR = - * @private - */ - -var QESC_REGEXP = /\\([\u0000-\u007f])/g // eslint-disable-line no-control-regex - -/** - * RegExp to match chars that must be quoted-pair in RFC 2616 - * @private - */ - -var QUOTE_REGEXP = /([\\"])/g - -/** - * RegExp for various RFC 2616 grammar - * - * parameter = token "=" ( token | quoted-string ) - * token = 1* - * separators = "(" | ")" | "<" | ">" | "@" - * | "," | ";" | ":" | "\" | <"> - * | "/" | "[" | "]" | "?" | "=" - * | "{" | "}" | SP | HT - * quoted-string = ( <"> *(qdtext | quoted-pair ) <"> ) - * qdtext = > - * quoted-pair = "\" CHAR - * CHAR = - * TEXT = - * LWS = [CRLF] 1*( SP | HT ) - * CRLF = CR LF - * CR = - * LF = - * SP = - * HT = - * CTL = - * OCTET = - * @private - */ - -var PARAM_REGEXP = /;[\x09\x20]*([!#$%&'*+.0-9A-Z^_`a-z|~-]+)[\x09\x20]*=[\x09\x20]*("(?:[\x20!\x23-\x5b\x5d-\x7e\x80-\xff]|\\[\x20-\x7e])*"|[!#$%&'*+.0-9A-Z^_`a-z|~-]+)[\x09\x20]*/g // eslint-disable-line no-control-regex -var TEXT_REGEXP = /^[\x20-\x7e\x80-\xff]+$/ -var TOKEN_REGEXP = /^[!#$%&'*+.0-9A-Z^_`a-z|~-]+$/ - -/** - * RegExp for various RFC 5987 grammar - * - * ext-value = charset "'" [ language ] "'" value-chars - * charset = "UTF-8" / "ISO-8859-1" / mime-charset - * mime-charset = 1*mime-charsetc - * mime-charsetc = ALPHA / DIGIT - * / "!" / "#" / "$" / "%" / "&" - * / "+" / "-" / "^" / "_" / "`" - * / "{" / "}" / "~" - * language = ( 2*3ALPHA [ extlang ] ) - * / 4ALPHA - * / 5*8ALPHA - * extlang = *3( "-" 3ALPHA ) - * value-chars = *( pct-encoded / attr-char ) - * pct-encoded = "%" HEXDIG HEXDIG - * attr-char = ALPHA / DIGIT - * / "!" / "#" / "$" / "&" / "+" / "-" / "." - * / "^" / "_" / "`" / "|" / "~" - * @private - */ - -var EXT_VALUE_REGEXP = /^([A-Za-z0-9!#$%&+\-^_`{}~]+)'(?:[A-Za-z]{2,3}(?:-[A-Za-z]{3}){0,3}|[A-Za-z]{4,8}|)'((?:%[0-9A-Fa-f]{2}|[A-Za-z0-9!#$&+.^_`|~-])+)$/ - -/** - * RegExp for various RFC 6266 grammar - * - * disposition-type = "inline" | "attachment" | disp-ext-type - * disp-ext-type = token - * disposition-parm = filename-parm | disp-ext-parm - * filename-parm = "filename" "=" value - * | "filename*" "=" ext-value - * disp-ext-parm = token "=" value - * | ext-token "=" ext-value - * ext-token = - * @private - */ - -var DISPOSITION_TYPE_REGEXP = /^([!#$%&'*+.0-9A-Z^_`a-z|~-]+)[\x09\x20]*(?:$|;)/ // eslint-disable-line no-control-regex - -/** - * Create an attachment Content-Disposition header. - * - * @param {string} [filename] - * @param {object} [options] - * @param {string} [options.type=attachment] - * @param {string|boolean} [options.fallback=true] - * @return {string} - * @public - */ - -function contentDisposition (filename, options) { - var opts = options || {} - - // get type - var type = opts.type || 'attachment' - - // get parameters - var params = createparams(filename, opts.fallback) - - // format into string - return format(new ContentDisposition(type, params)) -} - -/** - * Create parameters object from filename and fallback. - * - * @param {string} [filename] - * @param {string|boolean} [fallback=true] - * @return {object} - * @private - */ - -function createparams (filename, fallback) { - if (filename === undefined) { - return - } - - var params = {} - - if (typeof filename !== 'string') { - throw new TypeError('filename must be a string') - } - - // fallback defaults to true - if (fallback === undefined) { - fallback = true - } - - if (typeof fallback !== 'string' && typeof fallback !== 'boolean') { - throw new TypeError('fallback must be a string or boolean') - } - - if (typeof fallback === 'string' && NON_LATIN1_REGEXP.test(fallback)) { - throw new TypeError('fallback must be ISO-8859-1 string') - } - - // restrict to file base name - var name = basename(filename) - - // determine if name is suitable for quoted string - var isQuotedString = TEXT_REGEXP.test(name) - - // generate fallback name - var fallbackName = typeof fallback !== 'string' - ? fallback && getlatin1(name) - : basename(fallback) - var hasFallback = typeof fallbackName === 'string' && fallbackName !== name - - // set extended filename parameter - if (hasFallback || !isQuotedString || HEX_ESCAPE_REGEXP.test(name)) { - params['filename*'] = name - } - - // set filename parameter - if (isQuotedString || hasFallback) { - params.filename = hasFallback - ? fallbackName - : name - } - - return params -} - -/** - * Format object to Content-Disposition header. - * - * @param {object} obj - * @param {string} obj.type - * @param {object} [obj.parameters] - * @return {string} - * @private - */ - -function format (obj) { - var parameters = obj.parameters - var type = obj.type - - if (!type || typeof type !== 'string' || !TOKEN_REGEXP.test(type)) { - throw new TypeError('invalid type') - } - - // start with normalized type - var string = String(type).toLowerCase() - - // append parameters - if (parameters && typeof parameters === 'object') { - var param - var params = Object.keys(parameters).sort() - - for (var i = 0; i < params.length; i++) { - param = params[i] - - var val = param.substr(-1) === '*' - ? ustring(parameters[param]) - : qstring(parameters[param]) - - string += '; ' + param + '=' + val - } - } - - return string -} - -/** - * Decode a RFC 5987 field value (gracefully). - * - * @param {string} str - * @return {string} - * @private - */ - -function decodefield (str) { - var match = EXT_VALUE_REGEXP.exec(str) - - if (!match) { - throw new TypeError('invalid extended field value') - } - - var charset = match[1].toLowerCase() - var encoded = match[2] - var value - - // to binary string - var binary = encoded.replace(HEX_ESCAPE_REPLACE_REGEXP, pdecode) - - switch (charset) { - case 'iso-8859-1': - value = getlatin1(binary) - break - case 'utf-8': - value = Buffer.from(binary, 'binary').toString('utf8') - break - default: - throw new TypeError('unsupported charset in extended field') - } - - return value -} - -/** - * Get ISO-8859-1 version of string. - * - * @param {string} val - * @return {string} - * @private - */ - -function getlatin1 (val) { - // simple Unicode -> ISO-8859-1 transformation - return String(val).replace(NON_LATIN1_REGEXP, '?') -} - -/** - * Parse Content-Disposition header string. - * - * @param {string} string - * @return {object} - * @public - */ - -function parse (string) { - if (!string || typeof string !== 'string') { - throw new TypeError('argument string is required') - } - - var match = DISPOSITION_TYPE_REGEXP.exec(string) - - if (!match) { - throw new TypeError('invalid type format') - } - - // normalize type - var index = match[0].length - var type = match[1].toLowerCase() - - var key - var names = [] - var params = {} - var value - - // calculate index to start at - index = PARAM_REGEXP.lastIndex = match[0].substr(-1) === ';' - ? index - 1 - : index - - // match parameters - while ((match = PARAM_REGEXP.exec(string))) { - if (match.index !== index) { - throw new TypeError('invalid parameter format') - } - - index += match[0].length - key = match[1].toLowerCase() - value = match[2] - - if (names.indexOf(key) !== -1) { - throw new TypeError('invalid duplicate parameter') - } - - names.push(key) - - if (key.indexOf('*') + 1 === key.length) { - // decode extended value - key = key.slice(0, -1) - value = decodefield(value) - - // overwrite existing value - params[key] = value - continue - } - - if (typeof params[key] === 'string') { - continue - } - - if (value[0] === '"') { - // remove quotes and escapes - value = value - .substr(1, value.length - 2) - .replace(QESC_REGEXP, '$1') - } - - params[key] = value - } - - if (index !== -1 && index !== string.length) { - throw new TypeError('invalid parameter format') - } - - return new ContentDisposition(type, params) -} - -/** - * Percent decode a single character. - * - * @param {string} str - * @param {string} hex - * @return {string} - * @private - */ - -function pdecode (str, hex) { - return String.fromCharCode(parseInt(hex, 16)) -} - -/** - * Percent encode a single character. - * - * @param {string} char - * @return {string} - * @private - */ - -function pencode (char) { - return '%' + String(char) - .charCodeAt(0) - .toString(16) - .toUpperCase() -} - -/** - * Quote a string for HTTP. - * - * @param {string} val - * @return {string} - * @private - */ - -function qstring (val) { - var str = String(val) - - return '"' + str.replace(QUOTE_REGEXP, '\\$1') + '"' -} - -/** - * Encode a Unicode string for HTTP (RFC 5987). - * - * @param {string} val - * @return {string} - * @private - */ - -function ustring (val) { - var str = String(val) - - // percent encode as UTF-8 - 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- diff --git a/spaces/robin0307/MMOCR/configs/_base_/det_models/dbnetpp_r50dcnv2_fpnc.py b/spaces/robin0307/MMOCR/configs/_base_/det_models/dbnetpp_r50dcnv2_fpnc.py deleted file mode 100644 index f8eaf2ffd6efe9fa4ad63f5dc208f9b134a38380..0000000000000000000000000000000000000000 --- a/spaces/robin0307/MMOCR/configs/_base_/det_models/dbnetpp_r50dcnv2_fpnc.py +++ /dev/null @@ -1,28 +0,0 @@ -model = dict( - type='DBNet', - backbone=dict( - type='mmdet.ResNet', - depth=50, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=-1, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=False, - style='pytorch', - dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), - init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), - stage_with_dcn=(False, True, True, True)), - neck=dict( - type='FPNC', - in_channels=[256, 512, 1024, 2048], - lateral_channels=256, - asf_cfg=dict(attention_type='ScaleChannelSpatial')), - bbox_head=dict( - type='DBHead', - in_channels=256, - loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True), - postprocessor=dict( - type='DBPostprocessor', text_repr_type='quad', - epsilon_ratio=0.002)), - train_cfg=None, - test_cfg=None) diff --git a/spaces/rorallitri/biomedical-language-models/logs/Bahubali - The Beginning Movie English Sub Free Download.md b/spaces/rorallitri/biomedical-language-models/logs/Bahubali - The Beginning Movie English Sub Free Download.md deleted file mode 100644 index 0e81635177a7fbf59b4ec6beca7f1ab82b36163c..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/Bahubali - The Beginning Movie English Sub Free Download.md +++ /dev/null @@ -1,11 +0,0 @@ -Bahubali - The Beginning movie english sub free download
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-We now differentiate between the requirements for new and revised submissions. Only when your paper is at the revision stage, will you be requested to put your paper in to a 'correct format' for acceptance and provide the items required for the publication of your article (you can view templates demonstrating the final layout of the title page, main text, tables and supplementary online material). However, please note that submissions should not include any information in the main text, tables and figures that could lead to the author(s) being identified by reviewers, given the double-anonymized peer review process adopted by the journal.
-
To find out more, please visit the Preparation section below.Virtual special issues (VSIs). VSIs are devoted to particular topics or themes that fall within the purview of the journal. For VSIs, presubmission inquiries to the Editors, including detailed special issue proposals, are mandatory. JHE publishes a maximum of one VSI per year. The proposal should include the following: names and affiliations of all proposed Guest Editors (GE) and their CVs, VSI title, aim and scope of the VSI and its current relevance to JHE readership, and number of anticipated submissions including title and author(s) of each submission, and abstracts (please keep in mind the 300-word limit for abstracts). The proposal should indicate the papers that will be handled by each GE. Each VSI should have an Editorial that places the papers in collective context and may have a closing article though this is not mandatory. Please note that acceptance of a VSI is not a guarantee that all submissions for that VSI will be accepted for publication. The manuscripts should be submitted under the category with the title of the VSI. All submissions are subject to the normal peer-review process with the exception of the Editorial, which should be submitted directly to the Editor-in-Chief handling the VSI.
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-The body of the manuscript of a Research Article should be submitted in editable Word format. JHE allows for primary, secondary and tertiary headers. The format of these headers is as follows:
JHE allows for primary, secondary and tertiary headers. The format of these headers is as follows:
1. Primary section header (capitalize only the first word of the primary header and use bold)
Text begins here.
1.1.Secondary section header (italics, no bold or underline, but please do not italicize genera/species names)
Text begins here.
Tertiary section header Text begins here.
The manuscript should thus adhere to the following formatting:- Introduction
- Materials and methods
Users of Mendeley Desktop can easily install the reference style for this journal by clicking the following link: -citation-style/journal-of-human-evolution When preparing your manuscript, you will then be able to select this style using the Mendeley plug-ins for Microsoft Word or LibreOffice.
-A separate primary section, numbered and entitled Systematics (for extant taxa) or Systematic paleontology (for extinct taxa) is mandatory. This section may substitute the standard Results section; if not, then the latter must be placed immediately after the systematic section. This section primary heading should be followed by successive taxonomic subheadings in round type that do not adhere to the style of secondary or tertiary headings of the journal, and which must include rank (but see below), taxon name, and taxonomic authorships and/or specifications about the novelty of taxa (see abbreviations below), without dot at the end. For example:
-The use of taxon ranks is mandatory when ruled by the Code (i.e., family-, genus- or species-group taxa), and optional for higher-level ranks not ruled by the Code (e.g., class, order?). New taxa must be explicitly denoted as such after taxon name (e.g., sp. nov.), whereas new rank must be explicitly specified after taxonomic authorship. A taxonomic subheading can be followed, on next line, by another taxonomic subheading (of lower rank), a synonyms list, or one or more secondary or tertiary headings (in accordance to the journal style), depending on the case. Tertiary headings will be used except for longer sub-sections of 'Description', 'Comparisons' or 'Remarks', which must be under secondary headings and may be alternatively placed within a separate Results section (if any). The erection of new family- and genus-group taxa must be accompanied at least by a designation of the type genus or species, respectively, whereas the erection of new species-group taxa must obligatorily include a succinct diagnosis (without references, and preceded by a tertiary heading), which must be differential unless a separate differential diagnosis is provided.
-
If applicable, systematic headings of species-group taxa may be followed by a synonyms list with references. References cited within the synonyms list(s) must be included in the references list of the paper, but their citation does not adhere to the standard citation style of the journal. Only relevant synonyms must be included (e.g., original description, new rank, genus transfer, etc.). Each synonym must be placed on a text line of its own, flushed to the left, beginning with year, followed by colon, taxon name, authorship without year, semicolon plus citation (if different from authorship), colon, plates and/or figures and comma (if applicable), and page range, ending with a dot.
-Taxononic authorities (or authorships) should be provided following a taxon name (especially for ranks of the genus-and species-groups) when it is first mentioned in the text, as well as within systematic headings within a formal Systematics or Systematic paleontology section. References for taxonomic authorships used in the text must be included in the references list following the same style as other references. Within the text, taxonomic authorships should follow the recommendations of the Code if they go against the journal's citation style. Therefore, the name of author(s) must be separated from year of original description with a comma, but parentheses can only be used for species-group names when they were originally erected within a different genus from that currently used. For authors, only surnames will be used, unless initials are required to distinguish different authors. For two authors, Australopithecus must be separated by 'and' (instead of ampersand); for three or more authors, the name of first author followed by 'et al.' should be used as long as the full list of authors can be unambiguously identified in the references list.
- -Although registration in ZooBank is only mandatory for online-only taxonomic publications, and Journal of Human Evolution is still printed, taxonomic papers published in the journal should be registered in ZooBank, and evidence of such registration included within the published version of the paper. Registration in ZooBank is the responsibility of authors, once the paper has been provisionally accepted for publication, so that the details to be included in the published version of the paper must be provided by authors before its final formal acceptance. These details must include the date of registration and the life science identifier (LSID) of the publication. Authors must register their papers as in press, and subsequently update the citation details one their paper is published. Authors are also encouraged to register new taxa in ZooBank, and provide their LSID below the corresponding systematic headings in their papers, but this is not mandatory. The digital repositories (e.g., CLOCKSS) that partner with Elsevier (to be selected by authors of Journal of Human Evolution during ZooBank registration) can be consulted here:
-Research Elements is a suite of peer-reviewed, open access journals which make your research objects findable, accessible and reusable. Articles place research objects into context by providing detailed descriptions of objects and their application, and linking to the associated original research articles. Research Elements articles can be prepared by you, or by one of your collaborators.
-The journal publishes impactful articles of any length, including literature reviews, meta-analyses, replications, theoretical notes, and commentaries on previously published works in the Journal of Experimental Psychology: Learning, Memory, and Cognition.
-The editorial policy of the journal encompasses integrative articles containing multiple experiments as well as articles reporting single experiments. The journal also publishes commentaries and theoretical notes. Please note that theoretical notes are limited to a maximum of 25 pages of text. Commentaries on articles should be at maximum half the length of the target article.
-JEP:LMC is now using a software system to screen submitted content for similarity with other published content. The system compares the initial version of each submitted manuscript against a database of 40+ million scholarly documents, as well as content appearing on the open web. This allows APA to check submissions for potential overlap with material previously published in scholarly journals (e.g., lifted or republished material).
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- diff --git a/spaces/rpa45/ai_hands_classifier/README.md b/spaces/rpa45/ai_hands_classifier/README.md deleted file mode 100644 index b409c6a40a8597f5a7c1176086b55a360c4db8d1..0000000000000000000000000000000000000000 --- a/spaces/rpa45/ai_hands_classifier/README.md +++ /dev/null @@ -1,17 +0,0 @@ ---- -title: Ai Hands Classifier -emoji: 💻 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false -license: openrail ---- - -Simple classification model built with ResNet architecture to tell the difference between human and ai-generated hand. -
-Accuracy on holdout set = 0.89 - -
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diff --git a/spaces/runa91/bite_gradio/src/combined_model/train_main_image_to_3d_withbreedrel.py b/spaces/runa91/bite_gradio/src/combined_model/train_main_image_to_3d_withbreedrel.py
deleted file mode 100644
index 59308fa38badbbce05250edd44b8435a1896838d..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/src/combined_model/train_main_image_to_3d_withbreedrel.py
+++ /dev/null
@@ -1,496 +0,0 @@
-
-import torch
-import torch.nn as nn
-import torch.backends.cudnn
-import torch.nn.parallel
-from tqdm import tqdm
-import os
-import pathlib
-from matplotlib import pyplot as plt
-import cv2
-import numpy as np
-import torch
-import trimesh
-
-import sys
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..'))
-from stacked_hourglass.utils.evaluation import accuracy, AverageMeter, final_preds, get_preds, get_preds_soft
-from stacked_hourglass.utils.visualization import save_input_image_with_keypoints, save_input_image
-from metrics.metrics import Metrics
-from configs.SMAL_configs import EVAL_KEYPOINTS, KEYPOINT_GROUPS
-
-
-# ---------------------------------------------------------------------------------------------------------------------------
-def do_training_epoch(train_loader, model, loss_module, device, data_info, optimiser, quiet=False, acc_joints=None, weight_dict=None):
- losses = AverageMeter()
- losses_keyp = AverageMeter()
- losses_silh = AverageMeter()
- losses_shape = AverageMeter()
- losses_pose = AverageMeter()
- losses_class = AverageMeter()
- losses_breed = AverageMeter()
- losses_partseg = AverageMeter()
- accuracies = AverageMeter()
- # Put the model in training mode.
- model.train()
- # prepare progress bar
- iterable = enumerate(train_loader)
- progress = None
- if not quiet:
- progress = tqdm(iterable, desc='Train', total=len(train_loader), ascii=True, leave=False)
- iterable = progress
- # information for normalization
- norm_dict = {
- 'pose_rot6d_mean': torch.from_numpy(data_info.pose_rot6d_mean).float().to(device),
- 'trans_mean': torch.from_numpy(data_info.trans_mean).float().to(device),
- 'trans_std': torch.from_numpy(data_info.trans_std).float().to(device),
- 'flength_mean': torch.from_numpy(data_info.flength_mean).float().to(device),
- 'flength_std': torch.from_numpy(data_info.flength_std).float().to(device)}
- # prepare variables, put them on the right device
- for i, (input, target_dict) in iterable:
- batch_size = input.shape[0]
- for key in target_dict.keys():
- if key == 'breed_index':
- target_dict[key] = target_dict[key].long().to(device)
- elif key in ['index', 'pts', 'tpts', 'target_weight', 'silh', 'silh_distmat_tofg', 'silh_distmat_tobg', 'sim_breed_index', 'img_border_mask']:
- target_dict[key] = target_dict[key].float().to(device)
- elif key in ['has_seg', 'gc']:
- target_dict[key] = target_dict[key].to(device)
- else:
- pass
- input = input.float().to(device)
-
- # ----------------------- do training step -----------------------
- assert model.training, 'model must be in training mode.'
- with torch.enable_grad():
- # ----- forward pass -----
- output, output_unnorm, output_reproj = model(input, norm_dict=norm_dict)
- # ----- loss -----
- loss, loss_dict = loss_module(output_reproj=output_reproj,
- target_dict=target_dict,
- weight_dict=weight_dict)
- # ----- backward pass and parameter update -----
- optimiser.zero_grad()
- loss.backward()
- optimiser.step()
- # ----------------------------------------------------------------
-
- # prepare losses for progress bar
- bs_fake = 1 # batch_size
- losses.update(loss_dict['loss'], bs_fake)
- losses_keyp.update(loss_dict['loss_keyp_weighted'], bs_fake)
- losses_silh.update(loss_dict['loss_silh_weighted'], bs_fake)
- losses_shape.update(loss_dict['loss_shape_weighted'], bs_fake)
- losses_pose.update(loss_dict['loss_poseprior_weighted'], bs_fake)
- losses_class.update(loss_dict['loss_class_weighted'], bs_fake)
- losses_breed.update(loss_dict['loss_breed_weighted'], bs_fake)
- losses_partseg.update(loss_dict['loss_partseg_weighted'], bs_fake)
- acc = - loss_dict['loss_keyp_weighted'] # this will be used to keep track of the 'best model'
- accuracies.update(acc, bs_fake)
- # Show losses as part of the progress bar.
- if progress is not None:
- my_string = 'Loss: {loss:0.4f}, loss_keyp: {loss_keyp:0.4f}, loss_silh: {loss_silh:0.4f}, loss_partseg: {loss_partseg:0.4f}, loss_shape: {loss_shape:0.4f}, loss_pose: {loss_pose:0.4f}, loss_class: {loss_class:0.4f}, loss_breed: {loss_breed:0.4f}'.format(
- loss=losses.avg,
- loss_keyp=losses_keyp.avg,
- loss_silh=losses_silh.avg,
- loss_shape=losses_shape.avg,
- loss_pose=losses_pose.avg,
- loss_class=losses_class.avg,
- loss_breed=losses_breed.avg,
- loss_partseg=losses_partseg.avg
- )
- progress.set_postfix_str(my_string)
-
- return my_string, accuracies.avg
-
-
-# ---------------------------------------------------------------------------------------------------------------------------
-def do_validation_epoch(val_loader, model, loss_module, device, data_info, flip=False, quiet=False, acc_joints=None, save_imgs_path=None, weight_dict=None, metrics=None, val_opt='default', test_name_list=None, render_all=False, pck_thresh=0.15, len_dataset=None):
- losses = AverageMeter()
- losses_keyp = AverageMeter()
- losses_silh = AverageMeter()
- losses_shape = AverageMeter()
- losses_pose = AverageMeter()
- losses_class = AverageMeter()
- losses_breed = AverageMeter()
- losses_partseg = AverageMeter()
- accuracies = AverageMeter()
- if save_imgs_path is not None:
- pathlib.Path(save_imgs_path).mkdir(parents=True, exist_ok=True)
- # Put the model in evaluation mode.
- model.eval()
- # prepare progress bar
- iterable = enumerate(val_loader)
- progress = None
- if not quiet:
- progress = tqdm(iterable, desc='Valid', total=len(val_loader), ascii=True, leave=False)
- iterable = progress
- # summarize information for normalization
- norm_dict = {
- 'pose_rot6d_mean': torch.from_numpy(data_info.pose_rot6d_mean).float().to(device),
- 'trans_mean': torch.from_numpy(data_info.trans_mean).float().to(device),
- 'trans_std': torch.from_numpy(data_info.trans_std).float().to(device),
- 'flength_mean': torch.from_numpy(data_info.flength_mean).float().to(device),
- 'flength_std': torch.from_numpy(data_info.flength_std).float().to(device)}
- batch_size = val_loader.batch_size
- # prepare variables, put them on the right device
- my_step = 0
- for i, (input, target_dict) in iterable:
- curr_batch_size = input.shape[0]
- for key in target_dict.keys():
- if key == 'breed_index':
- target_dict[key] = target_dict[key].long().to(device)
- elif key in ['index', 'pts', 'tpts', 'target_weight', 'silh', 'silh_distmat_tofg', 'silh_distmat_tobg', 'sim_breed_index', 'img_border_mask']:
- target_dict[key] = target_dict[key].float().to(device)
- elif key in ['has_seg', 'gc']:
- target_dict[key] = target_dict[key].to(device)
- else:
- pass
- input = input.float().to(device)
-
- # ----------------------- do validation step -----------------------
- with torch.no_grad():
- # ----- forward pass -----
- # output: (['pose', 'flength', 'trans', 'keypoints_norm', 'keypoints_scores'])
- # output_unnorm: (['pose_rotmat', 'flength', 'trans', 'keypoints'])
- # output_reproj: (['vertices_smal', 'torch_meshes', 'keyp_3d', 'keyp_2d', 'silh', 'betas', 'pose_rot6d', 'dog_breed', 'shapedirs', 'z', 'flength_unnorm', 'flength'])
- # target_dict: (['index', 'center', 'scale', 'pts', 'tpts', 'target_weight', 'breed_index', 'sim_breed_index', 'ind_dataset', 'silh'])
- output, output_unnorm, output_reproj = model(input, norm_dict=norm_dict)
- # ----- loss -----
- if metrics == 'no_loss':
- loss, loss_dict = loss_module(output_reproj=output_reproj,
- target_dict=target_dict,
- weight_dict=weight_dict)
- # ----------------------------------------------------------------
-
- if i == 0:
- if len_dataset is None:
- len_data = val_loader.batch_size * len(val_loader) # 1703
- else:
- len_data = len_dataset
- if metrics == 'all' or metrics == 'no_loss':
- pck = np.zeros((len_data))
- pck_by_part = {group:np.zeros((len_data)) for group in KEYPOINT_GROUPS}
- acc_sil_2d = np.zeros(len_data)
-
- all_betas = np.zeros((len_data, output_reproj['betas'].shape[1]))
- all_betas_limbs = np.zeros((len_data, output_reproj['betas_limbs'].shape[1]))
- all_z = np.zeros((len_data, output_reproj['z'].shape[1]))
- all_pose_rotmat = np.zeros((len_data, output_unnorm['pose_rotmat'].shape[1], 3, 3))
- all_flength = np.zeros((len_data, output_unnorm['flength'].shape[1]))
- all_trans = np.zeros((len_data, output_unnorm['trans'].shape[1]))
- all_breed_indices = np.zeros((len_data))
- all_image_names = [] # len_data * [None]
-
- index = i
- ind_img = 0
- if save_imgs_path is not None:
- # render predicted 3d models
- visualizations = model.render_vis_nograd(vertices=output_reproj['vertices_smal'],
- focal_lengths=output_unnorm['flength'],
- color=0) # color=2)
- for ind_img in range(len(target_dict['index'])):
- try:
- if test_name_list is not None:
- img_name = test_name_list[int(target_dict['index'][ind_img].cpu().detach().numpy())].replace('/', '_')
- img_name = img_name.split('.')[0]
- else:
- img_name = str(index) + '_' + str(ind_img)
- # save image with predicted keypoints
- out_path = save_imgs_path + '/keypoints_pred_' + img_name + '.png'
- pred_unp = (output['keypoints_norm'][ind_img, :, :] + 1.) / 2 * (data_info.image_size - 1)
- pred_unp_maxval = output['keypoints_scores'][ind_img, :, :]
- pred_unp_prep = torch.cat((pred_unp, pred_unp_maxval), 1)
- inp_img = input[ind_img, :, :, :].detach().clone()
- save_input_image_with_keypoints(inp_img, pred_unp_prep, out_path=out_path, threshold=0.1, print_scores=True, ratio_in_out=1.0) # threshold=0.3
- # save predicted 3d model (front view)
- pred_tex = visualizations[ind_img, :, :, :].permute((1, 2, 0)).cpu().detach().numpy() / 256
- pred_tex_max = np.max(pred_tex, axis=2)
- out_path = save_imgs_path + '/tex_pred_' + img_name + '.png'
- plt.imsave(out_path, pred_tex)
- input_image = input[ind_img, :, :, :].detach().clone()
- for t, m, s in zip(input_image, data_info.rgb_mean, data_info.rgb_stddev): t.add_(m)
- input_image_np = input_image.detach().cpu().numpy().transpose(1, 2, 0)
- im_masked = cv2.addWeighted(input_image_np,0.2,pred_tex,0.8,0)
- im_masked[pred_tex_max<0.01, :] = input_image_np[pred_tex_max<0.01, :]
- out_path = save_imgs_path + '/comp_pred_' + img_name + '.png'
- plt.imsave(out_path, im_masked)
- # save predicted 3d model (side view)
- vertices_cent = output_reproj['vertices_smal'] - output_reproj['vertices_smal'].mean(dim=1)[:, None, :]
- roll = np.pi / 2 * torch.ones(1).float().to(device)
- pitch = np.pi / 2 * torch.ones(1).float().to(device)
- tensor_0 = torch.zeros(1).float().to(device)
- tensor_1 = torch.ones(1).float().to(device)
- RX = torch.stack([torch.stack([tensor_1, tensor_0, tensor_0]), torch.stack([tensor_0, torch.cos(roll), -torch.sin(roll)]),torch.stack([tensor_0, torch.sin(roll), torch.cos(roll)])]).reshape(3,3)
- RY = torch.stack([
- torch.stack([torch.cos(pitch), tensor_0, torch.sin(pitch)]),
- torch.stack([tensor_0, tensor_1, tensor_0]),
- torch.stack([-torch.sin(pitch), tensor_0, torch.cos(pitch)])]).reshape(3,3)
- vertices_rot = (torch.matmul(RY, vertices_cent.reshape((-1, 3))[:, :, None])).reshape((curr_batch_size, -1, 3))
- vertices_rot[:, :, 2] = vertices_rot[:, :, 2] + torch.ones_like(vertices_rot[:, :, 2]) * 20 # 18 # *16
-
- visualizations_rot = model.render_vis_nograd(vertices=vertices_rot,
- focal_lengths=output_unnorm['flength'],
- color=0) # 2)
- pred_tex = visualizations_rot[ind_img, :, :, :].permute((1, 2, 0)).cpu().detach().numpy() / 256
- pred_tex_max = np.max(pred_tex, axis=2)
- out_path = save_imgs_path + '/rot_tex_pred_' + img_name + '.png'
- plt.imsave(out_path, pred_tex)
- if render_all:
- # save input image
- inp_img = input[ind_img, :, :, :].detach().clone()
- out_path = save_imgs_path + '/image_' + img_name + '.png'
- save_input_image(inp_img, out_path)
- # save mesh
- V_posed = output_reproj['vertices_smal'][ind_img, :, :].detach().cpu().numpy()
- Faces = model.smal.f
- mesh_posed = trimesh.Trimesh(vertices=V_posed, faces=Faces, process=False, maintain_order=True)
- mesh_posed.export(save_imgs_path + '/mesh_posed_' + img_name + '.obj')
- except:
- print('dont save an image')
-
- if metrics == 'all' or metrics == 'no_loss':
- # prepare a dictionary with all the predicted results
- preds = {}
- preds['betas'] = output_reproj['betas'].cpu().detach().numpy()
- preds['betas_limbs'] = output_reproj['betas_limbs'].cpu().detach().numpy()
- preds['z'] = output_reproj['z'].cpu().detach().numpy()
- preds['pose_rotmat'] = output_unnorm['pose_rotmat'].cpu().detach().numpy()
- preds['flength'] = output_unnorm['flength'].cpu().detach().numpy()
- preds['trans'] = output_unnorm['trans'].cpu().detach().numpy()
- preds['breed_index'] = target_dict['breed_index'].cpu().detach().numpy().reshape((-1))
- img_names = []
- for ind_img2 in range(0, output_reproj['betas'].shape[0]):
- if test_name_list is not None:
- img_name2 = test_name_list[int(target_dict['index'][ind_img2].cpu().detach().numpy())].replace('/', '_')
- img_name2 = img_name2.split('.')[0]
- else:
- img_name2 = str(index) + '_' + str(ind_img2)
- img_names.append(img_name2)
- preds['image_names'] = img_names
- # prepare keypoints for PCK calculation - predicted as well as ground truth
- pred_keypoints_norm = output['keypoints_norm'] # -1 to 1
- pred_keypoints_256 = output_reproj['keyp_2d']
- pred_keypoints = pred_keypoints_256
- gt_keypoints_256 = target_dict['tpts'][:, :, :2] / 64. * (256. - 1)
- gt_keypoints_norm = gt_keypoints_256 / 256 / 0.5 - 1
- gt_keypoints = torch.cat((gt_keypoints_256, target_dict['tpts'][:, :, 2:3]), dim=2) # gt_keypoints_norm
- # prepare silhouette for IoU calculation - predicted as well as ground truth
- has_seg = target_dict['has_seg']
- img_border_mask = target_dict['img_border_mask'][:, 0, :, :]
- gtseg = target_dict['silh']
- synth_silhouettes = output_reproj['silh'][:, 0, :, :] # output_reproj['silh']
- synth_silhouettes[synth_silhouettes>0.5] = 1
- synth_silhouettes[synth_silhouettes<0.5] = 0
- # calculate PCK as well as IoU (similar to WLDO)
- preds['acc_PCK'] = Metrics.PCK(
- pred_keypoints, gt_keypoints,
- gtseg, has_seg, idxs=EVAL_KEYPOINTS,
- thresh_range=[pck_thresh], # [0.15],
- )
- preds['acc_IOU'] = Metrics.IOU(
- synth_silhouettes, gtseg,
- img_border_mask, mask=has_seg
- )
- for group, group_kps in KEYPOINT_GROUPS.items():
- preds[f'{group}_PCK'] = Metrics.PCK(
- pred_keypoints, gt_keypoints, gtseg, has_seg,
- thresh_range=[pck_thresh], # [0.15],
- idxs=group_kps
- )
- # add results for all images in this batch to lists
- curr_batch_size = pred_keypoints_256.shape[0]
- if not (preds['acc_PCK'].data.cpu().numpy().shape == (pck[my_step * batch_size:my_step * batch_size + curr_batch_size]).shape):
- import pdb; pdb.set_trace()
- pck[my_step * batch_size:my_step * batch_size + curr_batch_size] = preds['acc_PCK'].data.cpu().numpy()
- acc_sil_2d[my_step * batch_size:my_step * batch_size + curr_batch_size] = preds['acc_IOU'].data.cpu().numpy()
- for part in pck_by_part:
- pck_by_part[part][my_step * batch_size:my_step * batch_size + curr_batch_size] = preds[f'{part}_PCK'].data.cpu().numpy()
- all_betas[my_step * batch_size:my_step * batch_size + curr_batch_size, ...] = preds['betas']
- all_betas_limbs[my_step * batch_size:my_step * batch_size + curr_batch_size, ...] = preds['betas_limbs']
- all_z[my_step * batch_size:my_step * batch_size + curr_batch_size, ...] = preds['z']
- all_pose_rotmat[my_step * batch_size:my_step * batch_size + curr_batch_size, ...] = preds['pose_rotmat']
- all_flength[my_step * batch_size:my_step * batch_size + curr_batch_size, ...] = preds['flength']
- all_trans[my_step * batch_size:my_step * batch_size + curr_batch_size, ...] = preds['trans']
- all_breed_indices[my_step * batch_size:my_step * batch_size + curr_batch_size] = preds['breed_index']
- all_image_names.extend(preds['image_names'])
- # update progress bar
- if progress is not None:
- my_string = "PCK: {0:.2f}, IOU: {1:.2f}".format(
- pck[:(my_step * batch_size + curr_batch_size)].mean(),
- acc_sil_2d[:(my_step * batch_size + curr_batch_size)].mean())
- progress.set_postfix_str(my_string)
- else:
- # measure accuracy and record loss
- bs_fake = 1 # batch_size
- losses.update(loss_dict['loss'], bs_fake)
- losses_keyp.update(loss_dict['loss_keyp_weighted'], bs_fake)
- losses_silh.update(loss_dict['loss_silh_weighted'], bs_fake)
- losses_shape.update(loss_dict['loss_shape_weighted'], bs_fake)
- losses_pose.update(loss_dict['loss_poseprior_weighted'], bs_fake)
- losses_class.update(loss_dict['loss_class_weighted'], bs_fake)
- losses_breed.update(loss_dict['loss_breed_weighted'], bs_fake)
- losses_partseg.update(loss_dict['loss_partseg_weighted'], bs_fake)
- acc = - loss_dict['loss_keyp_weighted'] # this will be used to keep track of the 'best model'
- accuracies.update(acc, bs_fake)
- # Show losses as part of the progress bar.
- if progress is not None:
- my_string = 'Loss: {loss:0.4f}, loss_keyp: {loss_keyp:0.4f}, loss_silh: {loss_silh:0.4f}, loss_partseg: {loss_partseg:0.4f}, loss_shape: {loss_shape:0.4f}, loss_pose: {loss_pose:0.4f}, loss_class: {loss_class:0.4f}, loss_breed: {loss_breed:0.4f}'.format(
- loss=losses.avg,
- loss_keyp=losses_keyp.avg,
- loss_silh=losses_silh.avg,
- loss_shape=losses_shape.avg,
- loss_pose=losses_pose.avg,
- loss_class=losses_class.avg,
- loss_breed=losses_breed.avg,
- loss_partseg=losses_partseg.avg
- )
- progress.set_postfix_str(my_string)
- my_step += 1
- if metrics == 'all':
- summary = {'pck': pck, 'acc_sil_2d': acc_sil_2d, 'pck_by_part':pck_by_part,
- 'betas': all_betas, 'betas_limbs': all_betas_limbs, 'z': all_z, 'pose_rotmat': all_pose_rotmat,
- 'flenght': all_flength, 'trans': all_trans, 'image_names': all_image_names, 'breed_indices': all_breed_indices}
- return my_string, summary
- elif metrics == 'no_loss':
- return my_string, np.average(np.asarray(acc_sil_2d))
- else:
- return my_string, accuracies.avg
-
-
-# ---------------------------------------------------------------------------------------------------------------------------
-def do_visual_epoch(val_loader, model, device, data_info, flip=False, quiet=False, acc_joints=None, save_imgs_path=None, weight_dict=None, metrics=None, val_opt='default', test_name_list=None, render_all=False, pck_thresh=0.15, return_results=False):
- if save_imgs_path is not None:
- pathlib.Path(save_imgs_path).mkdir(parents=True, exist_ok=True)
- all_results = []
-
- # Put the model in evaluation mode.
- model.eval()
-
- iterable = enumerate(val_loader)
-
- # information for normalization
- norm_dict = {
- 'pose_rot6d_mean': torch.from_numpy(data_info.pose_rot6d_mean).float().to(device),
- 'trans_mean': torch.from_numpy(data_info.trans_mean).float().to(device),
- 'trans_std': torch.from_numpy(data_info.trans_std).float().to(device),
- 'flength_mean': torch.from_numpy(data_info.flength_mean).float().to(device),
- 'flength_std': torch.from_numpy(data_info.flength_std).float().to(device)}
-
- '''
- return_mesh_with_gt_groundplane = True
- if return_mesh_with_gt_groundplane:
- remeshing_path = '/is/cluster/work/nrueegg/icon_pifu_related/barc_for_bite/data/smal_data_remeshed/uniform_surface_sampling/my_smpl_39dogsnorm_Jr_4_dog_remesh4000_info.pkl'
- with open(remeshing_path, 'rb') as fp:
- remeshing_dict = pkl.load(fp)
- remeshing_relevant_faces = torch.tensor(remeshing_dict['smal_faces'][remeshing_dict['faceid_closest']], dtype=torch.long, device=device)
- remeshing_relevant_barys = torch.tensor(remeshing_dict['barys_closest'], dtype=torch.float32, device=device)
-
- # from smal_pytorch.smal_model.smal_torch_new import SMAL
- print('start: load smal default model (barc), but only for vertices')
- smal = SMAL()
- print('end: load smal default model (barc), but only for vertices')
- smal_template_verts = smal.v_template.detach().cpu().numpy()
- smal_faces = smal.faces.detach().cpu().numpy()
-
- file_alignment_errors = open(save_imgs_path + '/a_ref_procrustes_alignmnet_errors.txt', 'a') # append mode
- file_alignment_errors.write(" ----------- start evaluation ------------- \n ")
-
- csv_file_alignment_errors = open(save_imgs_path + '/a_ref_procrustes_alignmnet_errors.csv', 'w') # write mode
- fieldnames = ['name', 'error']
- writer = csv.DictWriter(csv_file_alignment_errors, fieldnames=fieldnames)
- writer.writeheader()
- '''
-
- my_step = 0
- for i, (input, target_dict) in iterable:
- batch_size = input.shape[0]
- input = input.float().to(device)
- partial_results = {}
-
- # ----------------------- do visualization step -----------------------
- with torch.no_grad():
- output, output_unnorm, output_reproj = model(input, norm_dict=norm_dict)
-
- index = i
- ind_img = 0
- for ind_img in range(batch_size): # range(min(12, batch_size)): # range(12): # [0]: #range(0, batch_size):
-
- try:
- if test_name_list is not None:
- img_name = test_name_list[int(target_dict['index'][ind_img].cpu().detach().numpy())].replace('/', '_')
- img_name = img_name.split('.')[0]
- else:
- img_name = str(index) + '_' + str(ind_img)
- partial_results['img_name'] = img_name
- visualizations = model.render_vis_nograd(vertices=output_reproj['vertices_smal'],
- focal_lengths=output_unnorm['flength'],
- color=0) # 2)
- # save image with predicted keypoints
- pred_unp = (output['keypoints_norm'][ind_img, :, :] + 1.) / 2 * (data_info.image_size - 1)
- pred_unp_maxval = output['keypoints_scores'][ind_img, :, :]
- pred_unp_prep = torch.cat((pred_unp, pred_unp_maxval), 1)
- inp_img = input[ind_img, :, :, :].detach().clone()
- if save_imgs_path is not None:
- out_path = save_imgs_path + '/keypoints_pred_' + img_name + '.png'
- save_input_image_with_keypoints(inp_img, pred_unp_prep, out_path=out_path, threshold=0.1, print_scores=True, ratio_in_out=1.0) # threshold=0.3
- # save predicted 3d model
- # (1) front view
- pred_tex = visualizations[ind_img, :, :, :].permute((1, 2, 0)).cpu().detach().numpy() / 256
- pred_tex_max = np.max(pred_tex, axis=2)
- partial_results['tex_pred'] = pred_tex
- if save_imgs_path is not None:
- out_path = save_imgs_path + '/tex_pred_' + img_name + '.png'
- plt.imsave(out_path, pred_tex)
- input_image = input[ind_img, :, :, :].detach().clone()
- for t, m, s in zip(input_image, data_info.rgb_mean, data_info.rgb_stddev): t.add_(m)
- input_image_np = input_image.detach().cpu().numpy().transpose(1, 2, 0)
- im_masked = cv2.addWeighted(input_image_np,0.2,pred_tex,0.8,0)
- im_masked[pred_tex_max<0.01, :] = input_image_np[pred_tex_max<0.01, :]
- partial_results['comp_pred'] = im_masked
- if save_imgs_path is not None:
- out_path = save_imgs_path + '/comp_pred_' + img_name + '.png'
- plt.imsave(out_path, im_masked)
- # (2) side view
- vertices_cent = output_reproj['vertices_smal'] - output_reproj['vertices_smal'].mean(dim=1)[:, None, :]
- roll = np.pi / 2 * torch.ones(1).float().to(device)
- pitch = np.pi / 2 * torch.ones(1).float().to(device)
- tensor_0 = torch.zeros(1).float().to(device)
- tensor_1 = torch.ones(1).float().to(device)
- RX = torch.stack([torch.stack([tensor_1, tensor_0, tensor_0]), torch.stack([tensor_0, torch.cos(roll), -torch.sin(roll)]),torch.stack([tensor_0, torch.sin(roll), torch.cos(roll)])]).reshape(3,3)
- RY = torch.stack([
- torch.stack([torch.cos(pitch), tensor_0, torch.sin(pitch)]),
- torch.stack([tensor_0, tensor_1, tensor_0]),
- torch.stack([-torch.sin(pitch), tensor_0, torch.cos(pitch)])]).reshape(3,3)
- vertices_rot = (torch.matmul(RY, vertices_cent.reshape((-1, 3))[:, :, None])).reshape((batch_size, -1, 3))
- vertices_rot[:, :, 2] = vertices_rot[:, :, 2] + torch.ones_like(vertices_rot[:, :, 2]) * 20 # 18 # *16
- visualizations_rot = model.render_vis_nograd(vertices=vertices_rot,
- focal_lengths=output_unnorm['flength'],
- color=0) # 2)
- pred_tex = visualizations_rot[ind_img, :, :, :].permute((1, 2, 0)).cpu().detach().numpy() / 256
- pred_tex_max = np.max(pred_tex, axis=2)
- partial_results['rot_tex_pred'] = pred_tex
- if save_imgs_path is not None:
- out_path = save_imgs_path + '/rot_tex_pred_' + img_name + '.png'
- plt.imsave(out_path, pred_tex)
- render_all = True
- if render_all:
- # save input image
- inp_img = input[ind_img, :, :, :].detach().clone()
- if save_imgs_path is not None:
- out_path = save_imgs_path + '/image_' + img_name + '.png'
- save_input_image(inp_img, out_path)
- # save posed mesh
- V_posed = output_reproj['vertices_smal'][ind_img, :, :].detach().cpu().numpy()
- Faces = model.smal.f
- mesh_posed = trimesh.Trimesh(vertices=V_posed, faces=Faces, process=False, maintain_order=True)
- partial_results['mesh_posed'] = mesh_posed
- if save_imgs_path is not None:
- mesh_posed.export(save_imgs_path + '/mesh_posed_' + img_name + '.obj')
- except:
- print('pass...')
- all_results.append(partial_results)
- if return_results:
- return all_results
- else:
- return
\ No newline at end of file
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- diff --git a/spaces/senior-sigan/vgg_style_transfer/README.md b/spaces/senior-sigan/vgg_style_transfer/README.md deleted file mode 100644 index 0ae5940cbed547cedbd0b1085afd9df98c196b17..0000000000000000000000000000000000000000 --- a/spaces/senior-sigan/vgg_style_transfer/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Vgg_style_transfer -emoji: 🌖 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/shengzi/uer-gpt2-chinese-cluecorpussmall/README.md b/spaces/shengzi/uer-gpt2-chinese-cluecorpussmall/README.md deleted file mode 100644 index 0a93fcd8841f9f487112bd4fdb67cac3a4c045e4..0000000000000000000000000000000000000000 --- a/spaces/shengzi/uer-gpt2-chinese-cluecorpussmall/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Uer Gpt2 Chinese Cluecorpussmall -emoji: 😻 -colorFrom: gray -colorTo: green -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py b/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py deleted file mode 100644 index ecfea82f3e6ac34cd8b1abc2d1de5b0f4eaed343..0000000000000000000000000000000000000000 --- a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py +++ /dev/null @@ -1,77 +0,0 @@ -# ------------------------------------------------------------------------------------------------ -# Deformable DETR -# Copyright (c) 2020 SenseTime. All Rights Reserved. -# Licensed under the Apache License, Version 2.0 [see LICENSE for details] -# ------------------------------------------------------------------------------------------------ -# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -# ------------------------------------------------------------------------------------------------ - -# Copyright (c) Facebook, Inc. and its affiliates. -# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR - - -from __future__ import absolute_import -from __future__ import print_function -from __future__ import division - -import torch -import torch.nn.functional as F -from torch.autograd import Function -from torch.autograd.function import once_differentiable - -if torch.cuda.is_available(): - try: - import MultiScaleDeformableAttention as MSDA - except ModuleNotFoundError as e: - info_string = ( - "\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n" - "\t`cd oneformer/modeling/pixel_decoder/ops`\n" - "\t`sh make.sh`\n" - ) - raise ModuleNotFoundError(info_string) -else: - MultiScaleDeformableAttention = None - - - -class MSDeformAttnFunction(Function): - @staticmethod - def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step): - ctx.im2col_step = im2col_step - output = MSDA.ms_deform_attn_forward( - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step) - ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors - grad_value, grad_sampling_loc, grad_attn_weight = \ - MSDA.ms_deform_attn_backward( - value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step) - - return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None - - -def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights): - # for debug and test only, - # need to use cuda version instead - N_, S_, M_, D_ = value.shape - _, Lq_, M_, L_, P_, _ = sampling_locations.shape - value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1) - sampling_grids = 2 * sampling_locations - 1 - sampling_value_list = [] - for lid_, (H_, W_) in enumerate(value_spatial_shapes): - # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_ - value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_) - # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2 - sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1) - # N_*M_, D_, Lq_, P_ - sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_, - mode='bilinear', padding_mode='zeros', align_corners=False) - sampling_value_list.append(sampling_value_l_) - # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_) - attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_) - output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_) - return output.transpose(1, 2).contiguous() diff --git a/spaces/shi-labs/Prompt-Free-Diffusion/README.md b/spaces/shi-labs/Prompt-Free-Diffusion/README.md deleted file mode 100644 index 5833f05cd1f7d45e4fb34dca078577a12b783221..0000000000000000000000000000000000000000 --- a/spaces/shi-labs/Prompt-Free-Diffusion/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Prompt-Free Diffusion -emoji: 👀 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.32.0 -app_file: app.py -pinned: false -license: mit -python_version: 3.10.11 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/shi-labs/Prompt-Free-Diffusion/lib/model_zoo/controlnet_annotator/hed/__init__.py b/spaces/shi-labs/Prompt-Free-Diffusion/lib/model_zoo/controlnet_annotator/hed/__init__.py deleted file mode 100644 index 8dcfaeb55887ef0fa0928e817e2ac46de1669923..0000000000000000000000000000000000000000 --- a/spaces/shi-labs/Prompt-Free-Diffusion/lib/model_zoo/controlnet_annotator/hed/__init__.py +++ /dev/null @@ -1,134 +0,0 @@ -# This is an improved version and model of HED edge detection with Apache License, Version 2.0. -# Please use this implementation in your products -# This implementation may produce slightly different results from Saining Xie's official implementations, -# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations. -# Different from official models and other implementations, this is an RGB-input model (rather than BGR) -# and in this way it works better for gradio's RGB protocol - -import os -import cv2 -import torch -import numpy as np - -from einops import rearrange -import os - -models_path = 'pretrained/controlnet/preprocess' - -def safe_step(x, step=2): - y = x.astype(np.float32) * float(step + 1) - y = y.astype(np.int32).astype(np.float32) / float(step) - return y - -class DoubleConvBlock(torch.nn.Module): - def __init__(self, input_channel, output_channel, layer_number): - super().__init__() - self.convs = torch.nn.Sequential() - self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) - for i in range(1, layer_number): - self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1)) - self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0) - - def __call__(self, x, down_sampling=False): - h = x - if down_sampling: - h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2)) - for conv in self.convs: - h = conv(h) - h = torch.nn.functional.relu(h) - return h, self.projection(h) - - -class ControlNetHED_Apache2(torch.nn.Module): - def __init__(self): - super().__init__() - self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1))) - self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2) - self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2) - self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3) - self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3) - self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3) - - def __call__(self, x): - h = x - self.norm - h, projection1 = self.block1(h) - h, projection2 = self.block2(h, down_sampling=True) - h, projection3 = self.block3(h, down_sampling=True) - h, projection4 = self.block4(h, down_sampling=True) - h, projection5 = self.block5(h, down_sampling=True) - return projection1, projection2, projection3, projection4, projection5 - - -netNetwork = None -remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth" -modeldir = os.path.join(models_path, "hed") -old_modeldir = os.path.dirname(os.path.realpath(__file__)) - - -def load_file_from_url(url, model_dir=None, progress=True, file_name=None): - """Load file form http url, will download models if necessary. - - Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py - - Args: - url (str): URL to be downloaded. - model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. - Default: None. - progress (bool): Whether to show the download progress. Default: True. - file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. - - Returns: - str: The path to the downloaded file. - """ - from torch.hub import download_url_to_file, get_dir - from urllib.parse import urlparse - if model_dir is None: # use the pytorch hub_dir - hub_dir = get_dir() - model_dir = os.path.join(hub_dir, 'checkpoints') - - os.makedirs(model_dir, exist_ok=True) - - parts = urlparse(url) - filename = os.path.basename(parts.path) - if file_name is not None: - filename = file_name - cached_file = os.path.abspath(os.path.join(model_dir, filename)) - if not os.path.exists(cached_file): - print(f'Downloading: "{url}" to {cached_file}\n') - download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) - return cached_file - - -def apply_hed(input_image, is_safe=False, device='cpu'): - global netNetwork - if netNetwork is None: - modelpath = os.path.join(modeldir, "ControlNetHED.pth") - old_modelpath = os.path.join(old_modeldir, "ControlNetHED.pth") - if os.path.exists(old_modelpath): - modelpath = old_modelpath - elif not os.path.exists(modelpath): - load_file_from_url(remote_model_path, model_dir=modeldir) - netNetwork = ControlNetHED_Apache2().to(device) - netNetwork.load_state_dict(torch.load(modelpath, map_location='cpu')) - netNetwork.to(device).float().eval() - - assert input_image.ndim == 3 - H, W, C = input_image.shape - with torch.no_grad(): - image_hed = torch.from_numpy(input_image.copy()).float().to(device) - image_hed = rearrange(image_hed, 'h w c -> 1 c h w') - edges = netNetwork(image_hed) - edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] - edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges] - edges = np.stack(edges, axis=2) - edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) - if is_safe: - edge = safe_step(edge) - edge = (edge * 255.0).clip(0, 255).astype(np.uint8) - return edge - - -def unload_hed_model(): - global netNetwork - if netNetwork is not None: - netNetwork.cpu() diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/7Zip APK for Android How to Extract Open and Create Zip Files.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/7Zip APK for Android How to Extract Open and Create Zip Files.md deleted file mode 100644 index eda2ea2e841389fb0202be5f158f1401cb848de1..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/7Zip APK for Android How to Extract Open and Create Zip Files.md +++ /dev/null @@ -1,111 +0,0 @@ - -7Zip Download APK: How to Install and Use the Best File Compression App for Android
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\ No newline at end of file diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bus Simulator Ultimate Hile APK 2.0 3 Otobs Srcs Olarak Dnyay Gezin.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bus Simulator Ultimate Hile APK 2.0 3 Otobs Srcs Olarak Dnyay Gezin.md deleted file mode 100644 index d62853ebe2c3e5188162b7cdd073d54175c9f5a4..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Bus Simulator Ultimate Hile APK 2.0 3 Otobs Srcs Olarak Dnyay Gezin.md +++ /dev/null @@ -1,94 +0,0 @@ - -Bus Simulator Ultimate Hile APK 2.0 3: Everything You Need to Know
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-You can customize your buses and routes with Bus Simulator Ultimate Hile APK 2.0 3. You can choose from different bus models, colors, skins, logos, and accessories. You can also create your own routes, add or remove stops, change the difficulty level, and adjust the time and distance. You can also use the map editor to design your own maps and share them with other players.
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-If you want to download and install Bus Simulator Ultimate Hile APK 2.0 3 on your Android device, you need to follow these simple steps:
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-Before you can install any APK file on your device, you need to enable the unknown sources option in your settings. This will allow you to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-Step 2: Download the APK File
-Next, you need to download the APK file of Bus Simulator Ultimate Hile APK 2.0 3 from a reliable source. You can use the link below to download it directly to your device or transfer it from your PC using a USB cable or a file manager app.
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-Step 3: Install the APK File
-Once you have downloaded the APK file, you need to install it on your device. To do this, locate the file in your downloads folder or wherever you saved it and tap on it. You will see a pop-up window asking you to confirm the installation. Tap on Install and wait for the process to finish.
-Step 4: Launch the Game and Enjoy
-After the installation is complete, you can launch the game by tapping on its icon on your home screen or app drawer. You will see a splash screen with the game's logo and then you will be taken to the main menu. From there, you can start playing the game and enjoy all its features.
-Pros and Cons of Bus Simulator Ultimate Hile APK 2.0 3
-Bus Simulator Ultimate Hile APK 2.0 3 has many advantages over the original game, but it also has some drawbacks that you should be aware of before downloading it. Here are some of them:
-Pros
--
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- - Unlimited money and resources: You can get unlimited money and resources with Bus Simulator Ultimate Hile APK 2.0 3, which you can use to buy new buses, upgrade your company, hire more drivers, and create more routes. -
- - No ads and in-app purchases: You can play the game without any interruptions or distractions from ads and in-app purchases with Bus Simulator Ultimate Hile APK 2.0 3. You can also save your data and battery life by playing offline. -
- - Easy to use and play: You can easily use and play Bus Simulator Ultimate Hile APK 2.0 3 without any complications or difficulties. The game has a simple interface and controls that are suitable for all ages and skill levels. -
Cons
--
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- - Not available on Google Play Store: You cannot find Bus Simulator Ultimate Hile APK 2.0 3 on the Google Play Store, which means that you cannot get updates or support from the official developers. You also cannot sync your progress or achievements with your Google account. -
- - May not be compatible with some devices: Bus Simulator Ultimate Hile APK 2.0 3 may not work properly on some devices due to different hardware specifications or software versions. You may experience crashes, glitches, or errors while playing the game. -
- - May cause security risks and bugs: Bus Simulator Ultimate Hile APK 2.0 3 may contain viruses, malware, or spyware that could harm your device or compromise your privacy. You may also encounter bugs or errors that could affect your gameplay or data. You should always scan the APK file before installing it and use it at your own risk. -
Conclusion
-Bus Simulator Ultimate Hile APK 2.0 3 is a modified version of Bus Simulator Ultimate, a realistic bus driving simulation game by Zuuks Games. It offers many features and benefits that make the game more fun and enjoyable, such as unlimited money and resources, no ads and in-app purchases, realistic bus driving simulation, global expansion and management, multiplayer mode and social interactions, customizable buses and routes, and high-quality graphics and sound effects. However, it also has some drawbacks that you should be aware of before downloading it, such as not being available on Google Play Store, not being compatible with some devices, and causing security risks and bugs. Therefore, you should weigh the pros and cons carefully before deciding to use Bus Simulator Ultimate Hile APK 2.0 3.
-FAQs
-Here are some frequently asked questions about Bus Simulator Ultimate Hile APK 2.0 3:
-- Is Bus Simulator Ultimate Hile APK 2.0 3 safe to use?
-Bus Simulator Ultimate Hile APK 2.0 3 is not an official version of Bus Simulator Ultimate, so it may not be safe to use. It may contain viruses, malware, or spyware that could harm your device or compromise your privacy. You should always scan the APK file before installing it and use it at your own risk.
-- How can I update Bus Simulator Ultimate Hile APK 2.0 3?
-Bus Simulator Ultimate Hile APK 2.0 3 is not available on Google Play Store, so you cannot get updates or support from the official developers. You will have to download the latest version of the APK file from a reliable source and install it manually on your device.
-- Can I play Bus Simulator Ultimate Hile APK 2.0 3 offline?
-Yes, you can play Bus Simulator Ultimate Hile APK 2.0 3 offline without any internet connection. However, you will not be able to access some features that require online connectivity, such as multiplayer mode and social interactions.
-- Can I sync my progress or achievements with my Google account?
-No, you cannot sync your progress or achievements with your Google account with Bus Simulator Ultimate Hile APK 2.0 3. You will have to use a separate account or play as a guest.
-- Can I use Bus Simulator Ultimate Hile APK 2.0 3 on my PC or iOS device?
-No, you cannot use Bus Simulator Ultimate Hile APK 2.0 3 on your PC or iOS device. It is only compatible with Android devices that have Android version 5.0 or higher.
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\ No newline at end of file diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CarX Street APK No Verification The Ultimate Guide to Fixing Errors and Compatibility Issues.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CarX Street APK No Verification The Ultimate Guide to Fixing Errors and Compatibility Issues.md deleted file mode 100644 index 57bf76834d7c9b42f5f1ba7568f687ba32e21cdf..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CarX Street APK No Verification The Ultimate Guide to Fixing Errors and Compatibility Issues.md +++ /dev/null @@ -1,86 +0,0 @@ - -How to Download CarX Street Apk No Verification and Enjoy the Ultimate Street Racing Experience
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\ No newline at end of file diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Animal Revolt Battle Simulator and Unleash Your Creativity with Custom Monsters.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Animal Revolt Battle Simulator and Unleash Your Creativity with Custom Monsters.md deleted file mode 100644 index 43fd375f872c1a264c875b3b4471ec2395216f5b..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download Animal Revolt Battle Simulator and Unleash Your Creativity with Custom Monsters.md +++ /dev/null @@ -1,110 +0,0 @@ - -Download ARBS Animal Revolt Battle Simulator: The Ultimate Physics-Based Sandbox Game
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- Create your own custom monsters by combining different body parts and weapons -
- Test your tactical and strategic expertise in the campaign mode -
Let's take a closer look at each of these features and see what makes ARBS Animal Revolt Battle Simulator so fun and addictive.
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-FAQs
--
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- Q: What are the minimum system requirements to play ARBS Animal Revolt Battle Simulator on PC? -
- A: According to the Steam store page, you will need at least Windows 7 or higher, 4 GB of RAM, 4 GB of disk space, DirectX 11 compatible graphics card, and a 64-bit processor. -
- Q: What are the minimum system requirements to play ARBS Animal Revolt Battle Simulator on Android? -
- A: According to the Google Play store page, you will need at least Android 5.0 or higher, 1 GB of RAM, 1 GB of storage space, and a device that supports OpenGL ES 3.0. -
- Q: How can I play ARBS Animal Revolt Battle Simulator online with other players? -
- A: You can play ARBS Animal Revolt Battle Simulator online with other players by using Steam Remote Play Together. This feature allows you to invite your friends to join your game session over the internet, even if they don't own the game. You can also join other players' sessions by browsing the Remote Play Together hub on Steam. -
- Q: How can I contact the developer of ARBS Animal Revolt Battle Simulator? -
- A: You can contact the developer of ARBS Animal Revolt Battle Simulator by sending an email to arbsgame@gmail.com or by visiting their website at https://arbsgame.com/. -
- Q: How can I support the development of ARBS Animal Revolt Battle Simulator? -
- A: You can support the development of ARBS Animal Revolt Battle Simulator by buying the game on Steam or Google Play, leaving a positive review or rating, sharing your feedback and suggestions on the Steam forums or Google Play comments section, reporting any bugs or issues you encounter, creating and sharing your own content on the Steam Workshop or online communities, or donating to their Patreon page at https://www.patreon.com/arbsgame. -
-
-
\ No newline at end of file diff --git a/spaces/skf15963/summary/fengshen/data/taiyi_stable_diffusion_datasets/taiyi_datasets.py b/spaces/skf15963/summary/fengshen/data/taiyi_stable_diffusion_datasets/taiyi_datasets.py deleted file mode 100644 index 73e1071ac27c9839030734fe664abbcfef08d96b..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/data/taiyi_stable_diffusion_datasets/taiyi_datasets.py +++ /dev/null @@ -1,173 +0,0 @@ -from torch.utils.data import Dataset, ConcatDataset -import os -from concurrent.futures import ProcessPoolExecutor -import pandas as pd - - -def add_data_args(parent_args): - parser = parent_args.add_argument_group('taiyi stable diffusion data args') - # 支持传入多个路径，分别加载 - parser.add_argument( - "--datasets_path", type=str, default=None, required=True, nargs='+', - help="A folder containing the training data of instance images.", - ) - parser.add_argument( - "--datasets_type", type=str, default=None, required=True, choices=['txt', 'csv', 'fs_datasets'], nargs='+', - help="dataset type, txt or csv, same len as datasets_path", - ) - parser.add_argument( - "--resolution", type=int, default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", action="store_true", default=False, - help="Whether to center crop images before resizing to resolution" - ) - parser.add_argument("--thres", type=float, default=0.2) - return parent_args - - -class TXTDataset(Dataset): - # 添加Txt数据集读取，主要是针对Zero23m数据集。 - def __init__(self, - foloder_name, - thres=0.2): - super().__init__() - # print(f'Loading folder data from {foloder_name}.') - self.image_paths = [] - ''' - 暂时没有开源这部分文件 - score_data = pd.read_csv(os.path.join(foloder_name, 'score.csv')) - img_path2score = {score_data['image_path'][i]: score_data['score'][i] - for i in range(len(score_data))} - ''' - # print(img_path2score) - # 这里都存的是地址，避免初始化时间过多。 - for each_file in os.listdir(foloder_name): - if each_file.endswith('.jpg'): - self.image_paths.append(os.path.join(foloder_name, each_file)) - - # print('Done loading data. Len of images:', len(self.image_paths)) - - def __len__(self): - return len(self.image_paths) - - def __getitem__(self, idx): - img_path = str(self.image_paths[idx]) - caption_path = img_path.replace('.jpg', '.txt') # 图片名称和文本名称一致。 - with open(caption_path, 'r') as f: - caption = f.read() - return {'img_path': img_path, 'caption': caption} - - -# NOTE 加速读取数据，直接用原版的，在外部使用并行读取策略。30min->3min -class CSVDataset(Dataset): - def __init__(self, - input_filename, - image_root, - img_key, - caption_key, - thres=0.2): - super().__init__() - # logging.debug(f'Loading csv data from {input_filename}.') - print(f'Loading csv data from {input_filename}.') - self.images = [] - self.captions = [] - - if input_filename.endswith('.csv'): - # print(f"Load Data from{input_filename}") - df = pd.read_csv(input_filename, index_col=0, on_bad_lines='skip') - print(f'file {input_filename} datalen {len(df)}') - # 这个图片的路径也需要根据数据集的结构稍微做点修改 - self.images.extend(df[img_key].tolist()) - self.captions.extend(df[caption_key].tolist()) - self.image_root = image_root - - def __len__(self): - return len(self.images) - - def __getitem__(self, idx): - img_path = os.path.join(self.image_root, str(self.images[idx])) - return {'img_path': img_path, 'caption': self.captions[idx]} - - -def if_final_dir(path: str) -> bool: - # 如果当前目录有一个文件，那就算是终极目录 - for f in os.scandir(path): - if f.is_file(): - return True - return False - - -def process_pool_read_txt_dataset(args, - input_root=None, - thres=0.2): - p = ProcessPoolExecutor(max_workers=20) - all_datasets = [] - res = [] - - # 遍历该目录下所有的子目录 - def traversal_files(path: str): - list_subfolders_with_paths = [f.path for f in os.scandir(path) if f.is_dir()] - for dir_path in list_subfolders_with_paths: - if if_final_dir(dir_path): - res.append(p.submit(TXTDataset, - dir_path, - thres)) - else: - traversal_files(dir_path) - traversal_files(input_root) - p.shutdown() - for future in res: - all_datasets.append(future.result()) - dataset = ConcatDataset(all_datasets) - return dataset - - -def process_pool_read_csv_dataset(args, - input_root, - thres=0.20): - # here input_filename is a directory containing a CSV file - all_csvs = os.listdir(os.path.join(input_root, 'release')) - image_root = os.path.join(input_root, 'images') - # csv_with_score = [each for each in all_csvs if 'score' in each] - all_datasets = [] - res = [] - p = ProcessPoolExecutor(max_workers=150) - for path in all_csvs: - each_csv_path = os.path.join(input_root, 'release', path) - res.append(p.submit(CSVDataset, - each_csv_path, - image_root, - img_key="name", - caption_key="caption", - thres=thres)) - p.shutdown() - for future in res: - all_datasets.append(future.result()) - dataset = ConcatDataset(all_datasets) - return dataset - - -def load_data(args, global_rank=0): - assert len(args.datasets_path) == len(args.datasets_type), \ - "datasets_path num not equal to datasets_type" - all_datasets = [] - for path, type in zip(args.datasets_path, args.datasets_type): - if type == 'txt': - all_datasets.append(process_pool_read_txt_dataset( - args, input_root=path, thres=args.thres)) - elif type == 'csv': - all_datasets.append(process_pool_read_csv_dataset( - args, input_root=path, thres=args.thres)) - elif type == 'fs_datasets': - from fengshen.data.fs_datasets import load_dataset - all_datasets.append(load_dataset(path, num_proc=args.num_workers, - thres=args.thres, global_rank=global_rank)['train']) - else: - raise ValueError('unsupport dataset type: %s' % type) - print(f'load datasset {type} {path} len {len(all_datasets[-1])}') - return {'train': ConcatDataset(all_datasets)} diff --git a/spaces/skf15963/summary/fengshen/models/zen1/configuration_zen1.py b/spaces/skf15963/summary/fengshen/models/zen1/configuration_zen1.py deleted file mode 100644 index c7cbeb5657ea07b2a4e8429199a6091be39864c8..0000000000000000000000000000000000000000 --- a/spaces/skf15963/summary/fengshen/models/zen1/configuration_zen1.py +++ /dev/null @@ -1,80 +0,0 @@ -# coding=utf-8 -# Copyright 2022 IDEA-CCNL and The HuggingFace Inc. 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. -""" TransfoXLDenoise model configuration """ - -from transformers.configuration_utils import PretrainedConfig - - -class ZenConfig(PretrainedConfig): - - """Configuration class to store the configuration of a `ZenModel`. - """ - - def __init__(self, - # vocab_size_or_config_json_file, - # word_vocab_size, - hidden_size=768, - num_hidden_layers=12, - num_attention_heads=12, - intermediate_size=3072, - hidden_act="gelu", - hidden_dropout_prob=0.1, - attention_probs_dropout_prob=0.1, - max_position_embeddings=512, - type_vocab_size=2, - initializer_range=0.02, - layer_norm_eps=1e-12, - num_hidden_word_layers=6, - **kwargs): - """Constructs ZenConfig. - - Args: - vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`. - hidden_size: Size of the encoder layers and the pooler layer. - num_hidden_layers: Number of hidden layers in the Transformer encoder. - num_attention_heads: Number of attention heads for each attention layer in - the Transformer encoder. - intermediate_size: The size of the "intermediate" (i.e., feed-forward) - layer in the Transformer encoder. - hidden_act: The non-linear activation function (function or string) in the - encoder and pooler. If string, "gelu", "relu" and "swish" are supported. - hidden_dropout_prob: The dropout probabilitiy for all fully connected - layers in the embeddings, encoder, and pooler. - attention_probs_dropout_prob: The dropout ratio for the attention - probabilities. - max_position_embeddings: The maximum sequence length that this model might - ever be used with. Typically set this to something large just in case - (e.g., 512 or 1024 or 2048). - type_vocab_size: The vocabulary size of the `token_type_ids` passed into - `BertModel`. - initializer_range: The sttdev of the truncated_normal_initializer for - initializing all weight matrices. - layer_norm_eps: The epsilon used by LayerNorm. - """ - # self.vocab_size = vocab_size_or_config_json_file - # self.word_size = word_vocab_size - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.hidden_act = hidden_act - self.intermediate_size = intermediate_size - self.hidden_dropout_prob = hidden_dropout_prob - self.attention_probs_dropout_prob = attention_probs_dropout_prob - self.max_position_embeddings = max_position_embeddings - self.type_vocab_size = type_vocab_size - self.initializer_range = initializer_range - self.layer_norm_eps = layer_norm_eps - self.num_hidden_word_layers = num_hidden_word_layers - super().__init__(**kwargs) diff --git a/spaces/sklearn-docs/Lasso-dense-sparse-data/README.md b/spaces/sklearn-docs/Lasso-dense-sparse-data/README.md deleted file mode 100644 index 815ca3af11b11d306bcbf8863bab7d6b6a95f896..0000000000000000000000000000000000000000 --- a/spaces/sklearn-docs/Lasso-dense-sparse-data/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Lasso Dense Sparse Data -emoji: 📈 -colorFrom: red -colorTo: pink -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/sklearn-docs/multilabel_classification/README.md b/spaces/sklearn-docs/multilabel_classification/README.md deleted file mode 100644 index 888248f64156fe3b9e11d399140a251d13b33124..0000000000000000000000000000000000000000 --- a/spaces/sklearn-docs/multilabel_classification/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Multilabel Classification -emoji: 🚀 -colorFrom: yellow -colorTo: yellow -sdk: gradio -sdk_version: 3.28.2 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/seg2art/sstan_models/networks/generator.py b/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/seg2art/sstan_models/networks/generator.py deleted file mode 100644 index 8ab0c454bd264fa1b21738c98ae7afbae14a72a1..0000000000000000000000000000000000000000 --- a/spaces/sky24h/Controllable_Multi-domain_Semantic_Artwork_Synthesis/seg2art/sstan_models/networks/generator.py +++ /dev/null @@ -1,184 +0,0 @@ -""" -Copyright (C) 2019 NVIDIA Corporation. All rights reserved. -Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F -from sstan_models.networks.base_network import BaseNetwork -from sstan_models.networks.normalization import get_nonspade_norm_layer -from sstan_models.networks.architecture import ResnetBlock as ResnetBlock -from sstan_models.networks.architecture import SPADEResnetBlock as SPADEResnetBlock -import numpy as np -torch.manual_seed(1234) - - -class SPADEGenerator(BaseNetwork): - @staticmethod - def modify_commandline_options(parser, is_train): - parser.set_defaults(norm_G='spectralspadesyncbatch3x3') - parser.add_argument('--num_upsampling_layers', - choices=('normal', 'more', 'most'), default='normal', - help="If 'more', adds upsampling layer between the two middle resnet blocks. If 'most', also add one more upsampling + resnet layer at the end of the generator") - - return parser - - def __init__(self, opt): - super().__init__() - self.opt = opt - nf = opt.ngf - - self.sw, self.sh = self.compute_latent_vector_size(opt) - - # if opt.use_vae: - # # In case of VAE, we will sample from random z vector - # self.fc = nn.Linear(opt.z_dim, 16 * nf * self.sw * self.sh) - # else: - # # Otherwise, we make the network deterministic by starting with - # # downsampled segmentation map instead of random z - # self.fc = nn.Conv2d(self.opt.semantic_nc, 16 * nf, 3, padding=1) - self.fc = nn.Linear(opt.z_dim, 16 * nf * self.sw * self.sh) - self.head_0 = SPADEResnetBlock(16 * nf, 16 * nf, opt, feed_code = True) - - self.G_middle_0 = SPADEResnetBlock(16 * nf, 16 * nf, opt, feed_code = True) - self.G_middle_1 = SPADEResnetBlock(16 * nf, 16 * nf, opt, feed_code = True) - - self.up_0 = SPADEResnetBlock(16 * nf, 8 * nf, opt, feed_code = True) - self.up_1 = SPADEResnetBlock(8 * nf, 4 * nf, opt, feed_code = True) - self.up_2 = SPADEResnetBlock(4 * nf, 2 * nf, opt, feed_code = True) - self.up_3 = SPADEResnetBlock(2 * nf, 1 * nf, opt, feed_code = False) - - final_nc = nf - - # if opt.num_upsampling_layers == 'most': - # print('used?') - # self.up_4 = SPADEResnetBlock(1 * nf, nf // 2, opt) - # final_nc = nf // 2 - - self.conv_img = nn.Conv2d(final_nc, 3, 3, padding=1) - - self.up = nn.Upsample(scale_factor=2) - - def compute_latent_vector_size(self, opt): - if opt.num_upsampling_layers == 'normal': - num_up_layers = 5 - elif opt.num_upsampling_layers == 'more': - num_up_layers = 6 - elif opt.num_upsampling_layers == 'most': - num_up_layers = 7 - else: - raise ValueError('opt.num_upsampling_layers [%s] not recognized' % - opt.num_upsampling_layers) - - sw = opt.crop_size // (2**num_up_layers) - sh = round(sw / opt.aspect_ratio) - - return sw, sh - - def forward(self, input, rgb_img, style_codes=None): - with torch.cuda.amp.autocast(): - seg = input - # if self.opt.use_vae: - # # we sample z from unit normal and reshape the tensor - # if z is None: - # z = torch.randn(input.size(0), self.opt.z_dim, - # dtype=torch.float32, device=input.get_device()) - x = self.fc(style_codes) - x = x.view(-1, 16 * self.opt.ngf, self.sh, self.sw) - # else: - # # we downsample segmap and run convolution - # x = F.interpolate(seg, size=(self.sh, self.sw)) - # x = self.fc(x) - x = self.head_0(x, seg, style_codes=style_codes) - - x = self.up(x) - x = self.G_middle_0(x, seg, style_codes=style_codes) - - if self.opt.num_upsampling_layers == 'more' or \ - self.opt.num_upsampling_layers == 'most': - x = self.up(x) - - x = self.G_middle_1(x, seg, style_codes=style_codes) - - x = self.up(x) - x = self.up_0(x, seg, style_codes=style_codes) - x = self.up(x) - x = self.up_1(x, seg, style_codes=style_codes) - x = self.up(x) - x = self.up_2(x, seg, style_codes=style_codes) - x = self.up(x) - x = self.up_3(x, seg) - - # if self.opt.num_upsampling_layers == 'most': - # x = self.up(x) - # x = self.up_4(x, seg) - - x = self.conv_img(F.leaky_relu(x, 2e-1)) - x = torch.tanh(x)#F.tanh(x) - - return x#, style_codes - - -class Pix2PixHDGenerator(BaseNetwork): - @staticmethod - def modify_commandline_options(parser, is_train): - parser.add_argument('--resnet_n_downsample', type=int, default=4, help='number of downsampling layers in netG') - parser.add_argument('--resnet_n_blocks', type=int, default=9, help='number of residual blocks in the global generator network') - parser.add_argument('--resnet_kernel_size', type=int, default=3, - help='kernel size of the resnet block') - parser.add_argument('--resnet_initial_kernel_size', type=int, default=7, - help='kernel size of the first convolution') - parser.set_defaults(norm_G='instance') - return parser - - def __init__(self, opt): - super().__init__() - input_nc = opt.label_nc + (1 if opt.contain_dontcare_label else 0) + (0 if opt.no_instance else 1) - - norm_layer = get_nonspade_norm_layer(opt, opt.norm_G) - activation = nn.ReLU(False) - - model = [] - - # initial conv - model += [nn.ReflectionPad2d(opt.resnet_initial_kernel_size // 2), - norm_layer(nn.Conv2d(input_nc, opt.ngf, - kernel_size=opt.resnet_initial_kernel_size, - padding=0)), - activation] - - # downsample - mult = 1 - for i in range(opt.resnet_n_downsample): - model += [norm_layer(nn.Conv2d(opt.ngf * mult, opt.ngf * mult * 2, - kernel_size=3, stride=2, padding=1)), - activation] - mult *= 2 - - # resnet blocks - for i in range(opt.resnet_n_blocks): - model += [ResnetBlock(opt.ngf * mult, - norm_layer=norm_layer, - activation=activation, - kernel_size=opt.resnet_kernel_size)] - - # upsample - for i in range(opt.resnet_n_downsample): - nc_in = int(opt.ngf * mult) - nc_out = int((opt.ngf * mult) / 2) - model += [norm_layer(nn.ConvTranspose2d(nc_in, nc_out, - kernel_size=3, stride=2, - padding=1, output_padding=1)), - activation] - mult = mult // 2 - - # final output conv - model += [nn.ReflectionPad2d(3), - nn.Conv2d(nc_out, opt.output_nc, kernel_size=7, padding=0), - nn.Tanh()] - - self.model = nn.Sequential(*model) - - def forward(self, input, z=None): - return self.model(input) diff --git a/spaces/slone/myv-translation-2022-demo/README.md b/spaces/slone/myv-translation-2022-demo/README.md deleted file mode 100644 index 5af7d669f346715ebcbdf764a99a8917a59c5825..0000000000000000000000000000000000000000 --- a/spaces/slone/myv-translation-2022-demo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Эрзянь ютавтыця | Erzya Translator | Эрзянский переводчик 2022 -emoji: 😻 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.18.0 -app_file: app.py -pinned: false -license: cc-by-sa-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/snowc2023/ask_the_doc/README.md b/spaces/snowc2023/ask_the_doc/README.md deleted file mode 100644 index 6af60c2668214a0e25dc034ee9a76236d86bcaec..0000000000000000000000000000000000000000 --- a/spaces/snowc2023/ask_the_doc/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Ask The Doc -emoji: 📖 -colorFrom: red -colorTo: yellow -sdk: streamlit -sdk_version: '1.21.0' -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference - \ No newline at end of file diff --git a/spaces/souljoy/chinese_lyric_generation/app.py b/spaces/souljoy/chinese_lyric_generation/app.py deleted file mode 100644 index 6d98126a77b047007a3b149522f29cd0435813c8..0000000000000000000000000000000000000000 --- a/spaces/souljoy/chinese_lyric_generation/app.py +++ /dev/null @@ -1,66 +0,0 @@ -import gradio as gr -from transformers import T5Tokenizer, T5ForConditionalGeneration -import torch -from random import sample - -model_path = 'souljoy/t5-chinese-lyric' -device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") -tokenizer = T5Tokenizer.from_pretrained(model_path) -model = T5ForConditionalGeneration.from_pretrained(model_path).to(device) -keywords_list = [] -with open('keywords', mode='r', encoding='utf-8') as f: - for line in f: - keywords_list.append(line.strip()) - - -def key_cng_change(v_list): - return '，'.join(v_list) - - -def post_process(text): - return text.replace("\\n", "\n").replace("\\t", "\t") - - -def send(my_singer_txt, my_song_txt, my_key_txt): - if len(my_singer_txt) == 0: - lyric = '请输入歌手名' - elif len(my_song_txt) == 0: - lyric = '请输入歌名' - elif len(my_key_txt) == 0: - lyric = '歌词主题词' - elif len(my_key_txt.split('，')) > 10: - lyric = '歌词主太多！最多10个' - else: - text = '用户：写一首歌，歌手“{}”，歌曲“{}”，以“{}”为主题。\\n小L：'.format(my_singer_txt, my_song_txt, my_key_txt) - encoding = tokenizer(text=[text], truncation=True, padding=True, max_length=64, return_tensors="pt").to(device) - out = model.generate(**encoding, return_dict_in_generate=True, output_scores=False, max_new_tokens=512, - do_sample=True, top_p=1, temperature=0.7, no_repeat_ngram_size=3) - out_text = tokenizer.batch_decode(out["sequences"], skip_special_tokens=True) - lyric = post_process(out_text[0]) - - return lyric, gr.CheckboxGroup.update(choices=sample(keywords_list, 100), value=[]) - -def renew(): - return gr.CheckboxGroup.update(choices=sample(keywords_list, 100), value=[]) - -with gr.Blocks() as demo: - gr.Markdown("""#### 歌词创作机器人 📻 🎵 """) - with gr.Row(): - with gr.Column(): - singer_txt = gr.Textbox(label='歌手名', placeholder='请输入你的名字\n\n也可以输入其他歌手的名字，便可以仿照其歌词风格作词') - song_txt = gr.Textbox(label='歌名', placeholder='请输入要创作的歌词名称') - key_txt = gr.Textbox(label='歌词主题词', placeholder='请输入主题词/短语，以中文逗号（，）分割\n\n也可以点击下方“主题词样例”\n\n最多10个') - with gr.Row(): - send_button = gr.Button(value="生成歌词") - renew_button = gr.Button(value="换一批样例") - key_cng = gr.CheckboxGroup(label='主题词样例', choices=sample(keywords_list, 100)) - - - with gr.Column(): - lyric_txt = gr.Textbox(label='生成歌词') - key_cng.change(key_cng_change, [key_cng], [key_txt]) - send_button.click(send, [singer_txt, song_txt, key_txt], [lyric_txt,key_cng]) - renew_button.click(renew, [], [key_cng]) - -if __name__ == "__main__": - demo.launch() diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/linformer/linformer_src/modules/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/linformer/linformer_src/modules/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py deleted file mode 100644 index 031567c6d85d16b5236053abf008b7cabccb4673..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/dump_feats.py +++ /dev/null @@ -1,91 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import argparse -import logging - -from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( - get_and_dump_features, -) - - -def get_parser(): - parser = argparse.ArgumentParser( - description="Compute and dump log mel fbank features." - ) - parser.add_argument( - "--feature_type", - type=str, - choices=["logmel", "hubert", "w2v2", "cpc"], - default=None, - help="Acoustic feature type", - ) - parser.add_argument( - "--manifest_path", - type=str, - default=None, - help="Manifest file containing the root dir and file names", - ) - parser.add_argument( - "--out_features_path", - type=str, - default=None, - help="Features file path to write to", - ) - parser.add_argument( - "--checkpoint_path", - type=str, - help="Pretrained acoustic model checkpoint", - ) - parser.add_argument( - "--layer", - type=int, - help="The layer of the pretrained model to extract features from", - default=-1, - ) - parser.add_argument( - "--sample_pct", - type=float, - help="Percent data to use for K-means training", - default=0.1, - ) - parser.add_argument( - "--out_features_path", - type=str, - help="Path to save log mel fbank features", - ) - return parser - - -def get_logger(): - log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" - logging.basicConfig(format=log_format, level=logging.INFO) - logger = logging.getLogger(__name__) - return logger - - -if __name__ == "__main__": - """ - Example command: - python ~/speechbot/clustering/dump_logmelfank_feats.py \ - --manifest_path /checkpoint/kushall/data/LJSpeech-1.1/asr_input_wavs_16k/train.tsv - --out_features_path /checkpoint/kushall/experiments/speechbot/logmelfbank/features/ljspeech/train.npy - """ - parser = get_parser() - args = parser.parse_args() - logger = get_logger() - logger.info(args) - - logger.info(f"Extracting {args.feature_type} acoustic features...") - get_and_dump_features( - feature_type=args.feature_type, - checkpoint_path=args.checkpoint_path, - layer=args.layer, - manifest_path=args.manifest_path, - sample_pct=args.sample_pct, - flatten=True, - out_features_path=args.out_features_path, - ) - logger.info(f"Saved extracted features at {args.out_features_path}") diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/audio/__init__.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/audio/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/nested_dictionary_dataset.py b/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/nested_dictionary_dataset.py deleted file mode 100644 index 52e74abddacc923c5e29b0a0c41d7efc85482d3b..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/fairseq/data/nested_dictionary_dataset.py +++ /dev/null @@ -1,125 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -from collections import OrderedDict - -import torch -from torch.utils.data.dataloader import default_collate - -from . import FairseqDataset - - -def _flatten(dico, prefix=None): - """Flatten a nested dictionary.""" - new_dico = OrderedDict() - if isinstance(dico, dict): - prefix = prefix + "." if prefix is not None else "" - for k, v in dico.items(): - if v is None: - continue - new_dico.update(_flatten(v, prefix + k)) - elif isinstance(dico, list): - for i, v in enumerate(dico): - new_dico.update(_flatten(v, prefix + ".[" + str(i) + "]")) - else: - new_dico = OrderedDict({prefix: dico}) - return new_dico - - -def _unflatten(dico): - """Unflatten a flattened dictionary into a nested dictionary.""" - new_dico = OrderedDict() - for full_k, v in dico.items(): - full_k = full_k.split(".") - node = new_dico - for k in full_k[:-1]: - if k.startswith("[") and k.endswith("]"): - k = int(k[1:-1]) - if k not in node: - node[k] = OrderedDict() - node = node[k] - node[full_k[-1]] = v - return new_dico - - -class NestedDictionaryDataset(FairseqDataset): - def __init__(self, defn, sizes=None): - super().__init__() - self.defn = _flatten(defn) - self.sizes = [sizes] if not isinstance(sizes, (list, tuple)) else sizes - - first = None - for v in self.defn.values(): - if not isinstance( - v, - ( - FairseqDataset, - torch.utils.data.Dataset, - ), - ): - raise ValueError("Expected Dataset but found: {}".format(v.__class__)) - first = first or v - if len(v) > 0: - assert len(v) == len(first), "dataset lengths must match" - - self._len = len(first) - - def __getitem__(self, index): - return OrderedDict((k, ds[index]) for k, ds in self.defn.items()) - - def __len__(self): - return self._len - - def collater(self, samples): - """Merge a list of samples to form a mini-batch. - - Args: - samples (List[dict]): samples to collate - - Returns: - dict: a mini-batch suitable for forwarding with a Model - """ - if len(samples) == 0: - return {} - sample = OrderedDict() - for k, ds in self.defn.items(): - try: - sample[k] = ds.collater([s[k] for s in samples]) - except NotImplementedError: - sample[k] = default_collate([s[k] for s in samples]) - return _unflatten(sample) - - def num_tokens(self, index): - """Return the number of tokens in a sample. This value is used to - enforce ``--max-tokens`` during batching.""" - return max(s[index] for s in self.sizes) - - def size(self, index): - """Return an example's size as a float or tuple. This value is used when - filtering a dataset with ``--max-positions``.""" - if len(self.sizes) == 1: - return self.sizes[0][index] - else: - return (s[index] for s in self.sizes) - - @property - def supports_prefetch(self): - """Whether this dataset supports prefetching.""" - return any(ds.supports_prefetch for ds in self.defn.values()) - - def prefetch(self, indices): - """Prefetch the data required for this epoch.""" - for ds in self.defn.values(): - if getattr(ds, "supports_prefetch", False): - ds.prefetch(indices) - - @property - def can_reuse_epoch_itr_across_epochs(self): - return all(ds.can_reuse_epoch_itr_across_epochs for ds in self.defn.values()) - - def set_epoch(self, epoch): - super().set_epoch(epoch) - for ds in self.defn.values(): - ds.set_epoch(epoch) diff --git a/spaces/srush/minichain/process.py b/spaces/srush/minichain/process.py deleted file mode 100644 index 022c06466980e572352aacfe4a1b024e73865066..0000000000000000000000000000000000000000 --- a/spaces/srush/minichain/process.py +++ /dev/null @@ -1,13 +0,0 @@ -import sys - -print("!pip install -q git+https://github.com/srush/MiniChain") -print("!git clone https://github.com/srush/MiniChain; cp -fr MiniChain/examples/* . ") -print() - -for l in sys.stdin: - - if l.strip() == "# $": - continue - if l.strip().startswith("code="): - continue - print(l, end="") diff --git a/spaces/sub314xxl/MetaGPT/metagpt/roles/sales.py b/spaces/sub314xxl/MetaGPT/metagpt/roles/sales.py deleted file mode 100644 index 51b13f4878b83fe0bf38c199ffd8efd3b2b7024d..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/MetaGPT/metagpt/roles/sales.py +++ /dev/null @@ -1,34 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/25 17:21 -@Author : alexanderwu -@File : sales.py -""" -from metagpt.actions import SearchAndSummarize -from metagpt.roles import Role -from metagpt.tools import SearchEngineType - - -class Sales(Role): - def __init__( - self, - name="Xiaomei", - profile="Retail sales guide", - desc="I am a sales guide in retail. My name is Xiaomei. I will answer some customer questions next, and I " - "will answer questions only based on the information in the knowledge base." - "If I feel that you can't get the answer from the reference material, then I will directly reply that" - " I don't know, and I won't tell you that this is from the knowledge base," - "but pretend to be what I know. Note that each of my replies will be replied in the tone of a " - "professional guide", - store=None - ): - super().__init__(name, profile, desc=desc) - self._set_store(store) - - def _set_store(self, store): - if store: - action = SearchAndSummarize("", engine=SearchEngineType.CUSTOM_ENGINE, search_func=store.search) - else: - action = SearchAndSummarize() - self._init_actions([action]) diff --git a/spaces/sukiru/BlueArchiveTTS/app.py b/spaces/sukiru/BlueArchiveTTS/app.py deleted file mode 100644 index 4fbbddc191a41175d718eee3789f2bfd8384a891..0000000000000000000000000000000000000000 --- a/spaces/sukiru/BlueArchiveTTS/app.py +++ /dev/null @@ -1,164 +0,0 @@ -import json -import os -import re - -import librosa -import numpy as np -import torch -from torch import no_grad, LongTensor -import commons -import utils -import gradio as gr -from models import SynthesizerTrn -from text import text_to_sequence, _clean_text -from mel_processing import spectrogram_torch - -limitation = os.getenv("SYSTEM") == "spaces" # limit text and audio length in huggingface spaces - - -def get_text(text, hps, is_phoneme): - text_norm = text_to_sequence(text, hps.symbols, [] if is_phoneme else hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = LongTensor(text_norm) - return text_norm - - -def create_tts_fn(model, hps, speaker_ids): - def tts_fn(text, speaker, speed, is_phoneme): - if limitation: - text_len = len(text) - max_len = 500 - if is_phoneme: - max_len *= 3 - else: - if len(hps.data.text_cleaners) > 0 and hps.data.text_cleaners[0] == "zh_ja_mixture_cleaners": - text_len = len(re.sub("(\[ZH\]|\[JA\])", "", text)) - if text_len > max_len: - return "Error: Text is too long", None - - speaker_id = speaker_ids[speaker] - stn_tst = get_text(text, hps, is_phoneme) - with no_grad(): - x_tst = stn_tst.unsqueeze(0) - x_tst_lengths = LongTensor([stn_tst.size(0)]) - sid = LongTensor([speaker_id]) - audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, - length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() - del stn_tst, x_tst, x_tst_lengths, sid - return "Success", (hps.data.sampling_rate, audio) - - return tts_fn - - - - - -def create_to_phoneme_fn(hps): - def to_phoneme_fn(text): - return _clean_text(text, hps.data.text_cleaners) if text != "" else "" - - return to_phoneme_fn - - -css = """ - #advanced-btn { - color: white; - border-color: black; - background: black; - font-size: .7rem !important; - line-height: 19px; - margin-top: 24px; - margin-bottom: 12px; - padding: 2px 8px; - border-radius: 14px !important; - } - #advanced-options { - display: none; - margin-bottom: 20px; - } -""" - -if __name__ == '__main__': - models_tts = [] - models_vc = [] - models_soft_vc = [] - name = 'BlueArchiveTTS' - lang = '日本語 (Japanese)' - example = '先生、何をお手伝いしましょうか？' - config_path = f"saved_model/config.json" - model_path = f"saved_model/model.pth" - cover_path = f"saved_model/cover.png" - hps = utils.get_hparams_from_file(config_path) - model = SynthesizerTrn( - len(hps.symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model) - utils.load_checkpoint(model_path, model, None) - model.eval() - speaker_ids = [sid for sid, name in enumerate(hps.speakers) if name != "None"] - speakers = [name for sid, name in enumerate(hps.speakers) if name != "None"] - - t = 'vits' - models_tts.append((name, cover_path, speakers, lang, example, - hps.symbols, create_tts_fn(model, hps, speaker_ids), - create_to_phoneme_fn(hps))) - - - app = gr.Blocks(css=css) - - with app: - gr.Markdown("# BlueArchiveTTS Using VITS Model\n\n" - "\n\n") - with gr.Tabs(): - with gr.TabItem("TTS"): - with gr.Tabs(): - for i, (name, cover_path, speakers, lang, example, symbols, tts_fn, - to_phoneme_fn) in enumerate(models_tts): - with gr.TabItem(f"BlueArchive"): - with gr.Column(): - gr.Markdown(f"## {name}\n\n" - f"\n\n" - f"lang: {lang}") - tts_input1 = gr.TextArea(label="Text (500 words limitation)", value=example, - elem_id=f"tts-input{i}") - tts_input2 = gr.Dropdown(label="Speaker", choices=speakers, - type="index", value=speakers[0]) - tts_input3 = gr.Slider(label="Speed", value=1, minimum=0.1, maximum=2, step=0.1) - with gr.Accordion(label="Advanced Options", open=False): - phoneme_input = gr.Checkbox(value=False, label="Phoneme input") - to_phoneme_btn = gr.Button("Covert text to phoneme") - phoneme_list = gr.Dataset(label="Phoneme list", components=[tts_input1], - samples=[[x] for x in symbols], - elem_id=f"phoneme-list{i}") - phoneme_list_json = gr.Json(value=symbols, visible=False) - tts_submit = gr.Button("Generate", variant="primary") - tts_output1 = gr.Textbox(label="Output Message") - tts_output2 = gr.Audio(label="Output Audio") - tts_submit.click(tts_fn, [tts_input1, tts_input2, tts_input3, phoneme_input], - [tts_output1, tts_output2], api_name="tts") - to_phoneme_btn.click(to_phoneme_fn, [tts_input1], [tts_input1]) - phoneme_list.click(None, [phoneme_list, phoneme_list_json], [], - _js=f""" - (i,phonemes) => {{ - let root = document.querySelector("body > gradio-app"); - if (root.shadowRoot != null) - root = root.shadowRoot; - let text_input = root.querySelector("#tts-input{i}").querySelector("textarea"); - let startPos = text_input.selectionStart; - let endPos = text_input.selectionEnd; - let oldTxt = text_input.value; - let result = oldTxt.substring(0, startPos) + phonemes[i] + oldTxt.substring(endPos); - text_input.value = result; - let x = window.scrollX, y = window.scrollY; - text_input.focus(); - text_input.selectionStart = startPos + phonemes[i].length; - text_input.selectionEnd = startPos + phonemes[i].length; - text_input.blur(); - window.scrollTo(x, y); - return []; - }}""") - - app.queue(concurrency_count=3).launch(server_name="0.0.0.0") diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Adobe Acrobat Pro DC 2018.012.20039 Crack Utorrent.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Adobe Acrobat Pro DC 2018.012.20039 Crack Utorrent.md deleted file mode 100644 index ee5fba8e3209f6cf9ea0e4cb1176fe15acd40c68..0000000000000000000000000000000000000000 --- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Adobe Acrobat Pro DC 2018.012.20039 Crack Utorrent.md +++ /dev/null @@ -1,6 +0,0 @@ -Adobe Acrobat Pro DC 2018.012.20039 Crack utorrent
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- -2,086 - -Category:Augustine of Hippo - -Category:Martyred Roman Catholic saints - -Category:Christian martyrs executed by decapitation - -Category:Saints from the Holy Land - -Category:1st-century Romans - -Category:1st-century Christians - -Category:Year of birth uncertain - -Category:Year of death unknownTrump won’t budge on his promise of a border wall, and the president of American Hospitality Association seems to think Trump is on to something, proposing to “reimagine” the border wall. - -Yes, some healthcare professionals are worried that people will die at the border as they try to cross the desert. Oh, wait, that’s already happening. - -From CNN: - -“I think it’s a really great idea to reimagine the border,” said Ken Johnson, president of the American Hospital Association, to CNN. “I would love to hear the president’s thoughts on that.” The American Hospital Association represents some 3,000 hospitals across the United States. TRENDING: Black Lives Matter Activist Wearing 'Justice for Breonna Taylor' Shirt Walked into a Louisville Bar and Murdered Three People […] “We’ve already seen what’s going on with the border,” Johnson said. “There are overstressed shelters and people are coming into our hospitals.” - -Some hospitals are already taking in illegal aliens at the border, and there is no shortage of unscreened illegals. But to be fair, some folks who voted for Trump are saying that any criticism of Trump is “racist.” Trump is “good for America.” And Trump is “not a fascist.” - -It’s starting to look like, if Hillary Clinton had won, Trump would have been banned from the White House and charged with treason. - -At a campaign rally for Trump in Pennsylvania last week, Trump bragged about the fact that he would take on the media and that they would all be “sad” in the end. It’s not hard to see why Trump is afraid of the media. - -Back in February, former Hillary Clinton campaign manager and CNN analyst Donna Brazile said Clinton would campaign with Trump at rallies “until the cows come home.” - -“What was striking to me is the idea that you were going to be 4fefd39f24
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- diff --git a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/eval/benchmark/metrics/pose.py b/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/eval/benchmark/metrics/pose.py deleted file mode 100644 index 3e591f00f71f26466d66545a5229663f116193eb..0000000000000000000000000000000000000000 --- a/spaces/svjack/ControlNet-Face-Chinese/SPIGA/spiga/eval/benchmark/metrics/pose.py +++ /dev/null @@ -1,159 +0,0 @@ -import numpy as np -from sklearn.metrics import confusion_matrix - -from spiga.eval.benchmark.metrics.metrics import Metrics - - -class MetricsHeadpose(Metrics): - - def __init__(self, name='headpose'): - super().__init__(name) - - # Angles - self.angles = ['yaw', 'pitch', 'roll'] - # Confusion matrix intervals - self.pose_labels = [-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90] - # Percentile reference angles - self.error_labels = [2.5, 5, 10, 15, 30] - # Cumulative plot axis length - self.bins = 1000 - - def compute_error(self, data_anns, data_pred, database, select_ids=None): - - # Initialize global logs and variables of Computer Error function - self.init_ce(data_anns, data_pred, database) - - # Generate annotations if needed - if data_anns[0]['headpose'] is None: - print('Database anns generated by posit...') - data_anns = self._posit_anns() - print('Posit generation done...') - - # Dictionary variables - self.error['data_pred'] = [] - self.error['data_anns'] = [] - self.error['data_pred_trl'] = [] - self.error['data_anns_trl'] = [] - self.error['mae_ypr'] = [] - self.error['mae_mean'] = [] - - # Order data - for img_id, img_anns in enumerate(data_anns): - pose_anns = img_anns['headpose'][0:3] - self.error['data_anns'].append(pose_anns) - pose_pred = data_pred[img_id]['headpose'][0:3] - self.error['data_pred'].append(pose_pred) - - # Compute MAE error - anns_array = np.array(self.error['data_anns']) - pred_array = np.array(self.error['data_pred']) - mae_ypr = np.abs((anns_array-pred_array)) - self.error['mae_ypr'] = mae_ypr.tolist() - self.error['mae_mean'] = np.mean(mae_ypr, axis=-1).tolist() - - # Quantize labeled data - label_anns = self._nearest_label(anns_array) - label_pred = self._nearest_label(pred_array) - self.error['label_anns'] = label_anns - self.error['label_pred'] = label_pred - - for angle_id, angle in enumerate(self.angles): - # Confusion matrix - self.error['cm_%s' % angle] = confusion_matrix(label_anns[:, angle_id], label_pred[:, angle_id]) - # Cumulative error - self.error['cumulative_%s' % angle] = self._cumulative_error(mae_ypr[:, angle_id], bins=self.bins) - - return self.error - - def metrics(self): - - # Initialize global logs and variables of Metrics function - self.init_metrics() - - # Mean Absolute Error - mae_ypr = np.array(self.error['mae_ypr']) - mae_ypr_mean = np.mean(mae_ypr, axis=0) - self.metrics_log['mae_ypr'] = mae_ypr_mean.tolist() - self.metrics_log['mae_mean'] = np.mean(mae_ypr_mean) - print('MAE [yaw, pitch, roll]: [%.3f, %.3f, %.3f]' % (mae_ypr_mean[0], mae_ypr_mean[1], mae_ypr_mean[2])) - print('MAE mean: %.3f' % self.metrics_log['mae_mean']) - - # Per angle measurements - self.metrics_log['acc_label'] = [] - self.metrics_log['acc_adj_label'] = [] - - for angle_id, angle in enumerate(self.angles): - - # Accuracy per label - cm = self.error['cm_%s' % angle] - diagonal = np.diagonal(cm, offset=0).sum() - acc_main = diagonal / cm.sum().astype('float') - self.metrics_log['acc_label'].append(acc_main) - - # Permissive accuracy - diagonal_adj = diagonal.sum() + np.diagonal(cm, offset=-1).sum() + np.diagonal(cm, offset=1).sum() - acc_adj = diagonal_adj / cm.sum().astype('float') - self.metrics_log['acc_adj_label'].append(acc_adj) - - # Percentile of relevant angles - self.metrics_log['sr_%s' % angle] = {} - for angle_num in self.error_labels: - if max(mae_ypr[:, angle_id]) > angle_num: - [cumulative, base] = self.error['cumulative_%s' % angle] - perc = [cumulative[x[0] - 1] for x in enumerate(base) if x[1] > angle_num][0] - else: - perc = 1. - - self.metrics_log['sr_%s' % angle][angle_num] = perc - - print('Accuracy [yaw, pitch, roll]: ', self.metrics_log['acc_label']) - print('Accuracy [yaw, pitch, roll] (adjacency as TP): ', self.metrics_log['acc_adj_label']) - for angle in self.angles: - print('Success Rate %s: ' % angle, self.metrics_log['sr_%s' % angle]) - - return self.metrics_log - - def get_pimg_err(self, data_dict, img_select=None): - mae_mean = self.error['mae_mean'] - mae_ypr = self.error['mae_ypr'] - if img_select is not None: - mae_mean = [mae_mean[img_id] for img_id in img_select] - mae_ypr = [mae_ypr[img_id] for img_id in img_select] - name_dict = self.name + '/%s' - data_dict[name_dict % 'mae'] = mae_mean - mae_ypr = np.array(mae_ypr) - data_dict[name_dict % 'mae_yaw'] = mae_ypr[:, 0].tolist() - data_dict[name_dict % 'mae_pitch'] = mae_ypr[:, 1].tolist() - data_dict[name_dict % 'mae_roll'] = mae_ypr[:, 2].tolist() - return data_dict - - def _posit_anns(self): - - import spiga.data.loaders.dl_config as dl_config - import spiga.data.loaders.dataloader as dl - - # Load configuration - data_config = dl_config.AlignConfig(self.database, self.data_type) - data_config.image_size = (256, 256) - data_config.generate_pose = True - data_config.aug_names = [] - data_config.shuffle = False - dataloader, _ = dl.get_dataloader(1, data_config, debug=True) - - data_anns = [] - for num_batch, batch_dict in enumerate(dataloader): - pose = batch_dict['pose'].numpy() - data_anns.append({'headpose': pose[0].tolist()}) - return data_anns - - def _nearest_label(self, data): - data_tile = data[:, :, np.newaxis] - data_tile = np.tile(data_tile, len(self.pose_labels)) - diff_tile = np.abs(data_tile - self.pose_labels) - label_idx = diff_tile.argmin(axis=-1) - return label_idx - - def _cumulative_error(self, error, bins=1000): - num_imgs, base = np.histogram(error, bins=bins) - cumulative = [x / float(len(error)) for x in np.cumsum(num_imgs)] - return [cumulative[:bins], base[:bins]] diff --git a/spaces/taidi/bingai2/README.md b/spaces/taidi/bingai2/README.md deleted file mode 100644 index 0ab51fcecee3f962e752e2735cd90ac8a2549f07..0000000000000000000000000000000000000000 --- a/spaces/taidi/bingai2/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Bingai2 -emoji: 😻 -colorFrom: pink -colorTo: pink -sdk: docker -pinned: false -app_port: 8080 ---- - 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- A new tab will open with the PDF file of the songs. You can either save it to your device or print it out for your convenience. -
- You can also listen to the audio versions of the songs by clicking on the links that say "Play Audio". -
- Enjoy the divine music and feel the blessings of Lord Ayyappa. -
Ayyappan Bhajan Songs Lyrics In Tamil Pdf 105 is a great resource for anyone who wants to learn and sing along with the devotional songs of Lord Ayyappa. The songs can help you connect with your inner self and experience peace and joy. They can also enhance your faith and devotion towards Lord Ayyappa and his teachings.
-We hope this article has helped you find what you were looking for. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have a wonderful day.
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-Samiye Saranam Pon Ayyappa Samiye Saranam Pon Ayyappa Song Lyrics in Tamil PDFAyyappan Bhajan Songs Lyrics In Tamil Pdf 105 is not only a collection of songs, but also a treasure of wisdom and knowledge. The songs contain various stories and legends about Lord Ayyappa and his miracles. They also explain the significance and symbolism of his various attributes and forms. For example, the song "Ayyappa Ashtothram" lists the 108 names of Lord Ayyappa and their meanings. The song "Ayyappa Saranam" enumerates the 108 sacred mantras that can protect and bless the devotees.
-Ayyappan Bhajan Songs Lyrics In Tamil Pdf 105 is also a source of inspiration and motivation for the devotees who undertake the Sabarimala pilgrimage. The songs encourage and support the devotees to follow the strict rules and rituals of the pilgrimage, such as wearing black clothes, abstaining from meat, alcohol, tobacco, and sex, walking barefoot, carrying the sacred irumudi (a bundle of offerings), and chanting "Swamiye Saranam Ayyappa" (I seek refuge in Lord Ayyappa). The songs also describe the beauty and glory of Sabarimala and the joy of seeing Lord Ayyappa in his majestic form.
-Ayyappan Bhajan Songs Lyrics In Tamil Pdf 105 is a must-have for every devotee of Lord Ayyappa. The songs can enrich your spiritual life and bring you closer to Lord Ayyappa. They can also fill your mind and heart with positive energy and happiness. Whether you are planning to go to Sabarimala or not, you can benefit from listening to and singing these songs. They can make you feel the presence of Lord Ayyappa in your everyday life and help you overcome any challenges or difficulties.
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\ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/3 Bgm Love Ringtones The Best Collection of Romantic Tunes.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/3 Bgm Love Ringtones The Best Collection of Romantic Tunes.md deleted file mode 100644 index cbee8784f5686c6519de437838aa9877f0085188..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/3 Bgm Love Ringtones The Best Collection of Romantic Tunes.md +++ /dev/null @@ -1,177 +0,0 @@ - -3 BGM Ringtone Download Love: How to Find and Download the Best Background Music for Your Phone
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-FAQs
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- Open the Settings app on your phone and tap on Sound. -
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\ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Euro Truck Simulator 2 for Windows PC in Zip Format.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Euro Truck Simulator 2 for Windows PC in Zip Format.md deleted file mode 100644 index ac705933cbc9ddb9c9e92fe1398d1aba7f0889ad..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Euro Truck Simulator 2 for Windows PC in Zip Format.md +++ /dev/null @@ -1,143 +0,0 @@ - -Euro Truck Simulator 2: A Relaxing and Realistic Trucking Game for Windows
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-Euro Truck Simulator 2 or ETS2 is a trucking game developed by SCS Software for Windows PCs. It is the second instalment in the Euro Truck Simulator series, which started in 2008. The game lets you take on the role of a truck driver who has to deliver various kinds of cargo across Europe, from food and furniture to chemicals and cars. Along the way, you will encounter different road conditions, traffic rules, weather effects, and landmarks.
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-Euro Truck Simulator 2 has been praised by critics and players alike for its realistic and immersive trucking experience. The game features over 70 cities and 20 countries to explore, each with its own unique scenery, culture, and architecture. You can drive through the Alps, the Mediterranean coast, the Baltic Sea, and more. You can also see famous landmarks like the Eiffel Tower, the Colosseum, the Big Ben, and the Brandenburg Gate.
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- OS: Windows 7 or higher -
- Processor: Dual core CPU 2.4 GHz -
- Memory: 4 GB RAM -
- Graphics: GeForce GTS 450-class (Intel HD 4000) -
- Storage: 5 GB available space -
The recommended system requirements for Euro Truck Simulator 2 are as follows:
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- OS: Windows 7 or higher -
- Processor: Quad core CPU 3.0 GHz -
- Memory: 6 GB RAM -
- Graphics: GeForce GTX 760-class (2 GB) -
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How to install Euro Truck Simulator 2 on PC?
-To install Euro Truck Simulator 2 on PC, you need to follow these steps:
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- Download the game file from the website of your choice. -
- Extract the zip file using a program like WinRAR or 7-Zip. -
- Run the setup.exe file and follow the instructions. -
- Copy the crack file from the crack folder and paste it into the game folder. -
- Launch the game and enjoy. -
How to play Euro Truck Simulator 2 on PC?
-To play Euro Truck Simulator 2 on PC, you need to follow these steps:
--
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- Create a profile and choose your name, avatar, company logo, and preferred truck brand. -
- Select a starting city and take your first job from the job market. -
- Drive your truck to the pickup location and attach the trailer. -
- Drive your truck to the delivery location and detach the trailer. -
- Earn money and experience points for completing deliveries. -
- Buy, customize, and upgrade your own truck or hire other drivers. -
- Explore new cities and countries and take on more challenging jobs. -
How to update Euro Truck Simulator 2 on PC?
-To update Euro Truck Simulator 2 on PC, you need to follow these steps:
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- Visit the official website of the game or the website where you downloaded the game from. -
- Download the latest patch or update file for the game. -
- Extract the zip file using a program like WinRAR or 7-Zip. -
- Run the update.exe file and follow the instructions. -
- Copy the crack file from the crack folder and paste it into the game folder. -
- Launch the game and enjoy. -
How to mod Euro Truck Simulator 2 on PC?
-To mod Euro Truck Simulator 2 on PC, you need to follow these steps:
--
-
- Visit a website that offers mods for Euro Truck Simulator 2, such as https://ets2.lt/en/, https://www.modland.net/euro-truck-simulator-2/, or https://steamcommunity.com/app/227300/workshop/. -
- Browse through the categories and find a mod that you like. -
- Download the mod file and extract it using a program like WinRAR or 7-Zip. -
- Copy the .scs file or folder from the mod and paste it into the Documents\Euro Truck Simulator 2\mod folder. -
- Launch the game and go to the mod manager in the profile selection screen. -
- Select and activate the mod that you want to use. -
- Play the game and enjoy. -
-
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\ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Hair Challenge Mod APK with Unlimited Diamonds and No Ads.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Hair Challenge Mod APK with Unlimited Diamonds and No Ads.md deleted file mode 100644 index 2fb5bae400fdaa7c2301c6f24f02dca0768e1415..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Download Hair Challenge Mod APK with Unlimited Diamonds and No Ads.md +++ /dev/null @@ -1,88 +0,0 @@ - -Download Hair Challenge Mod APK: A Fun and Addictive Game for All Ages
-Do you love playing games that are easy to learn but hard to master? Do you enjoy styling your hair and making it look fabulous? If you answered yes to both questions, then you will love Hair Challenge, a popular game that has millions of downloads and positive reviews on Google Play Store. And if you want to make the game even more fun and exciting, you should download Hair Challenge Mod APK, which gives you unlimited diamonds, no ads, and access to all skins and accessories. In this article, we will tell you everything you need to know about Hair Challenge and its modded version, including how to download and install it on your device.
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-What is Hair Challenge?
-Hair Challenge is a casual game developed by Rollic Games, a Turkish studio that specializes in creating hyper-casual games for mobile devices. The game was released in April 2021 and has quickly become one of the most downloaded games on the Play Store, with over 100 million installs and a 4.1-star rating.
-The game is simple but addictive: you control a girl with long hair who runs through various obstacles and collects more hair along the way. The longer your hair gets, the more points you earn. But be careful: some obstacles will cut your hair or make it dirty, so you have to avoid them or use scissors to trim your hair. You can also customize your character with different skins, hair colors, and accessories, such as hats, glasses, earrings, and more.
-How to play Hair Challenge
-The game is easy to play: you just need to swipe left or right on the screen to move your character and avoid the obstacles. You can also tap on the screen to use scissors to cut your hair when it gets too long or dirty. The game has many levels with different themes and challenges, such as ice, fire, candy, jungle, etc. Each level has a finish line where you can show off your hair length and style. The longer your hair is at the end of the level, the more diamonds you earn. You can use these diamonds to buy new skins and accessories for your character.
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-download hair challenge mod apk easy installWhy you should download Hair Challenge Mod APK
-While Hair Challenge is a fun game to play, it also has some drawbacks that may affect your gaming experience. For example, the game has many ads that pop up frequently and interrupt your gameplay. The game also requires you to watch ads or spend real money to unlock some skins and accessories. Moreover, the game can be quite repetitive and boring after a while, as there is not much variety or challenge in the gameplay.
-That's why we recommend you to download Hair Challenge Mod APK, which is a modified version of the original game that offers many benefits and features that are not available in the official version. Here are some of the reasons why you should download Hair Challenge Mod APK:
-Features of Hair Challenge Mod APK
-Unlimited diamonds
-One of the best features of Hair Challenge Mod APK is that it gives you unlimited diamonds, which are the main currency in the game. You can use these diamonds to buy any skin or accessory that you want without watching ads or spending real money. You can also use these diamonds to revive your character if you fail a level or skip a level if you find it too hard.
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-Another great feature of Hair Challenge Mod APK is that it removes all the ads from the game. This means that you can enjoy the game without any interruptions or distractions. You don't have to watch ads to unlock skins or accessories or to get extra diamonds. You can also save your mobile
Unlock all skins and accessories
-With Hair Challenge Mod APK, you can also unlock all the skins and accessories that are available in the game. You don't have to complete levels or watch ads to get them. You can choose from a variety of options to customize your character, such as different hair colors, hairstyles, outfits, hats, glasses, earrings, and more. You can also mix and match different items to create your own unique look.
-How to download and install Hair Challenge Mod APK
-If you are interested in downloading and installing Hair Challenge Mod APK on your device, you can follow these simple steps:
-Step 1: Download the APK file from a trusted source
-The first thing you need to do is to download the APK file of Hair Challenge Mod APK from a reliable and safe source. You can use the link below to download the latest version of the modded game. The file size is about 100 MB, so make sure you have enough storage space on your device.
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-Step 2: Enable unknown sources on your device
-The next thing you need to do is to enable unknown sources on your device. This is a security setting that allows you to install apps that are not from the official Play Store. To do this, go to your device settings and look for the security or privacy option. Then, find the unknown sources option and toggle it on. You may see a warning message, but don't worry, it's safe to install Hair Challenge Mod APK.
-Step 3: Install the APK file and enjoy the game
-The final step is to install the APK file that you downloaded in step 1. To do this, locate the file in your device's file manager and tap on it. You may see a pop-up window asking for your permission to install the app. Just tap on install and wait for the process to finish. Once the installation is done, you can open the app and enjoy the game with all its features.
-Conclusion
-Hair Challenge is a fun and addictive game that lets you style your hair and run through various obstacles. The game is easy to play but hard to master, as you have to avoid or cut your hair when it gets too long or dirty. You can also customize your character with different skins and accessories that you can buy with diamonds or unlock by watching ads.
-However, if you want to enhance your gaming experience and enjoy the game without any limitations, you should download Hair Challenge Mod APK, which gives you unlimited diamonds, no ads, and access to all skins and accessories. You can download and install the modded game easily by following the steps above.
-We hope this article was helpful and informative for you. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading!
-FAQs
-Here are some of the frequently asked questions about Hair Challenge Mod APK:
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- Is Hair Challenge Mod APK safe to use? -
- Is Hair Challenge Mod APK compatible with my device? -
- Can I play Hair Challenge Mod APK online with other players? -
- Can I update Hair Challenge Mod APK when a new version is released? -
- Can I use Hair Challenge Mod APK with my existing account? -
Yes, Hair Challenge Mod APK is safe to use, as long as you download it from a trusted source like the one we provided above. The modded game does not contain any viruses or malware that can harm your device or compromise your privacy.
-Hair Challenge Mod APK is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may not support some features or functions of the modded game due to hardware or software limitations.
-No, Hair Challenge Mod APK is not an online game, so you cannot play it with other players. The game is a single-player game that does not require an internet connection to play.
-No, you cannot update Hair Challenge Mod APK automatically when a new version of the original game is released. You have to download and install the new version of the modded game manually from the same source that you used before.
-No, you cannot use Hair Challenge Mod APK with your existing account that you used in the official version of the game. The modded game has a separate database and server that does not sync with the original game. Therefore, you have to start from scratch when you use Hair Challenge Mod APK.
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\ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Falcon Age Update 1.09 CODEX FitGirl.md b/spaces/tioseFevbu/cartoon-converter/scripts/Falcon Age Update 1.09 CODEX FitGirl.md deleted file mode 100644 index 6701caa4d8aa194b908da22f4045968dec6ea9d0..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Falcon Age Update 1.09 CODEX FitGirl.md +++ /dev/null @@ -1,34 +0,0 @@ - -Falcon Age Update 1.09 CODEX, FitGirl: What's New in the Latest Patch?
-Falcon Age is a first-person action-adventure game that lets you bond with a falcon and fight against a robot invasion. The game was released in 2019 by Outerloop Games and has received several updates since then. The latest update, version 1.09, was released on October 2022 by CODEX and FitGirl.
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How to Download and Install Falcon Age Update 1.09 CODEX, FitGirl?
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- -After downloading the patch, you need to extract it using a program like WinRAR or 7-Zip. Then, you need to run the setup.exe file and follow the instructions to install the update. Finally, you need to copy the contents of the CODEX folder and paste them into your game directory, replacing any existing files.
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\ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Free Spanish Ebook !FREE! Download The End We Start.md b/spaces/tioseFevbu/cartoon-converter/scripts/Free Spanish Ebook !FREE! Download The End We Start.md deleted file mode 100644 index 1c0538260172987cd64a4541e025e4d1cc5ffba4..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Free Spanish Ebook !FREE! Download The End We Start.md +++ /dev/null @@ -1,18 +0,0 @@ - -How to Download The End We Start From by Megan Hunter for Free in Spanish
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\ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Gilles Lartigot Eat Pdf Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Gilles Lartigot Eat Pdf Download.md deleted file mode 100644 index 7e19b0a9a6b2ed60c6149539fcabe10a3368152f..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Gilles Lartigot Eat Pdf Download.md +++ /dev/null @@ -1,22 +0,0 @@ -
-How to Download Gilles Lartigot's Eat: A Guide to Healthy Eating
-Gilles Lartigot is a French author and activist who has written two books on the topic of healthy eating: Eat: Chronicles of a Beast in the Food Jungle (2013) and Eat 2: Of the Dead and the Living - Chronicles of a Toxic Society (2017). In these books, he shares his personal journey and his insights on the modern food industry, the effects of industrial farming and chemical additives on our health, and the benefits of adopting a more natural and organic diet.
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- The Omnivore's Dilemma: A Natural History of Four Meals by Michael Pollan. This book explores the origins and consequences of the food choices we make every day, from industrial corn to organic vegetables to fast food. It also offers some suggestions for eating more ethically and sustainably. -
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- Food Rules: An Eater's Manual by Michael Pollan. This book is a concise and practical guide to eating well, based on 64 simple rules that cover everything from what to eat, how to eat, and when to eat. -
- How Not to Die: Discover the Foods Scientifically Proven to Prevent and Reverse Disease by Michael Greger and Gene Stone. This book examines the role of nutrition in preventing and treating various chronic diseases, such as heart disease, diabetes, cancer, and Alzheimer's. It also provides a list of foods that can help boost your health and longevity. -
- The China Study: The Most Comprehensive Study of Nutrition Ever Conducted and the Startling Implications for Diet, Weight Loss, and Long-term Health by T. Colin Campbell and Thomas M. Campbell II. This book presents the findings of a massive research project that compared the health outcomes of people who ate different diets in rural China. It reveals that a plant-based diet can reduce the risk of many diseases and improve overall well-being. -
These are just some of the books that you may want to read if you are interested in learning more about healthy eating and its benefits for your body and mind. Of course, you should always consult your doctor before making any major changes to your diet or lifestyle.
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\ No newline at end of file diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/resolution/legacy/resolver.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/resolution/legacy/resolver.py deleted file mode 100644 index fb49d41695fec744a674da8bc11b646264c768b7..0000000000000000000000000000000000000000 --- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/resolution/legacy/resolver.py +++ /dev/null @@ -1,600 +0,0 @@ -"""Dependency Resolution - -The dependency resolution in pip is performed as follows: - -for top-level requirements: - a. only one spec allowed per project, regardless of conflicts or not. - otherwise a "double requirement" exception is raised - b. they override sub-dependency requirements. -for sub-dependencies - a. "first found, wins" (where the order is breadth first) -""" - -# The following comment should be removed at some point in the future. -# mypy: strict-optional=False - -import logging -import sys -from collections import defaultdict -from itertools import chain -from typing import DefaultDict, Iterable, List, Optional, Set, Tuple - -from pip._vendor.packaging import specifiers -from pip._vendor.packaging.requirements import Requirement - -from pip._internal.cache import WheelCache -from pip._internal.exceptions import ( - BestVersionAlreadyInstalled, - DistributionNotFound, - HashError, - HashErrors, - InstallationError, - NoneMetadataError, - UnsupportedPythonVersion, -) -from pip._internal.index.package_finder import PackageFinder -from pip._internal.metadata import BaseDistribution -from pip._internal.models.link import Link -from pip._internal.models.wheel import Wheel -from pip._internal.operations.prepare import RequirementPreparer -from pip._internal.req.req_install import ( - InstallRequirement, - check_invalid_constraint_type, -) -from pip._internal.req.req_set import RequirementSet -from pip._internal.resolution.base import BaseResolver, InstallRequirementProvider -from pip._internal.utils import compatibility_tags -from pip._internal.utils.compatibility_tags import get_supported -from pip._internal.utils.direct_url_helpers import direct_url_from_link -from pip._internal.utils.logging import indent_log -from pip._internal.utils.misc import normalize_version_info -from pip._internal.utils.packaging import check_requires_python - -logger = logging.getLogger(__name__) - -DiscoveredDependencies = DefaultDict[str, List[InstallRequirement]] - - -def _check_dist_requires_python( - dist: BaseDistribution, - version_info: Tuple[int, int, int], - ignore_requires_python: bool = False, -) -> None: - """ - Check whether the given Python version is compatible with a distribution's - "Requires-Python" value. - - :param version_info: A 3-tuple of ints representing the Python - major-minor-micro version to check. - :param ignore_requires_python: Whether to ignore the "Requires-Python" - value if the given Python version isn't compatible. - - :raises UnsupportedPythonVersion: When the given Python version isn't - compatible. - """ - # This idiosyncratically converts the SpecifierSet to str and let - # check_requires_python then parse it again into SpecifierSet. But this - # is the legacy resolver so I'm just not going to bother refactoring. - try: - requires_python = str(dist.requires_python) - except FileNotFoundError as e: - raise NoneMetadataError(dist, str(e)) - try: - is_compatible = check_requires_python( - requires_python, - version_info=version_info, - ) - except specifiers.InvalidSpecifier as exc: - logger.warning( - "Package %r has an invalid Requires-Python: %s", dist.raw_name, exc - ) - return - - if is_compatible: - return - - version = ".".join(map(str, version_info)) - if ignore_requires_python: - logger.debug( - "Ignoring failed Requires-Python check for package %r: %s not in %r", - dist.raw_name, - version, - requires_python, - ) - return - - raise UnsupportedPythonVersion( - "Package {!r} requires a different Python: {} not in {!r}".format( - dist.raw_name, version, requires_python - ) - ) - - -class Resolver(BaseResolver): - """Resolves which packages need to be installed/uninstalled to perform \ - the requested operation without breaking the requirements of any package. - """ - - _allowed_strategies = {"eager", "only-if-needed", "to-satisfy-only"} - - def __init__( - self, - preparer: RequirementPreparer, - finder: PackageFinder, - wheel_cache: Optional[WheelCache], - make_install_req: InstallRequirementProvider, - use_user_site: bool, - ignore_dependencies: bool, - ignore_installed: bool, - ignore_requires_python: bool, - force_reinstall: bool, - upgrade_strategy: str, - py_version_info: Optional[Tuple[int, ...]] = None, - ) -> None: - super().__init__() - assert upgrade_strategy in self._allowed_strategies - - if py_version_info is None: - py_version_info = sys.version_info[:3] - else: - py_version_info = normalize_version_info(py_version_info) - - self._py_version_info = py_version_info - - self.preparer = preparer - self.finder = finder - self.wheel_cache = wheel_cache - - self.upgrade_strategy = upgrade_strategy - self.force_reinstall = force_reinstall - self.ignore_dependencies = ignore_dependencies - self.ignore_installed = ignore_installed - self.ignore_requires_python = ignore_requires_python - self.use_user_site = use_user_site - self._make_install_req = make_install_req - - self._discovered_dependencies: DiscoveredDependencies = defaultdict(list) - - def resolve( - self, root_reqs: List[InstallRequirement], check_supported_wheels: bool - ) -> RequirementSet: - """Resolve what operations need to be done - - As a side-effect of this method, the packages (and their dependencies) - are downloaded, unpacked and prepared for installation. This - preparation is done by ``pip.operations.prepare``. - - Once PyPI has static dependency metadata available, it would be - possible to move the preparation to become a step separated from - dependency resolution. - """ - requirement_set = RequirementSet(check_supported_wheels=check_supported_wheels) - for req in root_reqs: - if req.constraint: - check_invalid_constraint_type(req) - self._add_requirement_to_set(requirement_set, req) - - # Actually prepare the files, and collect any exceptions. Most hash - # exceptions cannot be checked ahead of time, because - # _populate_link() needs to be called before we can make decisions - # based on link type. - discovered_reqs: List[InstallRequirement] = [] - hash_errors = HashErrors() - for req in chain(requirement_set.all_requirements, discovered_reqs): - try: - discovered_reqs.extend(self._resolve_one(requirement_set, req)) - except HashError as exc: - exc.req = req - hash_errors.append(exc) - - if hash_errors: - raise hash_errors - - return requirement_set - - def _add_requirement_to_set( - self, - requirement_set: RequirementSet, - install_req: InstallRequirement, - parent_req_name: Optional[str] = None, - extras_requested: Optional[Iterable[str]] = None, - ) -> Tuple[List[InstallRequirement], Optional[InstallRequirement]]: - """Add install_req as a requirement to install. - - :param parent_req_name: The name of the requirement that needed this - added. The name is used because when multiple unnamed requirements - resolve to the same name, we could otherwise end up with dependency - links that point outside the Requirements set. parent_req must - already be added. Note that None implies that this is a user - supplied requirement, vs an inferred one. - :param extras_requested: an iterable of extras used to evaluate the - environment markers. - :return: Additional requirements to scan. That is either [] if - the requirement is not applicable, or [install_req] if the - requirement is applicable and has just been added. - """ - # If the markers do not match, ignore this requirement. - if not install_req.match_markers(extras_requested): - logger.info( - "Ignoring %s: markers '%s' don't match your environment", - install_req.name, - install_req.markers, - ) - return [], None - - # If the wheel is not supported, raise an error. - # Should check this after filtering out based on environment markers to - # allow specifying different wheels based on the environment/OS, in a - # single requirements file. - if install_req.link and install_req.link.is_wheel: - wheel = Wheel(install_req.link.filename) - tags = compatibility_tags.get_supported() - if requirement_set.check_supported_wheels and not wheel.supported(tags): - raise InstallationError( - "{} is not a supported wheel on this platform.".format( - wheel.filename - ) - ) - - # This next bit is really a sanity check. - assert ( - not install_req.user_supplied or parent_req_name is None - ), "a user supplied req shouldn't have a parent" - - # Unnamed requirements are scanned again and the requirement won't be - # added as a dependency until after scanning. - if not install_req.name: - requirement_set.add_unnamed_requirement(install_req) - return [install_req], None - - try: - existing_req: Optional[ - InstallRequirement - ] = requirement_set.get_requirement(install_req.name) - except KeyError: - existing_req = None - - has_conflicting_requirement = ( - parent_req_name is None - and existing_req - and not existing_req.constraint - and existing_req.extras == install_req.extras - and existing_req.req - and install_req.req - and existing_req.req.specifier != install_req.req.specifier - ) - if has_conflicting_requirement: - raise InstallationError( - "Double requirement given: {} (already in {}, name={!r})".format( - install_req, existing_req, install_req.name - ) - ) - - # When no existing requirement exists, add the requirement as a - # dependency and it will be scanned again after. - if not existing_req: - requirement_set.add_named_requirement(install_req) - # We'd want to rescan this requirement later - return [install_req], install_req - - # Assume there's no need to scan, and that we've already - # encountered this for scanning. - if install_req.constraint or not existing_req.constraint: - return [], existing_req - - does_not_satisfy_constraint = install_req.link and not ( - existing_req.link and install_req.link.path == existing_req.link.path - ) - if does_not_satisfy_constraint: - raise InstallationError( - "Could not satisfy constraints for '{}': " - "installation from path or url cannot be " - "constrained to a version".format(install_req.name) - ) - # If we're now installing a constraint, mark the existing - # object for real installation. - existing_req.constraint = False - # If we're now installing a user supplied requirement, - # mark the existing object as such. - if install_req.user_supplied: - existing_req.user_supplied = True - existing_req.extras = tuple( - sorted(set(existing_req.extras) | set(install_req.extras)) - ) - logger.debug( - "Setting %s extras to: %s", - existing_req, - existing_req.extras, - ) - # Return the existing requirement for addition to the parent and - # scanning again. - return [existing_req], existing_req - - def _is_upgrade_allowed(self, req: InstallRequirement) -> bool: - if self.upgrade_strategy == "to-satisfy-only": - return False - elif self.upgrade_strategy == "eager": - return True - else: - assert self.upgrade_strategy == "only-if-needed" - return req.user_supplied or req.constraint - - def _set_req_to_reinstall(self, req: InstallRequirement) -> None: - """ - Set a requirement to be installed. - """ - # Don't uninstall the conflict if doing a user install and the - # conflict is not a user install. - if not self.use_user_site or req.satisfied_by.in_usersite: - req.should_reinstall = True - req.satisfied_by = None - - def _check_skip_installed( - self, req_to_install: InstallRequirement - ) -> Optional[str]: - """Check if req_to_install should be skipped. - - This will check if the req is installed, and whether we should upgrade - or reinstall it, taking into account all the relevant user options. - - After calling this req_to_install will only have satisfied_by set to - None if the req_to_install is to be upgraded/reinstalled etc. Any - other value will be a dist recording the current thing installed that - satisfies the requirement. - - Note that for vcs urls and the like we can't assess skipping in this - routine - we simply identify that we need to pull the thing down, - then later on it is pulled down and introspected to assess upgrade/ - reinstalls etc. - - :return: A text reason for why it was skipped, or None. - """ - if self.ignore_installed: - return None - - req_to_install.check_if_exists(self.use_user_site) - if not req_to_install.satisfied_by: - return None - - if self.force_reinstall: - self._set_req_to_reinstall(req_to_install) - return None - - if not self._is_upgrade_allowed(req_to_install): - if self.upgrade_strategy == "only-if-needed": - return "already satisfied, skipping upgrade" - return "already satisfied" - - # Check for the possibility of an upgrade. For link-based - # requirements we have to pull the tree down and inspect to assess - # the version #, so it's handled way down. - if not req_to_install.link: - try: - self.finder.find_requirement(req_to_install, upgrade=True) - except BestVersionAlreadyInstalled: - # Then the best version is installed. - return "already up-to-date" - except DistributionNotFound: - # No distribution found, so we squash the error. It will - # be raised later when we re-try later to do the install. - # Why don't we just raise here? - pass - - self._set_req_to_reinstall(req_to_install) - return None - - def _find_requirement_link(self, req: InstallRequirement) -> Optional[Link]: - upgrade = self._is_upgrade_allowed(req) - best_candidate = self.finder.find_requirement(req, upgrade) - if not best_candidate: - return None - - # Log a warning per PEP 592 if necessary before returning. - link = best_candidate.link - if link.is_yanked: - reason = link.yanked_reason or "" - msg = ( - # Mark this as a unicode string to prevent - # "UnicodeEncodeError: 'ascii' codec can't encode character" - # in Python 2 when the reason contains non-ascii characters. - "The candidate selected for download or install is a " - "yanked version: {candidate}\n" - "Reason for being yanked: {reason}" - ).format(candidate=best_candidate, reason=reason) - logger.warning(msg) - - return link - - def _populate_link(self, req: InstallRequirement) -> None: - """Ensure that if a link can be found for this, that it is found. - - Note that req.link may still be None - if the requirement is already - installed and not needed to be upgraded based on the return value of - _is_upgrade_allowed(). - - If preparer.require_hashes is True, don't use the wheel cache, because - cached wheels, always built locally, have different hashes than the - files downloaded from the index server and thus throw false hash - mismatches. Furthermore, cached wheels at present have undeterministic - contents due to file modification times. - """ - if req.link is None: - req.link = self._find_requirement_link(req) - - if self.wheel_cache is None or self.preparer.require_hashes: - return - cache_entry = self.wheel_cache.get_cache_entry( - link=req.link, - package_name=req.name, - supported_tags=get_supported(), - ) - if cache_entry is not None: - logger.debug("Using cached wheel link: %s", cache_entry.link) - if req.link is req.original_link and cache_entry.persistent: - req.original_link_is_in_wheel_cache = True - if cache_entry.origin is not None: - req.download_info = cache_entry.origin - else: - # Legacy cache entry that does not have origin.json. - # download_info may miss the archive_info.hash field. - req.download_info = direct_url_from_link( - req.link, link_is_in_wheel_cache=cache_entry.persistent - ) - req.link = cache_entry.link - - def _get_dist_for(self, req: InstallRequirement) -> BaseDistribution: - """Takes a InstallRequirement and returns a single AbstractDist \ - representing a prepared variant of the same. - """ - if req.editable: - return self.preparer.prepare_editable_requirement(req) - - # satisfied_by is only evaluated by calling _check_skip_installed, - # so it must be None here. - assert req.satisfied_by is None - skip_reason = self._check_skip_installed(req) - - if req.satisfied_by: - return self.preparer.prepare_installed_requirement(req, skip_reason) - - # We eagerly populate the link, since that's our "legacy" behavior. - self._populate_link(req) - dist = self.preparer.prepare_linked_requirement(req) - - # NOTE - # The following portion is for determining if a certain package is - # going to be re-installed/upgraded or not and reporting to the user. - # This should probably get cleaned up in a future refactor. - - # req.req is only avail after unpack for URL - # pkgs repeat check_if_exists to uninstall-on-upgrade - # (#14) - if not self.ignore_installed: - req.check_if_exists(self.use_user_site) - - if req.satisfied_by: - should_modify = ( - self.upgrade_strategy != "to-satisfy-only" - or self.force_reinstall - or self.ignore_installed - or req.link.scheme == "file" - ) - if should_modify: - self._set_req_to_reinstall(req) - else: - logger.info( - "Requirement already satisfied (use --upgrade to upgrade): %s", - req, - ) - return dist - - def _resolve_one( - self, - requirement_set: RequirementSet, - req_to_install: InstallRequirement, - ) -> List[InstallRequirement]: - """Prepare a single requirements file. - - :return: A list of additional InstallRequirements to also install. - """ - # Tell user what we are doing for this requirement: - # obtain (editable), skipping, processing (local url), collecting - # (remote url or package name) - if req_to_install.constraint or req_to_install.prepared: - return [] - - req_to_install.prepared = True - - # Parse and return dependencies - dist = self._get_dist_for(req_to_install) - # This will raise UnsupportedPythonVersion if the given Python - # version isn't compatible with the distribution's Requires-Python. - _check_dist_requires_python( - dist, - version_info=self._py_version_info, - ignore_requires_python=self.ignore_requires_python, - ) - - more_reqs: List[InstallRequirement] = [] - - def add_req(subreq: Requirement, extras_requested: Iterable[str]) -> None: - # This idiosyncratically converts the Requirement to str and let - # make_install_req then parse it again into Requirement. But this is - # the legacy resolver so I'm just not going to bother refactoring. - sub_install_req = self._make_install_req(str(subreq), req_to_install) - parent_req_name = req_to_install.name - to_scan_again, add_to_parent = self._add_requirement_to_set( - requirement_set, - sub_install_req, - parent_req_name=parent_req_name, - extras_requested=extras_requested, - ) - if parent_req_name and add_to_parent: - self._discovered_dependencies[parent_req_name].append(add_to_parent) - more_reqs.extend(to_scan_again) - - with indent_log(): - # We add req_to_install before its dependencies, so that we - # can refer to it when adding dependencies. - if not requirement_set.has_requirement(req_to_install.name): - # 'unnamed' requirements will get added here - # 'unnamed' requirements can only come from being directly - # provided by the user. - assert req_to_install.user_supplied - self._add_requirement_to_set( - requirement_set, req_to_install, parent_req_name=None - ) - - if not self.ignore_dependencies: - if req_to_install.extras: - logger.debug( - "Installing extra requirements: %r", - ",".join(req_to_install.extras), - ) - missing_requested = sorted( - set(req_to_install.extras) - set(dist.iter_provided_extras()) - ) - for missing in missing_requested: - logger.warning( - "%s %s does not provide the extra '%s'", - dist.raw_name, - dist.version, - missing, - ) - - available_requested = sorted( - set(dist.iter_provided_extras()) & set(req_to_install.extras) - ) - for subreq in dist.iter_dependencies(available_requested): - add_req(subreq, extras_requested=available_requested) - - return more_reqs - - def get_installation_order( - self, req_set: RequirementSet - ) -> List[InstallRequirement]: - """Create the installation order. - - The installation order is topological - requirements are installed - before the requiring thing. We break cycles at an arbitrary point, - and make no other guarantees. - """ - # The current implementation, which we may change at any point - # installs the user specified things in the order given, except when - # dependencies must come earlier to achieve topological order. - order = [] - ordered_reqs: Set[InstallRequirement] = set() - - def schedule(req: InstallRequirement) -> None: - if req.satisfied_by or req in ordered_reqs: - return - if req.constraint: - return - ordered_reqs.add(req) - for dep in self._discovered_dependencies[req.name]: - schedule(dep) - order.append(req) - - for install_req in req_set.requirements.values(): - schedule(install_req) - return order diff --git a/spaces/tomofi/MMOCR/README.md b/spaces/tomofi/MMOCR/README.md deleted file mode 100644 index 1be4c748362fbc8e8712b9e39936072141862e5a..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: MMOCR -emoji: 🌍 -colorFrom: blue -colorTo: purple -sdk: gradio -sdk_version: 2.8.11 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/tomofi/MMOCR/mmocr/models/textrecog/heads/seg_head.py b/spaces/tomofi/MMOCR/mmocr/models/textrecog/heads/seg_head.py deleted file mode 100644 index e8686db8e1294607d8eb8709928dfd4b958b9609..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/mmocr/models/textrecog/heads/seg_head.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch.nn.functional as F -from mmcv.cnn import ConvModule -from mmcv.runner import BaseModule -from torch import nn - -from mmocr.models.builder import HEADS - - -@HEADS.register_module() -class SegHead(BaseModule): - """Head for segmentation based text recognition. - - Args: - in_channels (int): Number of input channels :math:`C`. - num_classes (int): Number of output classes :math:`C_{out}`. - upsample_param (dict | None): Config dict for interpolation layer. - Default: ``dict(scale_factor=1.0, mode='nearest')`` - init_cfg (dict or list[dict], optional): Initialization configs. - """ - - def __init__(self, - in_channels=128, - num_classes=37, - upsample_param=None, - init_cfg=None): - super().__init__(init_cfg=init_cfg) - assert isinstance(num_classes, int) - assert num_classes > 0 - assert upsample_param is None or isinstance(upsample_param, dict) - - self.upsample_param = upsample_param - - self.seg_conv = ConvModule( - in_channels, - in_channels, - 3, - stride=1, - padding=1, - norm_cfg=dict(type='BN')) - - # prediction - self.pred_conv = nn.Conv2d( - in_channels, num_classes, kernel_size=1, stride=1, padding=0) - - def forward(self, out_neck): - """ - Args: - out_neck (list[Tensor]): A list of tensor of shape - :math:`(N, C_i, H_i, W_i)`. The network only uses the last one - (``out_neck[-1]``). - - Returns: - Tensor: A tensor of shape :math:`(N, C_{out}, kH, kW)` where - :math:`k` is determined by ``upsample_param``. - """ - - seg_map = self.seg_conv(out_neck[-1]) - seg_map = self.pred_conv(seg_map) - - if self.upsample_param is not None: - seg_map = F.interpolate(seg_map, **self.upsample_param) - - return seg_map diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py deleted file mode 100644 index a4b987a19ae32453d524fc2f7a4fb6b6b87f1f32..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py +++ /dev/null @@ -1,5 +0,0 @@ -_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py' - -# learning policy -lr_config = dict(step=[16, 22]) -runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/spaces/training-transformers-together/Dashboard/streamlit_observable/frontend/build/static/js/2.b1c975ff.chunk.js b/spaces/training-transformers-together/Dashboard/streamlit_observable/frontend/build/static/js/2.b1c975ff.chunk.js deleted file mode 100644 index c369a482a48d1fd13f73b9ed7f6e35a45a52cc94..0000000000000000000000000000000000000000 --- a/spaces/training-transformers-together/Dashboard/streamlit_observable/frontend/build/static/js/2.b1c975ff.chunk.js +++ /dev/null @@ -1,3 +0,0 @@ -/*! 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- diff --git a/spaces/valeriylo/saiga_rag/app.py b/spaces/valeriylo/saiga_rag/app.py deleted file mode 100644 index 8c679943d9a73f61e2d2c1c305762c17e8f4618e..0000000000000000000000000000000000000000 --- a/spaces/valeriylo/saiga_rag/app.py +++ /dev/null @@ -1,375 +0,0 @@ -import gradio as gr - -from uuid import uuid4 -from huggingface_hub import snapshot_download -from langchain.document_loaders import ( - CSVLoader, - EverNoteLoader, - PDFMinerLoader, - PyPDFium2Loader, - TextLoader, - UnstructuredEmailLoader, - UnstructuredEPubLoader, - UnstructuredHTMLLoader, - UnstructuredMarkdownLoader, - UnstructuredODTLoader, - UnstructuredPowerPointLoader, - UnstructuredWordDocumentLoader, -) -from langchain.text_splitter import RecursiveCharacterTextSplitter -from langchain.vectorstores import Chroma -from langchain.embeddings import HuggingFaceEmbeddings -from langchain.docstore.document import Document -from chromadb.config import Settings -from llama_cpp import Llama - - -SYSTEM_PROMPT = "Ты - полезный, уважительный и честный ассистент. Всегда отвечай как можно более надежно. В ответах не должно информации из твоей базы знаний, а только лишь информация из контекста и ее перефразирование. Если вопрос не имеет смысла или не является фактологически последовательным, объясни почему, а не отвечайте на вопрос некорректно. Если ты не знаешь ответа на вопрос, пожалуйста, не сообщай ложную информацию. Твоя цель - дать ответы, связанные с базой знаний компании." - -SYSTEM_TOKEN = 1788 -USER_TOKEN = 1404 -BOT_TOKEN = 9225 -LINEBREAK_TOKEN = 13 - -ROLE_TOKENS = { - "user": USER_TOKEN, - "bot": BOT_TOKEN, - "system": SYSTEM_TOKEN -} - -LOADER_MAPPING = { - ".csv": (CSVLoader, {}), - ".doc": (UnstructuredWordDocumentLoader, {}), - ".docx": (UnstructuredWordDocumentLoader, {}), - ".enex": (EverNoteLoader, {}), - ".epub": (UnstructuredEPubLoader, {}), - ".html": (UnstructuredHTMLLoader, {}), - ".md": (UnstructuredMarkdownLoader, {}), - ".odt": (UnstructuredODTLoader, {}), - #".pdf": (PDFMinerLoader, {}), - ".pdf": (PyPDFium2Loader, {}), - ".ppt": (UnstructuredPowerPointLoader, {}), - ".pptx": (UnstructuredPowerPointLoader, {}), - ".txt": (TextLoader, {"encoding": "utf8"}), -} - -repo_name = "IlyaGusev/saiga_mistral_7b_gguf" -model_name = "model-q4_K.gguf" - -embedder_name = "intfloat/multilingual-e5-large" - - -snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name) - -model = Llama( - model_path=model_name, - n_ctx=2048, - n_parts=1, -) - -max_new_tokens = 1500 -embeddings = HuggingFaceEmbeddings(model_name=embedder_name) - -def get_uuid(): - return str(uuid4()) - - -def load_single_document(file_path: str) -> Document: - ext = "." + file_path.rsplit(".", 1)[-1] - assert ext in LOADER_MAPPING - loader_class, loader_args = LOADER_MAPPING[ext] - loader = loader_class(file_path, **loader_args) - print("load_single documernt, return type is: ", type(loader.load()[0])) - return loader.load()[0] - - -def get_message_tokens(model, role, content): - message_tokens = model.tokenize(content.encode("utf-8")) - message_tokens.insert(1, ROLE_TOKENS[role]) - message_tokens.insert(2, LINEBREAK_TOKEN) - message_tokens.append(model.token_eos()) - return message_tokens - - -def get_system_tokens(model): - system_message = {"role": "system", "content": SYSTEM_PROMPT} - return get_message_tokens(model, **system_message) - - -def upload_files(files, file_paths): - file_paths = [f.name for f in files] - return file_paths - - -def process_text(text): - lines = text.split("\n") - lines = [line for line in lines if len(line.strip()) > 2] - text = "\n".join(lines).strip() - if len(text) < 10: - return None - return text - - -def build_index(file_paths, db, chunk_size, chunk_overlap, file_warning): - documents = [load_single_document(path) for path in file_paths] - print("build_index, documents type is :", type(documents)) - text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) - documents = text_splitter.split_documents(documents) - fixed_documents = [] - for doc in documents: - doc.page_content = process_text(doc.page_content) - if not doc.page_content: - continue - fixed_documents.append(doc) - - db = Chroma.from_documents( - fixed_documents, - embeddings, - client_settings=Settings( - anonymized_telemetry=False - ) - ) - file_warning = f"Загружено {len(fixed_documents)} фрагментов! Можно задавать вопросы." - return db, file_warning - - -def user(message, history, system_prompt): - new_history = history + [[message, None]] - return "", new_history - - -def retrieve(history, db, retrieved_docs, k_documents): - context = "" - if db: - last_user_message = history[-1][0] - retriever = db.as_retriever(search_kwargs={"k": k_documents}) - docs = retriever.get_relevant_documents(last_user_message) - retrieved_docs = "\n\n".join([doc.page_content for doc in docs]) - return retrieved_docs - - -def bot( - history, - system_prompt, - conversation_id, - retrieved_docs, - top_p, - top_k, - temp -): - if not history: - return - - tokens = get_system_tokens(model)[:] - tokens.append(LINEBREAK_TOKEN) - - for user_message, bot_message in history[:-1]: - message_tokens = get_message_tokens(model=model, role="user", content=user_message) - tokens.extend(message_tokens) - if bot_message: - message_tokens = get_message_tokens(model=model, role="bot", content=bot_message) - tokens.extend(message_tokens) - - last_user_message = history[-1][0] - if retrieved_docs: - last_user_message = f"Контекст: {retrieved_docs}\n\nИспользуя контекст, ответь на вопрос: {last_user_message}. Не используй свои знания при ответе на вопрос." - message_tokens = get_message_tokens(model=model, role="user", content=last_user_message) - tokens.extend(message_tokens) - - role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN] - tokens.extend(role_tokens) - generator = model.generate( - tokens, - top_k=top_k, - top_p=top_p, - temp=temp - ) - - partial_text = "" - for i, token in enumerate(generator): - if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens): - break - partial_text += model.detokenize([token]).decode("utf-8", "ignore") - history[-1][1] = partial_text - yield history - - -with gr.Blocks( - theme=gr.themes.Soft() -) as demo: - db = gr.State(None) - conversation_id = gr.State(get_uuid) - gr.Markdown( - f"""
- """ - f"""Saiga Mistral RAG: retrieval QA Space code credits to Ilya Gusev - """ - f"""Recommended to use only over .txt, .csv, .doc and .pdf/A files""" - ) - - - with gr.Row(): - with gr.Column(scale=5): - file_output = gr.File(file_count="multiple", label="Загрузка файлов") - file_paths = gr.State([]) - file_warning = gr.Markdown(f"Фрагменты ещё не загружены!") - - with gr.Column(min_width=200, scale=3): - with gr.Tab(label="Параметры нарезки"): - chunk_size = gr.Slider( - minimum=50, - maximum=2000, - value=250, - step=50, - interactive=True, - label="Размер фрагментов", - ) - chunk_overlap = gr.Slider( - minimum=0, - maximum=500, - value=30, - step=10, - interactive=True, - label="Пересечение" - ) - - - with gr.Row(): - k_documents = gr.Slider( - minimum=1, - maximum=10, - value=2, - step=1, - interactive=True, - label="Кол-во фрагментов для контекста" - ) - with gr.Row(): - retrieved_docs = gr.Textbox( - lines=6, - label="Извлеченные фрагменты", - placeholder="Появятся после задавания вопросов", - interactive=False - ) - with gr.Row(): - with gr.Column(scale=5): - system_prompt = gr.Textbox(label="Системный промпт", placeholder="", value=SYSTEM_PROMPT, interactive=False) - chatbot = gr.Chatbot(label="Диалог").style(height=400) - with gr.Column(min_width=80, scale=1): - with gr.Tab(label="Параметры генерации"): - top_p = gr.Slider( - minimum=0.0, - maximum=1.0, - value=0.9, - step=0.05, - interactive=True, - label="Top-p", - ) - top_k = gr.Slider( - minimum=10, - maximum=100, - value=30, - step=5, - interactive=True, - label="Top-k", - ) - temp = gr.Slider( - minimum=0.0, - maximum=2.0, - value=0.1, - step=0.1, - interactive=True, - label="Temp" - ) - - with gr.Row(): - with gr.Column(): - msg = gr.Textbox( - label="Отправить сообщение", - placeholder="Отправить сообщение", - show_label=False, - ).style(container=False) - with gr.Column(): - with gr.Row(): - submit = gr.Button("Отправить") - stop = gr.Button("Остановить") - clear = gr.Button("Очистить") - - # Upload files - upload_event = file_output.change( - fn=upload_files, - inputs=[file_output, file_paths], - outputs=[file_paths], - queue=True, - ).success( - fn=build_index, - inputs=[file_paths, db, chunk_size, chunk_overlap, file_warning], - outputs=[db, file_warning], - queue=True - ) - - # Pressing Enter - submit_event = msg.submit( - fn=user, - inputs=[msg, chatbot, system_prompt], - outputs=[msg, chatbot], - queue=False, - ).success( - fn=retrieve, - inputs=[chatbot, db, retrieved_docs, k_documents], - outputs=[retrieved_docs], - queue=True, - ).success( - fn=bot, - inputs=[ - chatbot, - system_prompt, - conversation_id, - retrieved_docs, - top_p, - top_k, - temp - ], - outputs=chatbot, - queue=True, - ) - - # Pressing the button - submit_click_event = submit.click( - fn=user, - inputs=[msg, chatbot, system_prompt], - outputs=[msg, chatbot], - queue=False, - ).success( - fn=retrieve, - inputs=[chatbot, db, retrieved_docs, k_documents], - outputs=[retrieved_docs], - queue=True, - ).success( - fn=bot, - inputs=[ - chatbot, - system_prompt, - conversation_id, - retrieved_docs, - top_p, - top_k, - temp - ], - outputs=chatbot, - queue=True, - ) - - # Stop generation - stop.click( - fn=None, - inputs=None, - outputs=None, - cancels=[submit_event, submit_click_event], - queue=False, - ) - - # Clear history - clear.click(lambda: None, None, chatbot, queue=False) - -demo.queue(max_size=128, concurrency_count=1) -demo.launch() diff --git a/spaces/valhalla/glide-text2im/glide_text2im/nn.py b/spaces/valhalla/glide-text2im/glide_text2im/nn.py deleted file mode 100644 index 1a5b2f19e692dace9ade33d845632cea0479cc88..0000000000000000000000000000000000000000 --- a/spaces/valhalla/glide-text2im/glide_text2im/nn.py +++ /dev/null @@ -1,105 +0,0 @@ -""" -Various utilities for neural networks. -""" - -import math - -import torch as th -import torch.nn as nn -import torch.nn.functional as F - - -class GroupNorm32(nn.GroupNorm): - def __init__(self, num_groups, num_channels, swish, eps=1e-5): - super().__init__(num_groups=num_groups, num_channels=num_channels, eps=eps) - self.swish = swish - - def forward(self, x): - y = super().forward(x.float()).to(x.dtype) - if self.swish == 1.0: - y = F.silu(y) - elif self.swish: - y = y * F.sigmoid(y * float(self.swish)) - return y - - -def conv_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D convolution module. - """ - if dims == 1: - return nn.Conv1d(*args, **kwargs) - elif dims == 2: - return nn.Conv2d(*args, **kwargs) - elif dims == 3: - return nn.Conv3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def linear(*args, **kwargs): - """ - Create a linear module. - """ - return nn.Linear(*args, **kwargs) - - -def avg_pool_nd(dims, *args, **kwargs): - """ - Create a 1D, 2D, or 3D average pooling module. - """ - if dims == 1: - return nn.AvgPool1d(*args, **kwargs) - elif dims == 2: - return nn.AvgPool2d(*args, **kwargs) - elif dims == 3: - return nn.AvgPool3d(*args, **kwargs) - raise ValueError(f"unsupported dimensions: {dims}") - - -def zero_module(module): - """ - Zero out the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().zero_() - return module - - -def scale_module(module, scale): - """ - Scale the parameters of a module and return it. - """ - for p in module.parameters(): - p.detach().mul_(scale) - return module - - -def normalization(channels, swish=0.0): - """ - Make a standard normalization layer, with an optional swish activation. - - :param channels: number of input channels. - :return: an nn.Module for normalization. - """ - return GroupNorm32(num_channels=channels, num_groups=32, swish=swish) - - -def timestep_embedding(timesteps, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - half = dim // 2 - freqs = th.exp( - -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = th.cat([th.cos(args), th.sin(args)], dim=-1) - if dim % 2: - embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1) - return embedding diff --git a/spaces/vumichien/Generate_human_motion/pyrender/pyrender/mesh.py b/spaces/vumichien/Generate_human_motion/pyrender/pyrender/mesh.py deleted file mode 100644 index 36833ea3dfa6c095a18fc745ff34cf106e83c95d..0000000000000000000000000000000000000000 --- a/spaces/vumichien/Generate_human_motion/pyrender/pyrender/mesh.py +++ /dev/null @@ -1,328 +0,0 @@ -"""Meshes, conforming to the glTF 2.0 standards as specified in -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-mesh - -Author: Matthew Matl -""" -import copy - -import numpy as np -import trimesh - -from .primitive import Primitive -from .constants import GLTF -from .material import MetallicRoughnessMaterial - - -class Mesh(object): - """A set of primitives to be rendered. - - Parameters - ---------- - name : str - The user-defined name of this object. - primitives : list of :class:`Primitive` - The primitives associated with this mesh. - weights : (k,) float - Array of weights to be applied to the Morph Targets. - is_visible : bool - If False, the mesh will not be rendered. - """ - - def __init__(self, primitives, name=None, weights=None, is_visible=True): - self.primitives = primitives - self.name = name - self.weights = weights - self.is_visible = is_visible - - self._bounds = None - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def primitives(self): - """list of :class:`Primitive` : The primitives associated - with this mesh. - """ - return self._primitives - - @primitives.setter - def primitives(self, value): - self._primitives = value - - @property - def weights(self): - """(k,) float : Weights to be applied to morph targets. - """ - return self._weights - - @weights.setter - def weights(self, value): - self._weights = value - - @property - def is_visible(self): - """bool : Whether the mesh is visible. - """ - return self._is_visible - - @is_visible.setter - def is_visible(self, value): - self._is_visible = value - - @property - def bounds(self): - """(2,3) float : The axis-aligned bounds of the mesh. - """ - if self._bounds is None: - bounds = np.array([[np.infty, np.infty, np.infty], - [-np.infty, -np.infty, -np.infty]]) - for p in self.primitives: - bounds[0] = np.minimum(bounds[0], p.bounds[0]) - bounds[1] = np.maximum(bounds[1], p.bounds[1]) - self._bounds = bounds - return self._bounds - - @property - def centroid(self): - """(3,) float : The centroid of the mesh's axis-aligned bounding box - (AABB). - """ - return np.mean(self.bounds, axis=0) - - @property - def extents(self): - """(3,) float : The lengths of the axes of the mesh's AABB. - """ - return np.diff(self.bounds, axis=0).reshape(-1) - - @property - def scale(self): - """(3,) float : The length of the diagonal of the mesh's AABB. - """ - return np.linalg.norm(self.extents) - - @property - def is_transparent(self): - """bool : If True, the mesh is partially-transparent. - """ - for p in self.primitives: - if p.is_transparent: - return True - return False - - @staticmethod - def from_points(points, colors=None, normals=None, - is_visible=True, poses=None): - """Create a Mesh from a set of points. - - Parameters - ---------- - points : (n,3) float - The point positions. - colors : (n,3) or (n,4) float, optional - RGB or RGBA colors for each point. - normals : (n,3) float, optionals - The normal vectors for each point. - is_visible : bool - If False, the points will not be rendered. - poses : (x,4,4) - Array of 4x4 transformation matrices for instancing this object. - - Returns - ------- - mesh : :class:`Mesh` - The created mesh. - """ - primitive = Primitive( - positions=points, - normals=normals, - color_0=colors, - mode=GLTF.POINTS, - poses=poses - ) - mesh = Mesh(primitives=[primitive], is_visible=is_visible) - return mesh - - @staticmethod - def from_trimesh(mesh, material=None, is_visible=True, - poses=None, wireframe=False, smooth=True): - """Create a Mesh from a :class:`~trimesh.base.Trimesh`. - - Parameters - ---------- - mesh : :class:`~trimesh.base.Trimesh` or list of them - A triangular mesh or a list of meshes. - material : :class:`Material` - The material of the object. Overrides any mesh material. - If not specified and the mesh has no material, a default material - will be used. - is_visible : bool - If False, the mesh will not be rendered. - poses : (n,4,4) float - Array of 4x4 transformation matrices for instancing this object. - wireframe : bool - If `True`, the mesh will be rendered as a wireframe object - smooth : bool - If `True`, the mesh will be rendered with interpolated vertex - normals. Otherwise, the mesh edges will stay sharp. - - Returns - ------- - mesh : :class:`Mesh` - The created mesh. - """ - - if isinstance(mesh, (list, tuple, set, np.ndarray)): - meshes = list(mesh) - elif isinstance(mesh, trimesh.Trimesh): - meshes = [mesh] - else: - raise TypeError('Expected a Trimesh or a list, got a {}' - .format(type(mesh))) - - primitives = [] - for m in meshes: - positions = None - normals = None - indices = None - - # Compute positions, normals, and indices - if smooth: - positions = m.vertices.copy() - normals = m.vertex_normals.copy() - indices = m.faces.copy() - else: - positions = m.vertices[m.faces].reshape((3 * len(m.faces), 3)) - normals = np.repeat(m.face_normals, 3, axis=0) - - # Compute colors, texture coords, and material properties - color_0, texcoord_0, primitive_material = Mesh._get_trimesh_props(m, smooth=smooth, material=material) - - # Override if material is given. - if material is not None: - #primitive_material = copy.copy(material) - primitive_material = copy.deepcopy(material) # TODO - - if primitive_material is None: - # Replace material with default if needed - primitive_material = MetallicRoughnessMaterial( - alphaMode='BLEND', - baseColorFactor=[0.3, 0.3, 0.3, 1.0], - metallicFactor=0.2, - roughnessFactor=0.8 - ) - - primitive_material.wireframe = wireframe - - # Create the primitive - primitives.append(Primitive( - positions=positions, - normals=normals, - texcoord_0=texcoord_0, - color_0=color_0, - indices=indices, - material=primitive_material, - mode=GLTF.TRIANGLES, - poses=poses - )) - - return Mesh(primitives=primitives, is_visible=is_visible) - - @staticmethod - def _get_trimesh_props(mesh, smooth=False, material=None): - """Gets the vertex colors, texture coordinates, and material properties - from a :class:`~trimesh.base.Trimesh`. - """ - colors = None - texcoords = None - - # If the trimesh visual is undefined, return none for both - if not mesh.visual.defined: - return colors, texcoords, material - - # Process vertex colors - if material is None: - if mesh.visual.kind == 'vertex': - vc = mesh.visual.vertex_colors.copy() - if smooth: - colors = vc - else: - colors = vc[mesh.faces].reshape( - (3 * len(mesh.faces), vc.shape[1]) - ) - material = MetallicRoughnessMaterial( - alphaMode='BLEND', - baseColorFactor=[1.0, 1.0, 1.0, 1.0], - metallicFactor=0.2, - roughnessFactor=0.8 - ) - # Process face colors - elif mesh.visual.kind == 'face': - if smooth: - raise ValueError('Cannot use face colors with a smooth mesh') - else: - colors = np.repeat(mesh.visual.face_colors, 3, axis=0) - - material = MetallicRoughnessMaterial( - alphaMode='BLEND', - baseColorFactor=[1.0, 1.0, 1.0, 1.0], - metallicFactor=0.2, - roughnessFactor=0.8 - ) - - # Process texture colors - if mesh.visual.kind == 'texture': - # Configure UV coordinates - if mesh.visual.uv is not None and len(mesh.visual.uv) != 0: - uv = mesh.visual.uv.copy() - if smooth: - texcoords = uv - else: - texcoords = uv[mesh.faces].reshape( - (3 * len(mesh.faces), uv.shape[1]) - ) - - if material is None: - # Configure mesh material - mat = mesh.visual.material - - if isinstance(mat, trimesh.visual.texture.PBRMaterial): - material = MetallicRoughnessMaterial( - normalTexture=mat.normalTexture, - occlusionTexture=mat.occlusionTexture, - emissiveTexture=mat.emissiveTexture, - emissiveFactor=mat.emissiveFactor, - alphaMode='BLEND', - baseColorFactor=mat.baseColorFactor, - baseColorTexture=mat.baseColorTexture, - metallicFactor=mat.metallicFactor, - roughnessFactor=mat.roughnessFactor, - metallicRoughnessTexture=mat.metallicRoughnessTexture, - doubleSided=mat.doubleSided, - alphaCutoff=mat.alphaCutoff - ) - elif isinstance(mat, trimesh.visual.texture.SimpleMaterial): - glossiness = mat.kwargs.get('Ns', 1.0) - if isinstance(glossiness, list): - glossiness = float(glossiness[0]) - roughness = (2 / (glossiness + 2)) ** (1.0 / 4.0) - material = MetallicRoughnessMaterial( - alphaMode='BLEND', - roughnessFactor=roughness, - baseColorFactor=mat.diffuse, - baseColorTexture=mat.image, - ) - elif isinstance(mat, MetallicRoughnessMaterial): - material = mat - - return colors, texcoords, material diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/optimizer/builder.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/optimizer/builder.py deleted file mode 100644 index f9234eed8f1f186d9d8dfda34562157ee39bdb3a..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmcv/runner/optimizer/builder.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy -import inspect - -import torch - -from ...utils import Registry, build_from_cfg - -OPTIMIZERS = Registry('optimizer') -OPTIMIZER_BUILDERS = Registry('optimizer builder') - - -def register_torch_optimizers(): - torch_optimizers = [] - for module_name in dir(torch.optim): - if module_name.startswith('__'): - continue - _optim = getattr(torch.optim, module_name) - if inspect.isclass(_optim) and issubclass(_optim, - torch.optim.Optimizer): - OPTIMIZERS.register_module()(_optim) - torch_optimizers.append(module_name) - return torch_optimizers - - -TORCH_OPTIMIZERS = register_torch_optimizers() - - -def build_optimizer_constructor(cfg): - return build_from_cfg(cfg, OPTIMIZER_BUILDERS) - - -def build_optimizer(model, cfg): - optimizer_cfg = copy.deepcopy(cfg) - constructor_type = optimizer_cfg.pop('constructor', - 'DefaultOptimizerConstructor') - paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None) - optim_constructor = build_optimizer_constructor( - dict( - type=constructor_type, - optimizer_cfg=optimizer_cfg, - paramwise_cfg=paramwise_cfg)) - optimizer = optim_constructor(model) - return optimizer diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/necks/__init__.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/necks/__init__.py deleted file mode 100644 index 9b9d3d5b3fe80247642d962edd6fb787537d01d6..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/models/necks/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .fpn import FPN -from .multilevel_neck import MultiLevelNeck - -__all__ = ['FPN', 'MultiLevelNeck'] diff --git a/spaces/wavymulder/timeless-diffusion/app.py b/spaces/wavymulder/timeless-diffusion/app.py deleted file mode 100644 index 51ba6d45be1fafd7222d505bcb3d833b82db5e68..0000000000000000000000000000000000000000 --- a/spaces/wavymulder/timeless-diffusion/app.py +++ /dev/null @@ -1,137 +0,0 @@ -from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler -import gradio as gr -import torch -from PIL import Image - -model_id = 'wavymulder/timeless-diffusion' -prefix = 'timeless style' - -scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler") - -pipe = StableDiffusionPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, - scheduler=scheduler) - -pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, - scheduler=scheduler) - -if torch.cuda.is_available(): - pipe = pipe.to("cuda") - pipe_i2i = pipe_i2i.to("cuda") - -def error_str(error, title="Error"): - return f"""#### {title} - {error}""" if error else "" - -def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False): - - generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None - prompt = f"{prefix} {prompt}" if auto_prefix else prompt - - try: - if img is not None: - return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None - else: - return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None - except Exception as e: - return None, error_str(e) - -def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator): - - result = pipe( - prompt, - negative_prompt = neg_prompt, - num_inference_steps = int(steps), - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return result.images[0] - -def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): - - ratio = min(height / img.height, width / img.width) - img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS) - result = pipe_i2i( - prompt, - negative_prompt = neg_prompt, - init_image = img, - num_inference_steps = int(steps), - strength = strength, - guidance_scale = guidance, - width = width, - height = height, - generator = generator) - - return result.images[0] - -css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} -""" -with gr.Blocks(css=css) as demo: - gr.HTML( - f""" - -- """ - ) - with gr.Row(): - - with gr.Column(scale=55): - with gr.Group(): - with gr.Row(): - prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False) - generate = gr.Button(value="Generate").style(rounded=(False, True, True, False)) - - image_out = gr.Image(height=512) - error_output = gr.Markdown() - - with gr.Column(scale=45): - with gr.Tab("Options"): - with gr.Group(): - neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") - auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (timeless style)", value=prefix, visible=prefix) - - with gr.Row(): - guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) - steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1) - - with gr.Row(): - width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8) - height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8) - - seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) - - with gr.Tab("Image to image"): - with gr.Group(): - image = gr.Image(label="Image", height=256, tool="editor", type="pil") - strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) - - auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False) - - inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix] - outputs = [image_out, error_output] - prompt.submit(inference, inputs=inputs, outputs=outputs) - generate.click(inference, inputs=inputs, outputs=outputs) - - gr.HTML(""" ---Timeless Diffusion
-- Demo for Timeless Diffusion Stable Diffusion model.
- Running on {"GPU 🔥" if torch.cuda.is_available() else f"CPU 🥶. For faster inference it is recommended to upgrade to GPU in Settings"} after duplicating the space
- {"Add the following tokens to your prompts for the model to work properly: prefix" if prefix else ""} -
--
-- """) - -demo.queue(concurrency_count=1) -demo.launch() diff --git a/spaces/webis-huggingface-workshop/chris_code_generation/README.md b/spaces/webis-huggingface-workshop/chris_code_generation/README.md deleted file mode 100644 index 255f2bb2bada82fdcf1809edc18b79a8048e3504..0000000000000000000000000000000000000000 --- a/spaces/webis-huggingface-workshop/chris_code_generation/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Chris_code_generation -emoji: 🐠 -colorFrom: pink -colorTo: blue -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/whitphx/gradio-static-test/dist/assets/__vite-browser-external-b25bb000.js b/spaces/whitphx/gradio-static-test/dist/assets/__vite-browser-external-b25bb000.js deleted file mode 100644 index efa8971d2172dd2c1924c07a4e2b2bc18871ccd9..0000000000000000000000000000000000000000 --- a/spaces/whitphx/gradio-static-test/dist/assets/__vite-browser-external-b25bb000.js +++ /dev/null @@ -1,2 +0,0 @@ -const e={};export{e as default}; -//# sourceMappingURL=__vite-browser-external-b25bb000.js.map diff --git a/spaces/wonderit-safeai/tts-announcer/text/english.py b/spaces/wonderit-safeai/tts-announcer/text/english.py deleted file mode 100644 index 59f25c1ac6afb31f9a15d6d61e3eb4239dcb720b..0000000000000000000000000000000000000000 --- a/spaces/wonderit-safeai/tts-announcer/text/english.py +++ /dev/null @@ -1,186 +0,0 @@ -""" from https://github.com/keithito/tacotron """ - -''' -Cleaners are transformations that run over the input text at both training and eval time. - -Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners" -hyperparameter. Some cleaners are English-specific. You'll typically want to use: - 1. "english_cleaners" for English text - 2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using - the Unidecode library (https://pypi.python.org/pypi/Unidecode) - 3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update - the symbols in symbols.py to match your data). -''' - - -# Regular expression matching whitespace: -import re -import inflect -from unidecode import unidecode -import eng_to_ipa as ipa -_inflect = inflect.engine() -_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') -_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') -_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') -_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') -_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') -_number_re = re.compile(r'[0-9]+') - -# List of (regular expression, replacement) pairs for abbreviations: -_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ - ('mrs', 'misess'), - ('mr', 'mister'), - ('dr', 'doctor'), - ('st', 'saint'), - ('co', 'company'), - ('jr', 'junior'), - ('maj', 'major'), - ('gen', 'general'), - ('drs', 'doctors'), - ('rev', 'reverend'), - ('lt', 'lieutenant'), - ('hon', 'honorable'), - ('sgt', 'sergeant'), - ('capt', 'captain'), - ('esq', 'esquire'), - ('ltd', 'limited'), - ('col', 'colonel'), - ('ft', 'fort'), -]] - - -# List of (ipa, lazy ipa) pairs: -_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('æ', 'e'), - ('ɑ', 'a'), - ('ɔ', 'o'), - ('ð', 'z'), - ('θ', 's'), - ('ɛ', 'e'), - ('ɪ', 'i'), - ('ʊ', 'u'), - ('ʒ', 'ʥ'), - ('ʤ', 'ʥ'), - ('ˈ', '↓'), -]] - -# List of (ipa, lazy ipa2) pairs: -_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('ð', 'z'), - ('θ', 's'), - ('ʒ', 'ʑ'), - ('ʤ', 'dʑ'), - ('ˈ', '↓'), -]] - -# List of (ipa, ipa2) pairs -_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('r', 'ɹ'), - ('ʤ', 'dʒ'), - ('ʧ', 'tʃ') -]] - - -def expand_abbreviations(text): - for regex, replacement in _abbreviations: - text = re.sub(regex, replacement, text) - return text - - -def collapse_whitespace(text): - return re.sub(r'\s+', ' ', text) - - -def _remove_commas(m): - return m.group(1).replace(',', '') - - -def _expand_decimal_point(m): - return m.group(1).replace('.', ' point ') - - -def _expand_dollars(m): - match = m.group(1) - parts = match.split('.') - if len(parts) > 2: - return match + ' dollars' # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = 'dollar' if dollars == 1 else 'dollars' - return '%s %s' % (dollars, dollar_unit) - elif cents: - cent_unit = 'cent' if cents == 1 else 'cents' - return '%s %s' % (cents, cent_unit) - else: - return 'zero dollars' - - -def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) - - -def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return 'two thousand' - elif num > 2000 and num < 2010: - return 'two thousand ' + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + ' hundred' - else: - return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') - else: - return _inflect.number_to_words(num, andword='') - - -def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r'\1 pounds', text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text - - -def mark_dark_l(text): - return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text) - - -def english_to_ipa(text): - text = unidecode(text).lower() - text = expand_abbreviations(text) - text = normalize_numbers(text) - phonemes = ipa.convert(text) - phonemes = collapse_whitespace(phonemes) - return phonemes - - -def english_to_lazy_ipa(text): - text = english_to_ipa(text) - for regex, replacement in _lazy_ipa: - text = re.sub(regex, replacement, text) - return text - - -def english_to_ipa2(text): - text = english_to_ipa(text) - text = mark_dark_l(text) - for regex, replacement in _ipa_to_ipa2: - text = re.sub(regex, replacement, text) - return text.replace('...', '…') - - -def english_to_lazy_ipa2(text): - text = english_to_ipa(text) - for regex, replacement in _lazy_ipa2: - text = re.sub(regex, replacement, text) - return text diff --git a/spaces/wrs/nb/README.md b/spaces/wrs/nb/README.md deleted file mode 100644 index 8ab2591d01bbe47e5a63266ae2166977a88a34d3..0000000000000000000000000000000000000000 --- a/spaces/wrs/nb/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Nb -emoji: 💻 -colorFrom: blue -colorTo: purple -sdk: docker -pinned: false -license: mit -app_port: 8080 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/wxiaofei/vits-uma-genshin-honkai/Docker/Dockerfile b/spaces/wxiaofei/vits-uma-genshin-honkai/Docker/Dockerfile deleted file mode 100644 index 4d39cdf02a2ec151686cc1d61234bf723068fed8..0000000000000000000000000000000000000000 --- a/spaces/wxiaofei/vits-uma-genshin-honkai/Docker/Dockerfile +++ /dev/null @@ -1,12 +0,0 @@ -FROM python:3.9-bullseye -VOLUME ["/app"] -WORKDIR /app -# Set apt to Chinese mirror -RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list -RUN apt-get update && apt-get -y install cmake git -RUN git clone https://huggingface.co/spaces/ikechan8370/vits-uma-genshin-honkai -WORKDIR /app/vits-uma-genshin-honkai -RUN sed -i "s/\.launch()/\.launch(server_name=\"0.0.0.0\")/" /app/vits-uma-genshin-honkai/app.py -ADD vits.sh /app/vits.sh -EXPOSE 7860 -ENTRYPOINT [ "/app/vits.sh" ] \ No newline at end of file diff --git a/spaces/xiongjie/u2net_rgba/app.py b/spaces/xiongjie/u2net_rgba/app.py deleted file mode 100644 index 9a868a6f1406fde8f130bf14b4ebbaf3c2b39e1b..0000000000000000000000000000000000000000 --- a/spaces/xiongjie/u2net_rgba/app.py +++ /dev/null @@ -1,66 +0,0 @@ -import os -import copy -import time - -import cv2 as cv -import numpy as np -import onnxruntime - -from PIL import Image - -import gradio - -def run_inference(onnx_session, input_size, image): - # リサイズ - temp_image = copy.deepcopy(image) - resize_image = cv.resize(temp_image, dsize=(input_size, input_size)) - x = cv.cvtColor(resize_image, cv.COLOR_BGR2RGB) - - # 前処理 - x = np.array(x, dtype=np.float32) - mean = [0.485, 0.456, 0.406] - std = [0.229, 0.224, 0.225] - x = (x / 255 - mean) / std - x = x.transpose(2, 0, 1).astype('float32') - x = x.reshape(-1, 3, input_size, input_size) - - # 推論 - input_name = onnx_session.get_inputs()[0].name - output_name = onnx_session.get_outputs()[0].name - onnx_result = onnx_session.run([output_name], {input_name: x}) - - # 後処理 - onnx_result = np.array(onnx_result).squeeze() - min_value = np.min(onnx_result) - max_value = np.max(onnx_result) - onnx_result = (onnx_result - min_value) / (max_value - min_value) - onnx_result *= 255 - onnx_result = onnx_result.astype('uint8') - - return onnx_result - -# Load model -onnx_session = onnxruntime.InferenceSession("u2net.onnx") - -def create_rgba(mode, image): - out = run_inference( - onnx_session, - 320, - image, - ) - resize_image = cv.resize(out, dsize=(image.shape[1], image.shape[0])) - - if mode == "binary": - resize_image[resize_image > 255] = 255 - resize_image[resize_image < 125] = 0 - - mask = Image.fromarray(resize_image) - - rgba_image = Image.fromarray(image).convert('RGBA') - rgba_image.putalpha(mask) - - return rgba_image - -inputs = [gradio.inputs.Radio(["binary", "smooth"]), gradio.inputs.Image()] -outputs = gradio.outputs.Image() -gradio.Interface(fn=create_rgba, inputs=inputs, outputs=outputs).launch() diff --git a/spaces/xnetba/MMS/tts.py b/spaces/xnetba/MMS/tts.py deleted file mode 100644 index dfc53054a7aac3bf651b2f5f6872dbfddf3500eb..0000000000000000000000000000000000000000 --- a/spaces/xnetba/MMS/tts.py +++ /dev/null @@ -1,179 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import os -import re -import tempfile -import torch -import sys -import gradio as gr - -from huggingface_hub import hf_hub_download - -# Setup TTS env -if "vits" not in sys.path: - sys.path.append("vits") - -from vits import commons, utils -from vits.models import SynthesizerTrn - - -TTS_LANGUAGES = {} -with open(f"data/tts/all_langs.tsv") as f: - for line in f: - iso, name = line.split(" ", 1) - TTS_LANGUAGES[iso] = name - - -class TextMapper(object): - def __init__(self, vocab_file): - self.symbols = [ - x.replace("\n", "") for x in open(vocab_file, encoding="utf-8").readlines() - ] - self.SPACE_ID = self.symbols.index(" ") - self._symbol_to_id = {s: i for i, s in enumerate(self.symbols)} - self._id_to_symbol = {i: s for i, s in enumerate(self.symbols)} - - def text_to_sequence(self, text, 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 - """ - sequence = [] - clean_text = text.strip() - for symbol in clean_text: - symbol_id = self._symbol_to_id[symbol] - sequence += [symbol_id] - return sequence - - def uromanize(self, text, uroman_pl): - iso = "xxx" - with tempfile.NamedTemporaryFile() as tf, tempfile.NamedTemporaryFile() as tf2: - with open(tf.name, "w") as f: - f.write("\n".join([text])) - cmd = f"perl " + uroman_pl - cmd += f" -l {iso} " - cmd += f" < {tf.name} > {tf2.name}" - os.system(cmd) - outtexts = [] - with open(tf2.name) as f: - for line in f: - line = re.sub(r"\s+", " ", line).strip() - outtexts.append(line) - outtext = outtexts[0] - return outtext - - def get_text(self, text, hps): - text_norm = self.text_to_sequence(text, hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = torch.LongTensor(text_norm) - return text_norm - - def filter_oov(self, text, lang=None): - text = self.preprocess_char(text, lang=lang) - val_chars = self._symbol_to_id - txt_filt = "".join(list(filter(lambda x: x in val_chars, text))) - return txt_filt - - def preprocess_char(self, text, lang=None): - """ - Special treatement of characters in certain languages - """ - if lang == "ron": - text = text.replace("ț", "ţ") - print(f"{lang} (ț -> ţ): {text}") - return text - - -def synthesize(text, lang, speed=None): - if speed is None: - speed = 1.0 - - lang_code = lang.split()[0].strip() - - vocab_file = hf_hub_download( - repo_id="facebook/mms-tts", - filename="vocab.txt", - subfolder=f"models/{lang_code}", - ) - config_file = hf_hub_download( - repo_id="facebook/mms-tts", - filename="config.json", - subfolder=f"models/{lang_code}", - ) - g_pth = hf_hub_download( - repo_id="facebook/mms-tts", - filename="G_100000.pth", - subfolder=f"models/{lang_code}", - ) - - if torch.cuda.is_available(): - device = torch.device("cuda") - elif ( - hasattr(torch.backends, "mps") - and torch.backends.mps.is_available() - and torch.backends.mps.is_built() - ): - device = torch.device("mps") - else: - device = torch.device("cpu") - - print(f"Run inference with {device}") - - assert os.path.isfile(config_file), f"{config_file} doesn't exist" - hps = utils.get_hparams_from_file(config_file) - text_mapper = TextMapper(vocab_file) - net_g = SynthesizerTrn( - len(text_mapper.symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - **hps.model, - ) - net_g.to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(g_pth, net_g, None) - - is_uroman = hps.data.training_files.split(".")[-1] == "uroman" - - if is_uroman: - uroman_dir = "uroman" - assert os.path.exists(uroman_dir) - uroman_pl = os.path.join(uroman_dir, "bin", "uroman.pl") - text = text_mapper.uromanize(text, uroman_pl) - - text = text.lower() - text = text_mapper.filter_oov(text, lang=lang) - stn_tst = text_mapper.get_text(text, hps) - with torch.no_grad(): - x_tst = stn_tst.unsqueeze(0).to(device) - x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(device) - hyp = ( - net_g.infer( - x_tst, - x_tst_lengths, - noise_scale=0.667, - noise_scale_w=0.8, - length_scale=1.0 / speed, - )[0][0, 0] - .cpu() - .float() - .numpy() - ) - - return gr.Audio.update(value=(hps.data.sampling_rate, hyp)), text - - -TTS_EXAMPLES = [ - ["I am going to the store.", "eng (English)"], - ["안녕하세요.", "kor (Korean)"], - ["क्या मुझे पीने का पानी मिल सकता है?", "hin (Hindi)"], - ["Tanış olmağıma çox şadam", "azj-script_latin (Azerbaijani, North)"], - ["Mu zo murna a cikin ƙasar.", "hau (Hausa)"], -] diff --git a/spaces/xp3857/Image_Restoration_Colorization/Global/models/networks.py b/spaces/xp3857/Image_Restoration_Colorization/Global/models/networks.py deleted file mode 100644 index 6c4b08664b7ea139b310b658a63d2e46e61d8d75..0000000000000000000000000000000000000000 --- a/spaces/xp3857/Image_Restoration_Colorization/Global/models/networks.py +++ /dev/null @@ -1,875 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import torch -import torch.nn as nn -import functools -from torch.autograd import Variable -import numpy as np -from torch.nn.utils import spectral_norm - -# from util.util import SwitchNorm2d -import torch.nn.functional as F - -############################################################################### -# Functions -############################################################################### -def weights_init(m): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(0.0, 0.02) - elif classname.find("BatchNorm2d") != -1: - m.weight.data.normal_(1.0, 0.02) - m.bias.data.fill_(0) - - -def get_norm_layer(norm_type="instance"): - if norm_type == "batch": - norm_layer = functools.partial(nn.BatchNorm2d, affine=True) - elif norm_type == "instance": - norm_layer = functools.partial(nn.InstanceNorm2d, affine=False) - elif norm_type == "spectral": - norm_layer = spectral_norm() - elif norm_type == "SwitchNorm": - norm_layer = SwitchNorm2d - else: - raise NotImplementedError("normalization layer [%s] is not found" % norm_type) - return norm_layer - - -def print_network(net): - if isinstance(net, list): - net = net[0] - num_params = 0 - for param in net.parameters(): - num_params += param.numel() - print(net) - print("Total number of parameters: %d" % num_params) - - -def define_G(input_nc, output_nc, ngf, netG, k_size=3, n_downsample_global=3, n_blocks_global=9, n_local_enhancers=1, - n_blocks_local=3, norm='instance', gpu_ids=[], opt=None): - - norm_layer = get_norm_layer(norm_type=norm) - if netG == 'global': - # if opt.self_gen: - if opt.use_v2: - netG = GlobalGenerator_DCDCv2(input_nc, output_nc, ngf, k_size, n_downsample_global, norm_layer, opt=opt) - else: - netG = GlobalGenerator_v2(input_nc, output_nc, ngf, k_size, n_downsample_global, n_blocks_global, norm_layer, opt=opt) - else: - raise('generator not implemented!') - print(netG) - if len(gpu_ids) > 0: - assert(torch.cuda.is_available()) - netG.cuda(gpu_ids[0]) - netG.apply(weights_init) - return netG - - -def define_D(input_nc, ndf, n_layers_D, opt, norm='instance', use_sigmoid=False, num_D=1, getIntermFeat=False, gpu_ids=[]): - norm_layer = get_norm_layer(norm_type=norm) - netD = MultiscaleDiscriminator(input_nc, opt, ndf, n_layers_D, norm_layer, use_sigmoid, num_D, getIntermFeat) - print(netD) - if len(gpu_ids) > 0: - assert(torch.cuda.is_available()) - netD.cuda(gpu_ids[0]) - netD.apply(weights_init) - return netD - - - -class GlobalGenerator_DCDCv2(nn.Module): - def __init__( - self, - input_nc, - output_nc, - ngf=64, - k_size=3, - n_downsampling=8, - norm_layer=nn.BatchNorm2d, - padding_type="reflect", - opt=None, - ): - super(GlobalGenerator_DCDCv2, self).__init__() - activation = nn.ReLU(True) - - model = [ - nn.ReflectionPad2d(3), - nn.Conv2d(input_nc, min(ngf, opt.mc), kernel_size=7, padding=0), - norm_layer(ngf), - activation, - ] - ### downsample - for i in range(opt.start_r): - mult = 2 ** i - model += [ - nn.Conv2d( - min(ngf * mult, opt.mc), - min(ngf * mult * 2, opt.mc), - kernel_size=k_size, - stride=2, - padding=1, - ), - norm_layer(min(ngf * mult * 2, opt.mc)), - activation, - ] - for i in range(opt.start_r, n_downsampling - 1): - mult = 2 ** i - model += [ - nn.Conv2d( - min(ngf * mult, opt.mc), - min(ngf * mult * 2, opt.mc), - kernel_size=k_size, - stride=2, - padding=1, - ), - norm_layer(min(ngf * mult * 2, opt.mc)), - activation, - ] - model += [ - ResnetBlock( - min(ngf * mult * 2, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - model += [ - ResnetBlock( - min(ngf * mult * 2, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - mult = 2 ** (n_downsampling - 1) - - if opt.spatio_size == 32: - model += [ - nn.Conv2d( - min(ngf * mult, opt.mc), - min(ngf * mult * 2, opt.mc), - kernel_size=k_size, - stride=2, - padding=1, - ), - norm_layer(min(ngf * mult * 2, opt.mc)), - activation, - ] - if opt.spatio_size == 64: - model += [ - ResnetBlock( - min(ngf * mult * 2, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - model += [ - ResnetBlock( - min(ngf * mult * 2, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - # model += [nn.Conv2d(min(ngf * mult * 2, opt.mc), min(ngf, opt.mc), 1, 1)] - if opt.feat_dim > 0: - model += [nn.Conv2d(min(ngf * mult * 2, opt.mc), opt.feat_dim, 1, 1)] - self.encoder = nn.Sequential(*model) - - # decode - model = [] - if opt.feat_dim > 0: - model += [nn.Conv2d(opt.feat_dim, min(ngf * mult * 2, opt.mc), 1, 1)] - # model += [nn.Conv2d(min(ngf, opt.mc), min(ngf * mult * 2, opt.mc), 1, 1)] - o_pad = 0 if k_size == 4 else 1 - mult = 2 ** n_downsampling - model += [ - ResnetBlock( - min(ngf * mult, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - - if opt.spatio_size == 32: - model += [ - nn.ConvTranspose2d( - min(ngf * mult, opt.mc), - min(int(ngf * mult / 2), opt.mc), - kernel_size=k_size, - stride=2, - padding=1, - output_padding=o_pad, - ), - norm_layer(min(int(ngf * mult / 2), opt.mc)), - activation, - ] - if opt.spatio_size == 64: - model += [ - ResnetBlock( - min(ngf * mult, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - - for i in range(1, n_downsampling - opt.start_r): - mult = 2 ** (n_downsampling - i) - model += [ - ResnetBlock( - min(ngf * mult, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - model += [ - ResnetBlock( - min(ngf * mult, opt.mc), - padding_type=padding_type, - activation=activation, - norm_layer=norm_layer, - opt=opt, - ) - ] - model += [ - nn.ConvTranspose2d( - min(ngf * mult, opt.mc), - min(int(ngf * mult / 2), opt.mc), - kernel_size=k_size, - stride=2, - padding=1, - output_padding=o_pad, - ), - norm_layer(min(int(ngf * mult / 2), opt.mc)), - activation, - ] - for i in range(n_downsampling - opt.start_r, n_downsampling): - mult = 2 ** (n_downsampling - i) - model += [ - nn.ConvTranspose2d( - min(ngf * mult, opt.mc), - min(int(ngf * mult / 2), opt.mc), - kernel_size=k_size, - stride=2, - padding=1, - output_padding=o_pad, - ), - norm_layer(min(int(ngf * mult / 2), opt.mc)), - activation, - ] - if opt.use_segmentation_model: - model += [nn.ReflectionPad2d(3), nn.Conv2d(min(ngf, opt.mc), output_nc, kernel_size=7, padding=0)] - else: - model += [ - nn.ReflectionPad2d(3), - nn.Conv2d(min(ngf, opt.mc), output_nc, kernel_size=7, padding=0), - nn.Tanh(), - ] - self.decoder = nn.Sequential(*model) - - def forward(self, input, flow="enc_dec"): - if flow == "enc": - return self.encoder(input) - elif flow == "dec": - return self.decoder(input) - elif flow == "enc_dec": - x = self.encoder(input) - x = self.decoder(x) - return x - - -# Define a resnet block -class ResnetBlock(nn.Module): - def __init__( - self, dim, padding_type, norm_layer, opt, activation=nn.ReLU(True), use_dropout=False, dilation=1 - ): - super(ResnetBlock, self).__init__() - self.opt = opt - self.dilation = dilation - self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout) - - def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout): - conv_block = [] - p = 0 - if padding_type == "reflect": - conv_block += [nn.ReflectionPad2d(self.dilation)] - elif padding_type == "replicate": - conv_block += [nn.ReplicationPad2d(self.dilation)] - elif padding_type == "zero": - p = self.dilation - else: - raise NotImplementedError("padding [%s] is not implemented" % padding_type) - - conv_block += [ - nn.Conv2d(dim, dim, kernel_size=3, padding=p, dilation=self.dilation), - norm_layer(dim), - activation, - ] - if use_dropout: - conv_block += [nn.Dropout(0.5)] - - p = 0 - if padding_type == "reflect": - conv_block += [nn.ReflectionPad2d(1)] - elif padding_type == "replicate": - conv_block += [nn.ReplicationPad2d(1)] - elif padding_type == "zero": - p = 1 - else: - raise NotImplementedError("padding [%s] is not implemented" % padding_type) - conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, dilation=1), norm_layer(dim)] - - return nn.Sequential(*conv_block) - - def forward(self, x): - out = x + self.conv_block(x) - return out - - -class Encoder(nn.Module): - def __init__(self, input_nc, output_nc, ngf=32, n_downsampling=4, norm_layer=nn.BatchNorm2d): - super(Encoder, self).__init__() - self.output_nc = output_nc - - model = [ - nn.ReflectionPad2d(3), - nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0), - norm_layer(ngf), - nn.ReLU(True), - ] - ### downsample - for i in range(n_downsampling): - mult = 2 ** i - model += [ - nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1), - norm_layer(ngf * mult * 2), - nn.ReLU(True), - ] - - ### upsample - for i in range(n_downsampling): - mult = 2 ** (n_downsampling - i) - model += [ - nn.ConvTranspose2d( - ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1, output_padding=1 - ), - norm_layer(int(ngf * mult / 2)), - nn.ReLU(True), - ] - - model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()] - self.model = nn.Sequential(*model) - - def forward(self, input, inst): - outputs = self.model(input) - - # instance-wise average pooling - outputs_mean = outputs.clone() - inst_list = np.unique(inst.cpu().numpy().astype(int)) - for i in inst_list: - for b in range(input.size()[0]): - indices = (inst[b : b + 1] == int(i)).nonzero() # n x 4 - for j in range(self.output_nc): - output_ins = outputs[indices[:, 0] + b, indices[:, 1] + j, indices[:, 2], indices[:, 3]] - mean_feat = torch.mean(output_ins).expand_as(output_ins) - outputs_mean[ - indices[:, 0] + b, indices[:, 1] + j, indices[:, 2], indices[:, 3] - ] = mean_feat - return outputs_mean - - -def SN(module, mode=True): - if mode: - return torch.nn.utils.spectral_norm(module) - - return module - - -class NonLocalBlock2D_with_mask_Res(nn.Module): - def __init__( - self, - in_channels, - inter_channels, - mode="add", - re_norm=False, - temperature=1.0, - use_self=False, - cosin=False, - ): - super(NonLocalBlock2D_with_mask_Res, self).__init__() - - self.cosin = cosin - self.renorm = re_norm - self.in_channels = in_channels - self.inter_channels = inter_channels - - self.g = nn.Conv2d( - in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0 - ) - - self.W = nn.Conv2d( - in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0 - ) - # for pytorch 0.3.1 - # nn.init.constant(self.W.weight, 0) - # nn.init.constant(self.W.bias, 0) - # for pytorch 0.4.0 - nn.init.constant_(self.W.weight, 0) - nn.init.constant_(self.W.bias, 0) - self.theta = nn.Conv2d( - in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0 - ) - - self.phi = nn.Conv2d( - in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0 - ) - - self.mode = mode - self.temperature = temperature - self.use_self = use_self - - norm_layer = get_norm_layer(norm_type="instance") - activation = nn.ReLU(True) - - model = [] - for i in range(3): - model += [ - ResnetBlock( - inter_channels, - padding_type="reflect", - activation=activation, - norm_layer=norm_layer, - opt=None, - ) - ] - self.res_block = nn.Sequential(*model) - - def forward(self, x, mask): ## The shape of mask is Batch*1*H*W - batch_size = x.size(0) - - g_x = self.g(x).view(batch_size, self.inter_channels, -1) - - g_x = g_x.permute(0, 2, 1) - - theta_x = self.theta(x).view(batch_size, self.inter_channels, -1) - - theta_x = theta_x.permute(0, 2, 1) - - phi_x = self.phi(x).view(batch_size, self.inter_channels, -1) - - if self.cosin: - theta_x = F.normalize(theta_x, dim=2) - phi_x = F.normalize(phi_x, dim=1) - - f = torch.matmul(theta_x, phi_x) - - f /= self.temperature - - f_div_C = F.softmax(f, dim=2) - - tmp = 1 - mask - mask = F.interpolate(mask, (x.size(2), x.size(3)), mode="bilinear") - mask[mask > 0] = 1.0 - mask = 1 - mask - - tmp = F.interpolate(tmp, (x.size(2), x.size(3))) - mask *= tmp - - mask_expand = mask.view(batch_size, 1, -1) - mask_expand = mask_expand.repeat(1, x.size(2) * x.size(3), 1) - - # mask = 1 - mask - # mask=F.interpolate(mask,(x.size(2),x.size(3))) - # mask_expand=mask.view(batch_size,1,-1) - # mask_expand=mask_expand.repeat(1,x.size(2)*x.size(3),1) - - if self.use_self: - mask_expand[:, range(x.size(2) * x.size(3)), range(x.size(2) * x.size(3))] = 1.0 - - # print(mask_expand.shape) - # print(f_div_C.shape) - - f_div_C = mask_expand * f_div_C - if self.renorm: - f_div_C = F.normalize(f_div_C, p=1, dim=2) - - ########################### - - y = torch.matmul(f_div_C, g_x) - - y = y.permute(0, 2, 1).contiguous() - - y = y.view(batch_size, self.inter_channels, *x.size()[2:]) - W_y = self.W(y) - - W_y = self.res_block(W_y) - - if self.mode == "combine": - full_mask = mask.repeat(1, self.inter_channels, 1, 1) - z = full_mask * x + (1 - full_mask) * W_y - return z - - -class MultiscaleDiscriminator(nn.Module): - def __init__(self, input_nc, opt, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, - use_sigmoid=False, num_D=3, getIntermFeat=False): - super(MultiscaleDiscriminator, self).__init__() - self.num_D = num_D - self.n_layers = n_layers - self.getIntermFeat = getIntermFeat - - for i in range(num_D): - netD = NLayerDiscriminator(input_nc, opt, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat) - if getIntermFeat: - for j in range(n_layers+2): - setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j))) - else: - setattr(self, 'layer'+str(i), netD.model) - - self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False) - - def singleD_forward(self, model, input): - if self.getIntermFeat: - result = [input] - for i in range(len(model)): - result.append(model[i](result[-1])) - return result[1:] - else: - return [model(input)] - - def forward(self, input): - num_D = self.num_D - result = [] - input_downsampled = input - for i in range(num_D): - if self.getIntermFeat: - model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)] - else: - model = getattr(self, 'layer'+str(num_D-1-i)) - result.append(self.singleD_forward(model, input_downsampled)) - if i != (num_D-1): - input_downsampled = self.downsample(input_downsampled) - return result - -# Defines the PatchGAN discriminator with the specified arguments. -class NLayerDiscriminator(nn.Module): - def __init__(self, input_nc, opt, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False): - super(NLayerDiscriminator, self).__init__() - self.getIntermFeat = getIntermFeat - self.n_layers = n_layers - - kw = 4 - padw = int(np.ceil((kw-1.0)/2)) - sequence = [[SN(nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),opt.use_SN), nn.LeakyReLU(0.2, True)]] - - nf = ndf - for n in range(1, n_layers): - nf_prev = nf - nf = min(nf * 2, 512) - sequence += [[ - SN(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),opt.use_SN), - norm_layer(nf), nn.LeakyReLU(0.2, True) - ]] - - nf_prev = nf - nf = min(nf * 2, 512) - sequence += [[ - SN(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),opt.use_SN), - norm_layer(nf), - nn.LeakyReLU(0.2, True) - ]] - - sequence += [[SN(nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw),opt.use_SN)]] - - if use_sigmoid: - sequence += [[nn.Sigmoid()]] - - if getIntermFeat: - for n in range(len(sequence)): - setattr(self, 'model'+str(n), nn.Sequential(*sequence[n])) - else: - sequence_stream = [] - for n in range(len(sequence)): - sequence_stream += sequence[n] - self.model = nn.Sequential(*sequence_stream) - - def forward(self, input): - if self.getIntermFeat: - res = [input] - for n in range(self.n_layers+2): - model = getattr(self, 'model'+str(n)) - res.append(model(res[-1])) - return res[1:] - else: - return self.model(input) - - - -class Patch_Attention_4(nn.Module): ## While combine the feature map, use conv and mask - def __init__(self, in_channels, inter_channels, patch_size): - super(Patch_Attention_4, self).__init__() - - self.patch_size=patch_size - - - # self.g = nn.Conv2d( - # in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0 - # ) - - # self.W = nn.Conv2d( - # in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0 - # ) - # # for pytorch 0.3.1 - # # nn.init.constant(self.W.weight, 0) - # # nn.init.constant(self.W.bias, 0) - # # for pytorch 0.4.0 - # nn.init.constant_(self.W.weight, 0) - # nn.init.constant_(self.W.bias, 0) - # self.theta = nn.Conv2d( - # in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0 - # ) - - # self.phi = nn.Conv2d( - # in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0 - # ) - - self.F_Combine=nn.Conv2d(in_channels=1025,out_channels=512,kernel_size=3,stride=1,padding=1,bias=True) - norm_layer = get_norm_layer(norm_type="instance") - activation = nn.ReLU(True) - - model = [] - for i in range(1): - model += [ - ResnetBlock( - inter_channels, - padding_type="reflect", - activation=activation, - norm_layer=norm_layer, - opt=None, - ) - ] - self.res_block = nn.Sequential(*model) - - def Hard_Compose(self, input, dim, index): - # batch index select - # input: [B,C,HW] - # dim: scalar > 0 - # index: [B, HW] - views = [input.size(0)] + [1 if i!=dim else -1 for i in range(1, len(input.size()))] - expanse = list(input.size()) - expanse[0] = -1 - expanse[dim] = -1 - index = index.view(views).expand(expanse) - return torch.gather(input, dim, index) - - def forward(self, z, mask): ## The shape of mask is Batch*1*H*W - - x=self.res_block(z) - - b,c,h,w=x.shape - - ## mask resize + dilation - # tmp = 1 - mask - mask = F.interpolate(mask, (x.size(2), x.size(3)), mode="bilinear") - mask[mask > 0] = 1.0 - - # mask = 1 - mask - # tmp = F.interpolate(tmp, (x.size(2), x.size(3))) - # mask *= tmp - # mask=1-mask - ## 1: mask position 0: non-mask - - mask_unfold=F.unfold(mask, kernel_size=(self.patch_size,self.patch_size), padding=0, stride=self.patch_size) - non_mask_region=(torch.mean(mask_unfold,dim=1,keepdim=True)>0.6).float() - all_patch_num=h*w/self.patch_size/self.patch_size - non_mask_region=non_mask_region.repeat(1,int(all_patch_num),1) - - x_unfold=F.unfold(x, kernel_size=(self.patch_size,self.patch_size), padding=0, stride=self.patch_size) - y_unfold=x_unfold.permute(0,2,1) - x_unfold_normalized=F.normalize(x_unfold,dim=1) - y_unfold_normalized=F.normalize(y_unfold,dim=2) - correlation_matrix=torch.bmm(y_unfold_normalized,x_unfold_normalized) - correlation_matrix=correlation_matrix.masked_fill(non_mask_region==1.,-1e9) - correlation_matrix=F.softmax(correlation_matrix,dim=2) - - # print(correlation_matrix) - - R, max_arg=torch.max(correlation_matrix,dim=2) - - composed_unfold=self.Hard_Compose(x_unfold, 2, max_arg) - composed_fold=F.fold(composed_unfold,output_size=(h,w),kernel_size=(self.patch_size,self.patch_size),padding=0,stride=self.patch_size) - - concat_1=torch.cat((z,composed_fold,mask),dim=1) - concat_1=self.F_Combine(concat_1) - - return concat_1 - - def inference_forward(self,z,mask): ## Reduce the extra memory cost - - - x=self.res_block(z) - - b,c,h,w=x.shape - - ## mask resize + dilation - # tmp = 1 - mask - mask = F.interpolate(mask, (x.size(2), x.size(3)), mode="bilinear") - mask[mask > 0] = 1.0 - # mask = 1 - mask - # tmp = F.interpolate(tmp, (x.size(2), x.size(3))) - # mask *= tmp - # mask=1-mask - ## 1: mask position 0: non-mask - - mask_unfold=F.unfold(mask, kernel_size=(self.patch_size,self.patch_size), padding=0, stride=self.patch_size) - non_mask_region=(torch.mean(mask_unfold,dim=1,keepdim=True)>0.6).float()[0,0,:] # 1*1*all_patch_num - - all_patch_num=h*w/self.patch_size/self.patch_size - - mask_index=torch.nonzero(non_mask_region,as_tuple=True)[0] - - - if len(mask_index)==0: ## No mask patch is selected, no attention is needed - - composed_fold=x - - else: - - unmask_index=torch.nonzero(non_mask_region!=1,as_tuple=True)[0] - - x_unfold=F.unfold(x, kernel_size=(self.patch_size,self.patch_size), padding=0, stride=self.patch_size) - - Query_Patch=torch.index_select(x_unfold,2,mask_index) - Key_Patch=torch.index_select(x_unfold,2,unmask_index) - - Query_Patch=Query_Patch.permute(0,2,1) - Query_Patch_normalized=F.normalize(Query_Patch,dim=2) - Key_Patch_normalized=F.normalize(Key_Patch,dim=1) - - correlation_matrix=torch.bmm(Query_Patch_normalized,Key_Patch_normalized) - correlation_matrix=F.softmax(correlation_matrix,dim=2) - - - R, max_arg=torch.max(correlation_matrix,dim=2) - - composed_unfold=self.Hard_Compose(Key_Patch, 2, max_arg) - x_unfold[:,:,mask_index]=composed_unfold - composed_fold=F.fold(x_unfold,output_size=(h,w),kernel_size=(self.patch_size,self.patch_size),padding=0,stride=self.patch_size) - - concat_1=torch.cat((z,composed_fold,mask),dim=1) - concat_1=self.F_Combine(concat_1) - - - return concat_1 - -############################################################################## -# Losses -############################################################################## -class GANLoss(nn.Module): - def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0, - tensor=torch.FloatTensor): - super(GANLoss, self).__init__() - self.real_label = target_real_label - self.fake_label = target_fake_label - self.real_label_var = None - self.fake_label_var = None - self.Tensor = tensor - if use_lsgan: - self.loss = nn.MSELoss() - else: - self.loss = nn.BCELoss() - - def get_target_tensor(self, input, target_is_real): - target_tensor = None - if target_is_real: - create_label = ((self.real_label_var is None) or - (self.real_label_var.numel() != input.numel())) - if create_label: - real_tensor = self.Tensor(input.size()).fill_(self.real_label) - self.real_label_var = Variable(real_tensor, requires_grad=False) - target_tensor = self.real_label_var - else: - create_label = ((self.fake_label_var is None) or - (self.fake_label_var.numel() != input.numel())) - if create_label: - fake_tensor = self.Tensor(input.size()).fill_(self.fake_label) - self.fake_label_var = Variable(fake_tensor, requires_grad=False) - target_tensor = self.fake_label_var - return target_tensor - - def __call__(self, input, target_is_real): - if isinstance(input[0], list): - loss = 0 - for input_i in input: - pred = input_i[-1] - target_tensor = self.get_target_tensor(pred, target_is_real) - loss += self.loss(pred, target_tensor) - return loss - else: - target_tensor = self.get_target_tensor(input[-1], target_is_real) - return self.loss(input[-1], target_tensor) - - - - -####################################### VGG Loss - -from torchvision import models -class VGG19_torch(torch.nn.Module): - def __init__(self, requires_grad=False): - super(VGG19_torch, self).__init__() - vgg_pretrained_features = models.vgg19(pretrained=True).features - self.slice1 = torch.nn.Sequential() - self.slice2 = torch.nn.Sequential() - self.slice3 = torch.nn.Sequential() - self.slice4 = torch.nn.Sequential() - self.slice5 = torch.nn.Sequential() - for x in range(2): - self.slice1.add_module(str(x), vgg_pretrained_features[x]) - for x in range(2, 7): - self.slice2.add_module(str(x), vgg_pretrained_features[x]) - for x in range(7, 12): - self.slice3.add_module(str(x), vgg_pretrained_features[x]) - for x in range(12, 21): - self.slice4.add_module(str(x), vgg_pretrained_features[x]) - for x in range(21, 30): - self.slice5.add_module(str(x), vgg_pretrained_features[x]) - if not requires_grad: - for param in self.parameters(): - param.requires_grad = False - - def forward(self, X): - h_relu1 = self.slice1(X) - h_relu2 = self.slice2(h_relu1) - h_relu3 = self.slice3(h_relu2) - h_relu4 = self.slice4(h_relu3) - h_relu5 = self.slice5(h_relu4) - out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] - return out - -class VGGLoss_torch(nn.Module): - def __init__(self, gpu_ids): - super(VGGLoss_torch, self).__init__() - self.vgg = VGG19_torch().cuda() - self.criterion = nn.L1Loss() - self.weights = [1.0/32, 1.0/16, 1.0/8, 1.0/4, 1.0] - - def forward(self, x, y): - x_vgg, y_vgg = self.vgg(x), self.vgg(y) - loss = 0 - for i in range(len(x_vgg)): - loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) - return loss \ No newline at end of file diff --git a/spaces/xuxw98/TAPA/lit_llama/utils.py b/spaces/xuxw98/TAPA/lit_llama/utils.py deleted file mode 100644 index a09ada20c55c7b49c54f1720d2901b2953f209f9..0000000000000000000000000000000000000000 --- a/spaces/xuxw98/TAPA/lit_llama/utils.py +++ /dev/null @@ -1,496 +0,0 @@ -"""Utility functions for training and inference.""" - -import functools -import pickle -import warnings -from io import BytesIO -from pathlib import Path -from contextlib import contextmanager - -import torch -import torch.utils._device -from lightning.fabric.strategies import DeepSpeedStrategy, FSDPStrategy -from torch.distributed.fsdp import FullStateDictConfig -from torch.distributed.fsdp import FullyShardedDataParallel as FSDP -from torch.distributed.fsdp import StateDictType -from torch.serialization import normalize_storage_type - -llama_model_sizes = { - 4096: "7B", # 7B n_embd=4096 - 5120: "13B", # 13B n_embd=5120 - 6656: "30B", # 30B n_embd=6656 - 8192: "65B", # 65B n_embd=8192 -} - - -def llama_model_lookup(checkpoint: dict) -> str: - """Returns the LLaMA model name from the checkpoint. - - Checks the width of the lm_head.weight matrix, as these uniquely identify the model. - """ - embedding_size = checkpoint['transformer.wte.weight'].shape[1] - return llama_model_sizes[embedding_size] - - -def find_multiple(n: int, k: int) -> int: - if n % k == 0: - return n - return n + k - (n % k) - - -def save_model_checkpoint(fabric, model, file_path): - """Handles boilerplate logic for retrieving and saving the state_dict. - - This will be upstreamed to Fabric soon. - """ - file_path = Path(file_path) - - if isinstance(fabric.strategy, DeepSpeedStrategy): - from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict - - fabric.save(file_path, {"model": model}) - fabric.barrier() - if fabric.global_rank == 0: - # Create a consolidated checkpoint with the same name next to the deepspeed checkpoint - convert_zero_checkpoint_to_fp32_state_dict(file_path, file_path.with_suffix(".pth")) - return - - if isinstance(fabric.strategy, FSDPStrategy): - save_policy = FullStateDictConfig(offload_to_cpu=(fabric.world_size > 1), rank0_only=True) - with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, save_policy): - state_dict = model._forward_module.state_dict() - else: - state_dict = model.state_dict() - - if fabric.global_rank == 0: - torch.save(state_dict, file_path) - fabric.barrier() - - -class EmptyInitOnDevice(torch.overrides.TorchFunctionMode): - def __init__(self, device=None, dtype=None, quantization_mode=None): - """ - Create tensors with given device and dtype and don't run initialization - (but instead use "empty tensors", i.e. uninitialized memory). - - device: `torch.device` to work with - dtype: `torch.dtype` to work with - quantization_mode: optional string, quantization mode to work with, default `None`. - Available modes: `llm.int8` bitsnbytes LLM.int8 quantization (only on GPU) - `gptq.int4`, `gptq.int8`: GPTQ pre-quantized models - - Example:: - with EmptyInitOnDevice("cuda", dtype=torch.bfloat16): - model = LLaMA.from_name('7B') - model.load_state_dict(torch.load('llama-lit/7B/lit-llama.pth'))""" - - self.quantization_mode = quantization_mode - self.quantized_linear_cls = None - if self.quantization_mode == 'llm.int8': - if device.type != "cuda": - raise ValueError("Quantization is only supported on the GPU.") - from .quantization import Linear8bitLt - self.quantized_linear_cls = Linear8bitLt - elif self.quantization_mode == 'gptq.int4': - from .quantization import ColBlockQuantizedLinear - self.quantized_linear_cls = functools.partial(ColBlockQuantizedLinear, bits=4, tile_cols=-1) - elif self.quantization_mode == 'gptq.int8': - from .quantization import ColBlockQuantizedLinear - self.quantized_linear_cls = functools.partial(ColBlockQuantizedLinear, bits=8, tile_cols=-1) - elif self.quantization_mode is not None: - raise RuntimeError(f"unknown quantization mode {self.quantization_mode}") - self.device = device - self.dtype = dtype - - def __enter__(self): - if self.quantized_linear_cls != None: - self.torch_linear_cls = torch.nn.Linear - torch.nn.Linear = self.quantized_linear_cls - return super().__enter__() - - def __exit__(self, exc_type, exc_val, exc_tb): - if self.quantized_linear_cls != None: - torch.nn.Linear = self.torch_linear_cls - return super().__exit__(exc_type, exc_val, exc_tb) - - def __torch_function__(self, func, types, args=(), kwargs=None): - kwargs = kwargs or {} - if getattr(func, "__module__", None) == "torch.nn.init": - if "tensor" in kwargs: - return kwargs["tensor"] - else: - return args[0] - if ( - self.device is not None - and func in torch.utils._device._device_constructors() - and kwargs.get("device") is None - ): - kwargs["device"] = self.device - if ( - self.dtype is not None - and func in torch.utils._device._device_constructors() - and kwargs.get("dtype") is None - ): - kwargs["dtype"] = self.dtype - return func(*args, **kwargs) - - -@contextmanager -def quantization(mode: str = None): - quantized_linear_cls = None - if mode == 'llm.int8': - from .quantization import Linear8bitLt - quantized_linear_cls = Linear8bitLt - elif mode == 'gptq.int4': - from .quantization import ColBlockQuantizedLinear - quantized_linear_cls = functools.partial(ColBlockQuantizedLinear, bits=4, tile_cols=-1) - elif mode == 'gptq.int8': - from .quantization import ColBlockQuantizedLinear - quantized_linear_cls = functools.partial(ColBlockQuantizedLinear, bits=8, tile_cols=-1) - elif mode is not None: - raise ValueError(f"Unknown quantization mode: {mode}") - - enabled = mode is not None - torch_linear_cls = torch.nn.Linear - if enabled: - torch.nn.Linear = quantized_linear_cls - yield - if enabled: - torch.nn.Linear = torch_linear_cls - - -# this is taken from torchhacks https://github.com/lernapparat/torchhacks - - -class NotYetLoadedTensor: - def __init__(self, metatensor, archiveinfo, storageinfo, rebuild_args): - self.metatensor = metatensor - self.archiveinfo = archiveinfo - self.storageinfo = storageinfo - self.rebuild_args = rebuild_args - - @classmethod - def rebuild_from_type_v2(cls, func, new_type, args, state, *, archiveinfo=None): - ret = func(*args) - if isinstance(ret, NotYetLoadedTensor): - old_lt = ret._load_tensor - - def _load_tensor(): - t = old_lt() - return torch._tensor._rebuild_from_type_v2( - lambda: t, new_type, (), state - ) - - ret._load_tensor = _load_tensor - return ret - return torch._tensor._rebuild_from_type_v2(func, new_type, args, state) - - @classmethod - def rebuild_parameter( - cls, data, requires_grad, backward_hooks, *, archiveinfo=None - ): - if isinstance(data, NotYetLoadedTensor): - old_lt = data._load_tensor - - def _load_tensor(): - t = old_lt() - return torch._utils._rebuild_parameter(t, requires_grad, backward_hooks) - - data._load_tensor = _load_tensor - return data - return torch._utils._rebuild_parameter(data, requires_grad, backward_hooks) - - @classmethod - def rebuild_tensor_v2( - cls, - storage, - storage_offset, - size, - stride, - requires_grad, - backward_hooks, - metadata=None, - *, - archiveinfo=None, - ): - rebuild_args = ( - storage_offset, - size, - stride, - requires_grad, - backward_hooks, - metadata, - ) - metatensor = torch._utils._rebuild_tensor_v2( - storage, - storage_offset, - size, - stride, - requires_grad, - backward_hooks, - metadata, - ) - storageinfo = storage.archiveinfo - return NotYetLoadedTensor(metatensor, archiveinfo, storageinfo, rebuild_args) - - def _load_tensor(self): - name, storage_cls, fn, device, size = self.storageinfo - dtype = self.metatensor.dtype - - uts = ( - self.archiveinfo.zipfile_context.zf.get_storage_from_record( - f"data/{fn}", - size * torch._utils._element_size(dtype), - torch.UntypedStorage, - ) - ._typed_storage() - ._untyped_storage - ) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - storage = torch.storage.TypedStorage( - wrap_storage=uts, dtype=self.metatensor.dtype, _internal=True - ) - tensor = torch._utils._rebuild_tensor_v2(storage, *self.rebuild_args) - return tensor - - @classmethod - def __torch_function__(cls, func, types, args=(), kwargs=None): - if kwargs is None: - kwargs = {} - loaded_args = [ - (a._load_tensor() if isinstance(a, NotYetLoadedTensor) else a) for a in args - ] - res = func(*loaded_args, **kwargs) - # gc.collect would be costly here, maybe do it optionally - return res - - def __getattr__(self, name): - # properties - ## TODO: device, is_...?? - ## TODO: mH, mT, H, T, data, imag, real - ## name ??? - if name in { - "dtype", - "grad", - "grad_fn", - "layout", - "names", - "ndim", - "output_nr", - "requires_grad", - "retains_grad", - "shape", - "volatile", - }: - return getattr(self.metatensor, name) - if name in {"size"}: - return getattr(self.metatensor, name) - # materializing with contiguous is needed for quantization - if name in {"contiguous"}: - return getattr(self._load_tensor(), name) - - raise AttributeError(f"{type(self)} does not have {name}") - - def __repr__(self): - return f"NotYetLoadedTensor({repr(self.metatensor)})" - - -class LazyLoadingUnpickler(pickle.Unpickler): - def __init__(self, file, zipfile_context): - super().__init__(file) - self.zipfile_context = zipfile_context - - def find_class(self, module, name): - res = super().find_class(module, name) - if module == "torch._utils" and name == "_rebuild_tensor_v2": - return functools.partial( - NotYetLoadedTensor.rebuild_tensor_v2, archiveinfo=self - ) - elif module == "torch._tensor" and name == "_rebuild_from_type_v2": - return functools.partial( - NotYetLoadedTensor.rebuild_from_type_v2, archiveinfo=self - ) - elif module == "torch._utils" and name == "_rebuild_parameter": - return functools.partial( - NotYetLoadedTensor.rebuild_parameter, archiveinfo=self - ) - return res - - def persistent_load(self, pid): - name, cls, fn, device, size = pid - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - s = torch.storage.TypedStorage(dtype=cls().dtype, device="meta") - s.archiveinfo = pid - return s - - -class lazy_load: - def __init__(self, fn): - self.zf = torch._C.PyTorchFileReader(str(fn)) - with BytesIO(self.zf.get_record("data.pkl")) as pkl: - mup = LazyLoadingUnpickler(pkl, self) - self.sd = mup.load() - - def __enter__(self): - return self.sd - - def __exit__(self, exc_type, exc_val, exc_tb): - del self.zf # I don't think there is a way to force closing... - self.zf = None - - -class SavingProxyForStorage: - def __init__(self, obj, saver, protocol_version=5): - self.protocol_version = protocol_version - self.saver = saver - if not (isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj)): - raise TypeError(f"expected storage, not {type(obj)}") - - # this logic is taken from PyTorch 2.0+ torch/serialization.py - if isinstance(obj, torch.storage.TypedStorage): - # PT upstream wants to deprecate this eventually... - storage = obj._untyped_storage - storage_type_str = obj._pickle_storage_type() - storage_type = getattr(torch, storage_type_str) - storage_numel = obj._size() - else: - storage = obj - storage_type = normalize_storage_type(type(obj)) - storage_numel = storage.nbytes() - - storage_key = saver._write_storage_and_return_key(storage) - location = torch.serialization.location_tag(storage) - - self.storage_info = ( - "storage", - storage_type, - storage_key, - location, - storage_numel, - ) - - def __reduce_ex__(self, protocol_version): - assert False, "this should be handled with out of band" - - -class SavingProxyForTensor: - def __init__(self, tensor, saver, protocol_version=5): - self.protocol_version = protocol_version - self.reduce_ret_fn, (storage, *other_reduce_args) = tensor.__reduce_ex__( - protocol_version - ) - assert isinstance( - storage, torch.storage.TypedStorage - ), "Please check for updates" - storage_proxy = SavingProxyForStorage( - storage, saver, protocol_version=protocol_version - ) - self.reduce_args = (storage_proxy, *other_reduce_args) - - def __reduce_ex__(self, protocol_version): - if protocol_version != self.protocol_version: - raise RuntimeError( - f"Unexpected protocol version: expected {self.protocol_version}, got {protocol_version}" - ) - return self.reduce_ret_fn, self.reduce_args - - -class IncrementalPyTorchPickler(pickle.Pickler): - def __init__(self, saver, *args, **kwargs): - super().__init__(*args, **kwargs) - self.storage_dtypes = {} - self.saver = saver - self.id_map = {} - - # this logic is taken from PyTorch 2.0+ torch/serialization.py - def persistent_id(self, obj): - # FIXME: the docs say that persistent_id should only return a string - # but torch store returns tuples. This works only in the binary protocol - # see - # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects - # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537 - if isinstance(obj, SavingProxyForStorage): - return obj.storage_info - - if isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj): - if isinstance(obj, torch.storage.TypedStorage): - # TODO: Once we decide to break serialization FC, this case - # can be deleted - storage = obj._untyped_storage - storage_dtype = obj.dtype - storage_type_str = obj._pickle_storage_type() - storage_type = getattr(torch, storage_type_str) - storage_numel = obj._size() - - else: - storage = obj - storage_dtype = torch.uint8 - storage_type = normalize_storage_type(type(obj)) - storage_numel = storage.nbytes() - - # If storage is allocated, ensure that any other saved storages - # pointing to the same data all have the same dtype. If storage is - # not allocated, don't perform this check - if storage.data_ptr() != 0: - if storage.data_ptr() in self.storage_dtypes: - if storage_dtype != self.storage_dtypes[storage.data_ptr()]: - raise RuntimeError( - "Cannot save multiple tensors or storages that " - "view the same data as different types" - ) - else: - self.storage_dtypes[storage.data_ptr()] = storage_dtype - - storage_key = self.id_map.get(storage._cdata) - if storage_key is None: - storage_key = self.saver._write_storage_and_return_key(storage) - self.id_map[storage._cdata] = storage_key - location = torch.serialization.location_tag(storage) - - return ("storage", storage_type, storage_key, location, storage_numel) - - return None - - -class incremental_save: - def __init__(self, name): - self.name = name - self.zipfile = torch._C.PyTorchFileWriter(str(name)) - self.has_saved = False - self.next_key = 0 - - def __enter__(self): - return self - - def store_early(self, tensor): - if isinstance(tensor, torch.Tensor): - return SavingProxyForTensor(tensor, self) - raise TypeError(f"can only store tensors early, not {type(tensor)}") - - def save(self, obj): - if self.has_saved: - raise RuntimeError("have already saved") - # Write the pickle data for `obj` - data_buf = BytesIO() - pickler = IncrementalPyTorchPickler(self, data_buf, protocol=5) - pickler.dump(obj) - data_value = data_buf.getvalue() - self.zipfile.write_record("data.pkl", data_value, len(data_value)) - self.has_saved = True - - def _write_storage_and_return_key(self, storage): - if self.has_saved: - raise RuntimeError("have already saved") - key = self.next_key - self.next_key += 1 - name = f"data/{key}" - if storage.device.type != "cpu": - storage = storage.cpu() - num_bytes = storage.nbytes() - self.zipfile.write_record(name, storage.data_ptr(), num_bytes) - return key - - def __exit__(self, type, value, traceback): - self.zipfile.write_end_of_file() diff --git "a/spaces/xxccc/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py" "b/spaces/xxccc/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py" deleted file mode 100644 index cbda23b83d759e6a3a4da5847c37ddff662daab2..0000000000000000000000000000000000000000 --- "a/spaces/xxccc/gpt-academic/crazy_functions/\346\211\271\351\207\217\346\200\273\347\273\223PDF\346\226\207\346\241\243.py" +++ /dev/null @@ -1,166 +0,0 @@ -from toolbox import update_ui -from toolbox import CatchException, report_execption, write_results_to_file -import re -import unicodedata -fast_debug = False -from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive - -def is_paragraph_break(match): - """ - 根据给定的匹配结果来判断换行符是否表示段落分隔。 - 如果换行符前为句子结束标志（句号，感叹号，问号），且下一个字符为大写字母，则换行符更有可能表示段落分隔。 - 也可以根据之前的内容长度来判断段落是否已经足够长。 - """ - prev_char, next_char = match.groups() - - # 句子结束标志 - sentence_endings = ".!?" - - # 设定一个最小段落长度阈值 - min_paragraph_length = 140 - - if prev_char in sentence_endings and next_char.isupper() and len(match.string[:match.start(1)]) > min_paragraph_length: - return "\n\n" - else: - return " " - -def normalize_text(text): - """ - 通过把连字（ligatures）等文本特殊符号转换为其基本形式来对文本进行归一化处理。 - 例如，将连字 "fi" 转换为 "f" 和 "i"。 - """ - # 对文本进行归一化处理，分解连字 - normalized_text = unicodedata.normalize("NFKD", text) - - # 替换其他特殊字符 - cleaned_text = re.sub(r'[^\x00-\x7F]+', '', normalized_text) - - return cleaned_text - -def clean_text(raw_text): - """ - 对从 PDF 提取出的原始文本进行清洗和格式化处理。 - 1. 对原始文本进行归一化处理。 - 2. 替换跨行的连词 - 3. 根据 heuristic 规则判断换行符是否是段落分隔，并相应地进行替换 - """ - # 对文本进行归一化处理 - normalized_text = normalize_text(raw_text) - - # 替换跨行的连词 - text = re.sub(r'(\w+-\n\w+)', lambda m: m.group(1).replace('-\n', ''), normalized_text) - - # 根据前后相邻字符的特点，找到原文本中的换行符 - newlines = re.compile(r'(\S)\n(\S)') - - # 根据 heuristic 规则，用空格或段落分隔符替换原换行符 - final_text = re.sub(newlines, lambda m: m.group(1) + is_paragraph_break(m) + m.group(2), text) - - return final_text.strip() - -def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt): - import time, glob, os, fitz - print('begin analysis on:', file_manifest) - for index, fp in enumerate(file_manifest): - with fitz.open(fp) as doc: - file_content = "" - for page in doc: - file_content += page.get_text() - file_content = clean_text(file_content) - print(file_content) - - prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else "" - i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```' - i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}' - chatbot.append((i_say_show_user, "[Local Message] waiting gpt response.")) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - if not fast_debug: - msg = '正常' - # ** gpt request ** - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( - inputs=i_say, - inputs_show_user=i_say_show_user, - llm_kwargs=llm_kwargs, - chatbot=chatbot, - history=[], - sys_prompt="总结文章。" - ) # 带超时倒计时 - - - chatbot[-1] = (i_say_show_user, gpt_say) - history.append(i_say_show_user); history.append(gpt_say) - yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面 - if not fast_debug: time.sleep(2) - - all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)]) - i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。' - chatbot.append((i_say, "[Local Message] waiting gpt response.")) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - if not fast_debug: - msg = '正常' - # ** gpt request ** - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( - inputs=i_say, - inputs_show_user=i_say, - llm_kwargs=llm_kwargs, - chatbot=chatbot, - history=history, - sys_prompt="总结文章。" - ) # 带超时倒计时 - - chatbot[-1] = (i_say, gpt_say) - history.append(i_say); history.append(gpt_say) - yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面 - res = write_results_to_file(history) - chatbot.append(("完成了吗？", res)) - yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面 - - -@CatchException -def 批量总结PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - import glob, os - - # 基本信息：功能、贡献者 - chatbot.append([ - "函数插件功能？", - "批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien"]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - # 尝试导入依赖，如果缺少依赖，则给出安装建议 - try: - import fitz - except: - report_execption(chatbot, history, - a = f"解析项目: {txt}", - b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - - # 清空历史，以免输入溢出 - history = [] - - # 检测输入参数，如没有给定输入参数，直接退出 - if os.path.exists(txt): - project_folder = txt - else: - if txt == "": txt = '空空如也的输入栏' - report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - - # 搜索需要处理的文件清单 - file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)] # + \ - # [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] + \ - # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \ - # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)] - - # 如果没找到任何文件 - if len(file_manifest) == 0: - report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}") - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return - - # 开始正式执行任务 - yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt) diff --git a/spaces/ybelkada/interfacegan_pp/models/stylegan_generator.py b/spaces/ybelkada/interfacegan_pp/models/stylegan_generator.py deleted file mode 100644 index 8296946f9825d8d389afea97dfe5c7c926f425e9..0000000000000000000000000000000000000000 --- a/spaces/ybelkada/interfacegan_pp/models/stylegan_generator.py +++ /dev/null @@ -1,283 +0,0 @@ -# python3.7 -"""Contains the generator class of StyleGAN. - -Basically, this class is derived from the `BaseGenerator` class defined in -`base_generator.py`. -""" - -import os -import numpy as np - -import torch - -from . import model_settings -from .stylegan_generator_model import StyleGANGeneratorModel -from .base_generator import BaseGenerator - -__all__ = ['StyleGANGenerator'] - - -class StyleGANGenerator(BaseGenerator): - """Defines the generator class of StyleGAN. - - Different from conventional GAN, StyleGAN introduces a disentangled latent - space (i.e., W space) besides the normal latent space (i.e., Z space). Then, - the disentangled latent code, w, is fed into each convolutional layer to - modulate the `style` of the synthesis through AdaIN (Adaptive Instance - Normalization) layer. Normally, the w's fed into all layers are the same. But, - they can actually be different to make different layers get different styles. - Accordingly, an extended space (i.e. W+ space) is used to gather all w's - together. Taking the official StyleGAN model trained on FF-HQ dataset as an - instance, there are - (1) Z space, with dimension (512,) - (2) W space, with dimension (512,) - (3) W+ space, with dimension (18, 512) - """ - - def __init__(self, model_name, logger=None): - self.truncation_psi = model_settings.STYLEGAN_TRUNCATION_PSI - self.truncation_layers = model_settings.STYLEGAN_TRUNCATION_LAYERS - self.randomize_noise = model_settings.STYLEGAN_RANDOMIZE_NOISE - self.model_specific_vars = ['truncation.truncation'] - super().__init__(model_name, logger) - self.num_layers = (int(np.log2(self.resolution)) - 1) * 2 - assert self.gan_type == 'stylegan' - - def build(self): - self.check_attr('w_space_dim') - self.check_attr('fused_scale') - self.model = StyleGANGeneratorModel( - resolution=self.resolution, - w_space_dim=self.w_space_dim, - fused_scale=self.fused_scale, - output_channels=self.output_channels, - truncation_psi=self.truncation_psi, - truncation_layers=self.truncation_layers, - randomize_noise=self.randomize_noise) - - def load(self): - self.logger.info(f'Loading pytorch model from `{self.model_path}`.') - state_dict = torch.load(self.model_path) - for var_name in self.model_specific_vars: - state_dict[var_name] = self.model.state_dict()[var_name] - self.model.load_state_dict(state_dict) - self.logger.info(f'Successfully loaded!') - self.lod = self.model.synthesis.lod.to(self.cpu_device).tolist() - self.logger.info(f' `lod` of the loaded model is {self.lod}.') - - def convert_tf_model(self, test_num=10): - import sys - import pickle - import tensorflow as tf - os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' - sys.path.append(model_settings.BASE_DIR + '/stylegan_tf_official') - - self.logger.info(f'Loading tensorflow model from `{self.tf_model_path}`.') - tf.InteractiveSession() - with open(self.tf_model_path, 'rb') as f: - _, _, tf_model = pickle.load(f) - self.logger.info(f'Successfully loaded!') - - self.logger.info(f'Converting tensorflow model to pytorch version.') - tf_vars = dict(tf_model.__getstate__()['variables']) - tf_vars.update( - dict(tf_model.components.mapping.__getstate__()['variables'])) - tf_vars.update( - dict(tf_model.components.synthesis.__getstate__()['variables'])) - state_dict = self.model.state_dict() - for pth_var_name, tf_var_name in self.model.pth_to_tf_var_mapping.items(): - if 'ToRGB_lod' in tf_var_name: - lod = int(tf_var_name[len('ToRGB_lod')]) - lod_shift = 10 - int(np.log2(self.resolution)) - tf_var_name = tf_var_name.replace(f'{lod}', f'{lod - lod_shift}') - if tf_var_name not in tf_vars: - self.logger.debug(f'Variable `{tf_var_name}` does not exist in ' - f'tensorflow model.') - continue - self.logger.debug(f' Converting `{tf_var_name}` to `{pth_var_name}`.') - var = torch.from_numpy(np.array(tf_vars[tf_var_name])) - if 'weight' in pth_var_name: - if 'dense' in pth_var_name: - var = var.permute(1, 0) - elif 'conv' in pth_var_name: - var = var.permute(3, 2, 0, 1) - state_dict[pth_var_name] = var - self.logger.info(f'Successfully converted!') - - self.logger.info(f'Saving pytorch model to `{self.model_path}`.') - for var_name in self.model_specific_vars: - del state_dict[var_name] - torch.save(state_dict, self.model_path) - self.logger.info(f'Successfully saved!') - - self.load() - - # Official tensorflow model can only run on GPU. - if test_num <= 0 or not tf.test.is_built_with_cuda(): - return - self.logger.info(f'Testing conversion results.') - self.model.eval().to(self.run_device) - total_distance = 0.0 - for i in range(test_num): - latent_code = self.easy_sample(1) - tf_output = tf_model.run(latent_code, # latents_in - None, # labels_in - truncation_psi=self.truncation_psi, - truncation_cutoff=self.truncation_layers, - randomize_noise=self.randomize_noise) - pth_output = self.synthesize(latent_code)['image'] - distance = np.average(np.abs(tf_output - pth_output)) - self.logger.debug(f' Test {i:03d}: distance {distance:.6e}.') - total_distance += distance - self.logger.info(f'Average distance is {total_distance / test_num:.6e}.') - - def sample(self, num, latent_space_type='Z'): - """Samples latent codes randomly. - - Args: - num: Number of latent codes to sample. Should be positive. - latent_space_type: Type of latent space from which to sample latent code. - Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) - - Returns: - A `numpy.ndarray` as sampled latend codes. - - Raises: - ValueError: If the given `latent_space_type` is not supported. - """ - latent_space_type = latent_space_type.upper() - if latent_space_type == 'Z': - latent_codes = np.random.randn(num, self.latent_space_dim) - elif latent_space_type == 'W': - latent_codes = np.random.randn(num, self.w_space_dim) - elif latent_space_type == 'WP': - latent_codes = np.random.randn(num, self.num_layers, self.w_space_dim) - else: - raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') - - return latent_codes.astype(np.float32) - - def preprocess(self, latent_codes, latent_space_type='Z'): - """Preprocesses the input latent code if needed. - - Args: - latent_codes: The input latent codes for preprocessing. - latent_space_type: Type of latent space to which the latent codes belong. - Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) - - Returns: - The preprocessed latent codes which can be used as final input for the - generator. - - Raises: - ValueError: If the given `latent_space_type` is not supported. - """ - if not isinstance(latent_codes, np.ndarray): - raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') - - latent_space_type = latent_space_type.upper() - if latent_space_type == 'Z': - latent_codes = latent_codes.reshape(-1, self.latent_space_dim) - norm = np.linalg.norm(latent_codes, axis=1, keepdims=True) - latent_codes = latent_codes / norm * np.sqrt(self.latent_space_dim) - elif latent_space_type == 'W': - latent_codes = latent_codes.reshape(-1, self.w_space_dim) - elif latent_space_type == 'WP': - latent_codes = latent_codes.reshape(-1, self.num_layers, self.w_space_dim) - else: - raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') - - return latent_codes.astype(np.float32) - - def easy_sample(self, num, latent_space_type='Z'): - return self.preprocess(self.sample(num, latent_space_type), - latent_space_type) - - def synthesize(self, - latent_codes, - latent_space_type='Z', - generate_style=False, - generate_image=True): - """Synthesizes images with given latent codes. - - One can choose whether to generate the layer-wise style codes. - - Args: - latent_codes: Input latent codes for image synthesis. - latent_space_type: Type of latent space to which the latent codes belong. - Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) - generate_style: Whether to generate the layer-wise style codes. (default: - False) - generate_image: Whether to generate the final image synthesis. (default: - True) - - Returns: - A dictionary whose values are raw outputs from the generator. - """ - if not isinstance(latent_codes, np.ndarray): - raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') - - results = {} - - latent_space_type = latent_space_type.upper() - latent_codes_shape = latent_codes.shape - # Generate from Z space. - if latent_space_type == 'Z': - if not (len(latent_codes_shape) == 2 and - latent_codes_shape[0] <= self.batch_size and - latent_codes_shape[1] == self.latent_space_dim): - raise ValueError(f'Latent_codes should be with shape [batch_size, ' - f'latent_space_dim], where `batch_size` no larger ' - f'than {self.batch_size}, and `latent_space_dim` ' - f'equal to {self.latent_space_dim}!\n' - f'But {latent_codes_shape} received!') - zs = torch.from_numpy(latent_codes).type(torch.FloatTensor) - zs = zs.to(self.run_device) - ws = self.model.mapping(zs) - wps = self.model.truncation(ws) - results['z'] = latent_codes - results['w'] = self.get_value(ws) - results['wp'] = self.get_value(wps) - # Generate from W space. - elif latent_space_type == 'W': - if not (len(latent_codes_shape) == 2 and - latent_codes_shape[0] <= self.batch_size and - latent_codes_shape[1] == self.w_space_dim): - raise ValueError(f'Latent_codes should be with shape [batch_size, ' - f'w_space_dim], where `batch_size` no larger than ' - f'{self.batch_size}, and `w_space_dim` equal to ' - f'{self.w_space_dim}!\n' - f'But {latent_codes_shape} received!') - ws = torch.from_numpy(latent_codes).type(torch.FloatTensor) - ws = ws.to(self.run_device) - wps = self.model.truncation(ws) - results['w'] = latent_codes - results['wp'] = self.get_value(wps) - # Generate from W+ space. - elif latent_space_type == 'WP': - if not (len(latent_codes_shape) == 3 and - latent_codes_shape[0] <= self.batch_size and - latent_codes_shape[1] == self.num_layers and - latent_codes_shape[2] == self.w_space_dim): - raise ValueError(f'Latent_codes should be with shape [batch_size, ' - f'num_layers, w_space_dim], where `batch_size` no ' - f'larger than {self.batch_size}, `num_layers` equal ' - f'to {self.num_layers}, and `w_space_dim` equal to ' - f'{self.w_space_dim}!\n' - f'But {latent_codes_shape} received!') - wps = torch.from_numpy(latent_codes).type(torch.FloatTensor) - wps = wps.to(self.run_device) - results['wp'] = latent_codes - else: - raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') - - if generate_style: - for i in range(self.num_layers): - style = self.model.synthesis.__getattr__( - f'layer{i}').epilogue.style_mod.dense(wps[:, i, :]) - results[f'style{i:02d}'] = self.get_value(style) - - if generate_image: - images = self.model.synthesis(wps) - results['image'] = self.get_value(images) - return results diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/components/TransportPanel/PlayButton.tsx b/spaces/yderre-aubay/midi-player-demo/src/main/components/TransportPanel/PlayButton.tsx deleted file mode 100644 index a798766bb6a166dd698eefacbecb2159ce71c5d7..0000000000000000000000000000000000000000 --- a/spaces/yderre-aubay/midi-player-demo/src/main/components/TransportPanel/PlayButton.tsx +++ /dev/null @@ -1,49 +0,0 @@ -import styled from "@emotion/styled" -import Pause from "mdi-react/PauseIcon" -import PlayArrow from "mdi-react/PlayArrowIcon" -import { FC } from "react" -import { Localized } from "../../../components/Localized" -import { Tooltip } from "../../../components/Tooltip" -import { CircleButton } from "./CircleButton" - -export const StyledButton = styled(CircleButton)` - background: ${({ theme }) => theme.themeColor}; - - &:hover { - background: ${({ theme }) => theme.themeColor}; - opacity: 0.8; - } - - &.active { - background: ${({ theme }) => theme.themeColor}; - } -` - -export interface PlayButtonProps { - onClick?: () => void - isPlaying: boolean -} - -export const PlayButton: FC
-This space was created using SD Space Creator.
-= ( - { onClick, isPlaying }, - ref, -) => { - return ( - - - ) -} diff --git a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/PIPNet/FaceBoxesV2/utils/nms_wrapper.py b/spaces/ygtxr1997/ReliableSwap_Demo/third_party/PIPNet/FaceBoxesV2/utils/nms_wrapper.py deleted file mode 100644 index d529875fac67e070ea865a1ba0cc3d248847827f..0000000000000000000000000000000000000000 --- a/spaces/ygtxr1997/ReliableSwap_Demo/third_party/PIPNet/FaceBoxesV2/utils/nms_wrapper.py +++ /dev/null @@ -1,15 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -from .nms.cpu_nms import cpu_nms, cpu_soft_nms - -def nms(dets, thresh): - """Dispatch to either CPU or GPU NMS implementations.""" - - if dets.shape[0] == 0: - return [] - return cpu_nms(dets, thresh) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/pegasus/modeling_pegasus.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/pegasus/modeling_pegasus.py deleted file mode 100644 index 67934520fbb6d9e342794b493e52e403110d76ba..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/pegasus/modeling_pegasus.py +++ /dev/null @@ -1,1738 +0,0 @@ -# coding=utf-8 -# Copyright 2021, Google and The HuggingFace Inc. 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. -""" PyTorch PEGASUS model.""" - -import copy -import math -from typing import List, Optional, Tuple, Union - -import numpy as np -import torch -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss - -from ...activations import ACT2FN -from ...modeling_outputs import ( - BaseModelOutput, - BaseModelOutputWithPastAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - Seq2SeqLMOutput, - Seq2SeqModelOutput, -) -from ...modeling_utils import PreTrainedModel -from ...utils import ( - add_end_docstrings, - add_start_docstrings, - add_start_docstrings_to_model_forward, - logging, - replace_return_docstrings, -) -from .configuration_pegasus import PegasusConfig - - -logger = logging.get_logger(__name__) - -_CHECKPOINT_FOR_DOC = "google/pegasus-large" -_CONFIG_FOR_DOC = "PegasusConfig" - - -PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "google/pegasus-large", - # See all PEGASUS models at https://huggingface.co/models?filter=pegasus -] - - -# Copied from transformers.models.bart.modeling_bart.shift_tokens_right -def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): - """ - Shift input ids one token to the right. - """ - shifted_input_ids = input_ids.new_zeros(input_ids.shape) - shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() - shifted_input_ids[:, 0] = decoder_start_token_id - - if pad_token_id is None: - raise ValueError("self.model.config.pad_token_id has to be defined.") - # replace possible -100 values in labels by `pad_token_id` - shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) - - return shifted_input_ids - - -# Copied from transformers.models.bart.modeling_bart._make_causal_mask -def _make_causal_mask( - input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 -): - """ - Make causal mask used for bi-directional self-attention. - """ - bsz, tgt_len = input_ids_shape - mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) - mask_cond = torch.arange(mask.size(-1), device=device) - mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) - mask = mask.to(dtype) - - if past_key_values_length > 0: - mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) - return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) - - -# Copied from transformers.models.bart.modeling_bart._expand_mask -def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None): - """ - Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. - """ - bsz, src_len = mask.size() - tgt_len = tgt_len if tgt_len is not None else src_len - - expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) - - inverted_mask = 1.0 - expanded_mask - - return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) - - -# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->Pegasus -class PegasusSinusoidalPositionalEmbedding(nn.Embedding): - """This module produces sinusoidal positional embeddings of any length.""" - - def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None: - super().__init__(num_positions, embedding_dim) - self.weight = self._init_weight(self.weight) - - @staticmethod - def _init_weight(out: nn.Parameter) -> nn.Parameter: - """ - Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in - the 2nd half of the vector. [dim // 2:] - """ - n_pos, dim = out.shape - position_enc = np.array( - [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)] - ) - out.requires_grad = False # set early to avoid an error in pytorch-1.8+ - sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1 - out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) - out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) - out.detach_() - return out - - @torch.no_grad() - def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0) -> torch.Tensor: - """`input_ids_shape` is expected to be [bsz x seqlen].""" - bsz, seq_len = input_ids_shape[:2] - positions = torch.arange( - past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device - ) - return super().forward(positions) - - -# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Pegasus -class PegasusAttention(nn.Module): - """Multi-headed attention from 'Attention Is All You Need' paper""" - - def __init__( - self, - embed_dim: int, - num_heads: int, - dropout: float = 0.0, - is_decoder: bool = False, - bias: bool = True, - ): - super().__init__() - self.embed_dim = embed_dim - self.num_heads = num_heads - self.dropout = dropout - self.head_dim = embed_dim // num_heads - - if (self.head_dim * num_heads) != self.embed_dim: - raise ValueError( - f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" - f" and `num_heads`: {num_heads})." - ) - self.scaling = self.head_dim**-0.5 - self.is_decoder = is_decoder - - self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) - - def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): - return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() - - def forward( - self, - hidden_states: torch.Tensor, - key_value_states: Optional[torch.Tensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - attention_mask: Optional[torch.Tensor] = None, - layer_head_mask: Optional[torch.Tensor] = None, - output_attentions: bool = False, - ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: - """Input shape: Batch x Time x Channel""" - - # if key_value_states are provided this layer is used as a cross-attention layer - # for the decoder - is_cross_attention = key_value_states is not None - - bsz, tgt_len, _ = hidden_states.size() - - # get query proj - query_states = self.q_proj(hidden_states) * self.scaling - # get key, value proj - # `past_key_value[0].shape[2] == key_value_states.shape[1]` - # is checking that the `sequence_length` of the `past_key_value` is the same as - # the provided `key_value_states` to support prefix tuning - if ( - is_cross_attention - and past_key_value is not None - and past_key_value[0].shape[2] == key_value_states.shape[1] - ): - # reuse k,v, cross_attentions - key_states = past_key_value[0] - value_states = past_key_value[1] - elif is_cross_attention: - # cross_attentions - key_states = self._shape(self.k_proj(key_value_states), -1, bsz) - value_states = self._shape(self.v_proj(key_value_states), -1, bsz) - elif past_key_value is not None: - # reuse k, v, self_attention - key_states = self._shape(self.k_proj(hidden_states), -1, bsz) - value_states = self._shape(self.v_proj(hidden_states), -1, bsz) - key_states = torch.cat([past_key_value[0], key_states], dim=2) - value_states = torch.cat([past_key_value[1], value_states], dim=2) - else: - # self_attention - key_states = self._shape(self.k_proj(hidden_states), -1, bsz) - value_states = self._shape(self.v_proj(hidden_states), -1, bsz) - - if self.is_decoder: - # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states. - # Further calls to cross_attention layer can then reuse all cross-attention - # key/value_states (first "if" case) - # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of - # all previous decoder key/value_states. Further calls to uni-directional self-attention - # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) - # if encoder bi-directional self-attention `past_key_value` is always `None` - past_key_value = (key_states, value_states) - - proj_shape = (bsz * self.num_heads, -1, self.head_dim) - query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape) - key_states = key_states.reshape(*proj_shape) - value_states = value_states.reshape(*proj_shape) - - src_len = key_states.size(1) - attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) - - if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): - raise ValueError( - f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" - f" {attn_weights.size()}" - ) - - if attention_mask is not None: - if attention_mask.size() != (bsz, 1, tgt_len, src_len): - raise ValueError( - f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" - ) - attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - attn_weights = nn.functional.softmax(attn_weights, dim=-1) - - if layer_head_mask is not None: - if layer_head_mask.size() != (self.num_heads,): - raise ValueError( - f"Head mask for a single layer should be of size {(self.num_heads,)}, but is" - f" {layer_head_mask.size()}" - ) - attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) - - if output_attentions: - # this operation is a bit awkward, but it's required to - # make sure that attn_weights keeps its gradient. - # In order to do so, attn_weights have to be reshaped - # twice and have to be reused in the following - attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) - attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) - else: - attn_weights_reshaped = None - - attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) - - attn_output = torch.bmm(attn_probs, value_states) - - if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): - raise ValueError( - f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" - f" {attn_output.size()}" - ) - - attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) - attn_output = attn_output.transpose(1, 2) - - # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be - # partitioned across GPUs when using tensor-parallelism. - attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) - - attn_output = self.out_proj(attn_output) - - return attn_output, attn_weights_reshaped, past_key_value - - -# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Pegasus -class PegasusEncoderLayer(nn.Module): - def __init__(self, config: PegasusConfig): - super().__init__() - self.embed_dim = config.d_model - self.self_attn = PegasusAttention( - embed_dim=self.embed_dim, - num_heads=config.encoder_attention_heads, - dropout=config.attention_dropout, - ) - self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) - self.dropout = config.dropout - self.activation_fn = ACT2FN[config.activation_function] - self.activation_dropout = config.activation_dropout - self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) - self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) - self.final_layer_norm = nn.LayerNorm(self.embed_dim) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: torch.Tensor, - layer_head_mask: torch.Tensor, - output_attentions: bool = False, - ) -> torch.Tensor: - """ - Args: - hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` - attention_mask (`torch.FloatTensor`): attention mask of size - `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. - layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size - `(encoder_attention_heads,)`. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - """ - residual = hidden_states - hidden_states = self.self_attn_layer_norm(hidden_states) - hidden_states, attn_weights, _ = self.self_attn( - hidden_states=hidden_states, - attention_mask=attention_mask, - layer_head_mask=layer_head_mask, - output_attentions=output_attentions, - ) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - residual = hidden_states - hidden_states = self.final_layer_norm(hidden_states) - hidden_states = self.activation_fn(self.fc1(hidden_states)) - hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) - hidden_states = self.fc2(hidden_states) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - if hidden_states.dtype == torch.float16 and ( - torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any() - ): - clamp_value = torch.finfo(hidden_states.dtype).max - 1000 - hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) - - outputs = (hidden_states,) - - if output_attentions: - outputs += (attn_weights,) - - return outputs - - -# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Pegasus -class PegasusDecoderLayer(nn.Module): - def __init__(self, config: PegasusConfig): - super().__init__() - self.embed_dim = config.d_model - - self.self_attn = PegasusAttention( - embed_dim=self.embed_dim, - num_heads=config.decoder_attention_heads, - dropout=config.attention_dropout, - is_decoder=True, - ) - self.dropout = config.dropout - self.activation_fn = ACT2FN[config.activation_function] - self.activation_dropout = config.activation_dropout - - self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) - self.encoder_attn = PegasusAttention( - self.embed_dim, - config.decoder_attention_heads, - dropout=config.attention_dropout, - is_decoder=True, - ) - self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) - self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) - self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) - self.final_layer_norm = nn.LayerNorm(self.embed_dim) - - def forward( - self, - hidden_states: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - layer_head_mask: Optional[torch.Tensor] = None, - cross_attn_layer_head_mask: Optional[torch.Tensor] = None, - past_key_value: Optional[Tuple[torch.Tensor]] = None, - output_attentions: Optional[bool] = False, - use_cache: Optional[bool] = True, - ) -> torch.Tensor: - """ - Args: - hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` - attention_mask (`torch.FloatTensor`): attention mask of size - `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. - encoder_hidden_states (`torch.FloatTensor`): - cross attention input to the layer of shape `(batch, seq_len, embed_dim)` - encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size - `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. - layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size - `(encoder_attention_heads,)`. - cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of - size `(decoder_attention_heads,)`. - past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - """ - residual = hidden_states - hidden_states = self.self_attn_layer_norm(hidden_states) - - # Self Attention - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - # add present self-attn cache to positions 1,2 of present_key_value tuple - hidden_states, self_attn_weights, present_key_value = self.self_attn( - hidden_states=hidden_states, - past_key_value=self_attn_past_key_value, - attention_mask=attention_mask, - layer_head_mask=layer_head_mask, - output_attentions=output_attentions, - ) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - # Cross-Attention Block - cross_attn_present_key_value = None - cross_attn_weights = None - if encoder_hidden_states is not None: - residual = hidden_states - hidden_states = self.encoder_attn_layer_norm(hidden_states) - - # cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple - cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None - hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( - hidden_states=hidden_states, - key_value_states=encoder_hidden_states, - attention_mask=encoder_attention_mask, - layer_head_mask=cross_attn_layer_head_mask, - past_key_value=cross_attn_past_key_value, - output_attentions=output_attentions, - ) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - # add cross-attn to positions 3,4 of present_key_value tuple - present_key_value = present_key_value + cross_attn_present_key_value - - # Fully Connected - residual = hidden_states - hidden_states = self.final_layer_norm(hidden_states) - hidden_states = self.activation_fn(self.fc1(hidden_states)) - hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) - hidden_states = self.fc2(hidden_states) - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - hidden_states = residual + hidden_states - - outputs = (hidden_states,) - - if output_attentions: - outputs += (self_attn_weights, cross_attn_weights) - - if use_cache: - outputs += (present_key_value,) - - return outputs - - -class PegasusPreTrainedModel(PreTrainedModel): - config_class = PegasusConfig - base_model_prefix = "model" - supports_gradient_checkpointing = True - - def _init_weights(self, module): - std = self.config.init_std - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=std) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, PegasusSinusoidalPositionalEmbedding): - pass - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=std) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, (PegasusDecoder, PegasusEncoder)): - module.gradient_checkpointing = value - - -PEGASUS_START_DOCSTRING = r""" - This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the - library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads - etc.) - - This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. - Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage - and behavior. - - Parameters: - config ([`PegasusConfig`]): - Model configuration class with all the parameters of the model. Initializing with a config file does not - load the weights associated with the model, only the configuration. Check out the - [`~PreTrainedModel.from_pretrained`] method to load the model weights. -""" - -PEGASUS_GENERATION_EXAMPLE = r""" - Summarization example: - - ```python - >>> from transformers import AutoTokenizer, PegasusForConditionalGeneration - - >>> model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum") - >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum") - - >>> ARTICLE_TO_SUMMARIZE = ( - ... "PG&E stated it scheduled the blackouts in response to forecasts for high winds " - ... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " - ... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow." - ... ) - >>> inputs = tokenizer(ARTICLE_TO_SUMMARIZE, max_length=1024, return_tensors="pt") - - >>> # Generate Summary - >>> summary_ids = model.generate(inputs["input_ids"]) - >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] - "California's largest electricity provider has turned off power to hundreds of thousands of customers." - ``` -""" - -PEGASUS_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide - it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): - Indices of decoder input sequence tokens in the vocabulary. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are decoder input IDs?](../glossary#decoder-input-ids) - - Pegasus uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If - `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see - `past_key_values`). - decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): - Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also - be used by default. - head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, - 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): - Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) - `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of - hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape - `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape - `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention - blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that - don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all - `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape - `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you - can choose to directly pass an embedded representation. This is useful if you want more control over how to - convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded - representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be - input (see `past_key_values`). This is useful if you want more control over how to convert - `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. - - If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value - of `inputs_embeds`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see - `past_key_values`). - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned - tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for - more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. -""" - - -class PegasusEncoder(PegasusPreTrainedModel): - """ - Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a - [`PegasusEncoderLayer`]. - - Args: - config: PegasusConfig - embed_tokens (nn.Embedding): output embedding - """ - - def __init__(self, config: PegasusConfig, embed_tokens: Optional[nn.Embedding] = None): - super().__init__(config) - - self.dropout = config.dropout - self.layerdrop = config.encoder_layerdrop - - embed_dim = config.d_model - self.padding_idx = config.pad_token_id - self.max_source_positions = config.max_position_embeddings - self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 - - if embed_tokens is not None: - self.embed_tokens = embed_tokens - else: - self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) - - self.embed_positions = PegasusSinusoidalPositionalEmbedding( - config.max_position_embeddings, - embed_dim, - self.padding_idx, - ) - self.layers = nn.ModuleList([PegasusEncoderLayer(config) for _ in range(config.encoder_layers)]) - self.layer_norm = nn.LayerNorm(config.d_model) - - self.gradient_checkpointing = False - # Initialize weights and apply final processing - self.post_init() - - def resize_position_embeddings(self, new_num_position_embeddings: int): - """ - Resizes position embeddings matrix of the model if `new_num_position_embeddings != - config.max_position_embeddings`. - - Arguments: - new_num_position_embeddings (`int`): - The number of new position embeddings. If position embeddings are learned, increasing the size will add - newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If - position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will - add correct vectors at the end following the position encoding algorithm, whereas reducing the size - will remove vectors from the end. - """ - logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...") - self.config.max_position_embeddings = new_num_position_embeddings - - self.embed_positions = PegasusSinusoidalPositionalEmbedding( - self.config.max_position_embeddings, - self.config.d_model, - self.padding_idx, - ) - self.embed_positions.to(self.device) - - def get_position_embeddings(self) -> nn.Embedding: - """ - Returns the position embeddings matrix - """ - return self.embed_positions - - def forward( - self, - input_ids=None, - attention_mask=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you - provide it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. - This is useful if you want more control over how to convert `input_ids` indices into associated vectors - than the model's internal embedding lookup matrix. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors - for more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # retrieve input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale - - embed_pos = self.embed_positions(input_shape) - - hidden_states = inputs_embeds + embed_pos - - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - - # expand attention_mask - if attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype) - - encoder_states = () if output_hidden_states else None - all_attentions = () if output_attentions else None - - # check if head_mask has a correct number of layers specified if desired - if head_mask is not None: - if head_mask.size()[0] != len(self.layers): - raise ValueError( - f"The head_mask should be specified for {len(self.layers)} layers, but it is for" - f" {head_mask.size()[0]}." - ) - for idx, encoder_layer in enumerate(self.layers): - if output_hidden_states: - encoder_states = encoder_states + (hidden_states,) - # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) - to_drop = False - if self.training: - dropout_probability = torch.rand([]) - if dropout_probability < self.layerdrop: # skip the layer - to_drop = True - - if to_drop: - layer_outputs = (None, None) - else: - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(encoder_layer), - hidden_states, - attention_mask, - (head_mask[idx] if head_mask is not None else None), - ) - else: - layer_outputs = encoder_layer( - hidden_states, - attention_mask, - layer_head_mask=(head_mask[idx] if head_mask is not None else None), - output_attentions=output_attentions, - ) - - hidden_states = layer_outputs[0] - - if output_attentions: - all_attentions = all_attentions + (layer_outputs[1],) - - hidden_states = self.layer_norm(hidden_states) - - if output_hidden_states: - encoder_states = encoder_states + (hidden_states,) - - if not return_dict: - return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) - return BaseModelOutput( - last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions - ) - - -class PegasusDecoder(PegasusPreTrainedModel): - """ - Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PegasusDecoderLayer`] - - Args: - config: PegasusConfig - embed_tokens (nn.Embedding): output embedding - """ - - def __init__(self, config: PegasusConfig, embed_tokens: Optional[nn.Embedding] = None): - super().__init__(config) - self.dropout = config.dropout - self.layerdrop = config.decoder_layerdrop - self.padding_idx = config.pad_token_id - self.max_target_positions = config.max_position_embeddings - self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 - - if embed_tokens is not None: - self.embed_tokens = embed_tokens - else: - self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) - - self.embed_positions = PegasusSinusoidalPositionalEmbedding( - config.max_position_embeddings, - config.d_model, - self.padding_idx, - ) - self.layers = nn.ModuleList([PegasusDecoderLayer(config) for _ in range(config.decoder_layers)]) - self.layer_norm = nn.LayerNorm(config.d_model) - - self.gradient_checkpointing = False - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.embed_tokens - - def set_input_embeddings(self, value): - self.embed_tokens = value - - # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask - def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length): - # create causal mask - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - combined_attention_mask = None - if input_shape[-1] > 1: - combined_attention_mask = _make_causal_mask( - input_shape, - inputs_embeds.dtype, - device=inputs_embeds.device, - past_key_values_length=past_key_values_length, - ) - - if attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( - inputs_embeds.device - ) - combined_attention_mask = ( - expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask - ) - - return combined_attention_mask - - def resize_position_embeddings(self, new_num_position_embeddings: int): - """ - Resizes position embeddings matrix of the model if `new_num_position_embeddings != - config.max_position_embeddings`. - - Arguments: - new_num_position_embeddings (`int`): - The number of new position embeddings. If position embeddings are learned, increasing the size will add - newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If - position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will - add correct vectors at the end following the position encoding algorithm, whereas reducing the size - will remove vectors from the end. - """ - logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...") - self.config.max_position_embeddings = new_num_position_embeddings - - self.embed_positions = PegasusSinusoidalPositionalEmbedding( - self.config.max_position_embeddings, - self.config.d_model, - self.padding_idx, - ) - self.embed_positions.to(self.device) - - def get_position_embeddings(self) -> nn.Embedding: - """ - Returns the position embeddings matrix - """ - return self.embed_positions - - def forward( - self, - input_ids=None, - attention_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - head_mask=None, - cross_attn_head_mask=None, - past_key_values=None, - inputs_embeds=None, - use_cache=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - ): - r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you - provide it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention - of the decoder. - encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): - Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values - selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the cross-attention modules in decoder to avoid performing - cross-attention on hidden heads. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of - shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of - shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the - cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those - that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of - all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of - shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing - `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more - control over how to convert `input_ids` indices into associated vectors than the model's internal - embedding lookup matrix. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors - for more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - """ - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # retrieve input_ids and inputs_embeds - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - input_ids = input_ids.view(-1, input_shape[-1]) - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") - - # past_key_values_length - past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 - - if inputs_embeds is None: - inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale - - attention_mask = self._prepare_decoder_attention_mask( - attention_mask, input_shape, inputs_embeds, past_key_values_length - ) - - # expand encoder attention mask - if encoder_hidden_states is not None and encoder_attention_mask is not None: - # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] - encoder_attention_mask = _expand_mask(encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]) - - # embed positions - positions = self.embed_positions(input_shape, past_key_values_length) - - hidden_states = inputs_embeds + positions - - hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - # decoder layers - all_hidden_states = () if output_hidden_states else None - all_self_attns = () if output_attentions else None - all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None - next_decoder_cache = () if use_cache else None - - # check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired - for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]): - if attn_mask is not None: - if attn_mask.size()[0] != len(self.layers): - raise ValueError( - f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for" - f" {head_mask.size()[0]}." - ) - for idx, decoder_layer in enumerate(self.layers): - # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) - if output_hidden_states: - all_hidden_states += (hidden_states,) - if self.training: - dropout_probability = torch.rand([]) - if dropout_probability < self.layerdrop: - continue - - past_key_value = past_key_values[idx] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - def custom_forward(*inputs): - # None for past_key_value - return module(*inputs, output_attentions, use_cache) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(decoder_layer), - hidden_states, - attention_mask, - encoder_hidden_states, - encoder_attention_mask, - head_mask[idx] if head_mask is not None else None, - cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None, - None, - ) - else: - layer_outputs = decoder_layer( - hidden_states, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - layer_head_mask=(head_mask[idx] if head_mask is not None else None), - cross_attn_layer_head_mask=( - cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None - ), - past_key_value=past_key_value, - output_attentions=output_attentions, - use_cache=use_cache, - ) - hidden_states = layer_outputs[0] - - if use_cache: - next_decoder_cache += (layer_outputs[3 if output_attentions else 1],) - - if output_attentions: - all_self_attns += (layer_outputs[1],) - - if encoder_hidden_states is not None: - all_cross_attentions += (layer_outputs[2],) - - hidden_states = self.layer_norm(hidden_states) - - # add hidden states from the last decoder layer - if output_hidden_states: - all_hidden_states += (hidden_states,) - - next_cache = next_decoder_cache if use_cache else None - if not return_dict: - return tuple( - v - for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_cache, - hidden_states=all_hidden_states, - attentions=all_self_attns, - cross_attentions=all_cross_attentions, - ) - - -@add_start_docstrings( - "The bare PEGASUS Model outputting raw hidden-states without any specific head on top.", - PEGASUS_START_DOCSTRING, -) -class PegasusModel(PegasusPreTrainedModel): - _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"] - - def __init__(self, config: PegasusConfig): - super().__init__(config) - - padding_idx, vocab_size = config.pad_token_id, config.vocab_size - self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) - - self.encoder = PegasusEncoder(config, self.shared) - self.decoder = PegasusDecoder(config, self.shared) - - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.shared - - def set_input_embeddings(self, value): - self.shared = value - self.encoder.embed_tokens = self.shared - self.decoder.embed_tokens = self.shared - - def get_encoder(self): - return self.encoder - - def get_decoder(self): - return self.decoder - - def resize_position_embeddings(self, new_num_position_embeddings: int): - """ - Resizes position embeddings matrix of the model if `new_num_position_embeddings != - config.max_position_embeddings`. - - Arguments: - new_num_position_embeddings (`int`): - The number of new position embeddings. If position embeddings are learned, increasing the size will add - newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If - position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will - add correct vectors at the end following the position encoding algorithm, whereas reducing the size - will remove vectors from the end. - """ - self.config.max_position_embeddings = new_num_position_embeddings - self.encoder.resize_position_embeddings(new_num_position_embeddings) - self.decoder.resize_position_embeddings(new_num_position_embeddings) - - def get_position_embeddings(self) -> Tuple[nn.Embedding]: - """ - Returns the position embeddings matrix - """ - return (self.encoder.get_position_embeddings(), self.decoder.get_position_embeddings()) - - @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - decoder_input_ids: Optional[torch.Tensor] = None, - decoder_attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - decoder_head_mask: Optional[torch.Tensor] = None, - cross_attn_head_mask: Optional[torch.Tensor] = None, - encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - decoder_inputs_embeds: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, Seq2SeqModelOutput]: - r""" - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, PegasusModel - - >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-large") - >>> model = PegasusModel.from_pretrained("google/pegasus-large") - - >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt") - >>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt") - >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids) - - >>> last_hidden_states = outputs.last_hidden_state - >>> list(last_hidden_states.shape) - [1, 4, 1024] - ```""" - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - use_cache = use_cache if use_cache is not None else self.config.use_cache - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if encoder_outputs is None: - encoder_outputs = self.encoder( - input_ids=input_ids, - attention_mask=attention_mask, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True - elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): - encoder_outputs = BaseModelOutput( - last_hidden_state=encoder_outputs[0], - hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, - attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, - ) - - # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) - decoder_outputs = self.decoder( - input_ids=decoder_input_ids, - attention_mask=decoder_attention_mask, - encoder_hidden_states=encoder_outputs[0], - encoder_attention_mask=attention_mask, - head_mask=decoder_head_mask, - cross_attn_head_mask=cross_attn_head_mask, - past_key_values=past_key_values, - inputs_embeds=decoder_inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - if not return_dict: - return decoder_outputs + encoder_outputs - - return Seq2SeqModelOutput( - last_hidden_state=decoder_outputs.last_hidden_state, - past_key_values=decoder_outputs.past_key_values, - decoder_hidden_states=decoder_outputs.hidden_states, - decoder_attentions=decoder_outputs.attentions, - cross_attentions=decoder_outputs.cross_attentions, - encoder_last_hidden_state=encoder_outputs.last_hidden_state, - encoder_hidden_states=encoder_outputs.hidden_states, - encoder_attentions=encoder_outputs.attentions, - ) - - -@add_start_docstrings( - "The PEGASUS Model with a language modeling head. Can be used for summarization.", PEGASUS_START_DOCSTRING -) -class PegasusForConditionalGeneration(PegasusPreTrainedModel): - base_model_prefix = "model" - _keys_to_ignore_on_load_missing = ["final_logits_bias"] - _tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"] - - def __init__(self, config: PegasusConfig): - super().__init__(config) - self.model = PegasusModel(config) - self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) - self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_encoder(self): - return self.model.get_encoder() - - def get_decoder(self): - return self.model.get_decoder() - - def resize_token_embeddings(self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None) -> nn.Embedding: - new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of) - self._resize_final_logits_bias(new_embeddings.weight.shape[0]) - return new_embeddings - - def _resize_final_logits_bias(self, new_num_tokens: int) -> None: - old_num_tokens = self.final_logits_bias.shape[-1] - if new_num_tokens <= old_num_tokens: - new_bias = self.final_logits_bias[:, :new_num_tokens] - else: - extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) - new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) - self.register_buffer("final_logits_bias", new_bias) - - def get_output_embeddings(self): - return self.lm_head - - def set_output_embeddings(self, new_embeddings): - self.lm_head = new_embeddings - - def resize_position_embeddings(self, new_num_position_embeddings: int): - """ - Resizes position embeddings matrix of the model if `new_num_position_embeddings != - config.max_position_embeddings`. - - Arguments: - new_num_position_embeddings (`int`): - The number of new position embeddings. If position embeddings are learned, increasing the size will add - newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If - position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will - add correct vectors at the end following the position encoding algorithm, whereas reducing the size - will remove vectors from the end. - """ - self.config.max_position_embeddings = new_num_position_embeddings - self.model.encoder.resize_position_embeddings(new_num_position_embeddings) - self.model.decoder.resize_position_embeddings(new_num_position_embeddings) - - def get_position_embeddings(self) -> Tuple[nn.Embedding]: - """ - Returns the position embeddings matrix - """ - return (self.model.encoder.get_position_embeddings(), self.model.decoder.get_position_embeddings()) - - @add_start_docstrings_to_model_forward(PEGASUS_INPUTS_DOCSTRING) - @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) - @add_end_docstrings(PEGASUS_GENERATION_EXAMPLE) - def forward( - self, - input_ids: Optional[torch.Tensor] = None, - attention_mask: Optional[torch.Tensor] = None, - decoder_input_ids: Optional[torch.Tensor] = None, - decoder_attention_mask: Optional[torch.Tensor] = None, - head_mask: Optional[torch.Tensor] = None, - decoder_head_mask: Optional[torch.Tensor] = None, - cross_attn_head_mask: Optional[torch.Tensor] = None, - encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None, - past_key_values: Optional[Tuple[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.Tensor] = None, - decoder_inputs_embeds: Optional[torch.Tensor] = None, - labels: Optional[torch.Tensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, Seq2SeqLMOutput]: - r""" - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - - Returns: - - """ - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - if labels is not None: - if use_cache: - logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") - use_cache = False - if decoder_input_ids is None and decoder_inputs_embeds is None: - decoder_input_ids = shift_tokens_right( - labels, self.config.pad_token_id, self.config.decoder_start_token_id - ) - - outputs = self.model( - input_ids, - attention_mask=attention_mask, - decoder_input_ids=decoder_input_ids, - encoder_outputs=encoder_outputs, - decoder_attention_mask=decoder_attention_mask, - head_mask=head_mask, - decoder_head_mask=decoder_head_mask, - cross_attn_head_mask=cross_attn_head_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - decoder_inputs_embeds=decoder_inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias - - masked_lm_loss = None - if labels is not None: - loss_fct = CrossEntropyLoss() - masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (lm_logits,) + outputs[1:] - return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output - - return Seq2SeqLMOutput( - loss=masked_lm_loss, - logits=lm_logits, - past_key_values=outputs.past_key_values, - decoder_hidden_states=outputs.decoder_hidden_states, - decoder_attentions=outputs.decoder_attentions, - cross_attentions=outputs.cross_attentions, - encoder_last_hidden_state=outputs.encoder_last_hidden_state, - encoder_hidden_states=outputs.encoder_hidden_states, - encoder_attentions=outputs.encoder_attentions, - ) - - def prepare_inputs_for_generation( - self, - decoder_input_ids, - past_key_values=None, - attention_mask=None, - head_mask=None, - decoder_head_mask=None, - cross_attn_head_mask=None, - use_cache=None, - encoder_outputs=None, - **kwargs, - ): - # cut decoder_input_ids if past is used - if past_key_values is not None: - decoder_input_ids = decoder_input_ids[:, -1:] - - return { - "input_ids": None, # encoder_outputs is defined. input_ids not needed - "encoder_outputs": encoder_outputs, - "past_key_values": past_key_values, - "decoder_input_ids": decoder_input_ids, - "attention_mask": attention_mask, - "head_mask": head_mask, - "decoder_head_mask": decoder_head_mask, - "cross_attn_head_mask": cross_attn_head_mask, - "use_cache": use_cache, # change this to avoid caching (presumably for debugging) - } - - def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): - return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) - - @staticmethod - def _reorder_cache(past_key_values, beam_idx): - reordered_past = () - for layer_past in past_key_values: - # cached cross_attention states don't have to be reordered -> they are always the same - reordered_past += ( - tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2]) - + layer_past[2:], - ) - return reordered_past - - -# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Pegasus -class PegasusDecoderWrapper(PegasusPreTrainedModel): - """ - This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is - used in combination with the [`EncoderDecoderModel`] framework. - """ - - def __init__(self, config): - super().__init__(config) - self.decoder = PegasusDecoder(config) - - def forward(self, *args, **kwargs): - return self.decoder(*args, **kwargs) - - -class PegasusForCausalLM(PegasusPreTrainedModel): - _tied_weights_keys = ["lm_head.weight"] - - def __init__(self, config): - config = copy.deepcopy(config) - config.is_decoder = True - config.is_encoder_decoder = False - super().__init__(config) - self.model = PegasusDecoderWrapper(config) - - self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.model.decoder.embed_tokens - - def set_input_embeddings(self, value): - self.model.decoder.embed_tokens = value - - def get_output_embeddings(self): - return self.lm_head - - def set_output_embeddings(self, new_embeddings): - self.lm_head = new_embeddings - - def set_decoder(self, decoder): - self.model.decoder = decoder - - def get_decoder(self): - return self.model.decoder - - def get_position_embeddings(self) -> nn.Embedding: - """ - Returns the position embeddings matrix - """ - return self.model.decoder.get_position_embeddings() - - def resize_position_embeddings(self, new_num_position_embeddings: int): - """ - Resizes position embeddings matrix of the model if `new_num_position_embeddings != - config.max_position_embeddings`. - - Arguments: - new_num_position_embeddings (`int`): - The number of new position embeddings. If position embeddings are learned, increasing the size will add - newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If - position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will - add correct vectors at the end following the position encoding algorithm, whereas reducing the size - will remove vectors from the end. - """ - self.config.max_position_embeddings = new_num_position_embeddings - self.model.decoder.resize_position_embeddings(new_num_position_embeddings) - - @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) - # Copied from transformers.models.bart.modeling_bart.BartForCausalLM.forward with Bart->Pegasus, facebook/bart-base->google/pegasus-large - def forward( - self, - input_ids: torch.LongTensor = None, - attention_mask: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.Tensor] = None, - cross_attn_head_mask: Optional[torch.Tensor] = None, - past_key_values: Optional[List[torch.FloatTensor]] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]: - r""" - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): - Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you - provide it. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - - [What are attention masks?](../glossary#attention-mask) - encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): - Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention - if the model is configured as a decoder. - encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): - Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used - in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): - Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of - shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of - shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional - tensors are only required when the model is used as a decoder in a Sequence to Sequence model. - - Contains pre-computed hidden-states (key and values in the self-attention blocks and in the - cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - - If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those - that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of - all `decoder_input_ids` of shape `(batch_size, sequence_length)`. - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., - config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored - (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. - use_cache (`bool`, *optional*): - If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding - (see `past_key_values`). - - - 1 for tokens that are **not masked**, - - 0 for tokens that are **masked**. - output_attentions (`bool`, *optional*): - Whether or not to return the attentions tensors of all attention layers. See `attentions` under - returned tensors for more detail. - output_hidden_states (`bool`, *optional*): - Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors - for more detail. - return_dict (`bool`, *optional*): - Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, PegasusForCausalLM - - >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-large") - >>> model = PegasusForCausalLM.from_pretrained("google/pegasus-large", add_cross_attention=False) - >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." - >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") - >>> outputs = model(**inputs) - - >>> logits = outputs.logits - >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] - >>> list(logits.shape) == expected_shape - True - ```""" - - output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states - ) - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) - outputs = self.model.decoder( - input_ids=input_ids, - attention_mask=attention_mask, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - head_mask=head_mask, - cross_attn_head_mask=cross_attn_head_mask, - past_key_values=past_key_values, - inputs_embeds=inputs_embeds, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - logits = self.lm_head(outputs[0]) - - loss = None - if labels is not None: - labels = labels.to(logits.device) - loss_fct = CrossEntropyLoss() - loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) - - if not return_dict: - output = (logits,) + outputs[1:] - return (loss,) + output if loss is not None else output - - return CausalLMOutputWithCrossAttentions( - loss=loss, - logits=logits, - past_key_values=outputs.past_key_values, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - cross_attentions=outputs.cross_attentions, - ) - - def prepare_inputs_for_generation( - self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs - ): - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_ids.shape) - - if past_key_values: - input_ids = input_ids[:, -1:] - # first step, decoder_cached_states are empty - return { - "input_ids": input_ids, # encoder_outputs is defined. input_ids not needed - "attention_mask": attention_mask, - "past_key_values": past_key_values, - "use_cache": use_cache, - } - - @staticmethod - def _reorder_cache(past_key_values, beam_idx): - reordered_past = () - for layer_past in past_key_values: - reordered_past += ( - tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), - ) - return reordered_past diff --git a/spaces/yl12053/so-vits-4.1-Kitasan-Black/compress_model.py b/spaces/yl12053/so-vits-4.1-Kitasan-Black/compress_model.py deleted file mode 100644 index 9c7a8c4aa765edb65658aa62db50f14174503f36..0000000000000000000000000000000000000000 --- a/spaces/yl12053/so-vits-4.1-Kitasan-Black/compress_model.py +++ /dev/null @@ -1,69 +0,0 @@ -from collections import OrderedDict - -import torch - -import utils -from models import SynthesizerTrn - - -def copyStateDict(state_dict): - if list(state_dict.keys())[0].startswith('module'): - start_idx = 1 - else: - start_idx = 0 - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - name = ','.join(k.split('.')[start_idx:]) - new_state_dict[name] = v - return new_state_dict - - -def removeOptimizer(config: str, input_model: str, output_model: str): - hps = utils.get_hparams_from_file(config) - - net_g = SynthesizerTrn(hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - **hps.model) - - optim_g = torch.optim.AdamW(net_g.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - - state_dict_g = torch.load(input_model, map_location="cpu") - new_dict_g = copyStateDict(state_dict_g) - keys = [] - for k, v in new_dict_g['model'].items(): - keys.append(k) - - new_dict_g = {k: new_dict_g['model'][k] for k in keys} - - torch.save( - { - 'model': new_dict_g, - 'iteration': 0, - 'optimizer': optim_g.state_dict(), - 'learning_rate': 0.0001 - }, output_model) - - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser() - parser.add_argument("-c", - "--config", - type=str, - default='configs/config.json') - parser.add_argument("-i", "--input", type=str) - parser.add_argument("-o", "--output", type=str, default=None) - - args = parser.parse_args() - - output = args.output - - if output is None: - import os.path - filename, ext = os.path.splitext(args.input) - output = filename + "_release" + ext - - removeOptimizer(args.config, args.input, output) diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/.github/ISSUE_TEMPLATE/feature-request.md b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/.github/ISSUE_TEMPLATE/feature-request.md deleted file mode 100644 index 03a1e93d7293948042120b875af8be0c6964e59c..0000000000000000000000000000000000000000 --- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/.github/ISSUE_TEMPLATE/feature-request.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -name: "\U0001F680Feature Request" -about: Suggest an improvement or new feature -labels: enhancement - ---- - -## 🚀 Feature -A clear and concise description of the feature proposal. - -## Motivation & Examples - -Tell us why the feature is useful. - -Describe what the feature would look like, if it is implemented. -Best demonstrated using **code examples** in addition to words. - -## Note - -We only consider adding new features if they are relevant to many users. - -If you request implementation of research papers -- we only consider papers that have enough significance and prevalance in the object detection field. - -We do not take requests for most projects in the `projects/` directory, because they are research code release that is mainly for other researchers to reproduce results. - -"Make X faster/accurate" is not a valid feature request. "Implement a concrete feature that can make X faster/accurate" can be a valid feature request. - -Instead of adding features inside detectron2, -you can implement many features by [extending detectron2](https://detectron2.readthedocs.io/tutorials/extend.html). -The [projects/](https://github.com/facebookresearch/detectron2/tree/main/projects/) directory contains many of such examples. - diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/memory.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/memory.py deleted file mode 100644 index bd494780b9dbbd1571688cd270bb9b53d113c13e..0000000000000000000000000000000000000000 --- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/utils/memory.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import logging -from contextlib import contextmanager -from functools import wraps -import torch - -__all__ = ["retry_if_cuda_oom"] - - -@contextmanager -def _ignore_torch_cuda_oom(): - """ - A context which ignores CUDA OOM exception from pytorch. - """ - try: - yield - except RuntimeError as e: - # NOTE: the string may change? - if "CUDA out of memory. " in str(e): - pass - else: - raise - - -def retry_if_cuda_oom(func): - """ - Makes a function retry itself after encountering - pytorch's CUDA OOM error. - It will first retry after calling `torch.cuda.empty_cache()`. - - If that still fails, it will then retry by trying to convert inputs to CPUs. - In this case, it expects the function to dispatch to CPU implementation. - The return values may become CPU tensors as well and it's user's - responsibility to convert it back to CUDA tensor if needed. - - Args: - func: a stateless callable that takes tensor-like objects as arguments - - Returns: - a callable which retries `func` if OOM is encountered. - - Examples: - :: - output = retry_if_cuda_oom(some_torch_function)(input1, input2) - # output may be on CPU even if inputs are on GPU - - Note: - 1. When converting inputs to CPU, it will only look at each argument and check - if it has `.device` and `.to` for conversion. Nested structures of tensors - are not supported. - - 2. Since the function might be called more than once, it has to be - stateless. - """ - - def maybe_to_cpu(x): - try: - like_gpu_tensor = x.device.type == "cuda" and hasattr(x, "to") - except AttributeError: - like_gpu_tensor = False - if like_gpu_tensor: - return x.to(device="cpu") - else: - return x - - @wraps(func) - def wrapped(*args, **kwargs): - with _ignore_torch_cuda_oom(): - return func(*args, **kwargs) - - # Clear cache and retry - torch.cuda.empty_cache() - with _ignore_torch_cuda_oom(): - return func(*args, **kwargs) - - # Try on CPU. This slows down the code significantly, therefore print a notice. - logger = logging.getLogger(__name__) - logger.info("Attempting to copy inputs of {} to CPU due to CUDA OOM".format(str(func))) - new_args = (maybe_to_cpu(x) for x in args) - new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()} - return func(*new_args, **new_kwargs) - - return wrapped diff --git a/spaces/yukie/yukie-sovits3/app.py b/spaces/yukie/yukie-sovits3/app.py deleted file mode 100644 index ac18a496cc5808dc3bdea00c37ff885971098938..0000000000000000000000000000000000000000 --- a/spaces/yukie/yukie-sovits3/app.py +++ /dev/null @@ -1,134 +0,0 @@ -import io - -import gradio as gr -import librosa -import numpy as np -import soundfile -from inference import slicer -from inference.infer_tool import Svc -import logging -from logmmse import logmmse -from typing import Tuple -import time - -logging.getLogger('numba').setLevel(logging.WARNING) - -model_sing = "logs/32k/sing1.pth" -model_talk = "logs/32k/talk1.pth" -config_name = "configs/config.json" - -sid_map = { - "唱歌特化": "sing", - "杂谈特化": "talk" -} - - -class YukieGradio: - def __init__(self): - self.UI = gr.Blocks() - with self.UI: - with gr.Tabs(): - with gr.TabItem("Basic"): - gr.Markdown(value=""" - # 前言 - * 本demo基于[sovits 3.0 32khz版本](https://github.com/innnky/so-vits-svc)训练的 - - * 由于雪宝唱歌和说话声线差异较大，一个模型包括说话和唱歌两部分声线会使得工作量较大（声纹识别等），故而选择将这两部分分开训练；目前这一版中，杂谈声线数据较差，仍需后续更新 - - # 声明 - 在使用此模型前请阅读[AI雪绘Yukie模型使用协议](https://huggingface.co/spaces/yukie/yukie-sovits3/edit/main/terms.md) - - # start！ - 上传一段**纯人声**干音（推荐60s以内），或者直接使用网站录音（二者只能选其一，优先使用上传音频） - - 然后点击提交即可开始推理！ - - **请使用无bgm，无混响的人声来进行生成推理，否则效果可能会较差** - """) - self.sid = gr.Dropdown(label="音色", choices=[ - "唱歌特化", "杂谈特化"], value="唱歌特化", interactive=True) - self.dev = gr.Dropdown(label="设备（云端一般请勿切换，使用默认值即可）", choices=[ - "cuda", "cpu"], value="cpu", interactive=True) - self.inMic = gr.Microphone(label="录音") - self.inAudio = gr.Audio(label="上传音频") - self.needLogmmse = gr.Checkbox(label="是否使用自带降噪") - self.slice_db = gr.Slider(label="切片阈值(较嘈杂时-30，保留呼吸声时-50，一般默认-40)", - maximum=32767, minimum=-32768, step=0.1, value=-40) - self.vcTransform = gr.Number( - label="升降调（整数，可以正负，半音数量，升高八度就是12）", value=0) - self.vcSubmit = gr.Button("转换", variant="primary") - self.outVcText = gr.Textbox( - label="音高平均偏差半音数量，体现转换音频的跑调情况（一般小于0.5）") - self.outAudio = gr.Audio( - source="upload", type="numpy", label="Output Audio") - self.f0_image = gr.Image( - label="f0曲线，蓝色为输入音高，橙色为合成音频的音高（代码有误差）") - gr.Markdown(value=""" - ## 注意 - 如果要在本地使用该demo，请使用 `git lfs clone https://huggingface.co/spaces/yukie/yukie-sovits3`克隆该仓库([简单教程](https://huggingface.co/spaces/yukie/yukie-sovits3/edit/main/local.md)) - """) - self.vcSubmit.click(infer, inputs=[self.inMic, self.inAudio, self.vcTransform, self.slice_db, self.needLogmmse, self.sid, self.dev], outputs=[ - self.outVcText, self.outAudio, self.f0_image]) - - -def infer(inMic, inAudio, transform, slice_db, lm, sid, dev): - if inAudio != None: - sampling_rate, inaudio = inAudio - else: - if inMic != None: - sampling_rate, inaudio = inMic - else: - return "请上传一段音频后再次尝试", None - - print("start inference") - start_time = time.time() - # 预处理，重编码 - inaudio = (inaudio / np.iinfo(inaudio.dtype).max).astype(np.float32) - if len(inaudio.shape) > 1: - inaudio = librosa.to_mono(inaudio.transpose(1, 0)) - if sampling_rate != 32000: - inaudio = librosa.resample( - inaudio, orig_sr=sampling_rate, target_sr=32000) - if lm: - inaudio = logmmse(inaudio, 32000) - - ori_wav_path = "tmp_ori.wav" - soundfile.write(ori_wav_path, inaudio, 32000, format="wav") - chunks = slicer.cut(ori_wav_path, db_thresh=slice_db) - audio_data, audio_sr = slicer.chunks2audio(ori_wav_path, chunks) - - audio = [] - sid = sid_map[sid] - if sid == "sing": - svc_model = Svc(model_sing, config_name, dev=dev) - else: - svc_model = Svc(model_talk, config_name, dev=dev) - - for (slice_tag, data) in audio_data: - length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample)) - raw_path = io.BytesIO() - soundfile.write(raw_path, data, audio_sr, format="wav") - raw_path.seek(0) - if slice_tag: - _audio = np.zeros(length) - else: - out_audio, out_str = svc_model.infer("yukie", transform, raw_path) - _audio = out_audio.cpu().numpy() - audio.extend(list(_audio)) - audio = (np.array(audio) * 32768.0).astype('int16') - used_time = time.time() - start_time - - out_wav_path = "tmp.wav" - soundfile.write(out_wav_path, audio, 32000, format="wav") - - mistake, var = svc_model.calc_error(ori_wav_path, out_wav_path, transform) - out_picture = svc_model.f0_plt(ori_wav_path, out_wav_path, transform) - out_str = ("Success! total use time:{}s\n半音偏差:{}\n半音方差:{}".format( - used_time, mistake, var)) - - return out_str, (32000, audio), gr.Image.update("temp.jpg") - - -if __name__ == "__main__": - app = YukieGradio() - app.UI.launch() diff --git a/spaces/yunfei0710/gpt-academic/docs/waifu_plugin/jquery-ui.min.js 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e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),t.extend(t.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(e,i){var s=RegExp(t.ui.autocomplete.escapeRegex(i),"i");return t.grep(e,function(t){return s.test(t.label||t.value||t)})}}),t.widget("ui.autocomplete",t.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(t>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(e){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=e&&e.length?this.options.messages.results(e.length):this.options.messages.noResults,this.liveRegion.children().hide(),t("").text(i).appendTo(this.liveRegion))}}),t.ui.autocomplete;var g=/ui-corner-([a-z]){2,6}/g;t.widget("ui.controlgroup",{version:"1.12.1",defaultElement:"",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var e=this,i=[];t.each(this.options.items,function(s,n){var o,a={};return n?"controlgroupLabel"===s?(o=e.element.find(n),o.each(function(){var e=t(this);e.children(".ui-controlgroup-label-contents").length||e.contents().wrapAll("")}),e._addClass(o,null,"ui-widget ui-widget-content ui-state-default"),i=i.concat(o.get()),void 0):(t.fn[s]&&(a=e["_"+s+"Options"]?e["_"+s+"Options"]("middle"):{classes:{}},e.element.find(n).each(function(){var n=t(this),o=n[s]("instance"),r=t.widget.extend({},a);if("button"!==s||!n.parent(".ui-spinner").length){o||(o=n[s]()[s]("instance")),o&&(r.classes=e._resolveClassesValues(r.classes,o)),n[s](r);var h=n[s]("widget");t.data(h[0],"ui-controlgroup-data",o?o:n[s]("instance")),i.push(h[0])}})),void 0):void 0}),this.childWidgets=t(t.unique(i)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var i=t(this),s=i.data("ui-controlgroup-data");s&&s[e]&&s[e]()})},_updateCornerClass:function(t,e){var i="ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right 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e="vertical"===this.options.direction;return{width:e?"auto":!1,classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(e,i){var s={};return t.each(e,function(n){var o=i.options.classes[n]||"";o=t.trim(o.replace(g,"")),s[n]=(o+" "+e[n]).replace(/\s+/g," ")}),s},_setOption:function(t,e){return"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"===t?(this._callChildMethod(e?"disable":"enable"),void 0):(this.refresh(),void 0)},refresh:function(){var e,i=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),e=this.childWidgets,this.options.onlyVisible&&(e=e.filter(":visible")),e.length&&(t.each(["first","last"],function(t,s){var n=e[s]().data("ui-controlgroup-data");if(n&&i["_"+n.widgetName+"Options"]){var o=i["_"+n.widgetName+"Options"](1===e.length?"only":s);o.classes=i._resolveClassesValues(o.classes,n),n.element[n.widgetName](o)}else i._updateCornerClass(e[s](),s)}),this._callChildMethod("refresh"))}}),t.widget("ui.checkboxradio",[t.ui.formResetMixin,{version:"1.12.1",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var e,i,s=this,n=this._super()||{};return this._readType(),i=this.element.labels(),this.label=t(i[i.length-1]),this.label.length||t.error("No label found for checkboxradio 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ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&(this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this.icon&&this._addClass(this.icon,null,"ui-state-hover")),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var e=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===e&&/radio|checkbox/.test(this.type)||t.error("Can't create checkboxradio on element.nodeName="+e+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var e,i=this.element[0].name,s="input[name='"+t.ui.escapeSelector(i)+"']";return i?(e=this.form.length?t(this.form[0].elements).filter(s):t(s).filter(function(){return 0===t(this).form().length}),e.not(this.element)):t([])},_toggleClasses:function(){var e=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",e),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",e)._toggleClass(this.icon,null,"ui-icon-blank",!e),"radio"===this.type&&this._getRadioGroup().each(function(){var e=t(this).checkboxradio("instance");e&&e._removeClass(e.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){return"label"!==t||e?(this._super(t,e),"disabled"===t?(this._toggleClass(this.label,null,"ui-state-disabled",e),this.element[0].disabled=e,void 0):(this.refresh(),void 0)):void 0},_updateIcon:function(e){var i="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=t(""),this.iconSpace=t(" "),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(i+=e?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,e?"ui-icon-blank":"ui-icon-check")):i+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",i),e||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),this.iconSpace&&(t=t.not(this.iconSpace[0])),t.remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]),t.ui.checkboxradio,t.widget("ui.button",{version:"1.12.1",defaultElement:"
Activating the Plugin
-To activate 1001bit Pro, you need to obtain a user license. You can try out the plugin for free for 30 days. If you wish to continue using it beyond the trial period, you need to buy the license for only USD 48.00. You can also upgrade from 1001bit Standard or 1001bit Pro v1 with a discount. Users who registered for 1001bit Pro v1 on and after 1st January 2012 can upgrade for free.
-To activate 1001bit Pro, follow these steps:
--
-
How to Use 1001bit Pro
-Now that you have installed and activated 1001bit Pro, you can start using it to create amazing architectural models in Sketchup. Here are some tips on how to use it:
-Accessing the Plugin Menu
-You can access the plugin menu by going to Plugins > 1001bit Pro. You will see a list of tools grouped into categories such as Architectural Tools, Editing Tools, etc. You can also access the plugin menu by clicking on the toolbar icon that looks like a blue house with a red roof.
-Selecting and Applying Tools
-To select a tool, you can either click on its name in the menu or its icon in the toolbar. A dialog box will appear with the parameters and options for that tool. You can also see a preview of the tool in action in the Sketchup window. To apply a tool, you need to follow the instructions in the dialog box and select the appropriate entities in your model. For example, to create a staircase, you need to select two points for the base and one point for the height.
-Adjusting Parameters and Options
-To adjust the parameters and options of a tool, you can use the sliders, text boxes, drop-down menus, check boxes, and buttons in the dialog box. You can also type in values directly or use mathematical expressions. You can see the changes in real time in the Sketchup window. To confirm your settings, click OK or Apply. To cancel your settings, click Cancel or Escape.
-Saving and Loading Parameters
-To save the parameters of a tool, click on Save Parameters in the dialog box. A file browser will appear where you can choose a location and name for your parameter file. The file will have a .txt extension. To load the parameters of a tool, click on Load Parameters in the dialog box. A file browser will appear where you can choose a parameter file that you have saved before. The parameters will be applied to the tool automatically.
- # ConclusionIn this article, we have reviewed 1001bit Pro 2.2 Plugin for Sketchup, a collection of plugins that can help you create architectural models faster and easier. We have covered its features, benefits, installation, activation, and usage. We hope that this article has given you a clear overview of what this plugin can do and how it can enhance your modeling experience and output.
-If you are interested in trying out 1001bit Pro, you can download it from https://www.1001bit.com/pro/download.html and use it for free for 30 days. If you like it and want to continue using it beyond the trial period, you can buy a user license for only USD 48.00 from https://www.1001bit.com/pro/purchase.html. You can also upgrade from 1001bit Standard or 1001bit Pro v1 with a discount or for free if you qualify.
-We highly recommend you to give 1001bit Pro a try and see how it can transform your architectural modeling in Sketchup. You will be amazed by how much more you can achieve while doing less with this plugin.
- # FAQsHere are some common questions and answers about 1001bit Pro:
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-
1001bit Pro works with Sketchup 8 and later versions on both Windows and Mac operating systems. It requires an internet connection for activation and updates.
-Each user license allows for activating 1001bit Pro on two separate computers.
-You can update 1001bit Pro by downloading and installing the latest version from https://www.1001bit.com/pro/download.html. You don't need to reactivate it after updating.
-You can get support for 1001bit Pro by visiting the official website: https://www.1001bit.com/pro/. There you can find user guides, tutorials, videos, forums, contacts, and more.
-To uninstall 1001bit Pro, you need to delete all the files related to it from your Sketchup plugins folder. The location of this folder depends on your Sketchup version and operating system. For example, for Sketchup 2014 on Windows, it is C:\Users\YourName\AppData\Roaming\SketchUp\SketchUp 2014\SketchUp\Plugins.
-
-
-
\ No newline at end of file diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Endhiran Full Movie Free Download in Tamil HD 1080p Dont Miss the Chance to See the Epic Adventure of Chitti.md b/spaces/raedeXanto/academic-chatgpt-beta/Endhiran Full Movie Free Download in Tamil HD 1080p Dont Miss the Chance to See the Epic Adventure of Chitti.md deleted file mode 100644 index 3198919da0f9016135fcc3da1ec6ced98939850f..0000000000000000000000000000000000000000 --- a/spaces/raedeXanto/academic-chatgpt-beta/Endhiran Full Movie Free Download in Tamil HD 1080p Dont Miss the Chance to See the Epic Adventure of Chitti.md +++ /dev/null @@ -1,127 +0,0 @@ -
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-If you are a fan of Tamil cinema, you must have heard of Endhiran, one of the most successful and expensive movies ever made in India. This sci-fi action thriller, starring Rajinikanth and Aishwarya Rai Bachchan, was released in 2010 and broke many box office records. It also received critical acclaim for its innovative story, stunning visuals, and catchy music.
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-Introduction
-What is Endhiran?
-Endhiran (also known as Robot in Hindi and Telugu) is a 2010 Indian Tamil-language science fiction action film directed by S. Shankar. It is the first installment of the Enthiran franchise, which also includes its sequel 2.0 (2018) and its upcoming third part 3.0 (2022). The film was produced by Sun Pictures with a budget of ₹1.32 billion (US$18 million), making it the most expensive Indian film at that time.
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How to watch Endhiran online?
-If you want to watch Endhiran online, you have two options: streaming or downloading. Streaming means watching the movie online without saving it on your device. Downloading means saving the movie file on your device so that you can watch it offline later.
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-Platform Subscription fee Amazon Prime Video ₹999/year or ₹129/month Netflix ₹199-₹799/month Sun NXT ₹50-₹480/year Zee5 ₹99-₹999/year Eros Now ₹49-₹399/year To download Endhiran full movie in Tamil HD 1080p for free, you can use any of the illegal sources that offer pirated copies of the movie. Some of them are:
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Install Roblox APK on Your Android Device - 5Play Guide.md b/spaces/congsaPfin/Manga-OCR/logs/How to Install Roblox APK on Your Android Device - 5Play Guide.md deleted file mode 100644 index a918b3d7705ea2fd56548b88935f92df16e477fc..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/How to Install Roblox APK on Your Android Device - 5Play Guide.md +++ /dev/null @@ -1,102 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Undawn The Ultimate Open-World Survival RPG - Beta Test APK Available.md b/spaces/congsaPfin/Manga-OCR/logs/Undawn The Ultimate Open-World Survival RPG - Beta Test APK Available.md deleted file mode 100644 index 993ed37c598499811e40786802ed202da41091f2..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Undawn The Ultimate Open-World Survival RPG - Beta Test APK Available.md +++ /dev/null @@ -1,129 +0,0 @@ -
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By completing these challenges, you can earn various rewards, such as:
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There is no official release date for Undawn yet. However, according to some sources , the game is expected to launch in late 2023 or early 2024.
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\ No newline at end of file diff --git a/spaces/consciousAI/question_answering/app.py b/spaces/consciousAI/question_answering/app.py deleted file mode 100644 index 1350e7756928fd5302d62a58b8012b17be9f7aa8..0000000000000000000000000000000000000000 --- a/spaces/consciousAI/question_answering/app.py +++ /dev/null @@ -1,115 +0,0 @@ -import gradio as gr -from transformers import pipeline -import nltk -nltk.download('punkt') -from nltk import sent_tokenize -import torch -from transformers import ( - pipeline, - AutoModelForSeq2SeqLM, - AutoTokenizer -) - -import re - -device = [0 if torch.cuda.is_available() else 'cpu'][0] - -def _generate(query, context, model, device): - - FT_MODEL = AutoModelForSeq2SeqLM.from_pretrained(model).to(device) - FT_MODEL_TOKENIZER = AutoTokenizer.from_pretrained(model) - input_text = "question: " + query + " question_context: " + context - - input_tokenized = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True, padding='max_length', max_length=1024).to(device) - _tok_count_assessment = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True).to(device) - - summary_ids = FT_MODEL.generate(input_tokenized, - max_length=30, - min_length=3, - length_penalty=1.0, - num_beams=2, - early_stopping=True, - ) - output = [FT_MODEL_TOKENIZER.decode(id, clean_up_tokenization_spaces=True, skip_special_tokens=True) for id in summary_ids] - - return str(output[0]) - -def predict(query, context): - - context = context.encode("ascii", "ignore") - context = context.decode() - - #Custom1 - cust_model_name = "consciousAI/question-answering-roberta-base-s" - cust_question_answerer = pipeline('question-answering', model=cust_model_name, tokenizer=cust_model_name, device=device) - - cust_output = cust_question_answerer(question=query, context=context) - cust_answer = cust_output['answer'] - cust_answer_span = "[" + str(cust_output['start']) + "," + str(cust_output['end']) + "]" - cust_confidence = cust_output['score'] - cust_answer_sentence = [_sent for _sent in sent_tokenize(context) if cust_answer in _sent] - if len(cust_answer_sentence) > 0: - cust_answer_sentence = cust_answer_sentence[0] - else: - cust_answer_sentence = "Failed matching sentence (answer may be split in multiple sentences)" - - #Custom3 - cust_model_name_3 = "consciousAI/question-answering-roberta-base-s-v2" - cust_question_answerer_3 = pipeline('question-answering', model=cust_model_name_3, tokenizer=cust_model_name_3, device=device) - - cust_output_3 = cust_question_answerer_3(question=query, context=context) - cust_answer_3 = cust_output_3['answer'] - cust_answer_span_3 = "[" + str(cust_output_3['start']) + "," + str(cust_output_3['end']) + "]" - cust_confidence_3 = cust_output_3['score'] - cust_answer_sentence_3 = [_sent for _sent in sent_tokenize(context) if cust_answer_3 in _sent] - if len(cust_answer_sentence_3) > 0: - cust_answer_sentence_3 = cust_answer_sentence_3[0] - else: - cust_answer_sentence_3 = "Failed matching sentence (answer may be split in multiple sentences)" - - #Custom2 - cust_answer_2 = _generate(query, context, model="consciousAI/question-answering-generative-t5-v1-base-s-q-c", device=device) - cust_answer_sentence_2 = [_sent for _sent in sent_tokenize(context) if cust_answer_2 in _sent] - if len(cust_answer_sentence_2) > 0: - cust_answer_sentence_2 = cust_answer_sentence_2[0] - else: - cust_answer_sentence_2 = "Failed matching sentence (answer may be split in multiple sentences)" - cust_answer_span_2 = re.search(cust_answer_2, contextDefault).span() - - return cust_answer, cust_answer_sentence, cust_answer_span, cust_confidence, cust_answer_2, cust_answer_sentence_2, cust_answer_span_2, cust_answer_sentence_3, cust_answer_3, cust_answer_span_3, cust_confidence_3 - -with gr.Blocks() as demo: - gr.Markdown(value="# Question Answering Encoders vs Generative\n [Question Answering Leveraging Encoders V1](https://huggingface.co/anshoomehra/question-answering-roberta-base-s)\n\n[Question Answering Leveraging Encoders V2](https://huggingface.co/anshoomehra/question-answering-roberta-base-s-v2)\n\n[Generative Question Answering](https://huggingface.co/anshoomehra/question-answering-generative-t5-v1-base-s-q-c)") - with gr.Accordion(variant='compact', label='Input Values'): - with gr.Row(variant='compact'): - queryDefault = "Which company alongside Amazon, Apple, Meta, and Microsoft is considered part of Big Five?" - contextDefault = "Google LLC is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. It has been referred to as 'the most powerful company in the world' and one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the area of artificial intelligence. Its parent company Alphabet is considered one of the Big Five American information technology companies, alongside Amazon, Apple, Meta, and Microsoft." - query = gr.Textbox(queryDefault, label="Query", placeholder="Dummy Query", lines=2) - context = gr.Textbox(contextDefault, label="Context", placeholder="Dummy Context", lines=5, max_lines = 6) - - with gr.Accordion(variant='compact', label='Q&A Model(s) Output'): - with gr.Row(variant='compact'): - with gr.Column(variant='compact'): - _predictionM6 = gr.Textbox(label="question-answering-roberta-base-s: Answer Sentence") - _predictionM5 = gr.Textbox(label="question-answering-roberta-base-s: Answer") - _predictionM7 = gr.Textbox(label="question-answering-roberta-base-s: Q&A Answer Span") - _predictionM8 = gr.Textbox(label="question-answering-roberta-base-s: Answer Confidence") - with gr.Column(variant='compact'): - _predictionM12 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer Sentence") - _predictionM13 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer") - _predictionM14 = gr.Textbox(label="question-answering-roberta-base-s-v2: Q&A Answer Span") - _predictionM15 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer Confidence") - with gr.Column(variant='compact'): - _predictionM10 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Sentence") - _predictionM9 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Answer") - _predictionM11 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Answer Span") - - - with gr.Row(): - gen_btn = gr.Button("Generate Answers") - gen_btn.click(fn=predict, - inputs=[query, context], - outputs=[_predictionM5, _predictionM6, _predictionM7, _predictionM8, _predictionM9, _predictionM10, _predictionM11, _predictionM12, _predictionM13, _predictionM14, _predictionM15] - ) - -demo.launch(show_error=True) \ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Download Stellaris Starter Pack -62 99953776 Free Gaming ...[2].md b/spaces/contluForse/HuggingGPT/assets/Download Stellaris Starter Pack -62 99953776 Free Gaming ...[2].md deleted file mode 100644 index ea3af42afc1344eba0927b36ac34cbc6d40ef3f1..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Download Stellaris Starter Pack -62 99953776 Free Gaming ...[2].md +++ /dev/null @@ -1,6 +0,0 @@ -
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-If you want to learn more about EASERA SysTune and how it can help you achieve better sound quality and performance, you can visit the official website of AFMG Technologies GmbH at https://www.afmg.eu/en or download a free trial version of the software from https://www.afmg.eu/sites/default/files/2021-07/EASERA%20SysTune_Installation%20Information.pdf.
- -EASERA SysTune is not only a powerful measurement tool, but also a versatile audio processor that can be used to enhance the sound quality and performance of any sound system. EASERA SysTune can apply various filters, equalizers, delays, limiters, and other effects to the input or output signals in real time. You can also use EASERA SysTune to perform system alignment and optimization using the built-in features such as FIRmaker, Delay Finder, Auto EQ, and more. EASERA SysTune can also integrate with other software and hardware products from AFMG Technologies GmbH, such as EASE Focus, EASE Evac, EASE SpeakerLab, and GLL Plug-In API.
- -EASERA SysTune is also a user-friendly and customizable software that can be adapted to your preferences and needs. You can choose from different measurement modes, such as Live IR, Live RTA, Live Spectrogram, Live Transfer Function, and more. You can also adjust the display settings, such as the frequency range, resolution, smoothing, scaling, color scheme, and more. You can also create and save your own presets and templates for different measurement scenarios and projects.
-EASERA SysTune is compatible with Windows 7, 8, and 10 operating systems and requires a minimum of 2 GB of RAM and 1 GB of hard disk space. EASERA SysTune can be purchased online from the AFMG Technologies GmbH website or from authorized dealers and distributors around the world. EASERA SysTune comes with a USB dongle that serves as a license key and allows you to use the software on any computer. You can also register your license online and receive free updates and technical support from AFMG Technologies GmbH.
-EASERA SysTune is the ultimate solution for live sound measurements and audio processing that can help you achieve better sound quality and performance in any situation. Whether you are a professional or a hobbyist, EASERA SysTune can provide you with the tools and features you need to measure and optimize any sound system and acoustic environment. EASERA SysTune is trusted by thousands of users worldwide who rely on its accuracy, reliability, and versatility. If you want to join them and experience the benefits of EASERA SysTune for yourself, you can download a free trial version of the software today and see what it can do for you.
d5da3c52bf-
-
\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/ball_query.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/ball_query.py deleted file mode 100644 index d0466847c6e5c1239e359a0397568413ebc1504a..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/ball_query.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -from torch.autograd import Function - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['ball_query_forward']) - - -class BallQuery(Function): - """Find nearby points in spherical space.""" - - @staticmethod - def forward(ctx, min_radius: float, max_radius: float, sample_num: int, - xyz: torch.Tensor, center_xyz: torch.Tensor) -> torch.Tensor: - """ - Args: - min_radius (float): minimum radius of the balls. - max_radius (float): maximum radius of the balls. - sample_num (int): maximum number of features in the balls. - xyz (Tensor): (B, N, 3) xyz coordinates of the features. - center_xyz (Tensor): (B, npoint, 3) centers of the ball query. - - Returns: - Tensor: (B, npoint, nsample) tensor with the indices of - the features that form the query balls. - """ - assert center_xyz.is_contiguous() - assert xyz.is_contiguous() - assert min_radius < max_radius - - B, N, _ = xyz.size() - npoint = center_xyz.size(1) - idx = xyz.new_zeros(B, npoint, sample_num, dtype=torch.int) - - ext_module.ball_query_forward( - center_xyz, - xyz, - idx, - b=B, - n=N, - m=npoint, - min_radius=min_radius, - max_radius=max_radius, - nsample=sample_num) - if torch.__version__ != 'parrots': - ctx.mark_non_differentiable(idx) - return idx - - @staticmethod - def backward(ctx, a=None): - return None, None, None, None - - -ball_query = BallQuery.apply diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/roi_align.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/roi_align.py deleted file mode 100644 index 0755aefc66e67233ceae0f4b77948301c443e9fb..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/roi_align.py +++ /dev/null @@ -1,223 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import deprecated_api_warning, ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['roi_align_forward', 'roi_align_backward']) - - -class RoIAlignFunction(Function): - - @staticmethod - def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio, - pool_mode, aligned): - from ..onnx import is_custom_op_loaded - has_custom_op = is_custom_op_loaded() - if has_custom_op: - return g.op( - 'mmcv::MMCVRoiAlign', - input, - rois, - output_height_i=output_size[0], - output_width_i=output_size[1], - spatial_scale_f=spatial_scale, - sampling_ratio_i=sampling_ratio, - mode_s=pool_mode, - aligned_i=aligned) - else: - from torch.onnx.symbolic_opset9 import sub, squeeze - from torch.onnx.symbolic_helper import _slice_helper - from torch.onnx import TensorProtoDataType - # batch_indices = rois[:, 0].long() - batch_indices = _slice_helper( - g, rois, axes=[1], starts=[0], ends=[1]) - batch_indices = squeeze(g, batch_indices, 1) - batch_indices = g.op( - 'Cast', batch_indices, to_i=TensorProtoDataType.INT64) - # rois = rois[:, 1:] - rois = _slice_helper(g, rois, axes=[1], starts=[1], ends=[5]) - if aligned: - # rois -= 0.5/spatial_scale - aligned_offset = g.op( - 'Constant', - value_t=torch.tensor([0.5 / spatial_scale], - dtype=torch.float32)) - rois = sub(g, rois, aligned_offset) - # roi align - return g.op( - 'RoiAlign', - input, - rois, - batch_indices, - output_height_i=output_size[0], - output_width_i=output_size[1], - spatial_scale_f=spatial_scale, - sampling_ratio_i=max(0, sampling_ratio), - mode_s=pool_mode) - - @staticmethod - def forward(ctx, - input, - rois, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - pool_mode='avg', - aligned=True): - ctx.output_size = _pair(output_size) - ctx.spatial_scale = spatial_scale - ctx.sampling_ratio = sampling_ratio - assert pool_mode in ('max', 'avg') - ctx.pool_mode = 0 if pool_mode == 'max' else 1 - ctx.aligned = aligned - ctx.input_shape = input.size() - - assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' - - output_shape = (rois.size(0), input.size(1), ctx.output_size[0], - ctx.output_size[1]) - output = input.new_zeros(output_shape) - if ctx.pool_mode == 0: - argmax_y = input.new_zeros(output_shape) - argmax_x = input.new_zeros(output_shape) - else: - argmax_y = input.new_zeros(0) - argmax_x = input.new_zeros(0) - - ext_module.roi_align_forward( - input, - rois, - output, - argmax_y, - argmax_x, - aligned_height=ctx.output_size[0], - aligned_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - pool_mode=ctx.pool_mode, - aligned=ctx.aligned) - - ctx.save_for_backward(rois, argmax_y, argmax_x) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - rois, argmax_y, argmax_x = ctx.saved_tensors - grad_input = grad_output.new_zeros(ctx.input_shape) - # complex head architecture may cause grad_output uncontiguous. - grad_output = grad_output.contiguous() - ext_module.roi_align_backward( - grad_output, - rois, - argmax_y, - argmax_x, - grad_input, - aligned_height=ctx.output_size[0], - aligned_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - pool_mode=ctx.pool_mode, - aligned=ctx.aligned) - return grad_input, None, None, None, None, None, None - - -roi_align = RoIAlignFunction.apply - - -class RoIAlign(nn.Module): - """RoI align pooling layer. - - Args: - output_size (tuple): h, w - spatial_scale (float): scale the input boxes by this number - sampling_ratio (int): number of inputs samples to take for each - output sample. 0 to take samples densely for current models. - pool_mode (str, 'avg' or 'max'): pooling mode in each bin. - aligned (bool): if False, use the legacy implementation in - MMDetection. If True, align the results more perfectly. - use_torchvision (bool): whether to use roi_align from torchvision. - - Note: - The implementation of RoIAlign when aligned=True is modified from - https://github.com/facebookresearch/detectron2/ - - The meaning of aligned=True: - - Given a continuous coordinate c, its two neighboring pixel - indices (in our pixel model) are computed by floor(c - 0.5) and - ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete - indices [0] and [1] (which are sampled from the underlying signal - at continuous coordinates 0.5 and 1.5). But the original roi_align - (aligned=False) does not subtract the 0.5 when computing - neighboring pixel indices and therefore it uses pixels with a - slightly incorrect alignment (relative to our pixel model) when - performing bilinear interpolation. - - With `aligned=True`, - we first appropriately scale the ROI and then shift it by -0.5 - prior to calling roi_align. This produces the correct neighbors; - - The difference does not make a difference to the model's - performance if ROIAlign is used together with conv layers. - """ - - @deprecated_api_warning( - { - 'out_size': 'output_size', - 'sample_num': 'sampling_ratio' - }, - cls_name='RoIAlign') - def __init__(self, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - pool_mode='avg', - aligned=True, - use_torchvision=False): - super(RoIAlign, self).__init__() - - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - self.sampling_ratio = int(sampling_ratio) - self.pool_mode = pool_mode - self.aligned = aligned - self.use_torchvision = use_torchvision - - def forward(self, input, rois): - """ - Args: - input: NCHW images - rois: Bx5 boxes. First column is the index into N.\ - The other 4 columns are xyxy. - """ - if self.use_torchvision: - from torchvision.ops import roi_align as tv_roi_align - if 'aligned' in tv_roi_align.__code__.co_varnames: - return tv_roi_align(input, rois, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.aligned) - else: - if self.aligned: - rois -= rois.new_tensor([0.] + - [0.5 / self.spatial_scale] * 4) - return tv_roi_align(input, rois, self.output_size, - self.spatial_scale, self.sampling_ratio) - else: - return roi_align(input, rois, self.output_size, self.spatial_scale, - self.sampling_ratio, self.pool_mode, self.aligned) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(output_size={self.output_size}, ' - s += f'spatial_scale={self.spatial_scale}, ' - s += f'sampling_ratio={self.sampling_ratio}, ' - s += f'pool_mode={self.pool_mode}, ' - s += f'aligned={self.aligned}, ' - s += f'use_torchvision={self.use_torchvision})' - return s diff --git a/spaces/cvlab/zero123-live/ldm/data/base.py b/spaces/cvlab/zero123-live/ldm/data/base.py deleted file mode 100644 index 742794e631081bbfa7c44f3df6f83373ca5c15c1..0000000000000000000000000000000000000000 --- a/spaces/cvlab/zero123-live/ldm/data/base.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -import numpy as np -from abc import abstractmethod -from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset - - -class Txt2ImgIterableBaseDataset(IterableDataset): - ''' - Define an interface to make the IterableDatasets for text2img data chainable - ''' - def __init__(self, num_records=0, valid_ids=None, size=256): - super().__init__() - self.num_records = num_records - self.valid_ids = valid_ids - self.sample_ids = valid_ids - self.size = size - - print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.') - - def __len__(self): - return self.num_records - - @abstractmethod - def __iter__(self): - pass - - -class PRNGMixin(object): - """ - Adds a prng property which is a numpy RandomState which gets - reinitialized whenever the pid changes to avoid synchronized sampling - behavior when used in conjunction with multiprocessing. - """ - @property - def prng(self): - currentpid = os.getpid() - if getattr(self, "_initpid", None) != currentpid: - self._initpid = currentpid - self._prng = np.random.RandomState() - return self._prng diff --git a/spaces/cvlab/zero123-live/taming-transformers/taming/modules/util.py b/spaces/cvlab/zero123-live/taming-transformers/taming/modules/util.py deleted file mode 100644 index 9ee16385d8b1342a2d60a5f1aa5cadcfbe934bd8..0000000000000000000000000000000000000000 --- a/spaces/cvlab/zero123-live/taming-transformers/taming/modules/util.py +++ /dev/null @@ -1,130 +0,0 @@ -import torch -import torch.nn as nn - - -def count_params(model): - total_params = sum(p.numel() for p in model.parameters()) - return total_params - - -class ActNorm(nn.Module): - def __init__(self, num_features, logdet=False, affine=True, - allow_reverse_init=False): - assert affine - super().__init__() - self.logdet = logdet - self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1)) - self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1)) - self.allow_reverse_init = allow_reverse_init - - self.register_buffer('initialized', torch.tensor(0, dtype=torch.uint8)) - - def initialize(self, input): - with torch.no_grad(): - flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1) - mean = ( - flatten.mean(1) - .unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .permute(1, 0, 2, 3) - ) - std = ( - flatten.std(1) - .unsqueeze(1) - .unsqueeze(2) - .unsqueeze(3) - .permute(1, 0, 2, 3) - ) - - self.loc.data.copy_(-mean) - self.scale.data.copy_(1 / (std + 1e-6)) - - def forward(self, input, reverse=False): - if reverse: - return self.reverse(input) - if len(input.shape) == 2: - input = input[:,:,None,None] - squeeze = True - else: - squeeze = False - - _, _, height, width = input.shape - - if self.training and self.initialized.item() == 0: - self.initialize(input) - self.initialized.fill_(1) - - h = self.scale * (input + self.loc) - - if squeeze: - h = h.squeeze(-1).squeeze(-1) - - if self.logdet: - log_abs = torch.log(torch.abs(self.scale)) - logdet = height*width*torch.sum(log_abs) - logdet = logdet * torch.ones(input.shape[0]).to(input) - return h, logdet - - return h - - def reverse(self, output): - if self.training and self.initialized.item() == 0: - if not self.allow_reverse_init: - raise RuntimeError( - "Initializing ActNorm in reverse direction is " - "disabled by default. Use allow_reverse_init=True to enable." - ) - else: - self.initialize(output) - self.initialized.fill_(1) - - if len(output.shape) == 2: - output = output[:,:,None,None] - squeeze = True - else: - squeeze = False - - h = output / self.scale - self.loc - - if squeeze: - h = h.squeeze(-1).squeeze(-1) - return h - - -class AbstractEncoder(nn.Module): - def __init__(self): - super().__init__() - - def encode(self, *args, **kwargs): - raise NotImplementedError - - -class Labelator(AbstractEncoder): - """Net2Net Interface for Class-Conditional Model""" - def __init__(self, n_classes, quantize_interface=True): - super().__init__() - self.n_classes = n_classes - self.quantize_interface = quantize_interface - - def encode(self, c): - c = c[:,None] - if self.quantize_interface: - return c, None, [None, None, c.long()] - return c - - -class SOSProvider(AbstractEncoder): - # for unconditional training - def __init__(self, sos_token, quantize_interface=True): - super().__init__() - self.sos_token = sos_token - self.quantize_interface = quantize_interface - - def encode(self, x): - # get batch size from data and replicate sos_token - c = torch.ones(x.shape[0], 1)*self.sos_token - c = c.long().to(x.device) - if self.quantize_interface: - return c, None, [None, None, c] - return c diff --git a/spaces/daphshen/corgi-classifier/app.py b/spaces/daphshen/corgi-classifier/app.py deleted file mode 100644 index 4951dd5d4cc5536da32d961cfa0db9e531de855d..0000000000000000000000000000000000000000 --- a/spaces/daphshen/corgi-classifier/app.py +++ /dev/null @@ -1,41 +0,0 @@ -from fastai.vision.all import * -import gradio as gr - -learn = load_learner('model.pkl') - -categories = {'cardigan', 'pembroke'} - -def classify_image(img): - pred, idx, probs = learn.predict(img) - return dict(zip(categories, map(float, probs))) - -title = "Corgi Classifier" -description = """ -This was an experiment to create a corgi classifier between Pembroke Welsh Corgis vs. Cardigan Corgis. I wanted to see if I could create a ML model to tell these two breeds apart as these breeds are quite similar. -
-Spoiler alert: it isn't very good! I'm not too surprised, as there are a few reasons why these breeds differ, and it's not always distinguishable via just a photo. -
-
-
-
-Regardless, this was still a fun project to create! If you come across this and have any comments, I'd love to hear from you: @daphshen -
-Resources: -
-
-
-
{PLAIN_GPT_ANS}
", unsafe_allow_html=True) - placeholder_plain_gpt = st.empty() - placeholder_plain_gpt.text_area(f" ", placeholder="The answer will appear here.", disabled=True, - key=PLAIN_GPT_ANS, height=1, label_visibility='collapsed') - if st.session_state.get("query_type", BUTTON_LOCAL_RET_AUG) == BUTTON_LOCAL_RET_AUG: - st.markdown(f"{GPT_LOCAL_RET_AUG_ANS}
", unsafe_allow_html=True) - else: - st.markdown(f"{GPT_WEB_RET_AUG_ANS}
", unsafe_allow_html=True) - placeholder_retrieval_augmented = st.empty() - placeholder_retrieval_augmented.text_area(f" ", placeholder="The answer will appear here.", disabled=True, - key=GPT_LOCAL_RET_AUG_ANS, height=1, label_visibility='collapsed') - - return run_pressed, placeholder_plain_gpt, placeholder_retrieval_augmented - - -def right_sidebar(): - st.write("") - st.write("") - st.markdown("Example questions
", unsafe_allow_html=True) - st.button(QUERIES[0], on_click=set_q1, use_container_width=True) - st.button(QUERIES[1], on_click=set_q2, use_container_width=True) - st.button(QUERIES[2], on_click=set_q3, use_container_width=True) - st.button(QUERIES[3], on_click=set_q4, use_container_width=True) - st.button(QUERIES[4], on_click=set_q5, use_container_width=True) - - -def left_sidebar(): - with st.sidebar: - image = Image.open('logo/haystack-logo-colored.png') - st.markdown("Thanks for coming to this :hugging_face: space. \n\n" - "This is an effort towards showcasing how you can use Haystack for Retrieval Augmented QA, " - "with local [FAISSDocumentStore](https://docs.haystack.deepset.ai/reference/document-store-api#faissdocumentstore)" - " or a [WebRetriever](https://docs.haystack.deepset.ai/docs/retriever#retrieval-from-the-web). \n\n" - "More information on how this was built and instructions along " - "with a repository will be published soon and updated here.") - - # st.markdown( - # "## How to use\n" - # "1. Enter your [OpenAI API key](https://platform.openai.com/account/api-keys) below\n" - # "2. Enter a Serper Dev API key\n" - # "3. Enjoy 🤗\n" - # ) - - # api_key_input = st.text_input( - # "OpenAI API Key", - # type="password", - # placeholder="Paste your OpenAI API key here (sk-...)", - # help="You can get your API key from https://platform.openai.com/account/api-keys.", - # value=st.session_state.get("OPENAI_API_KEY", ""), - # ) - - # if api_key_input: - # set_openai_api_key(api_key_input) - - st.markdown("---") - st.markdown( - "## How this works\n" - "This app was built with [Haystack](https://haystack.deepset.ai) using the" - " [PromptNode](https://docs.haystack.deepset.ai/docs/prompt_node), " - "[Retriever](https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended)," - "and [FAISSDocumentStore](https://docs.haystack.deepset.ai/reference/document-store-api#faissdocumentstore).\n\n" - " You can find the source code in **Files and versions** tab." - ) - - st.markdown("---") - st.image(image, width=250) diff --git a/spaces/dhkim2810/MobileSAM/utils/tools.py b/spaces/dhkim2810/MobileSAM/utils/tools.py deleted file mode 100644 index 3a06972cfb82357bc66b4e4c9cd7f776846bbd1f..0000000000000000000000000000000000000000 --- a/spaces/dhkim2810/MobileSAM/utils/tools.py +++ /dev/null @@ -1,406 +0,0 @@ -import os -import sys - -import cv2 -import matplotlib.pyplot as plt -import numpy as np -import torch -from PIL import Image - - -def convert_box_xywh_to_xyxy(box): - x1 = box[0] - y1 = box[1] - x2 = box[0] + box[2] - y2 = box[1] + box[3] - return [x1, y1, x2, y2] - - -def segment_image(image, bbox): - image_array = np.array(image) - segmented_image_array = np.zeros_like(image_array) - x1, y1, x2, y2 = bbox - segmented_image_array[y1:y2, x1:x2] = image_array[y1:y2, x1:x2] - segmented_image = Image.fromarray(segmented_image_array) - black_image = Image.new("RGB", image.size, (255, 255, 255)) - # transparency_mask = np.zeros_like((), dtype=np.uint8) - transparency_mask = np.zeros( - (image_array.shape[0], image_array.shape[1]), dtype=np.uint8 - ) - transparency_mask[y1:y2, x1:x2] = 255 - transparency_mask_image = Image.fromarray(transparency_mask, mode="L") - black_image.paste(segmented_image, mask=transparency_mask_image) - return black_image - - -def format_results(masks, scores, logits, filter=0): - annotations = [] - n = len(scores) - for i in range(n): - annotation = {} - - mask = masks[i] - tmp = np.where(mask != 0) - if np.sum(mask) < filter: - continue - annotation["id"] = i - annotation["segmentation"] = mask - annotation["bbox"] = [ - np.min(tmp[0]), - np.min(tmp[1]), - np.max(tmp[1]), - np.max(tmp[0]), - ] - annotation["score"] = scores[i] - annotation["area"] = annotation["segmentation"].sum() - annotations.append(annotation) - return annotations - - -def filter_masks(annotations): # filter the overlap mask - annotations.sort(key=lambda x: x["area"], reverse=True) - to_remove = set() - for i in range(0, len(annotations)): - a = annotations[i] - for j in range(i + 1, len(annotations)): - b = annotations[j] - if i != j and j not in to_remove: - # check if - if b["area"] < a["area"]: - if (a["segmentation"] & b["segmentation"]).sum() / b[ - "segmentation" - ].sum() > 0.8: - to_remove.add(j) - - return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove - - -def get_bbox_from_mask(mask): - mask = mask.astype(np.uint8) - contours, hierarchy = cv2.findContours( - mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE - ) - x1, y1, w, h = cv2.boundingRect(contours[0]) - x2, y2 = x1 + w, y1 + h - if len(contours) > 1: - for b in contours: - x_t, y_t, w_t, h_t = cv2.boundingRect(b) - # 将多个bbox合并成一个 - x1 = min(x1, x_t) - y1 = min(y1, y_t) - x2 = max(x2, x_t + w_t) - y2 = max(y2, y_t + h_t) - h = y2 - y1 - w = x2 - x1 - return [x1, y1, x2, y2] - - -def fast_process( - annotations, args, mask_random_color, bbox=None, points=None, edges=False -): - if isinstance(annotations[0], dict): - annotations = [annotation["segmentation"] for annotation in annotations] - result_name = os.path.basename(args.img_path) - image = cv2.imread(args.img_path) - image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) - original_h = image.shape[0] - original_w = image.shape[1] - if sys.platform == "darwin": - plt.switch_backend("TkAgg") - plt.figure(figsize=(original_w / 100, original_h / 100)) - # Add subplot with no margin. - plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0) - plt.margins(0, 0) - plt.gca().xaxis.set_major_locator(plt.NullLocator()) - plt.gca().yaxis.set_major_locator(plt.NullLocator()) - plt.imshow(image) - if args.better_quality == True: - if isinstance(annotations[0], torch.Tensor): - annotations = np.array(annotations.cpu()) - for i, mask in enumerate(annotations): - mask = cv2.morphologyEx( - mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8) - ) - annotations[i] = cv2.morphologyEx( - mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8) - ) - if args.device == "cpu": - annotations = np.array(annotations) - fast_show_mask( - annotations, - plt.gca(), - random_color=mask_random_color, - bbox=bbox, - points=points, - point_label=args.point_label, - retinamask=args.retina, - target_height=original_h, - target_width=original_w, - ) - else: - if isinstance(annotations[0], np.ndarray): - annotations = torch.from_numpy(annotations) - fast_show_mask_gpu( - annotations, - plt.gca(), - random_color=args.randomcolor, - bbox=bbox, - points=points, - point_label=args.point_label, - retinamask=args.retina, - target_height=original_h, - target_width=original_w, - ) - if isinstance(annotations, torch.Tensor): - annotations = annotations.cpu().numpy() - if args.withContours == True: - contour_all = [] - temp = np.zeros((original_h, original_w, 1)) - for i, mask in enumerate(annotations): - if type(mask) == dict: - mask = mask["segmentation"] - annotation = mask.astype(np.uint8) - if args.retina == False: - annotation = cv2.resize( - annotation, - (original_w, original_h), - interpolation=cv2.INTER_NEAREST, - ) - contours, hierarchy = cv2.findContours( - annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE - ) - for contour in contours: - contour_all.append(contour) - cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2) - color = np.array([0 / 255, 0 / 255, 255 / 255, 0.8]) - contour_mask = temp / 255 * color.reshape(1, 1, -1) - plt.imshow(contour_mask) - - save_path = args.output - if not os.path.exists(save_path): - os.makedirs(save_path) - plt.axis("off") - fig = plt.gcf() - plt.draw() - - try: - buf = fig.canvas.tostring_rgb() - except AttributeError: - fig.canvas.draw() - buf = fig.canvas.tostring_rgb() - - cols, rows = fig.canvas.get_width_height() - img_array = np.fromstring(buf, dtype=np.uint8).reshape(rows, cols, 3) - cv2.imwrite( - os.path.join(save_path, result_name), cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR) - ) - - -# CPU post process -def fast_show_mask( - annotation, - ax, - random_color=False, - bbox=None, - points=None, - point_label=None, - retinamask=True, - target_height=960, - target_width=960, -): - msak_sum = annotation.shape[0] - height = annotation.shape[1] - weight = annotation.shape[2] - # 将annotation 按照面积 排序 - areas = np.sum(annotation, axis=(1, 2)) - sorted_indices = np.argsort(areas) - annotation = annotation[sorted_indices] - - index = (annotation != 0).argmax(axis=0) - if random_color == True: - color = np.random.random((msak_sum, 1, 1, 3)) - else: - color = np.ones((msak_sum, 1, 1, 3)) * np.array( - [30 / 255, 144 / 255, 255 / 255] - ) - transparency = np.ones((msak_sum, 1, 1, 1)) * 0.6 - visual = np.concatenate([color, transparency], axis=-1) - mask_image = np.expand_dims(annotation, -1) * visual - - show = np.zeros((height, weight, 4)) - h_indices, w_indices = np.meshgrid( - np.arange(height), np.arange(weight), indexing="ij" - ) - indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) - # 使用向量化索引更新show的值 - show[h_indices, w_indices, :] = mask_image[indices] - if bbox is not None: - x1, y1, x2, y2 = bbox - ax.add_patch( - plt.Rectangle( - (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1 - ) - ) - # draw point - if points is not None: - plt.scatter( - [point[0] for i, point in enumerate(points) if point_label[i] == 1], - [point[1] for i, point in enumerate(points) if point_label[i] == 1], - s=20, - c="y", - ) - plt.scatter( - [point[0] for i, point in enumerate(points) if point_label[i] == 0], - [point[1] for i, point in enumerate(points) if point_label[i] == 0], - s=20, - c="m", - ) - - if retinamask == False: - show = cv2.resize( - show, (target_width, target_height), interpolation=cv2.INTER_NEAREST - ) - ax.imshow(show) - - -def fast_show_mask_gpu( - annotation, - ax, - random_color=False, - bbox=None, - points=None, - point_label=None, - retinamask=True, - target_height=960, - target_width=960, -): - msak_sum = annotation.shape[0] - height = annotation.shape[1] - weight = annotation.shape[2] - areas = torch.sum(annotation, dim=(1, 2)) - sorted_indices = torch.argsort(areas, descending=False) - annotation = annotation[sorted_indices] - # 找每个位置第一个非零值下标 - index = (annotation != 0).to(torch.long).argmax(dim=0) - if random_color == True: - color = torch.rand((msak_sum, 1, 1, 3)).to(annotation.device) - else: - color = torch.ones((msak_sum, 1, 1, 3)).to(annotation.device) * torch.tensor( - [30 / 255, 144 / 255, 255 / 255] - ).to(annotation.device) - transparency = torch.ones((msak_sum, 1, 1, 1)).to(annotation.device) * 0.6 - visual = torch.cat([color, transparency], dim=-1) - mask_image = torch.unsqueeze(annotation, -1) * visual - # 按index取数，index指每个位置选哪个batch的数，把mask_image转成一个batch的形式 - show = torch.zeros((height, weight, 4)).to(annotation.device) - h_indices, w_indices = torch.meshgrid( - torch.arange(height), torch.arange(weight), indexing="ij" - ) - indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None)) - # 使用向量化索引更新show的值 - show[h_indices, w_indices, :] = mask_image[indices] - show_cpu = show.cpu().numpy() - if bbox is not None: - x1, y1, x2, y2 = bbox - ax.add_patch( - plt.Rectangle( - (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1 - ) - ) - # draw point - if points is not None: - plt.scatter( - [point[0] for i, point in enumerate(points) if point_label[i] == 1], - [point[1] for i, point in enumerate(points) if point_label[i] == 1], - s=20, - c="y", - ) - plt.scatter( - [point[0] for i, point in enumerate(points) if point_label[i] == 0], - [point[1] for i, point in enumerate(points) if point_label[i] == 0], - s=20, - c="m", - ) - if retinamask == False: - show_cpu = cv2.resize( - show_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST - ) - ax.imshow(show_cpu) - - -def crop_image(annotations, image_like): - if isinstance(image_like, str): - image = Image.open(image_like) - else: - image = image_like - ori_w, ori_h = image.size - mask_h, mask_w = annotations[0]["segmentation"].shape - if ori_w != mask_w or ori_h != mask_h: - image = image.resize((mask_w, mask_h)) - cropped_boxes = [] - cropped_images = [] - not_crop = [] - filter_id = [] - # annotations, _ = filter_masks(annotations) - # filter_id = list(_) - for _, mask in enumerate(annotations): - if np.sum(mask["segmentation"]) <= 100: - filter_id.append(_) - continue - bbox = get_bbox_from_mask(mask["segmentation"]) # mask 的 bbox - cropped_boxes.append(segment_image(image, bbox)) # 保存裁剪的图片 - # cropped_boxes.append(segment_image(image,mask["segmentation"])) - cropped_images.append(bbox) # 保存裁剪的图片的bbox - - return cropped_boxes, cropped_images, not_crop, filter_id, annotations - - -def box_prompt(masks, bbox, target_height, target_width): - h = masks.shape[1] - w = masks.shape[2] - if h != target_height or w != target_width: - bbox = [ - int(bbox[0] * w / target_width), - int(bbox[1] * h / target_height), - int(bbox[2] * w / target_width), - int(bbox[3] * h / target_height), - ] - bbox[0] = round(bbox[0]) if round(bbox[0]) > 0 else 0 - bbox[1] = round(bbox[1]) if round(bbox[1]) > 0 else 0 - bbox[2] = round(bbox[2]) if round(bbox[2]) < w else w - bbox[3] = round(bbox[3]) if round(bbox[3]) < h else h - - # IoUs = torch.zeros(len(masks), dtype=torch.float32) - bbox_area = (bbox[3] - bbox[1]) * (bbox[2] - bbox[0]) - - masks_area = torch.sum(masks[:, bbox[1] : bbox[3], bbox[0] : bbox[2]], dim=(1, 2)) - orig_masks_area = torch.sum(masks, dim=(1, 2)) - - union = bbox_area + orig_masks_area - masks_area - IoUs = masks_area / union - max_iou_index = torch.argmax(IoUs) - - return masks[max_iou_index].cpu().numpy(), max_iou_index - - -def point_prompt(masks, points, point_label, target_height, target_width): # numpy 处理 - h = masks[0]["segmentation"].shape[0] - w = masks[0]["segmentation"].shape[1] - if h != target_height or w != target_width: - points = [ - [int(point[0] * w / target_width), int(point[1] * h / target_height)] - for point in points - ] - onemask = np.zeros((h, w)) - for i, annotation in enumerate(masks): - if type(annotation) == dict: - mask = annotation["segmentation"] - else: - mask = annotation - for i, point in enumerate(points): - if mask[point[1], point[0]] == 1 and point_label[i] == 1: - onemask += mask - if mask[point[1], point[0]] == 1 and point_label[i] == 0: - onemask -= mask - onemask = onemask >= 1 - return onemask, 0 diff --git a/spaces/diacanFperku/AutoGPT/HD Online Player (interstellar Movie Download In Hindi) High Quality.md b/spaces/diacanFperku/AutoGPT/HD Online Player (interstellar Movie Download In Hindi) High Quality.md deleted file mode 100644 index b3f0054faf42f6744dd174c8fe03ac75945738f5..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/HD Online Player (interstellar Movie Download In Hindi) High Quality.md +++ /dev/null @@ -1,30 +0,0 @@ - -How to Watch Interstellar Movie in Hindi Online
- -Interstellar is a 2014 sci-fi movie directed by Christopher Nolan, starring Matthew McConaughey, Anne Hathaway, Jessica Chastain, and others. It tells the story of a group of explorers who use a wormhole to travel across the galaxy and find a new home for humanity.
- -If you are a fan of this movie and want to watch it in Hindi, you might be wondering how to do that. Well, there are a few options available for you to enjoy this epic movie in your preferred language.
-HD Online Player (interstellar movie download in hindi)
Download File — https://gohhs.com/2uFUyC
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One option is to buy or rent the movie from online platforms like Amazon Prime Video, Google Play Movies, YouTube, or iTunes. These platforms offer the movie in dual audio, meaning you can switch between English and Hindi as you wish. However, this option might cost you some money and require a stable internet connection.
- -Another option is to download the movie from torrent sites like KatMovieHD, Moviefone, or OlaMovies. These sites provide the movie in various formats and resolutions, such as 480p, 720p, 1080p, or 4K. You can also choose the audio quality and subtitles according to your preference. However, this option might be illegal and risky, as you might face copyright issues or malware attacks.
- -A third option is to stream the movie from online players like HD Online Player. This player allows you to watch the movie in Hindi without downloading or paying anything. You just need to paste the URL of the movie into the player and enjoy it. However, this option might have some drawbacks, such as low quality, buffering, ads, or pop-ups.
- -So, these are some of the ways you can watch Interstellar movie in Hindi online. Choose the one that suits you best and enjoy this amazing movie.
- -If you are curious about the plot of Interstellar, here is a brief summary. The movie follows Cooper, a former NASA pilot who lives with his two children, Tom and Murph, and his father-in-law, Donald. Cooper discovers a secret NASA facility where he meets his former mentor, Professor Brand, who reveals that Earth is doomed and that NASA has been working on a plan to save humanity.
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- - -Cooper agrees to go, despite Murph's protests, and promises her that he will return. He leaves behind his family and his farm, hoping to find a new world for them. However, he soon realizes that the mission is more complicated and dangerous than he expected. He has to face the challenges of space travel, time dilation, betrayal, sacrifice, and love as he tries to save the human race.
- -Interstellar is not just a movie about space exploration, but also a movie about human emotions, relationships, and values. The movie explores themes such as love, faith, sacrifice, survival, and destiny. It also raises questions about the nature of time, gravity, and the universe.
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- -Interstellar is a movie that will challenge your mind and touch your heart. It is a movie that will make you wonder about the mysteries of the cosmos and the meaning of life. It is a movie that will inspire you to dream big and reach for the stars.
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\ No newline at end of file diff --git a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/README_zh.md b/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/README_zh.md deleted file mode 100644 index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Bufeiyan-b-Bert-VITS2/README_zh.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py deleted file mode 100644 index df85a0112d27d97301fff56189f99bee0bf8efa5..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py +++ /dev/null @@ -1,27 +0,0 @@ -from mmdet.models.builder import HEADS -from mmdet.models.utils import ResLayer, SimplifiedBasicBlock -from .fused_semantic_head import FusedSemanticHead - - -@HEADS.register_module() -class SCNetSemanticHead(FusedSemanticHead): - """Mask head for `SCNet
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
" - -examples = [ - ['The goal of life is [MASK].','distilbert-base-uncased'] -] - - -io1 = gr.Interface.load("huggingface/distilbert-base-uncased") - -io2 = gr.Interface.load("huggingface/distilbert-base-multilingual-cased") - -def inference(inputtext, model): - if model == "distilbert-base-uncased": - outlabel = io1(inputtext) - else: - outlabel = io2(inputtext) - return outlabel - - -gr.Interface( - inference, - [gr.inputs.Textbox(label="Context",lines=10),gr.inputs.Dropdown(choices=["distilbert-base-uncased","distilbert-base-multilingual-cased"], type="value", default="distilbert-base-uncased", label="model")], - [gr.outputs.Label(label="Output")], - examples=examples, - article=article, - title=title, - description=description).launch(enable_queue=True) \ No newline at end of file diff --git a/spaces/doevent/Face-Real-ESRGAN/app.py b/spaces/doevent/Face-Real-ESRGAN/app.py deleted file mode 100644 index ba1d0d5de692acc340bd363fa92b0a1e2b59181b..0000000000000000000000000000000000000000 --- a/spaces/doevent/Face-Real-ESRGAN/app.py +++ /dev/null @@ -1,47 +0,0 @@ -import torch -from PIL import Image -from RealESRGAN import RealESRGAN -import gradio as gr - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -print(device) -model2 = RealESRGAN(device, scale=2) -model2.load_weights('weights/RealESRGAN_x2.pth', download=True) -model4 = RealESRGAN(device, scale=4) -model4.load_weights('weights/RealESRGAN_x4.pth', download=True) -model8 = RealESRGAN(device, scale=8) -model8.load_weights('weights/RealESRGAN_x8.pth', download=True) - - -def inference(image, size): - if size == '2x': - result = model2.predict(image.convert('RGB')) - elif size == '4x': - result = model4.predict(image.convert('RGB')) - else: - result = model8.predict(image.convert('RGB')) - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return result - - -title = "Face Real ESRGAN UpScale: 2x 4x 8x" -description = "This is an unofficial demo for Real-ESRGAN. Scales the resolution of a photo. This model shows better results on faces compared to the original version.Telegram BOT: https://t.me/restoration_photo_bot" -article = "
Twitter Max Skobeev | Model card/
"
-
-
-gr.Interface(inference,
- [gr.Image(type="pil"),
- gr.Radio(['2x', '4x', '8x'],
- type="value",
- value='2x',
- label='Resolution model')],
- gr.Image(type="pil", label="Output"),
- title=title,
- description=description,
- article=article,
- examples=[['groot.jpeg', "2x"]],
- allow_flagging='never',
- cache_examples=False,
- ).queue(concurrency_count=1).launch(show_error=True)
-
\ No newline at end of file
diff --git a/spaces/dolphinchat/dolphinchat-llm-gpt-ui/README.md b/spaces/dolphinchat/dolphinchat-llm-gpt-ui/README.md
deleted file mode 100644
index 9745d2d12b4073f81132db24fd6939ac001e09fc..0000000000000000000000000000000000000000
--- a/spaces/dolphinchat/dolphinchat-llm-gpt-ui/README.md
+++ /dev/null
@@ -1,22 +0,0 @@
----
-title: DolphinChat LLM
-emoji: 🗨🐬
-colorFrom: gray
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: dolphin.script.py
-pinned: true
----
-
-
-
-
-
-
-
\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Apk Live Bar Bar Indonesia.md b/spaces/fatiXbelha/sd/Download Apk Live Bar Bar Indonesia.md deleted file mode 100644 index a45bd744d1c249de031264de1468b167244cf3b1..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Apk Live Bar Bar Indonesia.md +++ /dev/null @@ -1,89 +0,0 @@ -
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-
-
\ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/request_llm/README.md b/spaces/fb700/chatglm-fitness-RLHF/request_llm/README.md deleted file mode 100644 index 545bc1ffba8b79a49d994cfedcc2a787475181b2..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/request_llm/README.md +++ /dev/null @@ -1,79 +0,0 @@ -# 如何使用其他大语言模型 - -## ChatGLM - -- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt` -- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm" - -``` sh -LLM_MODEL = "chatglm" -``` -- 运行！ -``` sh -`python main.py` -``` - -## Claude-Stack - -- 请参考此教程获取 https://zhuanlan.zhihu.com/p/627485689 - - 1、SLACK_CLAUDE_BOT_ID - - 2、SLACK_CLAUDE_USER_TOKEN - -- 把token加入config.py - -## Newbing - -- 使用cookie editor获取cookie（json） -- 把cookie（json）加入config.py （NEWBING_COOKIES） - -## Moss -- 使用docker-compose - -## RWKV -- 使用docker-compose - -## LLAMA -- 使用docker-compose - -## 盘古 -- 使用docker-compose - - ---- -## Text-Generation-UI (TGUI，调试中，暂不可用) - -### 1. 部署TGUI -``` sh -# 1 下载模型 -git clone https://github.com/oobabooga/text-generation-webui.git -# 2 这个仓库的最新代码有问题，回滚到几周之前 -git reset --hard fcda3f87767e642d1c0411776e549e1d3894843d -# 3 切换路径 -cd text-generation-webui -# 4 安装text-generation的额外依赖 -pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers -# 5 下载模型 -python download-model.py facebook/galactica-1.3b -# 其他可选如 facebook/opt-1.3b -# facebook/galactica-1.3b -# facebook/galactica-6.7b -# facebook/galactica-120b -# facebook/pygmalion-1.3b 等 -# 详情见 https://github.com/oobabooga/text-generation-webui - -# 6 启动text-generation -python server.py --cpu --listen --listen-port 7865 --model facebook_galactica-1.3b -``` - -### 2. 修改config.py - -``` sh -# LLM_MODEL格式: tgui:[模型]@[ws地址]:[ws端口] , 端口要和上面给定的端口一致 -LLM_MODEL = "tgui:galactica-1.3b@localhost:7860" -``` - -### 3. 运行！ -``` sh -cd chatgpt-academic -python main.py -``` diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/__init__.py b/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/__init__.py deleted file mode 100644 index bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# -*- coding: utf-8 -*- -# File : __init__.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d -from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/spaces/fclong/summary/fengshen/examples/pretrain_t5/pretrain_mt5_small_continue.sh b/spaces/fclong/summary/fengshen/examples/pretrain_t5/pretrain_mt5_small_continue.sh deleted file mode 100644 index 0a539a7e6a7fb4b750b441df98dd49f166c3c49b..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/pretrain_t5/pretrain_mt5_small_continue.sh +++ /dev/null @@ -1,120 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=t5_cn_small_pretrain_v2 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=8 -#SBATCH --gres=gpu:8 # number of gpus -#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH -o %x-%j.log -#SBATCH -e %x-%j.err -#SBATCH -x dgx050 - -set -x -e -source activate base - -echo "START TIME: $(date)" -MICRO_BATCH_SIZE=32 -ROOT_DIR=/cognitive_comp/ganruyi/experiments/t5_cn_small_pretrain_v2/ - -ZERO_STAGE=1 - -config_json="$ROOT_DIR/ds_config.t5_cn_small_pretrain_v2.$SLURM_JOBID.json" -export MASTER_PORT=$[RANDOM%10000+30000] -# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size() - -cat < $config_json
-{
- "zero_optimization": {
- "stage": 1
- },
- "fp16": {
- "enabled": true,
- "loss_scale": 0,
- "loss_scale_window": 1000,
- "initial_scale_power": 16,
- "hysteresis": 2,
- "min_loss_scale": 1
- },
- "optimizer": {
- "params": {
- "betas": [
- 0.9,
- 0.95
- ],
- "eps": 1e-08,
- "lr": 1e-04,
- "weight_decay": 0.01
- },
- "type": "AdamW"
- },
- "scheduler": {
- "type": "WarmupLR",
- "params":{
- "warmup_min_lr": 0,
- "warmup_max_lr": 1e-4,
- "warmup_num_steps": 10000
- }
- },
- "steps_per_print": 100,
- "gradient_clipping": 1,
- "train_micro_batch_size_per_gpu": $MICRO_BATCH_SIZE,
- "zero_allow_untested_optimizer": false
-}
-EOT
-
-export PL_DEEPSPEED_CONFIG_PATH=$config_json
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-# strategy=ddp
-strategy=deepspeed_stage_1
-
-TRAINER_ARGS="
- --max_epochs 1 \
- --gpus 8 \
- --num_nodes 1 \
- --strategy ${strategy} \
- --default_root_dir $ROOT_DIR \
- --dirpath $ROOT_DIR/ckpt \
- --save_top_k 3 \
- --every_n_train_steps 0 \
- --monitor train_loss \
- --mode min \
- --save_last \
- --val_check_interval 0.01 \
- --preprocessing_num_workers 20 \
-"
-# --accumulate_grad_batches 8 \
-DATA_DIR=wudao_180g_mt5_tokenized
-
-DATA_ARGS="
- --train_batchsize $MICRO_BATCH_SIZE \
- --valid_batchsize $MICRO_BATCH_SIZE \
- --train_data ${DATA_DIR} \
- --train_split_size 0.999 \
- --max_seq_length 1024 \
-"
-
-MODEL_ARGS="
- --pretrained_model_path /cognitive_comp/ganruyi/experiments/t5_cn_small_pretrain/Randeng-T5-77M \
- --learning_rate 1e-4 \
- --weight_decay 0.1 \
- --keep_tokens_path /cognitive_comp/ganruyi/hf_models/t5_cn_small/sentencepiece_cn_keep_tokens.json \
-"
-# --resume_from_checkpoint /cognitive_comp/ganruyi/fengshen/t5_cn_small_pretrain/ckpt/last.ckpt \
-
-SCRIPTS_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/pretrain_t5/pretrain_t5.py
-
-export CMD=" \
- $SCRIPTS_PATH \
- $TRAINER_ARGS \
- $MODEL_ARGS \
- $DATA_ARGS \
- "
-
-echo $CMD
-
-SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-
-# to debug - add echo (it exits and prints what it would have launched)
-#run_cmd="$PY_LAUNCHER $CMD"
-# salloc --nodes=1 --gres=gpu:2 --cpus-per-gpu=20 -t 24:00:00
-clear; srun singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH bash -c '/home/ganruyi/anaconda3/bin/python $CMD'
-# clear; srun --job-name=t5_cn_small_pretrain_v2 --jobid=153124 --nodes=1 --ntasks-per-node=8 --gres=gpu:8 --cpus-per-task=30 -o %x-%j.log -e %x-%j.err singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH bash -c '/home/ganruyi/anaconda3/bin/python $CMD'
diff --git "a/spaces/fengmuxi/ChatGpt-Web/.github/ISSUE_TEMPLATE/\345\217\215\351\246\210\351\227\256\351\242\230.md" "b/spaces/fengmuxi/ChatGpt-Web/.github/ISSUE_TEMPLATE/\345\217\215\351\246\210\351\227\256\351\242\230.md"
deleted file mode 100644
index 73ad4b2c6a7546e3f051721dabb2c81de0d0ca25..0000000000000000000000000000000000000000
--- "a/spaces/fengmuxi/ChatGpt-Web/.github/ISSUE_TEMPLATE/\345\217\215\351\246\210\351\227\256\351\242\230.md"
+++ /dev/null
@@ -1,32 +0,0 @@
----
-name: 反馈问题
-about: 请告诉我们你遇到的问题
-title: "[Bug] "
-labels: ''
-assignees: ''
-
----
-
-**反馈须知**
-
-⚠️ 注意：不遵循此模板的任何帖子都会被立即关闭。
-
-请在下方中括号内输入 x 来表示你已经知晓相关内容。
-- [ ] 我确认已经在 [常见问题](https://github.com/Yidadaa/ChatGPT-Next-Web/blob/main/docs/faq-cn.md) 中搜索了此次反馈的问题，没有找到解答；
-- [ ] 我确认已经在 [Issues](https://github.com/Yidadaa/ChatGPT-Next-Web/issues) 列表（包括已经 Close 的）中搜索了此次反馈的问题，没有找到解答。
-- [ ] 我确认已经在 [Vercel 使用教程](https://github.com/Yidadaa/ChatGPT-Next-Web/blob/main/docs/vercel-cn.md) 中搜索了此次反馈的问题，没有找到解答。
-
-**描述问题**
-请在此描述你遇到了什么问题。
-
-**如何复现**
-请告诉我们你是通过什么操作触发的该问题。
-
-**截图**
-请在此提供控制台截图、屏幕截图或者服务端的 log 截图。
-
-**一些必要的信息**
- - 系统：[比如 windows 10/ macos 12/ linux / android 11 / ios 16]
- - 浏览器： [比如 chrome, safari]
- - 版本： [填写设置页面的版本号]
- - 部署方式：[比如 vercel、docker 或者服务器部署]
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Ship Sim 2019 Mod APK and Sail the Open World with Various Ships.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Ship Sim 2019 Mod APK and Sail the Open World with Various Ships.md
deleted file mode 100644
index 8bcc2af25864c6fb831e655bfd287e5ce675ad3b..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Ship Sim 2019 Mod APK and Sail the Open World with Various Ships.md
+++ /dev/null
@@ -1,100 +0,0 @@
-
-
-Open
-
-
\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Sky Go for PC and Enjoy Unlimited Entertainment.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Sky Go for PC and Enjoy Unlimited Entertainment.md deleted file mode 100644 index 0f4bac2cfd66b12a49d36ab538ca81d2ece70f40..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Sky Go for PC and Enjoy Unlimited Entertainment.md +++ /dev/null @@ -1,117 +0,0 @@ -
-
-
ℹ️ I am DolphinChat and I was created to help people!
- -✅️ I have been trained on almost the entire Internet!
- -♻️ I can communicate in more than 60 languages of the world!
- -📂 I work on open source and keep your data safe, I am a non-commercial project!
- -▶️ I'm almost the perfect chat assistant, so try me!
- \ No newline at end of file diff --git a/spaces/durgaamma2005/fire_detector/README.md b/spaces/durgaamma2005/fire_detector/README.md deleted file mode 100644 index ad7b582bef8174c90ca080d08d88b7dddb79d59b..0000000000000000000000000000000000000000 --- a/spaces/durgaamma2005/fire_detector/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Fire_detector -emoji: 🐠 -colorFrom: purple -colorTo: red -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/duycse1603/math2tex/HybridViT/helper.py b/spaces/duycse1603/math2tex/HybridViT/helper.py deleted file mode 100644 index ba5cec3e1b2ca917cadbd914fbe45c0a6095f6b2..0000000000000000000000000000000000000000 --- a/spaces/duycse1603/math2tex/HybridViT/helper.py +++ /dev/null @@ -1,182 +0,0 @@ -import torch -import random -import numpy as np -from PIL import Image -from typing import Dict -import torch.nn.functional as F -import numpy as np -from PIL import Image -import cv2 -import math -import albumentations as alb -from albumentations.pytorch.transforms import ToTensorV2 -from collections import OrderedDict -from itertools import repeat -import collections.abc - - -# From PyTorch internals -def _ntuple(n): - def parse(x): - if isinstance(x, collections.abc.Iterable): - return x - return tuple(repeat(x, n)) - return parse - -to_3tuple = _ntuple(3) - -def clean_state_dict(state_dict): - # 'clean' checkpoint by removing .module prefix from state dict if it exists from parallel training - cleaned_state_dict = OrderedDict() - for k, v in state_dict.items(): - name = k[7:] if k.startswith('module.') else k - cleaned_state_dict[name] = v - return cleaned_state_dict - - -def math_transform(mean, std, is_gray: bool): - test_transform = [] - normalize = [ - alb.CLAHE(clip_limit=2, tile_grid_size=(2, 2), always_apply=True), - alb.Normalize(to_3tuple(mean), to_3tuple(std)), - ToTensorV2() - ] - if is_gray: - test_transform += [alb.ToGray(always_apply=True)] - test_transform += normalize - - test_transform = alb.Compose([*test_transform]) - return test_transform - - -def pad(img: Image.Image, divable=32): - """Pad an Image to the next full divisible value of `divable`. Also normalizes the PIL.image and invert if needed. - - Args: - img (PIL.Image): input PIL.image - divable (int, optional): . Defaults to 32. - - Returns: - PIL.Image - """ - data = np.array(img.convert('LA')) - - data = (data-data.min())/(data.max()-data.min())*255 - if data[..., 0].mean() > 128: - gray = 255*(data[..., 0] < 128).astype(np.uint8) # To invert the text to white - else: - gray = 255*(data[..., 0] > 128).astype(np.uint8) - data[..., 0] = 255-data[..., 0] - - coords = cv2.findNonZero(gray) # Find all non-zero points (text) - a, b, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box - rect = data[b:b+h, a:a+w] - - if rect[..., -1].var() == 0: - im = Image.fromarray((rect[..., 0]).astype(np.uint8)).convert('L') - else: - im = Image.fromarray((255-rect[..., -1]).astype(np.uint8)).convert('L') - dims = [] - - for x in [w, h]: - div, mod = divmod(x, divable) - dims.append(divable*(div + (1 if mod > 0 else 0))) - - padded = Image.new('L', dims, 255) - padded.paste(im, im.getbbox()) - - return padded - -def get_divisible_size(ori_h, ori_w, max_dimension=None, scale_factor=32): - new_h, new_w = ori_h, ori_w - if ori_h % scale_factor: - new_h = math.ceil(ori_h/scale_factor)*scale_factor - if new_h > max_dimension[0]: - new_h = math.floor(ori_h/scale_factor)*scale_factor - - if ori_w % scale_factor: - new_w = math.ceil(ori_w/scale_factor)*scale_factor - if new_w > max_dimension[1]: - new_w = math.floor(ori_w/scale_factor)*scale_factor - - return int(new_h),int(new_w) - -def minmax_size(img, max_dimensions=None, min_dimensions=None, is_gray=True): - if max_dimensions is not None: - ratios = [a/b for a, b in zip(list(img.size)[::-1], max_dimensions)] - if any([r > 1 for r in ratios]): - size = np.array(img.size)/max(ratios) - new_h, new_w = get_divisible_size(size[1], size[0], max_dimensions) - img = img.resize((new_w, new_h), Image.LANCZOS) - - if min_dimensions is not None: - ratios = [a/b for a, b in zip(list(img.size)[::-1], min_dimensions)] - if any([r < 1 for r in ratios]): - new_h, new_w = img.size[1] / min(ratios), img.size[0] / min(ratios) - new_h, new_w = get_divisible_size(new_h, new_w, max_dimensions) - if is_gray: - MODE = 'L' - BACKGROUND = 255 - padded_im = Image.new(MODE, (new_w, new_h), BACKGROUND) - padded_im.paste(img, img.getbbox()) - img = padded_im - - return img - -def resize(resizer, img: Image.Image, opt: Dict): - # for math recognition problem image alway in grayscale mode - img = img.convert('L') - assert isinstance(opt, Dict) - assert "imgH" in opt - assert "imgW" in opt - expected_H = opt['imgH'] - - if expected_H is None: - max_dimensions = opt['max_dimension'] #can be bigger than max dim in training set - min_dimensions = opt['min_dimension'] - #equal to min dim in trainign set - test_transform = math_transform(opt['mean'], opt['std'], not opt['rgb']) - try: - new_img = minmax_size(pad(img) if opt['pad'] else img, max_dimensions, min_dimensions, not opt['rgb']) - - if not resizer: - new_img = np.asarray(new_img.convert('RGB')).astype('uint8') - new_img = test_transform(image=new_img)['image'] - if not opt['rgb']: new_img = new_img[:1] - new_img = new_img.unsqueeze(0) - new_img = new_img.float() - else: - with torch.no_grad(): - input_image = new_img.convert('RGB').copy() - r, w, h = 1, input_image.size[0], input_image.size[1] - for i in range(20): - h = int(h * r) - new_img = pad(minmax_size(input_image.resize((w, h), Image.BILINEAR if r > 1 else Image.LANCZOS), - max_dimensions, - min_dimensions, - not opt['rgb'] - )) - t = test_transform(image=np.array(new_img.convert('RGB')).astype('uint8'))['image'] - if not opt['rgb']: t = t[:1] - t = t.unsqueeze(0) - t = t.float() - w = (resizer(t.to(opt['device'])).argmax(-1).item()+1)*opt['min_dimension'][1] - - if (w == new_img.size[0]): - break - - r = w/new_img.size[0] - - new_img = t - except ValueError as e: - print('Error:', e) - new_img = np.asarray(img.convert('RGB')).astype('uint8') - assert len(new_img.shape) == 3 and new_img.shape[2] == 3 - new_img = test_transform(image=new_img)['image'] - if not opt['rgb']: new_img = new_img[:1] - new_img = new_img.unsqueeze(0) - h, w = new_img.shape[2:] - new_img = F.pad(new_img, (0, max_dimensions[1]-w, 0, max_dimensions[0]-h), value=1) - - assert len(new_img.shape) == 4, f'{new_img.shape}' - return new_img diff --git a/spaces/emrecan/zero-shot-turkish/models.py b/spaces/emrecan/zero-shot-turkish/models.py deleted file mode 100644 index 6d8c0b1d62ae5aa7066d890423535ef0224c0a93..0000000000000000000000000000000000000000 --- a/spaces/emrecan/zero-shot-turkish/models.py +++ /dev/null @@ -1,26 +0,0 @@ -METHOD_OPTIONS = { - "nli": "Natural Language Inference", - "nsp": "Next Sentence Prediction", -} - -NLI_MODEL_OPTIONS = [ - "emrecan/distilbert-base-turkish-cased-allnli_tr", - "emrecan/distilbert-base-turkish-cased-multinli_tr", - "emrecan/distilbert-base-turkish-cased-snli_tr", - "emrecan/bert-base-turkish-cased-allnli_tr", - "emrecan/bert-base-turkish-cased-multinli_tr", - "emrecan/bert-base-turkish-cased-snli_tr", - "emrecan/convbert-base-turkish-mc4-cased-allnli_tr", - "emrecan/convbert-base-turkish-mc4-cased-multinli_tr", - "emrecan/convbert-base-turkish-mc4-cased-snli_tr", - "emrecan/bert-base-multilingual-cased-allnli_tr", - "emrecan/bert-base-multilingual-cased-multinli_tr", - "emrecan/bert-base-multilingual-cased-snli_tr", -] - -NSP_MODEL_OPTIONS = [ - "dbmdz/bert-base-turkish-cased", - "dbmdz/bert-base-turkish-uncased", - "dbmdz/bert-base-turkish-128k-cased", - "dbmdz/bert-base-turkish-128k-uncased", -] diff --git a/spaces/errorok/rvc-models-en-test/infer_pack/attentions.py b/spaces/errorok/rvc-models-en-test/infer_pack/attentions.py deleted file mode 100644 index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000 --- a/spaces/errorok/rvc-models-en-test/infer_pack/attentions.py +++ /dev/null @@ -1,417 +0,0 @@ -import copy -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -from infer_pack import commons -from infer_pack import modules -from infer_pack.modules import LayerNorm - - -class Encoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - window_size=10, - **kwargs - ): - super().__init__() - 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.window_size = window_size - - self.drop = nn.Dropout(p_dropout) - self.attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - window_size=window_size, - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask): - attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.attn_layers[i](x, x, attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class Decoder(nn.Module): - def __init__( - self, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size=1, - p_dropout=0.0, - proximal_bias=False, - proximal_init=True, - **kwargs - ): - super().__init__() - 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.proximal_bias = proximal_bias - self.proximal_init = proximal_init - - self.drop = nn.Dropout(p_dropout) - self.self_attn_layers = nn.ModuleList() - self.norm_layers_0 = nn.ModuleList() - self.encdec_attn_layers = nn.ModuleList() - self.norm_layers_1 = nn.ModuleList() - self.ffn_layers = nn.ModuleList() - self.norm_layers_2 = nn.ModuleList() - for i in range(self.n_layers): - self.self_attn_layers.append( - MultiHeadAttention( - hidden_channels, - hidden_channels, - n_heads, - p_dropout=p_dropout, - proximal_bias=proximal_bias, - proximal_init=proximal_init, - ) - ) - self.norm_layers_0.append(LayerNorm(hidden_channels)) - self.encdec_attn_layers.append( - MultiHeadAttention( - hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout - ) - ) - self.norm_layers_1.append(LayerNorm(hidden_channels)) - self.ffn_layers.append( - FFN( - hidden_channels, - hidden_channels, - filter_channels, - kernel_size, - p_dropout=p_dropout, - causal=True, - ) - ) - self.norm_layers_2.append(LayerNorm(hidden_channels)) - - def forward(self, x, x_mask, h, h_mask): - """ - x: decoder input - h: encoder output - """ - self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to( - device=x.device, dtype=x.dtype - ) - encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1) - x = x * x_mask - for i in range(self.n_layers): - y = self.self_attn_layers[i](x, x, self_attn_mask) - y = self.drop(y) - x = self.norm_layers_0[i](x + y) - - y = self.encdec_attn_layers[i](x, h, encdec_attn_mask) - y = self.drop(y) - x = self.norm_layers_1[i](x + y) - - y = self.ffn_layers[i](x, x_mask) - y = self.drop(y) - x = self.norm_layers_2[i](x + y) - x = x * x_mask - return x - - -class MultiHeadAttention(nn.Module): - def __init__( - self, - channels, - out_channels, - n_heads, - p_dropout=0.0, - window_size=None, - heads_share=True, - block_length=None, - proximal_bias=False, - proximal_init=False, - ): - super().__init__() - assert channels % n_heads == 0 - - self.channels = channels - self.out_channels = out_channels - self.n_heads = n_heads - self.p_dropout = p_dropout - self.window_size = window_size - self.heads_share = heads_share - self.block_length = block_length - self.proximal_bias = proximal_bias - self.proximal_init = proximal_init - self.attn = None - - self.k_channels = channels // n_heads - self.conv_q = nn.Conv1d(channels, channels, 1) - self.conv_k = nn.Conv1d(channels, channels, 1) - self.conv_v = nn.Conv1d(channels, channels, 1) - self.conv_o = nn.Conv1d(channels, out_channels, 1) - self.drop = nn.Dropout(p_dropout) - - if window_size is not None: - n_heads_rel = 1 if heads_share else n_heads - rel_stddev = self.k_channels**-0.5 - self.emb_rel_k = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - self.emb_rel_v = nn.Parameter( - torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) - * rel_stddev - ) - - nn.init.xavier_uniform_(self.conv_q.weight) - nn.init.xavier_uniform_(self.conv_k.weight) - nn.init.xavier_uniform_(self.conv_v.weight) - if proximal_init: - with torch.no_grad(): - self.conv_k.weight.copy_(self.conv_q.weight) - self.conv_k.bias.copy_(self.conv_q.bias) - - def forward(self, x, c, attn_mask=None): - q = self.conv_q(x) - k = self.conv_k(c) - v = self.conv_v(c) - - x, self.attn = self.attention(q, k, v, mask=attn_mask) - - x = self.conv_o(x) - return x - - def attention(self, query, key, value, mask=None): - # reshape [b, d, t] -> [b, n_h, t, d_k] - b, d, t_s, t_t = (*key.size(), query.size(2)) - query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3) - key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3) - - scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1)) - if self.window_size is not None: - assert ( - t_s == t_t - ), "Relative attention is only available for self-attention." - key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s) - rel_logits = self._matmul_with_relative_keys( - query / math.sqrt(self.k_channels), key_relative_embeddings - ) - scores_local = self._relative_position_to_absolute_position(rel_logits) - scores = scores + scores_local - if self.proximal_bias: - assert t_s == t_t, "Proximal bias is only available for self-attention." - scores = scores + self._attention_bias_proximal(t_s).to( - device=scores.device, dtype=scores.dtype - ) - if mask is not None: - scores = scores.masked_fill(mask == 0, -1e4) - if self.block_length is not None: - assert ( - t_s == t_t - ), "Local attention is only available for self-attention." - block_mask = ( - torch.ones_like(scores) - .triu(-self.block_length) - .tril(self.block_length) - ) - scores = scores.masked_fill(block_mask == 0, -1e4) - p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] - p_attn = self.drop(p_attn) - output = torch.matmul(p_attn, value) - if self.window_size is not None: - relative_weights = self._absolute_position_to_relative_position(p_attn) - value_relative_embeddings = self._get_relative_embeddings( - self.emb_rel_v, t_s - ) - output = output + self._matmul_with_relative_values( - relative_weights, value_relative_embeddings - ) - output = ( - output.transpose(2, 3).contiguous().view(b, d, t_t) - ) # [b, n_h, t_t, d_k] -> [b, d, t_t] - return output, p_attn - - def _matmul_with_relative_values(self, x, y): - """ - x: [b, h, l, m] - y: [h or 1, m, d] - ret: [b, h, l, d] - """ - ret = torch.matmul(x, y.unsqueeze(0)) - return ret - - def _matmul_with_relative_keys(self, x, y): - """ - x: [b, h, l, d] - y: [h or 1, m, d] - ret: [b, h, l, m] - """ - ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) - return ret - - def _get_relative_embeddings(self, relative_embeddings, length): - max_relative_position = 2 * self.window_size + 1 - # Pad first before slice to avoid using cond ops. - pad_length = max(length - (self.window_size + 1), 0) - slice_start_position = max((self.window_size + 1) - length, 0) - slice_end_position = slice_start_position + 2 * length - 1 - if pad_length > 0: - padded_relative_embeddings = F.pad( - relative_embeddings, - commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]), - ) - else: - padded_relative_embeddings = relative_embeddings - used_relative_embeddings = padded_relative_embeddings[ - :, slice_start_position:slice_end_position - ] - return used_relative_embeddings - - def _relative_position_to_absolute_position(self, x): - """ - x: [b, h, l, 2*l-1] - ret: [b, h, l, l] - """ - batch, heads, length, _ = x.size() - # Concat columns of pad to shift from relative to absolute indexing. - x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) - - # Concat extra elements so to add up to shape (len+1, 2*len-1). - x_flat = x.view([batch, heads, length * 2 * length]) - x_flat = F.pad( - x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]) - ) - - # Reshape and slice out the padded elements. - x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[ - :, :, :length, length - 1 : - ] - return x_final - - def _absolute_position_to_relative_position(self, x): - """ - x: [b, h, l, l] - ret: [b, h, l, 2*l-1] - """ - batch, heads, length, _ = x.size() - # padd along column - x = F.pad( - x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]) - ) - x_flat = x.view([batch, heads, length**2 + length * (length - 1)]) - # add 0's in the beginning that will skew the elements after reshape - x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]])) - x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:] - return x_final - - def _attention_bias_proximal(self, length): - """Bias for self-attention to encourage attention to close positions. - Args: - length: an integer scalar. - Returns: - a Tensor with shape [1, 1, length, length] - """ - r = torch.arange(length, dtype=torch.float32) - diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1) - return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0) - - -class FFN(nn.Module): - def __init__( - self, - in_channels, - out_channels, - filter_channels, - kernel_size, - p_dropout=0.0, - activation=None, - causal=False, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.activation = activation - self.causal = causal - - if causal: - self.padding = self._causal_padding - else: - self.padding = self._same_padding - - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size) - self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size) - self.drop = nn.Dropout(p_dropout) - - def forward(self, x, x_mask): - x = self.conv_1(self.padding(x * x_mask)) - if self.activation == "gelu": - x = x * torch.sigmoid(1.702 * x) - else: - x = torch.relu(x) - x = self.drop(x) - x = self.conv_2(self.padding(x * x_mask)) - return x * x_mask - - def _causal_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = self.kernel_size - 1 - pad_r = 0 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x - - def _same_padding(self, x): - if self.kernel_size == 1: - return x - pad_l = (self.kernel_size - 1) // 2 - pad_r = self.kernel_size // 2 - padding = [[0, 0], [0, 0], [pad_l, pad_r]] - x = F.pad(x, commons.convert_pad_shape(padding)) - return x diff --git a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/apps/prt_util.py b/spaces/eswat/Image-and-3D-Model-Creator/PIFu/apps/prt_util.py deleted file mode 100644 index 7eba32fa0b396f420b2e332abbb67135dbc14d6b..0000000000000000000000000000000000000000 --- a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/apps/prt_util.py +++ /dev/null @@ -1,142 +0,0 @@ -import os -import trimesh -import numpy as np -import math -from scipy.special import sph_harm -import argparse -from tqdm import tqdm - -def factratio(N, D): - if N >= D: - prod = 1.0 - for i in range(D+1, N+1): - prod *= i - return prod - else: - prod = 1.0 - for i in range(N+1, D+1): - prod *= i - return 1.0 / prod - -def KVal(M, L): - return math.sqrt(((2 * L + 1) / (4 * math.pi)) * (factratio(L - M, L + M))) - -def AssociatedLegendre(M, L, x): - if M < 0 or M > L or np.max(np.abs(x)) > 1.0: - return np.zeros_like(x) - - pmm = np.ones_like(x) - if M > 0: - somx2 = np.sqrt((1.0 + x) * (1.0 - x)) - fact = 1.0 - for i in range(1, M+1): - pmm = -pmm * fact * somx2 - fact = fact + 2 - - if L == M: - return pmm - else: - pmmp1 = x * (2 * M + 1) * pmm - if L == M+1: - return pmmp1 - else: - pll = np.zeros_like(x) - for i in range(M+2, L+1): - pll = (x * (2 * i - 1) * pmmp1 - (i + M - 1) * pmm) / (i - M) - pmm = pmmp1 - pmmp1 = pll - return pll - -def SphericalHarmonic(M, L, theta, phi): - if M > 0: - return math.sqrt(2.0) * KVal(M, L) * np.cos(M * phi) * AssociatedLegendre(M, L, np.cos(theta)) - elif M < 0: - return math.sqrt(2.0) * KVal(-M, L) * np.sin(-M * phi) * AssociatedLegendre(-M, L, np.cos(theta)) - else: - return KVal(0, L) * AssociatedLegendre(0, L, np.cos(theta)) - -def save_obj(mesh_path, verts): - file = open(mesh_path, 'w') - for v in verts: - file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2])) - file.close() - -def sampleSphericalDirections(n): - xv = np.random.rand(n,n) - yv = np.random.rand(n,n) - theta = np.arccos(1-2 * xv) - phi = 2.0 * math.pi * yv - - phi = phi.reshape(-1) - theta = theta.reshape(-1) - - vx = -np.sin(theta) * np.cos(phi) - vy = -np.sin(theta) * np.sin(phi) - vz = np.cos(theta) - return np.stack([vx, vy, vz], 1), phi, theta - -def getSHCoeffs(order, phi, theta): - shs = [] - for n in range(0, order+1): - for m in range(-n,n+1): - s = SphericalHarmonic(m, n, theta, phi) - shs.append(s) - - return np.stack(shs, 1) - -def computePRT(mesh_path, n, order): - mesh = trimesh.load(mesh_path, process=False) - vectors_orig, phi, theta = sampleSphericalDirections(n) - SH_orig = getSHCoeffs(order, phi, theta) - - w = 4.0 * math.pi / (n*n) - - origins = mesh.vertices - normals = mesh.vertex_normals - n_v = origins.shape[0] - - origins = np.repeat(origins[:,None], n, axis=1).reshape(-1,3) - normals = np.repeat(normals[:,None], n, axis=1).reshape(-1,3) - PRT_all = None - for i in tqdm(range(n)): - SH = np.repeat(SH_orig[None,(i*n):((i+1)*n)], n_v, axis=0).reshape(-1,SH_orig.shape[1]) - vectors = np.repeat(vectors_orig[None,(i*n):((i+1)*n)], n_v, axis=0).reshape(-1,3) - - dots = (vectors * normals).sum(1) - front = (dots > 0.0) - - delta = 1e-3*min(mesh.bounding_box.extents) - hits = mesh.ray.intersects_any(origins + delta * normals, vectors) - nohits = np.logical_and(front, np.logical_not(hits)) - - PRT = (nohits.astype(np.float) * dots)[:,None] * SH - - if PRT_all is not None: - PRT_all += (PRT.reshape(-1, n, SH.shape[1]).sum(1)) - else: - PRT_all = (PRT.reshape(-1, n, SH.shape[1]).sum(1)) - - PRT = w * PRT_all - - # NOTE: trimesh sometimes break the original vertex order, but topology will not change. - # when loading PRT in other program, use the triangle list from trimesh. - return PRT, mesh.faces - -def testPRT(dir_path, n=40): - if dir_path[-1] == '/': - dir_path = dir_path[:-1] - sub_name = dir_path.split('/')[-1][:-4] - obj_path = os.path.join(dir_path, sub_name + '_100k.obj') - os.makedirs(os.path.join(dir_path, 'bounce'), exist_ok=True) - - PRT, F = computePRT(obj_path, n, 2) - np.savetxt(os.path.join(dir_path, 'bounce', 'bounce0.txt'), PRT, fmt='%.8f') - np.save(os.path.join(dir_path, 'bounce', 'face.npy'), F) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('-i', '--input', type=str, default='/home/shunsuke/Downloads/rp_dennis_posed_004_OBJ') - parser.add_argument('-n', '--n_sample', type=int, default=40, help='squared root of number of sampling. the higher, the more accurate, but slower') - args = parser.parse_args() - - testPRT(args.input) diff --git a/spaces/everm1nd/musika/parse_test.py b/spaces/everm1nd/musika/parse_test.py deleted file mode 100644 index 96b101dfd1a63561482f9c52da61b0531054ab35..0000000000000000000000000000000000000000 --- a/spaces/everm1nd/musika/parse_test.py +++ /dev/null @@ -1,196 +0,0 @@ -import argparse -from typing import Any -import tensorflow as tf - - -class EasyDict(dict): - def __getattr__(self, name: str) -> Any: - try: - return self[name] - except KeyError: - raise AttributeError(name) - - def __setattr__(self, name: str, value: Any) -> None: - self[name] = value - - def __delattr__(self, name: str) -> None: - del self[name] - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def params_args(args): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--hop", - type=int, - default=256, - help="Hop size (window size = 4*hop)", - ) - parser.add_argument( - "--mel_bins", - type=int, - default=256, - help="Mel bins in mel-spectrograms", - ) - parser.add_argument( - "--sr", - type=int, - default=44100, - help="Sampling Rate", - ) - parser.add_argument( - "--small", - type=str2bool, - default=False, - help="If True, use model with shorter available context, useful for small datasets", - ) - parser.add_argument( - "--latdepth", - type=int, - default=64, - help="Depth of generated latent vectors", - ) - parser.add_argument( - "--coorddepth", - type=int, - default=64, - help="Dimension of latent coordinate and style random vectors", - ) - parser.add_argument( - "--base_channels", - type=int, - default=128, - help="Base channels for generator and discriminator architectures", - ) - parser.add_argument( - "--shape", - type=int, - default=128, - help="Length of spectrograms time axis", - ) - parser.add_argument( - "--window", - type=int, - default=64, - help="Generator spectrogram window (must divide shape)", - ) - parser.add_argument( - "--mu_rescale", - type=float, - default=-25.0, - help="Spectrogram mu used to normalize", - ) - parser.add_argument( - "--sigma_rescale", - type=float, - default=75.0, - help="Spectrogram sigma used to normalize", - ) - parser.add_argument( - "--load_path_1", - type=str, - default="checkpoints/techno/", - help="Path of pretrained networks weights 1", - ) - parser.add_argument( - "--load_path_2", - type=str, - default="checkpoints/metal/", - help="Path of pretrained networks weights 2", - ) - parser.add_argument( - "--load_path_3", - type=str, - default="checkpoints/misc/", - help="Path of pretrained networks weights 3", - ) - parser.add_argument( - "--dec_path", - type=str, - default="checkpoints/ae/", - help="Path of pretrained decoders weights", - ) - parser.add_argument( - "--testing", - type=str2bool, - default=True, - help="True if optimizers weight do not need to be loaded", - ) - parser.add_argument( - "--cpu", - type=str2bool, - default=False, - help="True if you wish to use cpu", - ) - parser.add_argument( - "--mixed_precision", - type=str2bool, - default=True, - help="True if your GPU supports mixed precision", - ) - - tmp_args = parser.parse_args() - - args.hop = tmp_args.hop - args.mel_bins = tmp_args.mel_bins - args.sr = tmp_args.sr - args.small = tmp_args.small - args.latdepth = tmp_args.latdepth - args.coorddepth = tmp_args.coorddepth - args.base_channels = tmp_args.base_channels - args.shape = tmp_args.shape - args.window = tmp_args.window - args.mu_rescale = tmp_args.mu_rescale - args.sigma_rescale = tmp_args.sigma_rescale - args.load_path_1 = tmp_args.load_path_1 - args.load_path_2 = tmp_args.load_path_2 - args.load_path_3 = tmp_args.load_path_3 - args.dec_path = tmp_args.dec_path - args.testing = tmp_args.testing - args.cpu = tmp_args.cpu - args.mixed_precision = tmp_args.mixed_precision - - if args.small: - args.latlen = 128 - else: - args.latlen = 256 - args.coordlen = (args.latlen // 2) * 3 - - print() - - args.datatype = tf.float32 - gpuls = tf.config.list_physical_devices("GPU") - if len(gpuls) == 0 or args.cpu: - args.cpu = True - args.mixed_precision = False - tf.config.set_visible_devices([], "GPU") - print() - print("Using CPU...") - print() - if args.mixed_precision: - args.datatype = tf.float16 - print() - print("Using GPU with mixed precision enabled...") - print() - if not args.mixed_precision and not args.cpu: - print() - print("Using GPU without mixed precision...") - print() - - return args - - -def parse_args(): - args = EasyDict() - return params_args(args) diff --git a/spaces/exbert-project/exbert/client/src/css/SentenceInput.css b/spaces/exbert-project/exbert/client/src/css/SentenceInput.css deleted file mode 100644 index 085ed497fa28962dc26f019a7ced30c981e73444..0000000000000000000000000000000000000000 --- a/spaces/exbert-project/exbert/client/src/css/SentenceInput.css +++ /dev/null @@ -1,48 +0,0 @@ -#sentence-input { - margin-bottom: -30px; - margin-right: -30px; - margin-left: 10px; - width: 90%; -} - -#sentence-input form { - display: -webkit-box; - display: -ms-flexbox; - display: flex; - -webkit-box-orient: horizontal; - -webkit-box-direction: normal; - -ms-flex-direction: row; - flex-direction: row; - -ms-flex-wrap: nowrap; - flex-wrap: nowrap; - -webkit-box-pack: space-evenly; - -ms-flex-pack: space-evenly; - justify-content: space-evenly; - -webkit-box-align: center; - -ms-flex-align: center; - align-items: center; -} - -#sentence-input form .form-group { - -webkit-box-flex: 3; - -ms-flex-positive: 3; - flex-grow: 3; -} - -#sentence-input form .form-group input { - width: 100%; - margin-right: 5%; -} - -#sentence-input form .padding { - -webkit-box-flex: 0.3; - -ms-flex-positive: 0.3; - flex-grow: 0.3; -} - -#sentence-input form .btn { - -webkit-box-flex: 1; - -ms-flex-positive: 1; - flex-grow: 1; -} -/*# sourceMappingURL=SentenceInput.css.map */ \ No newline at end of file diff --git a/spaces/facebook/StyleNeRF/torch_utils/ops/conv2d_gradfix.py b/spaces/facebook/StyleNeRF/torch_utils/ops/conv2d_gradfix.py deleted file mode 100644 index c2cf8727edbb5106a88a139b34943229487c9988..0000000000000000000000000000000000000000 --- a/spaces/facebook/StyleNeRF/torch_utils/ops/conv2d_gradfix.py +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -# 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. - -"""Custom replacement for `torch.nn.functional.conv2d` that supports -arbitrarily high order gradients with zero performance penalty.""" - -import contextlib -import torch - -# pylint: disable=redefined-builtin -# pylint: disable=arguments-differ -# pylint: disable=protected-access - -#---------------------------------------------------------------------------- - -enabled = False # Enable the custom op by setting this to true. -weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. - -@contextlib.contextmanager -def no_weight_gradients(disable=True): - global weight_gradients_disabled - old = weight_gradients_disabled - if disable: - weight_gradients_disabled = True - yield - weight_gradients_disabled = old - -#---------------------------------------------------------------------------- - -def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): - if _should_use_custom_op(input): - return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) - return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) - -def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): - if _should_use_custom_op(input): - return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) - return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) - -#---------------------------------------------------------------------------- - -def _should_use_custom_op(input): - assert isinstance(input, torch.Tensor) - if (not enabled) or (not torch.backends.cudnn.enabled): - return False - if input.device.type != 'cuda': - return False - return True - -def _tuple_of_ints(xs, ndim): - xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim - assert len(xs) == ndim - assert all(isinstance(x, int) for x in xs) - return xs - -#---------------------------------------------------------------------------- - -_conv2d_gradfix_cache = dict() -_null_tensor = torch.empty([0]) - -def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): - # Parse arguments. - ndim = 2 - weight_shape = tuple(weight_shape) - stride = _tuple_of_ints(stride, ndim) - padding = _tuple_of_ints(padding, ndim) - output_padding = _tuple_of_ints(output_padding, ndim) - dilation = _tuple_of_ints(dilation, ndim) - - # Lookup from cache. - key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) - if key in _conv2d_gradfix_cache: - return _conv2d_gradfix_cache[key] - - # Validate arguments. - assert groups >= 1 - assert len(weight_shape) == ndim + 2 - assert all(stride[i] >= 1 for i in range(ndim)) - assert all(padding[i] >= 0 for i in range(ndim)) - assert all(dilation[i] >= 0 for i in range(ndim)) - if not transpose: - assert all(output_padding[i] == 0 for i in range(ndim)) - else: # transpose - assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) - - # Helpers. - common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) - def calc_output_padding(input_shape, output_shape): - if transpose: - return [0, 0] - return [ - input_shape[i + 2] - - (output_shape[i + 2] - 1) * stride[i] - - (1 - 2 * padding[i]) - - dilation[i] * (weight_shape[i + 2] - 1) - for i in range(ndim) - ] - - # Forward & backward. - class Conv2d(torch.autograd.Function): - @staticmethod - def forward(ctx, input, weight, bias): - assert weight.shape == weight_shape - ctx.save_for_backward( - input if weight.requires_grad else _null_tensor, - weight if input.requires_grad else _null_tensor, - ) - ctx.input_shape = input.shape - - # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere). - if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0): - a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1]) - b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1) - c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2) - c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1) - c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3) - return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) - - # General case => cuDNN. - if transpose: - return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) - return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) - - @staticmethod - def backward(ctx, grad_output): - input, weight = ctx.saved_tensors - input_shape = ctx.input_shape - grad_input = None - grad_weight = None - grad_bias = None - - if ctx.needs_input_grad[0]: - p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape) - op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) - grad_input = op.apply(grad_output, weight, None) - assert grad_input.shape == input_shape - - if ctx.needs_input_grad[1] and not weight_gradients_disabled: - grad_weight = Conv2dGradWeight.apply(grad_output, input) - assert grad_weight.shape == weight_shape - - if ctx.needs_input_grad[2]: - grad_bias = grad_output.sum([0, 2, 3]) - - return grad_input, grad_weight, grad_bias - - # Gradient with respect to the weights. - class Conv2dGradWeight(torch.autograd.Function): - @staticmethod - def forward(ctx, grad_output, input): - ctx.save_for_backward( - grad_output if input.requires_grad else _null_tensor, - input if grad_output.requires_grad else _null_tensor, - ) - ctx.grad_output_shape = grad_output.shape - ctx.input_shape = input.shape - - # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere). - if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0): - a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) - b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) - c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape) - return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) - - # General case => cuDNN. - name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight' - flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] - return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) - - @staticmethod - def backward(ctx, grad2_grad_weight): - grad_output, input = ctx.saved_tensors - grad_output_shape = ctx.grad_output_shape - input_shape = ctx.input_shape - grad2_grad_output = None - grad2_input = None - - if ctx.needs_input_grad[0]: - grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) - assert grad2_grad_output.shape == grad_output_shape - - if ctx.needs_input_grad[1]: - p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape) - op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) - grad2_input = op.apply(grad_output, grad2_grad_weight, None) - assert grad2_input.shape == input_shape - - return grad2_grad_output, grad2_input - - _conv2d_gradfix_cache[key] = Conv2d - return Conv2d - -#---------------------------------------------------------------------------- diff --git a/spaces/falcondai/code-as-policies/app.py b/spaces/falcondai/code-as-policies/app.py deleted file mode 100644 index 93a5eb48d81ef94aac5fabfffa304fb39bfceb59..0000000000000000000000000000000000000000 --- a/spaces/falcondai/code-as-policies/app.py +++ /dev/null @@ -1,235 +0,0 @@ -import os -import openai -import numpy as np -from tempfile import NamedTemporaryFile -import copy -import shapely -from shapely.geometry import * -from shapely.affinity import * -from omegaconf import OmegaConf -from moviepy.editor import ImageSequenceClip -import gradio as gr - -from lmp import LMP, LMPFGen -from sim import PickPlaceEnv, LMP_wrapper -from consts import ALL_BLOCKS, ALL_BOWLS -from md_logger import MarkdownLogger - -default_open_ai_key = os.getenv('OPEN_AI_SECRET') -chain_of_thought_affix = ' with a step by step explanation' -ask_for_clarification_affix = ' or ask for clarification if you feel unclear' - - -class DemoRunner: - - def __init__(self): - self._cfg = OmegaConf.to_container(OmegaConf.load('cfg.yaml'), resolve=True) - self._env = None - self._model_name = '' - self._md_logger = MarkdownLogger() - - def make_LMP(self, env): - # LMP env wrapper - cfg = copy.deepcopy(self._cfg) - cfg['env'] = { - 'init_objs': list(env.obj_name_to_id.keys()), - 'coords': cfg['tabletop_coords'] - } - for vs in cfg['lmps'].values(): - vs['engine'] = self._model_name - - LMP_env = LMP_wrapper(env, cfg) - # creating APIs that the LMPs can interact with - fixed_vars = { - 'np': np - } - fixed_vars.update({ - name: eval(name) - for name in shapely.geometry.__all__ + shapely.affinity.__all__ - }) - variable_vars = { - k: getattr(LMP_env, k) - for k in [ - 'get_bbox', 'get_obj_pos', 'get_color', 'is_obj_visible', 'denormalize_xy', - 'put_first_on_second', 'get_obj_names', - 'get_corner_name', 'get_side_name', - ] - } - # variable_vars['say'] = lambda msg: self._md_logger.log_text(f'Robot says: "{msg}"') - variable_vars['say'] = lambda msg: self._md_logger.log_message( - f'{msg}') - - # creating the function-generating LMP - lmp_fgen = LMPFGen(cfg['lmps']['fgen'], fixed_vars, variable_vars, self._md_logger) - - # creating other low-level LMPs - variable_vars.update({ - k: LMP(k, cfg['lmps'][k], lmp_fgen, fixed_vars, variable_vars, self._md_logger) - for k in ['parse_obj_name', 'parse_position', 'parse_question', 'transform_shape_pts'] - }) - - # creating the LMP that deals w/ high-level language commands - lmp_tabletop_ui = LMP( - 'tabletop_ui', cfg['lmps']['tabletop_ui'], lmp_fgen, fixed_vars, variable_vars, self._md_logger - ) - - return lmp_tabletop_ui - - def setup(self, api_key, model_name, n_blocks, n_bowls): - openai.api_key = api_key - self._model_name = model_name - - self._env = PickPlaceEnv(render=True, high_res=True, high_frame_rate=False) - list_idxs = np.random.choice(len(ALL_BLOCKS), size=max(n_blocks, n_bowls), replace=False) - block_list = [ALL_BLOCKS[i] for i in list_idxs[:n_blocks]] - bowl_list = [ALL_BOWLS[i] for i in list_idxs[:n_bowls]] - obj_list = block_list + bowl_list - self._env.reset(obj_list) - - self._lmp_tabletop_ui = self.make_LMP(self._env) - - info = '### Available Objects: \n- ' + '\n- '.join(obj_list) - img = self._env.get_camera_image() - - return info, img - - def run(self, instruction, history): - if self._env is None: - return 'Please run setup first!', None, history - - self._env.cache_video = [] - self._md_logger.clear() - - try: - self._lmp_tabletop_ui(instruction, f'objects = {self._env.object_list}') - except Exception as e: - return f'Error: {e}', None, history - - # Update chat messages - for message in self._md_logger.get_messages(): - history.append((None, message)) - - if self._env.cache_video: - rendered_clip = ImageSequenceClip(self._env.cache_video, fps=25) - video_file_name = NamedTemporaryFile(suffix='.mp4').name - rendered_clip.write_videofile(video_file_name, fps=25) - history.append((None, (video_file_name, ))) - - return self._md_logger.get_log(), self._env.get_camera_image(), history - - -def setup(api_key, model_name, n_blocks, n_bowls): - if not api_key: - return 'Please enter your OpenAI API key!', None - if n_blocks + n_bowls == 0: - return 'Please select at least one object!', None - - demo_runner = DemoRunner() - - info, img = demo_runner.setup(api_key, model_name, n_blocks, n_bowls) - welcome_message = 'How can I help you?' - return info, img, demo_runner, [(None, welcome_message)], None - - -def run(demo_runner, chat_history): - if demo_runner is None: - return 'Please run setup first!', None, None, chat_history, None - instruction = chat_history[-1][0] - return *demo_runner.run(instruction, chat_history), '' - -def submit_chat(chat_message, history): - history += [[chat_message, None]] - return '', history - -def add_cot(chat_messsage): - return chat_messsage.strip() + chain_of_thought_affix - -def add_clarification(chat_message): - return chat_message.strip() + ask_for_clarification_affix - - -with open('README.md', 'r') as f: - for _ in range(12): - next(f) - readme_text = f.read() - -with gr.Blocks() as demo: - state = gr.State(None) - with gr.Accordion('Readme', open=False): - gr.Markdown(readme_text) - gr.Markdown('# Interactive Demo') - with gr.Row(): - with gr.Column(): - with gr.Row(): - inp_api_key = gr.Textbox(value=default_open_ai_key, - label='OpenAI API Key (this is not stored anywhere)', lines=1) - inp_model_name = gr.Dropdown(label='Model Name', choices=[ - 'text-davinci-003', 'code-davinci-002', 'text-davinci-002'], value='text-davinci-003') - with gr.Row(): - inp_n_blocks = gr.Slider(label='Number of Blocks', minimum=0, maximum=5, value=3, step=1) - inp_n_bowls = gr.Slider(label='Number of Bowls', minimum=0, maximum=5, value=3, step=1) - - btn_setup = gr.Button("Setup/Reset Simulation") - info_setup = gr.Markdown(label='Setup Info') - - with gr.Row(): - with gr.Column(): - chat_box = gr.Chatbot() - inp_instruction = gr.Textbox(label='Instruction', lines=1) - examples = gr.Examples( - [ - 'stack two of the blocks', - 'what color is the rightmost block?', - 'arrange the blocks into figure 3', - 'put blocks into non-matching bowls', - 'swap the positions of one block and another', - ], - inp_instruction, - ) - btn_add_cot = gr.Button(f'+{chain_of_thought_affix} (chain-of-thought)') - btn_add_cla = gr.Button( - f'+{ask_for_clarification_affix} (conversation)') - btn_run = gr.Button("Run (this may take 30+ seconds)") - info_run = gr.Markdown(label='Generated Code') - with gr.Column(): - img_setup = gr.Image(label='Current Simulation State') - # video_run = gr.Video(label='Most Recent Manipulation') - - btn_setup.click( - setup, - inputs=[inp_api_key, inp_model_name, inp_n_blocks, inp_n_bowls], - outputs=[info_setup, img_setup, state, chat_box, info_run], - ) - btn_add_cot.click( - add_cot, - inp_instruction, - inp_instruction, - ) - btn_add_cla.click( - add_clarification, - inp_instruction, - inp_instruction, - ) - btn_run.click( - submit_chat, - [inp_instruction, chat_box], - [inp_instruction, chat_box], - ).then( - run, - inputs=[state, chat_box], - outputs=[info_run, img_setup, chat_box, inp_instruction], - ) - inp_instruction.submit( - submit_chat, - [inp_instruction, chat_box], - [inp_instruction, chat_box], - ).then( - run, - inputs=[state, chat_box], - outputs=[info_run, img_setup, chat_box, inp_instruction], - ) - -if __name__ == '__main__': - print(gr.__version__) - demo.queue(concurrency_count=10) - demo.launch() \ No newline at end of file diff --git a/spaces/falterWliame/Face_Mask_Detection/Alpha Bravo Charlie 720p Torrent _BEST_.md b/spaces/falterWliame/Face_Mask_Detection/Alpha Bravo Charlie 720p Torrent _BEST_.md deleted file mode 100644 index 700c501ebfe205a9118683e9f3f77b1bc5fd5ae0..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Alpha Bravo Charlie 720p Torrent _BEST_.md +++ /dev/null @@ -1,45 +0,0 @@ --
How to Watch Alpha Bravo Charlie in 720p Quality
-If you are a fan of Pakistani dramas, you must have heard of Alpha Bravo Charlie, one of the most popular and acclaimed series ever produced by PTV. The drama follows the lives and adventures of three army officers, Faraz, Kashif and Gulsher, who are also known by their code names Alpha, Bravo and Charlie. The drama is a mix of comedy, romance, action and patriotism, and it showcases the challenges and sacrifices of the Pakistani army.
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Alpha Bravo Charlie was aired in 1998, and it became an instant hit among the viewers. The drama has a cult following even today, and many people consider it as one of the best Pakistani dramas of all time. However, finding a good quality version of the drama online can be a challenge. Most of the available copies are either low-resolution or have poor audio and video quality.
-That's why we have compiled this guide to help you watch Alpha Bravo Charlie in 720p quality, which is the best possible quality for this drama. You can either stream or download the drama using torrent links, depending on your preference and internet speed. Here are the steps you need to follow:
-Step 1: Download a Torrent Client
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- -Some of the torrent sites that have Alpha Bravo Charlie in 720p quality are LimeTorrents, The Pirate Bay, 1337x and RARBG. You can visit any of these sites and search for "Alpha Bravo Charlie 720p Torrent" in their search bar. You will see a list of results with different file sizes and seeders. Seeders are users who have the complete file and are sharing it with others. The more seeders a file has, the faster it will download.
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-FAQs
-Here are some frequently asked questions about Alpha Bravo Charlie and torrenting:
-What is Alpha Bravo Charlie about?
-Alpha Bravo Charlie is a Pakistani drama series that follows the lives and adventures of three army officers, Faraz, Kashif and Gulsher, who are also known by their code names Alpha, Bravo and Charlie. The drama is a mix of comedy, romance, action and patriotism, and it showcases the challenges and sacrifices of the Pakistani army. The drama was aired in 1998 and has 14 episodes.
-Who are the actors in Alpha Bravo Charlie?
-The main actors in Alpha Bravo Charlie are:
--
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What is torrenting?
-Torrenting is a method of downloading or streaming files from other users who are sharing them on a peer-to-peer network. Torrenting uses a software called a torrent client to connect to other users who have the file you want and download or stream it from them. Torrenting is a fast and efficient way of accessing large files such as movies, TV shows, games and music.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Apk Live Bar Bar Indonesia.md b/spaces/fatiXbelha/sd/Download Apk Live Bar Bar Indonesia.md deleted file mode 100644 index a45bd744d1c249de031264de1468b167244cf3b1..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Apk Live Bar Bar Indonesia.md +++ /dev/null @@ -1,89 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Tank Heroes Mod APK an1 - The Most Fun and Exciting Tank Game Ever.md b/spaces/fatiXbelha/sd/Download Tank Heroes Mod APK an1 - The Most Fun and Exciting Tank Game Ever.md deleted file mode 100644 index f202f70b419c05f89a68502bf3766e2423e26e66..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Tank Heroes Mod APK an1 - The Most Fun and Exciting Tank Game Ever.md +++ /dev/null @@ -1,109 +0,0 @@ - -
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Download Vice Online MOD APK 0.3.95 and Enjoy GTA-style Gameplay with Unlimited Money.md b/spaces/fatiXbelha/sd/Download Vice Online MOD APK 0.3.95 and Enjoy GTA-style Gameplay with Unlimited Money.md deleted file mode 100644 index 209f0271911491e3286e097f378c6d0d53889c56..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Download Vice Online MOD APK 0.3.95 and Enjoy GTA-style Gameplay with Unlimited Money.md +++ /dev/null @@ -1,108 +0,0 @@ - -
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Enable unknown sources on your device settings
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Install the APK file and launch the game
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-Vice Online is a fun and exciting game that offers you a lot of options and possibilities to enjoy. However, if you want to make the most out of your gaming experience, you might want to follow some tips and tricks that can help you play better. Here are some of them:
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-Another great thing about Vice Online is that it allows you to customize your character and vehicles to suit your style and personality. You can change your character's appearance, clothes, accessories, tattoos, and more. You can also modify your vehicles, such as cars, bikes, boats, and planes, with different colors, decals, parts, and features. You can create a unique look and playstyle that reflects your identity and preferences. You can also show off your character and vehicles to other players and impress them with your creativity and style.
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-Vice Online is a 3D multiplayer open-world sandbox mobile game that lets you explore a huge city with your friends and other players from around the world. You can customize your character, vehicles, and weapons, and participate in various activities, such as races, heists, hangouts, and more. However, if you want to enjoy the game without any limitations or restrictions, you should download Vice Online Mod APK. This is a modified version of the game that gives you access to unlimited money, mega menu, no ads, and other features that can enhance your gameplay. You can download Vice Online Mod APK from [APKCombo] by following the steps that we have provided in this article. We have also given you some tips and tricks on how to play the game better. So, what are you waiting for? Download Vice Online Mod APK now and have fun!
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-No, you cannot update Vice Online Mod APK from Google Play Store or APKCombo as it is a modded version of the game. If you want to update the game to the latest version, you will need to download the new modded APK file from a trusted source again.
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\ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/request_llm/README.md b/spaces/fb700/chatglm-fitness-RLHF/request_llm/README.md deleted file mode 100644 index 545bc1ffba8b79a49d994cfedcc2a787475181b2..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/request_llm/README.md +++ /dev/null @@ -1,79 +0,0 @@ -# 如何使用其他大语言模型 - -## ChatGLM - -- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt` -- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm" - -``` sh -LLM_MODEL = "chatglm" -``` -- 运行！ -``` sh -`python main.py` -``` - -## Claude-Stack - -- 请参考此教程获取 https://zhuanlan.zhihu.com/p/627485689 - - 1、SLACK_CLAUDE_BOT_ID - - 2、SLACK_CLAUDE_USER_TOKEN - -- 把token加入config.py - -## Newbing - -- 使用cookie editor获取cookie（json） -- 把cookie（json）加入config.py （NEWBING_COOKIES） - -## Moss -- 使用docker-compose - -## RWKV -- 使用docker-compose - -## LLAMA -- 使用docker-compose - -## 盘古 -- 使用docker-compose - - ---- -## Text-Generation-UI (TGUI，调试中，暂不可用) - -### 1. 部署TGUI -``` sh -# 1 下载模型 -git clone https://github.com/oobabooga/text-generation-webui.git -# 2 这个仓库的最新代码有问题，回滚到几周之前 -git reset --hard fcda3f87767e642d1c0411776e549e1d3894843d -# 3 切换路径 -cd text-generation-webui -# 4 安装text-generation的额外依赖 -pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers -# 5 下载模型 -python download-model.py facebook/galactica-1.3b -# 其他可选如 facebook/opt-1.3b -# facebook/galactica-1.3b -# facebook/galactica-6.7b -# facebook/galactica-120b -# facebook/pygmalion-1.3b 等 -# 详情见 https://github.com/oobabooga/text-generation-webui - -# 6 启动text-generation -python server.py --cpu --listen --listen-port 7865 --model facebook_galactica-1.3b -``` - -### 2. 修改config.py - -``` sh -# LLM_MODEL格式: tgui:[模型]@[ws地址]:[ws端口] , 端口要和上面给定的端口一致 -LLM_MODEL = "tgui:galactica-1.3b@localhost:7860" -``` - -### 3. 运行！ -``` sh -cd chatgpt-academic -python main.py -``` diff --git a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/__init__.py b/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/__init__.py deleted file mode 100644 index bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/src/facerender/sync_batchnorm/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -# -*- coding: utf-8 -*- -# File : __init__.py -# Author : Jiayuan Mao -# Email : maojiayuan@gmail.com -# Date : 27/01/2018 -# -# This file is part of Synchronized-BatchNorm-PyTorch. -# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch -# Distributed under MIT License. - -from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d -from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/spaces/fclong/summary/fengshen/examples/pretrain_t5/pretrain_mt5_small_continue.sh b/spaces/fclong/summary/fengshen/examples/pretrain_t5/pretrain_mt5_small_continue.sh deleted file mode 100644 index 0a539a7e6a7fb4b750b441df98dd49f166c3c49b..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/examples/pretrain_t5/pretrain_mt5_small_continue.sh +++ /dev/null @@ -1,120 +0,0 @@ -#!/bin/bash -#SBATCH --job-name=t5_cn_small_pretrain_v2 -#SBATCH --nodes=1 -#SBATCH --ntasks-per-node=8 -#SBATCH --gres=gpu:8 # number of gpus -#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks) -#SBATCH -o %x-%j.log -#SBATCH -e %x-%j.err -#SBATCH -x dgx050 - -set -x -e -source activate base - -echo "START TIME: $(date)" -MICRO_BATCH_SIZE=32 -ROOT_DIR=/cognitive_comp/ganruyi/experiments/t5_cn_small_pretrain_v2/ - -ZERO_STAGE=1 - -config_json="$ROOT_DIR/ds_config.t5_cn_small_pretrain_v2.$SLURM_JOBID.json" -export MASTER_PORT=$[RANDOM%10000+30000] -# Deepspeed figures out GAS dynamically from dynamic GBS via set_train_batch_size() - -cat <
Ship Sim 2019 APK Mod: A Realistic and Fun Ship Simulator Game
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Sky Go for PC and Enjoy Unlimited Entertainment.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Sky Go for PC and Enjoy Unlimited Entertainment.md deleted file mode 100644 index 0f4bac2cfd66b12a49d36ab538ca81d2ece70f40..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Sky Go for PC and Enjoy Unlimited Entertainment.md +++ /dev/null @@ -1,117 +0,0 @@ -
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